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Molecular and Translational Research on Colorectal Cancer

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Pathology, Diagnostics, and Therapeutics".

Deadline for manuscript submissions: closed (31 March 2020) | Viewed by 131147

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Guest Editor
Unit of Biostatistics and Clinical Trials, IRCCS-Istituto Romagnolo per lo Studio dei Tumori "Dino Amadori"- IRST-Srl, Via P. Maroncelli 40, 47014 Meldola, Italy
Interests: biostatistics; clinical trials; observational study; tumor epidemiology; oncology; palliative care; biomarkers
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Colorectal cancer (CRC) is the third most commonly diagnosed cancer in males and the second in females. The genome of colon cancer cells is altered at several sites as a result of point mutations or changes in chromosome integrity. The mutation-associated changes affect oncogenes, tumor suppressor genes, and several metastasis-related genes. Other factors including epigenetic alterations as well as the deregulation of miRNA-mediated control of mRNA functions, contribute to the incidence of cancer and metastasis. 

Translational research has led to significant benefits in CRC screening and patient management, and precision medicine is fast becoming the aim of scientific research. Individualized treatment for CRC in both adjuvant and metastatic settings is increasingly emphasized. The introduction of molecular-targeted agents with anti-epidermal growth factor receptor (EGFR) or anti-angiogenic mechanisms of action has significantly improved patient outcome, but predictive markers of efficacy, especially for angiogenesis inhibition, are still lacking. Furthermore immunotherapy has recently been implemented into clinical practice. 

A new approach to biomarker detection is the use of liquid biopsy. Free circulating tumor DNA (fctDNA) can be monitored quantitatively and qualitatively for diagnostic, prognostic, or predictive purposes. Liquid biopsy has the potential to replace tumor tissue analysis in clinical practice and could be used to monitor the extent of tumor burden and to detect tumor heterogeneity and molecular resistance to therapy.

Prof. Dr. Emanuela Scarpi
Dr. Paola Ulivi
Dr. Alessandro Passardi
Guest Editors

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Keywords

  • Adenoma-carcinoma sequence
  • Predictive biomarkers of response and toxicity in the adjuvant and metastatic settings
  • Genetic and epigenetic marker
  • Immunotherapy
  • Prognostic biomarkers
  • Angiogenesis
  • EGFR pathways
  • Tumor biopsies
  • Circulating tumor cells
  • Tumor heterogeneity
  • Early diagnosis
  • Screening
  • Liquid biopsy
  • Molecular pathology
  • Tumor biology

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Published Papers (22 papers)

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Editorial

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3 pages, 171 KiB  
Editorial
Molecular and Translational Research on Colorectal Cancer
by Alessandro Passardi, Emanuela Scarpi and Paola Ulivi
Int. J. Mol. Sci. 2020, 21(11), 4105; https://doi.org/10.3390/ijms21114105 - 9 Jun 2020
Cited by 4 | Viewed by 2118
Abstract
Colorectal cancer (CRC) is the third most frequently diagnosed cancer in the world [...] Full article
(This article belongs to the Special Issue Molecular and Translational Research on Colorectal Cancer)

Research

Jump to: Editorial, Review

13 pages, 637 KiB  
Article
Instability of Non-Standard Microsatellites in Relation to Prognosis in Metastatic Colorectal Cancer Patients
by Francesca Pirini, Luigi Pasini, Gianluca Tedaldi, Emanuela Scarpi, Giorgia Marisi, Chiara Molinari, Daniele Calistri, Alessandro Passardi and Paola Ulivi
Int. J. Mol. Sci. 2020, 21(10), 3532; https://doi.org/10.3390/ijms21103532 - 16 May 2020
Cited by 5 | Viewed by 2896
Abstract
Very few data are reported in the literature on the association between elevated microsatellite alterations at selected tetranucleotide repeats (EMAST) and prognosis in advanced colorectal cancer. Moreover, there is no information available in relation to the response to antiangiogenic treatment. We analyzed EMAST [...] Read more.
Very few data are reported in the literature on the association between elevated microsatellite alterations at selected tetranucleotide repeats (EMAST) and prognosis in advanced colorectal cancer. Moreover, there is no information available in relation to the response to antiangiogenic treatment. We analyzed EMAST and vascular endothelial growth factor-B (VEGF-B) microsatellite status, together with standard microsatellite instability (MSI), in relation to prognosis in 141 patients with metastatic colorectal cancer (mCRC) treated with chemotherapy (CT) alone (n = 51) or chemotherapy with bevacizumab (B) (CT + B; n = 90). High MSI (MSI-H) was detected in 3% of patients and was associated with progression-free survival (PFS; p = 0.005) and overall survival (OS; p < 0.0001). A total of 8% of cases showed EMAST instability, which was associated with worse PFS (p = 0.0006) and OS (p < 0.0001) in patients treated with CT + B. A total of 24.2% of patients showed VEGF-B instability associated with poorer outcome in (p = 0.005) in the CT arm. In conclusion, our analysis indicated that EMAST instability is associated with worse prognosis, particularly evident in patients receiving CT + B. Full article
(This article belongs to the Special Issue Molecular and Translational Research on Colorectal Cancer)
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Figure 1

Figure 1
<p>(<b>A</b>) Progression-free survival (PFS) in high microsatellite instability (MSI-H) and microsatellite stability (MSS) patients. (<b>B</b>) Overall survival (OS) in MSI-H and compared to MSS patients.</p>
Full article ">Figure 2
<p>(<b>A</b>) PFS in EMAST-unstable and -stable patients in CT + B arm. (<b>B</b>) OS in EMAST-unstable and -stable patients in CT + B arm.</p>
Full article ">
15 pages, 1501 KiB  
Article
MicroRNA Expression Profiling of Normal and Malignant Human Colonic Stem Cells Identifies miRNA92a as a Regulator of the LRIG1 Stem Cell Gene
by Vignesh Viswanathan, Lynn Opdenaker, Shirin Modarai, Jeremy Z. Fields, Gregory Gonye and Bruce M. Boman
Int. J. Mol. Sci. 2020, 21(8), 2804; https://doi.org/10.3390/ijms21082804 - 17 Apr 2020
Cited by 7 | Viewed by 2625
Abstract
MicroRNAs (miRNAs) have a critical role in regulating stem cells (SCs) during development, and because aberrant expression of miRNAs occurs in various cancers, our goal was to determine if dysregulation of miRNAs is involved in the SC origin of colorectal cancer (CRC). We [...] Read more.
MicroRNAs (miRNAs) have a critical role in regulating stem cells (SCs) during development, and because aberrant expression of miRNAs occurs in various cancers, our goal was to determine if dysregulation of miRNAs is involved in the SC origin of colorectal cancer (CRC). We previously reported that aldehyde dehydrogenase (ALDH) is a marker for normal and malignant human colonic SCs and tracks SC overpopulation during colon tumorigenesis. MicroRNA expression was studied in ALDH-positive SCs from normal and malignant human colon tissues by Nanostring miRNA profiling. Our findings show that: (1) A unique miRNA signature distinguishes ALDH-positive CRC cells from ALDH-positive normal colonic epithelial cells, (2) Expression of four miRNAs (miRNA200c, miRNA92a, miRNA20a, miRNA93) are significantly altered in CRC SCs compared to normal colonic SCs, (3) miRNA92a expression is also upregulated in ALDH-positive HT29 CRC SCs as compared to ALDH-negative SCs, (4) miRNA92a targets the 3′UTR of LRIG1 SC gene, and (5) miRNA92a modulates proliferation of HT29 CRC cells. Thus, our findings indicate that overexpression of miRNA92a contributes to the SC origin of CRC. Strategies designed to modulate miRNA expression, such as miRNA92a, may provide ways to target malignant SCs and to develop more effective therapies against CRC. Full article
(This article belongs to the Special Issue Molecular and Translational Research on Colorectal Cancer)
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Figure 1

Figure 1
<p>Identification and isolation of ALDEFLUOR-positive stem cells from fresh patient normal and tumor samples. Panels A, B—ALDH activity in the bottom of fresh normal isolated crypts. Normal isolated colonic crypts subjected to ALDEFLUOR assay in the presence (<b>A</b>) or absence of the inhibitor of ALDH activity (<b>B</b>). This image was taken using a Zeiss Epi-fluorescence microscope using the 10X objective. (<b>C</b>) Panel C shows ALDH activity in fresh dissociated patient tumor cells. Dissociated cells from fresh normal and tumor tissue show small populations of ALDEFLUOR positive (green) cells. Image was taken using a Zeiss Epi-fluorescence microscope using the 10X objective. (<b>D</b>) Panel D shows a histogram for flow cytometric isolation of stem cells from fresh patient normal and tumor samples. Gates showing representative percentages of isolated cells from fresh normal and tumor tissue positive for ALDH activity, when the DEAB control was set to 0.1%. Tumor cells were selected for EpCAM positivity (carcinoma cells) and normal cells negative for Propidium iodide (viable cells) for ALDEFLUOR assay and sorting.</p>
Full article ">Figure 1 Cont.
<p>Identification and isolation of ALDEFLUOR-positive stem cells from fresh patient normal and tumor samples. Panels A, B—ALDH activity in the bottom of fresh normal isolated crypts. Normal isolated colonic crypts subjected to ALDEFLUOR assay in the presence (<b>A</b>) or absence of the inhibitor of ALDH activity (<b>B</b>). This image was taken using a Zeiss Epi-fluorescence microscope using the 10X objective. (<b>C</b>) Panel C shows ALDH activity in fresh dissociated patient tumor cells. Dissociated cells from fresh normal and tumor tissue show small populations of ALDEFLUOR positive (green) cells. Image was taken using a Zeiss Epi-fluorescence microscope using the 10X objective. (<b>D</b>) Panel D shows a histogram for flow cytometric isolation of stem cells from fresh patient normal and tumor samples. Gates showing representative percentages of isolated cells from fresh normal and tumor tissue positive for ALDH activity, when the DEAB control was set to 0.1%. Tumor cells were selected for EpCAM positivity (carcinoma cells) and normal cells negative for Propidium iodide (viable cells) for ALDEFLUOR assay and sorting.</p>
Full article ">Figure 2
<p>Differential expression of microRNAs in normal and tumor ALDEFLUOR positive and negative cells. This figure shows a focused heatmap for the subset of miRNAs based on statistical analysis (cutoff of <span class="html-italic">p</span> &lt; 0.1) of all patient cases assessed by Nanostring profiling. The results are expressed as the average of normalized counts for the four types of sorted cell samples, (ALDH-positive and -negative cells for normal (N) and tumor (T)), which is converted to log2 and scaled to the mean of each sample. The list of differentially expressed miRNAs shown in <a href="#ijms-21-02804-f002" class="html-fig">Figure 2</a> is given in <a href="#app1-ijms-21-02804" class="html-app">Supplementary Table S2</a>.</p>
Full article ">Figure 3
<p><span class="html-italic">MicroRNA92a</span> is overexpressed in ALDEFLUOR positive cells and regulates the <span class="html-italic">LRIG1</span> gene expression. (<b>A</b>) <span class="html-italic">MicroRNA92a</span> expression in tumor and normal ALDEFLUOR positive cells compared to ALDEFLUOR negative cells in patient samples. The results show <span class="html-italic">miRNA92a</span> expression is upregulated in ALDH-positive SCs from CRCs compared to ALDH-positive SCs from normal colonic epithelium. (<b>B</b>) Normalized <span class="html-italic">miRNA92a</span> expression levels in sorted ALDEFLUOR positive and negative HT29 cells. The results show <span class="html-italic">miRNA92a</span> expression is upregulated in ALDH-positive cells compared to ALDH-negative cells from the HT29 CRC line. (<b>C</b>) Normalized fold change in cell count of HT29 cells with increased and decreased levels of <span class="html-italic">miRNA92a</span>. The results show transfecting HT29 cells with <span class="html-italic">miRNA92a</span> antimir significantly reduces cell numbers and <span class="html-italic">miRNA92a</span> precursor has the opposite effect. (<b>D</b>) Luciferase assay shows that <span class="html-italic">miRNA92a</span> targets 3′UTR of <span class="html-italic">LRIG1</span> gene indicated by the significant decrease in the relative luminescence intensity as compared to the control. The results indicate <span class="html-italic">miRNA92a</span> down-modulates LRIG expression. Error bars represent standard error of mean and * represents a significant <span class="html-italic">p</span> value &lt; 0.05.</p>
Full article ">
16 pages, 3584 KiB  
Article
Obatoclax, a Pan-BCL-2 Inhibitor, Downregulates Survivin to Induce Apoptosis in Human Colorectal Carcinoma Cells Via Suppressing WNT/?-catenin Signaling
by Chi-Hung R. Or, Chiao-Wen Huang, Ching-Chin Chang, You-Chen Lai, Yi-Ju Chen and Chia-Che Chang
Int. J. Mol. Sci. 2020, 21(5), 1773; https://doi.org/10.3390/ijms21051773 - 5 Mar 2020
Cited by 37 | Viewed by 4165
Abstract
Colorectal cancer (CRC) is a highly prevailing cancer and the fourth leading cause of cancer mortality worldwide. Aberrant expression of antiapoptotic BCL-2 family proteins is closely linked to neoplastic progression and chemoresistance. Obatoclax is a clinically developed drug, which binds antiapoptotic BCL-2, BCL-xL, [...] Read more.
Colorectal cancer (CRC) is a highly prevailing cancer and the fourth leading cause of cancer mortality worldwide. Aberrant expression of antiapoptotic BCL-2 family proteins is closely linked to neoplastic progression and chemoresistance. Obatoclax is a clinically developed drug, which binds antiapoptotic BCL-2, BCL-xL, and MCL-1 for inhibition to elicit apoptosis. Survivin is an antiapoptotic protein, whose upregulation correlates with pathogenesis, therapeutic resistance, and poor prognosis in CRC. Herein, we provide the first evidence delineating the functional linkage between Obatoclax and survivin in the context of human CRC cells. In detail, Obatoclax was found to markedly downregulate survivin. This downregulation was mainly achieved via transcriptional repression, as Obatoclax lowered the levels of both survivin mRNA and promoter activity, while blocking proteasomal degradation failed to prevent survivin from downregulation by Obatoclax. Notably, ectopic survivin expression curtailed Obatoclax-induced apoptosis and cytotoxicity, confirming an essential role of survivin downregulation in Obatoclax-elicited anti-CRC effect. Moreover, Obatoclax was found to repress hyperactive WNT/β-catenin signaling activity commonly present in human CRC cells, and, markedly, ectopic expression of dominant-active β-catenin mutant rescued the levels of survivin along with elevated cell viability. We further revealed that, depending on the cell context, Obatoclax suppresses WNT/β-catenin signaling in HCT 116 cells likely via inducing β-catenin destabilization, or by downregulating LEF1 in DLD-1 cells. Collectively, we for the first time define survivin downregulation as a novel, pro-apoptotic mechanism of Obatoclax as a consequence of Obatocalx acting as an antagonist to WNT/β-catenin signaling. Full article
(This article belongs to the Special Issue Molecular and Translational Research on Colorectal Cancer)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Obatoclax is cytotoxic to human colorectal carcinoma (CRC) cell lines. (<b>A</b>) Obatoclax suppresses CRC cell viability. CRC cell lines DLD-1, HCT 116, LoVo, and WiDr were treated with 0~400 nM of Obatoclax for 48 h, followed by cell viability analysis using MTS assay. (<b>B</b>) Obatoclax blocks the clonogenicity of CRC cells. CRC cells (2 × 10<sup>2</sup>) after Obatoclax treatment for 24 h were seeded onto 6-well plates and grown in drug-free culture media for 10 days to form colonies. **: <span class="html-italic">p</span> &lt; 0.01; ***: <span class="html-italic">p</span> &lt; 0.001. (<b>C</b>) Obatoclax triggers CRC cell apoptosis. CRC cells were treated with 0~200 nM of Obatoclax for 24 h and then subjected to immunoblot analysis for the status of caspase activation revealed by the levels of cleaved poly(ADP-ribose) polymerase (PARP) (c-PARP), caspase 8 (c-casp 8), caspase 9 (c-casp 9), and caspase 3 (c-casp 3). β-tubulin was used as the equal loading control.</p>
Full article ">Figure 1 Cont.
<p>Obatoclax is cytotoxic to human colorectal carcinoma (CRC) cell lines. (<b>A</b>) Obatoclax suppresses CRC cell viability. CRC cell lines DLD-1, HCT 116, LoVo, and WiDr were treated with 0~400 nM of Obatoclax for 48 h, followed by cell viability analysis using MTS assay. (<b>B</b>) Obatoclax blocks the clonogenicity of CRC cells. CRC cells (2 × 10<sup>2</sup>) after Obatoclax treatment for 24 h were seeded onto 6-well plates and grown in drug-free culture media for 10 days to form colonies. **: <span class="html-italic">p</span> &lt; 0.01; ***: <span class="html-italic">p</span> &lt; 0.001. (<b>C</b>) Obatoclax triggers CRC cell apoptosis. CRC cells were treated with 0~200 nM of Obatoclax for 24 h and then subjected to immunoblot analysis for the status of caspase activation revealed by the levels of cleaved poly(ADP-ribose) polymerase (PARP) (c-PARP), caspase 8 (c-casp 8), caspase 9 (c-casp 9), and caspase 3 (c-casp 3). β-tubulin was used as the equal loading control.</p>
Full article ">Figure 2
<p>Obatoclax downregulates survivin mainly through transcriptional repression of the <span class="html-italic">survivin</span> gene. (<b>A</b>) Obatoclax downregulates survivin in CRC cells. DLD-1, HCT 116, LoVo, and WiDr cells were treated with Obatoclax (0~400 nM) for 24 h and then subjected to survivin immunoblotting. Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) levels were used as the equal loading control. (<b>B</b>) Obatoclax lowers <span class="html-italic">survivin</span> mRNA levels. CRC cells were treated for 24 h with Obatoclax (0, 100, 200 nM), followed by total RNA extraction, reverse transcription, and real-time PCR for the levels of <span class="html-italic">survivin</span> mRNA expression. The mRNA levels of TATA box-binding protein (TBP) were used to normalize <span class="html-italic">survivin</span> mRNA expression. (<b>C</b>) Obatoclax represses the <span class="html-italic">survivin</span> promoter activity. CRC cells transiently transfected with pSRVN-Luc (the luciferase reporter construct for the human <span class="html-italic">survivin</span> promoter activity) were treated with Obatoclax (0, 100, 200 nM) for 24 h, and then the activity of firefly luciferase was determined thereafter. (<b>D</b>) Obatoclax downregulates survivin irrespective to blockage of proteasomal degradation. CRC cells were treated with Obatoclax (200 nM) for 24 h in the absence or presence of MG132 (20 μM) to inhibit proteasome-mediated survivin degradation, followed by survivin immunoblotting. β-tubulin served as the equal loading control. *: <span class="html-italic">p</span> &lt; 0.05; **: <span class="html-italic">p</span> &lt; 0.01; ***: <span class="html-italic">p</span> &lt; 0.001.</p>
Full article ">Figure 2 Cont.
<p>Obatoclax downregulates survivin mainly through transcriptional repression of the <span class="html-italic">survivin</span> gene. (<b>A</b>) Obatoclax downregulates survivin in CRC cells. DLD-1, HCT 116, LoVo, and WiDr cells were treated with Obatoclax (0~400 nM) for 24 h and then subjected to survivin immunoblotting. Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) levels were used as the equal loading control. (<b>B</b>) Obatoclax lowers <span class="html-italic">survivin</span> mRNA levels. CRC cells were treated for 24 h with Obatoclax (0, 100, 200 nM), followed by total RNA extraction, reverse transcription, and real-time PCR for the levels of <span class="html-italic">survivin</span> mRNA expression. The mRNA levels of TATA box-binding protein (TBP) were used to normalize <span class="html-italic">survivin</span> mRNA expression. (<b>C</b>) Obatoclax represses the <span class="html-italic">survivin</span> promoter activity. CRC cells transiently transfected with pSRVN-Luc (the luciferase reporter construct for the human <span class="html-italic">survivin</span> promoter activity) were treated with Obatoclax (0, 100, 200 nM) for 24 h, and then the activity of firefly luciferase was determined thereafter. (<b>D</b>) Obatoclax downregulates survivin irrespective to blockage of proteasomal degradation. CRC cells were treated with Obatoclax (200 nM) for 24 h in the absence or presence of MG132 (20 μM) to inhibit proteasome-mediated survivin degradation, followed by survivin immunoblotting. β-tubulin served as the equal loading control. *: <span class="html-italic">p</span> &lt; 0.05; **: <span class="html-italic">p</span> &lt; 0.01; ***: <span class="html-italic">p</span> &lt; 0.001.</p>
Full article ">Figure 3
<p>Survivin downregulation is required for the anti-CRC effect of Obatoclax. (<b>A</b>) Ectopic expression of survivin mitigates Obatoclax-induced CRC cell apoptosis. DLD-1 and HCT 116 cells stably expressing HA-tagged survivin and their respective vector control clones were treated with Obatoclax (0, 100, 200 nM) for 24 h, followed by immunoblotting for the levels of c-PARP, survivin, and HA. β-tubulin was used as the equal loading control. (<b>B</b>) Survivin overexpression attenuates Obatoclax-induced cytotoxicity. DLD-1 and HCT 116 HA-survivin stable clones and their respective vector controls were treated with Obatoclax (0, 50, 100, 200 nM) for 24 h, followed by MTS assay to evaluate cell viability. (<b>C</b>) Ectopic survivin expression sabotages Obatoclax-induced suppression of clonogenicity. DLD-1 and HCT 116 HA-survivin stable clones and their respective vector controls were treated with Obatoclax (0, 200 nM) for 24 h, followed by colony formation analysis. **: <span class="html-italic">p</span> &lt; 0.01; ***: <span class="html-italic">p</span> &lt; 0.001.</p>
Full article ">Figure 4
<p>Obatoclax antagonizes WNT/β-catenin signaling. (<b>A</b>) Obatoclax decreases β-catenin-mediated TCF/LEF-dependent transcriptional activity. CRC cell lines were transiently transfected with M50 Super 8x TOPFlash (TOPflash), a luciferase reporter plasmid for the transcriptional activity of TCF/LEF, followed by 200 nM of Obatoclax treatment and ensuing luciferase activity assay. M51 Super 8x FOPFlash (FOPflash), a TOPFlash mutant, was used as a negative control. ***: <span class="html-italic">p</span> &lt; 0.001. (<b>B</b>) Obatoclax downregulates WNT/β-catenin target genes c-MYC and cyclin D1. DLD-1 and HCT 116 cells were treated with Obatoclax (0, 100, 200 nM), followed by immunoblotting for the levels of c-MYC and cyclin D1. GAPDH was used as a control for equal loading.</p>
Full article ">Figure 5
<p>Blockade of WNT/β-catenin signaling is pivotal to the anti-CRC effect of Obatoclax. (<b>A</b>) Constitutive activation of β-catenin sustains survivin expression and confers resistance to Obatoclax-induced CRC cell apoptosis. DLD-1 and HCT 116 cells stably expressing HA-tagged dominant-active β-catenin (∆N90-β-catenin) and their corresponding vector control clones were treated with Obatoclax (0, 100, 200 nM) for 24 h, followed by immunoblotting for the levels of c-PARP, survivin, and HA. β-tubulin was used as the control for equal loading. (<b>B</b>) Constitutive activation of β-catenin attenuates Obatoclax-induced cytotoxicity. DLD-1 and HCT 116 HA-∆N90-β-catenin stable clones and their corresponding vector controls were treated with Obatoclax (0, 50, 100,200 nM) for 24 h, followed by MTS assay to evaluate cell viability. (<b>C</b>) ∆N90-β-catenin overexpression mitigates Obatoclax-evoked inhibition of clonogenicity. DLD-1 and HCT 116 HA-∆N90-β-catenin stable clones and their corresponding vector controls were treated with Obatoclax (0, 200 nM) for 24 h, followed by clonogenicity assay. **: <span class="html-italic">p</span> &lt; 0.01; ***: <span class="html-italic">p</span> &lt; 0.001.</p>
Full article ">Figure 6
<p>How Obatoclax antagonizes WNT/β-catenin signaling depends on the cell context. (<b>A</b>) Obatoclax induces proteasomal degradation of β-catenin in HCT 116 cells but not DLD-1 cells. CRC cells were treated with Obatoclax (200 nM) for 24 h without or with 2 h co-treatment of MG132 (20 μM). (<b>B</b>) Obatoclax downregulates LEF1 in DLD-1 cells. HCT 116 and DLD-1 cells were treated with Obatoclax (0, 100, 200 nM) for 24 h, followed by immunoblotting for the levels of TCF4 and LEF1. GAPDH levels were used as the equal loading controls.</p>
Full article ">Figure 7
<p>Schematic depiction of the mechanisms underlying the anti-CRC effect of Obatoclax, which likely involves the suppression of WNT/β-catenin–survivin signaling axis in addition to inhibiting the activity of antiapoptotic BCL-2 proteins.</p>
Full article ">
15 pages, 1601 KiB  
Article
Proteomics-Based Evidence for a Pro-Oncogenic Role of ESRP1 in Human Colorectal Cancer Cells
by Ugo Ala, Marta Manco, Giorgia Mandili, Emanuela Tolosano, Francesco Novelli, Paolo Provero, Fiorella Altruda and Sharmila Fagoonee
Int. J. Mol. Sci. 2020, 21(2), 575; https://doi.org/10.3390/ijms21020575 - 16 Jan 2020
Cited by 13 | Viewed by 3345
Abstract
The RNA-binding protein, Epithelial Splicing Regulatory Protein 1 (ESRP1) can promote or suppress tumorigenesis depending on the cell type and disease context. In colorectal cancer, we have previously shown that aberrantly high ESRP1 expression can drive tumor progression. In order to unveil the [...] Read more.
The RNA-binding protein, Epithelial Splicing Regulatory Protein 1 (ESRP1) can promote or suppress tumorigenesis depending on the cell type and disease context. In colorectal cancer, we have previously shown that aberrantly high ESRP1 expression can drive tumor progression. In order to unveil the mechanisms by which ESRP1 can modulate cancer traits, we searched for proteins affected by modulation of Esrp1 in two human colorectal cancer cell lines, HCA24 and COLO320DM, by proteomics analysis. Proteins hosted by endogenous ESRP1 ribonucleoprotein complex in HCA24 cells were also analyzed following RNA-immunoprecipitation. Proteomics data were complemented with bioinformatics approach to exploit publicly available data on protein-protein interaction (PPI). Gene Ontology was analysed to identify a common molecular signature possibly explaining the pro-tumorigenic role of ESRP1. Interestingly, proteins identified herein support a role for ESRP1 in response to external stimulus, regulation of cell cycle and hypoxia. Our data provide further insights into factors affected by and entwined with ESRP1 in colorectal cancer. Full article
(This article belongs to the Special Issue Molecular and Translational Research on Colorectal Cancer)
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Figure 1

Figure 1
<p>ESRP1 expression modulation in COLO320DM cells and proteomic analysis. (<b>A</b>). ESRP1 over-expression (ESRP1) in COLO320DM cells versus Empty controls (Empty) was analyzed by qRT-PCR and western blotting. (<b>B</b>). Proteins revealed as differentially expressed by MALDI-TOF analysis are shown. (<b>C</b>). Validation of results by qRT-PCR (<span class="html-italic">n</span> = 3) and western blotting/densitometric analysis (representative results of 2 independent experiments) of SF3A1 is shown. *** <span class="html-italic">p</span> &lt; 0.0001 (<b>D</b>). Validation of results by qRT-PCR (<span class="html-italic">n</span> = 3) and western blotting/densitometric analysis (representative results) of FBF1 is shown.</p>
Full article ">Figure 2
<p>ESRP1 expression modulation in HCA24 cells and proteomic analysis. (<b>A</b>). ESRP1 silencing (Sh4) in HCA24 versus scrambled (Scr) controls was analyzed by qRT-PCR and western blotting. *** <span class="html-italic">p</span> &lt; 0.0001. (<b>B</b>). Proteins revealed as differentially expressed by MALDI-TOF analysis are shown. (<b>C</b>). Validation of results by qRT-PCR (<span class="html-italic">n</span> = 3) and western blotting/densitometric analysis (representative results) for Hsp90AA1 is shown. (<b>D</b>). Validation of results by qRT-PCR (<span class="html-italic">n</span> = 3) for TPI1 is shown.</p>
Full article ">Figure 3
<p>ESRP1 RCS Functional Characterization. (<b>A</b>) ESRP1 RCS Biological Process Enrichments; (<b>B</b>) ESRP1 RCS Molecular Function Enrichments; on the x-axis the number of proteins driving the significance is reported; the Benjamini-Hochberg method was used to correct <span class="html-italic">p values</span> for multiple comparisons.</p>
Full article ">Figure 4
<p>RIP-derived endogenous ESRP1 interactors in HCA24 cells. (<b>A</b>). Western blot showing ESRP1-antibody specificity in RIP. COLO320DM cells were used as negative controls. (<b>B</b>). Proteins co-immunoprecipitating with ESRP1 and identified by MALDI-TOF analysis are shown. (<b>C</b>). Validation of candidate ESRP1 interactor (CCAR2) by RIP and western blotting (representative of 3 independent experiments).</p>
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<p>ESRP1 Extended Functional Network. Connections of some GO enriched keywords (depicted in green rhombus) and some ESRP1 partners highlight ESRP1 role and its extended biological influences from RNA splicing to response to growth factors, from mitosis and transcription to proliferation and post-transcriptional regulation. Edges connect GO terms with their associated proteins; red rectangle highlights ESRP1; orange hexagons proteins found experimentally by proteomics; blue ellipses proteins associated to our RCSs by STRING database.</p>
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26 pages, 3378 KiB  
Article
Newly Developed CK1-Specific Inhibitors Show Specifically Stronger Effects on CK1 Mutants and Colon Cancer Cell Lines
by Congxing Liu, Lydia Witt, Chiara Ianes, Joachim Bischof, Marie-Thérèse Bammert, Joana Baier, Stefan Kirschner, Doris Henne-Bruns, Pengfei Xu, Marko Kornmann, Christian Peifer and Uwe Knippschild
Int. J. Mol. Sci. 2019, 20(24), 6184; https://doi.org/10.3390/ijms20246184 - 7 Dec 2019
Cited by 14 | Viewed by 3842
Abstract
Protein kinases of the CK1 family can be involved in numerous physiological and pathophysiological processes. Dysregulated expression and/or activity as well as mutation of CK1 isoforms have previously been linked to tumorigenesis. Among all neoplastic diseases, colon and rectal cancer (CRC) represent the [...] Read more.
Protein kinases of the CK1 family can be involved in numerous physiological and pathophysiological processes. Dysregulated expression and/or activity as well as mutation of CK1 isoforms have previously been linked to tumorigenesis. Among all neoplastic diseases, colon and rectal cancer (CRC) represent the fourth leading cause of cancer related deaths. Since mutations in CK1δ previously found in CRC patients exhibited increased oncogenic features, inhibition of CK1δ is supposed to have promising therapeutic potential for tumors, which present overexpression or mutations of this CK1 isoform. Therefore, it is important to develop new small molecule inhibitors exhibiting higher affinity toward CK1δ mutants. In the present study, we first characterized the kinetic properties of CK1δ mutants, which were detected in different tumor entities. Subsequently, we characterized the ability of several newly developed IWP-based inhibitors to inhibit wild type and CK1δ mutants and we furthermore analyzed their effects on growth inhibition of various cultured colon cancer cell lines. Our results indicate, that these compounds represent a promising base for the development of novel CRC therapy concepts. Full article
(This article belongs to the Special Issue Molecular and Translational Research on Colorectal Cancer)
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Figure 1

Figure 1
<p>Mutations in <span class="html-italic">CSNK1D</span> identified in different types of cancer. According to the database cBioPortal for Cancer Genomics, 123 mutations have been reported in a curated set of 159 non-redundant studies, including 42,199 samples [<a href="#B5-ijms-20-06184" class="html-bibr">5</a>,<a href="#B6-ijms-20-06184" class="html-bibr">6</a>]. Positions of the respective mutations in the CK1δ protein and their frequency of detection are indicated. Missense mutations are displayed in green, truncating mutations in black, and other types of mutations (e.g., protein fusion mutations) in purple. Highlighted mutations were selected for further analysis. Abbreviations: A, alanine; aa, amino acids, E, glutamic acid; G, glycine; H, histidine; I, isoleucine; K, lysine; L, leucine; M, methionine; P, proline; Q, glutamine; R, arginine; S, serine; V, valine; W, tryptophan; Y, tyrosine; *, stop codon.</p>
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<p>Kinetic parameters V<sub>max</sub> and k<sub>cat</sub> of CK1δ wild type and mutants with different substrates. Michaelis–Menten kinetics have been analyzed for CK1δ wild type and mutants using either α-casein, GST-β-catenin<sup>1−181</sup>, or GST-p53<sup>1−64</sup> as substrate. The kinetic parameters V<sub>max</sub> (<b>a</b>) and k<sub>cat</sub> (<b>b</b>) were normalized toward the respective parameters determined for CK1δ wild type. Data is presented as mean values and standard deviation (SD) for experiments performed in triplicate. Abbreviations: A, alanine; E, glutamic acid; G, glycine; GST, glutathione S-transferase; H, histidine; I, isoleucine; K, lysine; k<sub>cat</sub>, turnover number; L, leucine; M, methionine; P, proline; Q, glutamine; R, arginine; S, serine; V, valine; V<sub>max</sub>, maximum enzyme reaction velocity; W, tryptophan; WT, wild type; Y, tyrosine; *, stop codon.</p>
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<p>Molecular modeling of MgATP in mutated CK1δ. (<b>a</b>) CK1δ<sup>R115H</sup>, (<b>b</b>) CK1δ<sup>R127Q</sup>, and (<b>c</b>) CK1δ<sup>R168H</sup> with MgATP (red/white atoms) bound in the ATP-binding pocket. The mutated residues are highlighted in purple and are indicated with white arrowheads. Induced fit calculations were performed based on a homology model of 4twc [<a href="#B14-ijms-20-06184" class="html-bibr">14</a>] on 1csn [<a href="#B23-ijms-20-06184" class="html-bibr">23</a>] CK1δ crystal structures (obtained from protein data bank (PDB) [<a href="#B24-ijms-20-06184" class="html-bibr">24</a>]) in order to realize the adjustment of the P-loop.</p>
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<p>Compounds <b>16</b>, <b>17</b>, <b>18</b>, and <b>19</b> significantly inhibit kinase activity of CK1δ and ε. (<b>a</b>) Kinase activity of CK1α, γ, δ, and ε was analyzed in presence of 10 µM of newly designed IWP-derivatives <b>16</b>, <b>17</b>, <b>18</b>, and <b>19</b>. Results are presented as mean values of experiments performed in triplicate. Error bars indicate the standard error. (<b>b</b>) In vitro IC<sub>50</sub> values were determined for CK1δ and a dilution series of compounds <b>16</b>, <b>17</b>, <b>18</b>, or <b>19</b>. IC<sub>50</sub> values were calculated using GraphPad Prism 6. Results are presented as mean values ± standard error of experiments performed in triplicate. (<b>c</b>) Structures of inhibitor compounds <b>16</b>, <b>17</b>, <b>18</b>, and <b>19</b>. Abbreviations: DMSO, dimethyl sulfoxide; IC<sub>50</sub>; 50% inhibitory concentration; µM, micromolar.</p>
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<p>CK1δ<sup>WT</sup> and selected hyperactive CK1δ mutants are inhibited by newly developed CK1-specific inhibitors. Inhibition of CK1δ<sup>WT</sup> and mutants CK1δ<sup>T67S</sup> (<b>a</b>), CK1δ<sup>R127Q</sup> (<b>b</b>), or CK1δ<sup>R168H</sup> (<b>c</b>) by IWP-derivatives <b>16</b>, <b>17</b>, <b>18</b>, and <b>19</b> at a concentration of 10 µM has been determined by in vitro kinase assays. Please note that the <span class="html-italic">Y</span>-axis in (<b>a</b>) is set to a maximum of 80%, while in (<b>b</b>,<b>c</b>) it is set to 40%. Residual kinase activity has been normalized toward the respective DMSO control activity (100%). Results are presented as mean values of experiments performed in triplicate. Error bars represent the SD. Statistical analysis was done by performing two-way ANOVA using following levels of significance: * <span class="html-italic">p</span> ≤ 0.05; ** <span class="html-italic">p</span> ≤ 0.01; *** <span class="html-italic">p</span> ≤ 0.001; ns, not significant. Abbreviations: H, histidine; Q, glutamine; R, arginine; S, serine; T, threonine; TV, transcription variant; WT, wild type.</p>
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<p>Compounds <b>18</b> and <b>19</b> show CK1δ-specific effects on cell viability. (<b>a</b>) MTT viability assays have been performed by treating parental HeLa cells, HeLa CK1δ<sup>−/−</sup>, or HeLa CK1δ<sup>+/+</sup> cells with 25 μM of compounds <b>16</b>, <b>17</b>, <b>18</b>, <b>19</b>, or DMSO as control for 48 h. Cell viability of treated cells has been normalized toward the viability of DMSO-treated cells. Results are presented as mean values of experiments performed in triplicate. Error bars represent the SD. Statistical analysis was done by performing two-way ANOVA using following levels of significance: * <span class="html-italic">p</span> ≤ 0.05; **** <span class="html-italic">p</span> ≤ 0.0001; ns, not significant. (<b>b</b>) EC<sub>50</sub> values were determined by treating HeLa CK1δ<sup>−/−</sup> and HeLa CK1δ<sup>+/+</sup> cells with compound <b>18</b> in a concentration range between 0.313 and 70 µM for 48 h. Cell viability of treated cells has been normalized toward the viability of DMSO-treated cells. Results are presented as mean values of experiments performed in triplicate. Error bars represent the SD. EC<sub>50</sub> values have been calculated using GraphPad Prism 6. Abbreviations: EC<sub>50</sub>, 50% effective concentration; µM, micromolar.</p>
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<p>Compound <b>18</b> shows stronger effects on cell viability of HeLa cells expressing hyperactive CK1δ mutants. Cell viability of HeLa CK1δ<sup>−/−</sup> cells expressing CK1δ<sup>WT</sup>, CK1δ<sup>T67S</sup> (<b>a</b>), CK1δ<sup>R127Q</sup> (<b>b</b>), or CK1δ<sup>R168H</sup> (<b>c</b>) has been determined by MTT viability assay after treatment with <b>18</b> (20 μM, 10 μM, or 5 μM) or DMSO as control for 48 h. Cell viability of treated cells has been normalized toward the viability of DMSO-treated cells. Results are presented as mean values of experiments performed in triplicate. Error bars represent the SD. Statistical analysis was done by performing two-way ANOVA using following levels of significance: * <span class="html-italic">p</span> ≤ 0.05; ** <span class="html-italic">p</span> ≤ 0.01; ns, not significant. Abbreviations: H, histidine; Q, glutamine; R, arginine; S, serine; T, threonine; TV, transcription variant; WT, wild type.</p>
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<p>Compounds <b>20</b>, <b>21</b>, and <b>22</b> significantly inhibit kinase activity of CK1δ and ε. (<b>a</b>) Kinase activity of CK1α, γ, δ, and ε was analyzed in presence of 10 µM of newly designed IWP-derivatives <b>20</b>, <b>21</b>, and <b>22</b>. Results are presented as mean values of experiments performed in triplicate. Error bars indicate the standard error. (<b>b</b>) In vitro IC<sub>50</sub> values were determined for CK1δ and a dilution series of compounds <b>20</b>, <b>21</b>, and <b>22</b>. IC<sub>50</sub> values were calculated using GraphPad Prism 6. Results are presented as mean values ± standard error of experiments performed in triplicate. (<b>c</b>) Structures of inhibitor compounds <b>20</b>, <b>21</b>, and <b>22</b>. Abbreviations: DMSO, dimethyl sulfoxide; IC<sub>50</sub>; 50% inhibitory concentration; µM, micromolar.</p>
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<p>Effects mediated by compound <b>20</b> are CK1δ-specific but not mutant-specific. (<b>a</b>) MTT viability assays have been performed by treating parental HeLa cells, HeLa CK1δ<sup>−/−</sup>, or HeLa CK1δ<sup>+/+</sup> cells with 5 μM of compound <b>20</b> or DMSO as control for 48 h. Cell viability of treated cells has been normalized toward the viability of DMSO-treated cells. Results are presented as mean values of experiments performed in triplicate. Error bars represent the SD. (<b>b–d</b>) Cell viability of HeLa CK1δ<sup>−/−</sup> cells expressing CK1δ<sup>WT</sup>, CK1δ<sup>T67S</sup> (<b>b</b>), CK1δ<sup>R127Q</sup> (<b>c</b>), or CK1δ<sup>R168H</sup> (<b>d</b>) has been determined by MTT viability assay after treatment with compound <b>20</b> (5 μM) or DMSO as control for 48 h. Cell viability of treated cells has been normalized toward the viability of DMSO-treated cells. Results are presented as mean values of experiments performed in triplicate. Error bars represent the SD. Statistical analysis was done by performing two-way ANOVA using following levels of significance: *** <span class="html-italic">p</span> ≤ 0.001; **** <span class="html-italic">p</span> ≤ 0.0001; ns, not significant. Abbreviations: H, histidine; Q, glutamine; R, arginine; S, serine; T, threonine; TV, transcription variant; WT, wild type.</p>
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<p>Inhibitory effects of compounds <b>20</b>, <b>21</b>, and <b>22</b> on cell viability of established colon cancer cell lines. (<b>a</b>) Inhibitors were used at a concentration of 10 µM to treat HCT-116, HT-29, SW480, and SW620 for 48 h in an initial screening. Cell viability of treated cells has been normalized toward the viability of DMSO-treated cells. Results are presented as mean values of experiments performed in triplicate. Error bars represent the SD. (<b>b</b>) EC<sub>50</sub> value was determined by treating SW620 cells with compound <b>20</b> in a concentration range between 0.625 and 20 µM for 48 h. Cell viability of treated cells has been normalized toward the viability of DMSO-treated cells. Results are presented as mean values of experiments performed in triplicate. Error bars represent the SD. EC<sub>50</sub> values have been calculated using GraphPad Prism 6. Abbreviations: DMSO, dimethyl sulfoxide; EC<sub>50</sub>, 50% effective concentration; µM, micromolar.</p>
Full article ">Scheme 1
<p>Scheme of synthesis of compounds <b>1</b>–<b>7</b> and <b>8</b>–<b>14</b>. Compounds <b>1</b>–<b>7</b>: <b>1</b> (R1−R4 = H); <b>2</b> (R1 = O CH<sub>3</sub>, R2−R3 = H, R4 = O CH<sub>3</sub>); <b>3</b> (R1 = O CH<sub>3</sub>, R2 = H, R3 = O CH<sub>3</sub>, R4 = H); <b>4</b> (R1 = H, R2 = O CH<sub>3</sub>, R3 = H, R4 = O CH<sub>3</sub>); <b>5</b> (R1–R2 = H, R3 = O CH<sub>3</sub>); <b>6</b> (R1–R2 = H, R3 = CF<sub>3</sub>); <b>7</b> (R1 = F, R2-R3 = H). Compounds <b>8</b>–<b>14</b>: <b>8</b> (n = 0, R1–R4 = H); <b>9</b> (n = 0, R1 = O CH<sub>3</sub>, R2-R3 = H, R4 = O CH<sub>3</sub>); <b>10</b> (n = 0, R1 = O CH<sub>3</sub>, R2 = H, R3 = O CH<sub>3</sub>, R4 = H); <b>11</b> (n = 0, R1 = H, R2 = O CH<sub>3</sub>, R3 = H, R4 = O CH<sub>3</sub>); <b>12</b> (n = 1, R1–R2 = H, R3 = O CH<sub>3</sub>); <b>13</b> (n = 1, R1–R2 = H, R3 = CF<sub>3</sub>); <b>14</b> (n = 1, R1 = F, R2-R3 = H).</p>
Full article ">Scheme 2
<p>Scheme of synthesis of compounds <b>16</b>–<b>22</b>. Coupling of compounds <b>8</b>–<b>14</b> with the benzothiazole–linker <b>15</b> was performed according to the literature [<a href="#B30-ijms-20-06184" class="html-bibr">30</a>]. Compounds: <b>16</b> (n = 0, R1–R4 = H); <b>17</b> (n = 0, R1 = O CH<sub>3</sub>, R2-R3 = H, R4 = O CH<sub>3</sub>); <b>18</b> (n = 0, R1 = O CH<sub>3</sub>, R2 = H, R3 = O CH<sub>3</sub>, R4 = H); <b>19</b> (n = 0, R1 = H, R2 = O CH<sub>3</sub>, R3 = H, R4 = O CH<sub>3</sub>); <b>20</b> (n = 1, R1–R2 = H, R3 = O CH<sub>3</sub>); <b>21</b> (n = 1, R1–R2 = H, R3 = CF<sub>3</sub>); <b>22</b> (n = 1, R1 = F, R2–R3 = H).</p>
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14 pages, 1529 KiB  
Article
Human Recombinant Arginase I [HuArgI (Co)-PEG5000]-Induced Arginine Depletion Inhibits Colorectal Cancer Cell Migration and Invasion
by Houssam Al-Koussa, Maria Al-Haddad, Ralph Abi-Habib and Mirvat El-Sibai
Int. J. Mol. Sci. 2019, 20(23), 6018; https://doi.org/10.3390/ijms20236018 - 29 Nov 2019
Cited by 26 | Viewed by 4158
Abstract
Purpose: Colorectal cancer (CRC) is the third most common type of cancer worldwide, and it represents over half of all gastrointestinal cancer deaths. Knowing that cancer cells have a high proliferation rate, they require high amounts of amino acids, including arginine. In addition, [...] Read more.
Purpose: Colorectal cancer (CRC) is the third most common type of cancer worldwide, and it represents over half of all gastrointestinal cancer deaths. Knowing that cancer cells have a high proliferation rate, they require high amounts of amino acids, including arginine. In addition, several tumor types have been shown to downregulate ASS-1 expression, becoming auxotrophic for arginine. Therefore, Arginine deprivation is one of the promising therapeutic approaches to target cancer cells. This can be achieved through the use of a recombinant human arginase, HuArgI(Co)-PEG5000, an arginine degrading enzyme. Methods: In this present study, the cytotoxic effect of HuArgI(Co)-PEG5000 on CRC cell lines (HT-29, Caco-2, Sw837) is examined though cytotoxicity assays. Wound healing assays, invasion assays, and adhesion assays were also performed to detect the effect on metastasis. Results: Wound healing and invasion assays revealed a decrease in cell migration and invasion after treatment with arginase. Cells that were treated with arginase also showed a decrease in adhesion, which coincided with a decrease in RhoA activation, demonstrated though the use of a FRET biosensor to detect RhoA activation in a single cell assay, and a decrease in MMP-9 expression. Treating cells with both arginase and L-citrulline, which significantly restores intracellular arginine levels, reversed the effect of HuArgI(Co)-PEG5000 on cell viability, migration, and invasion. Conclusion: We can, therefore, conclude that colorectal cancer is partially auxotrophic to arginine and that arginine depletion is a potential selective inhibitory approach for motility and invasion in colon cancer cells. Full article
(This article belongs to the Special Issue Molecular and Translational Research on Colorectal Cancer)
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Figure 1

Figure 1
<p>HuArgI(Co)-PEG5000 decreased viability of CRC cells. Non-linear regression curve of the cytotoxicity of the indicated CRC cell lines, HT-29 (<b>A</b>)<b>,</b> Caco-2 (<b>B</b>), and SW837 (<b>C</b>) treated with HuArgI(Co)-PEG5000 alone (square) at concentration ranging from 10<sup>−12</sup> to 10<sup>−7</sup> M or with L-citrulline (11.4 mM) (triangle) at time 72 h. Data are the mean ± SEM. <span class="html-italic">p</span> &lt; 0.05 indicates a statistically significant difference.</p>
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<p>HuArgI(Co)-PEG5000 inhibits motility in CRC cells. Caco-2 cell were treated with HuArgI(Co)-PEG5000 (100 pM) with or without L-citrulline (11.4 mM). Cell monolayers were wounded and images were taken at time 0 h and 72 h. (<b>A</b>) Representative wound closure images. The scale bar is 100 μm. (<b>B</b>) Frames were quantitated using ImageJ (National Institutes of Health, MA, USA). The width of each wound was measured at 11 different points. The average rate of wound closure was calculated and expressed as µm/h. (<b>C</b>) Caco-2 cells were untreated or treated with HuArgI(Co)-PEG5000 with or without L-citrulline for 72 h. Cells were then lysed and blotted for ASS-1 and Actin. (<b>D</b>) Quantitation of (<b>C</b>) using imageJ software, expressed as the fold change to control. Data are the mean ± SEM. <span class="html-italic">p</span> &lt; 0.05 (* <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.005) indicates a statistically significant difference.</p>
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<p>HuArgI(Co)-PEG5000 decreased cell adhesion in Caco-2 cells. (<b>A</b>) Representative micrographs of Caco-2 cells plated at the different indicated conditions, fixed and stained with crystal violet as described in the methods. Scale is 100 μm. (<b>B</b>) Quantitation of (<b>A</b>) expressed as fold difference to control. Absorption of the solubilized Crystal violet was measured at 550 nm using an ELISA plate reader. (<b>C</b>) Representative micrographs of Caco-2 cells untreated or treated with HuArgI (Co)-PEG5000 (100 pM) with or without L-citrulline (11.4 mM) and stained with anti-vinculin. Cells were imaged using a 60× objective. Scale is 10 μm. The red inset indicates a region in the cell magnified 10X in the right panel to better show the FAs. (<b>D</b>) Quantitation of areas (<b>E</b>) or numbers of focal adhesions, Data are the mean ± SEM. <span class="html-italic">p</span> &lt; 0.05 (* <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.001) indicates a statistically significant difference.</p>
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<p>HuArgI(Co)-PEG5000 decreased RhoA activation in CRC cells. (<b>A</b>) Representative micrographs of Caco-2 cells transfected with the RhoA FRET biosensors, cells were then untreated or treated with HuArgI(Co)-PEG5000 alone (100 pM) or with without L-citrulline (11.4 mM). The ratiometric images were obtained by normalizing the raw FRET image to the CFP image as described before [<a href="#B13-ijms-20-06018" class="html-bibr">13</a>]. Scale is 10 μm. (<b>B</b>) Quantitation of (<b>A</b>) expressed as fold difference to control. Data are the mean ± SEM. * is <span class="html-italic">p</span> &lt; 0.05 indicating a statistically significant difference.</p>
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<p>HuArgI(Co)-PEG5000 decreased invasion and MMP expression in CRC cells. (<b>A</b>) Representative micrographs of Caco-2 untreated or treated with HuArgI(Co)-PEG5000 (100 pM) without or with L-citrulline (11.4 mM) and subjected to a 72 h cell invasion assay. Scale is 100 μm. (<b>B</b>) After cells invaded, they were stained, stain was then extracted and absorbance measured at 560 nm. Data are fold change compared to control ± SEM. (<b>C</b>/<b>D</b>) Caco-2 cells were untreated or treated with HuArgI(Co)-PEG5000 with or without L-citrulline for 72 h. Cells were then lysed and blotted for MMP-2 (<b>C</b>) and MMP-9 (<b>D</b>). (<b>E</b>/<b>F</b>) Quantitation of (<b>C</b>) and (<b>D</b>) using the imageJ software expressed as fold change from control. Data are the mean ± SEM. <span class="html-italic">p</span> &lt; 0.05 (** <span class="html-italic">p</span> &lt; 0.001 and *** <span class="html-italic">p</span> &lt; 0.0001) indicates a statistically significant difference.</p>
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17 pages, 3334 KiB  
Article
VEGFR-1 Regulates EGF-R to Promote Proliferation in Colon Cancer Cells
by Hikaru Nagano, Chisato Tomida, Naoko Yamagishi and Shigetada Teshima-Kondo
Int. J. Mol. Sci. 2019, 20(22), 5608; https://doi.org/10.3390/ijms20225608 - 9 Nov 2019
Cited by 14 | Viewed by 4869
Abstract
The relationship between epidermal growth factor (EGF) and vascular endothelial growth factor (VEGF) pathways in tumor growth is well established. EGF induces VEGF production in cancer cells, and the paracrine VEGF activates vascular endothelial cells to promote tumor angiogenesis and thus supports tumor [...] Read more.
The relationship between epidermal growth factor (EGF) and vascular endothelial growth factor (VEGF) pathways in tumor growth is well established. EGF induces VEGF production in cancer cells, and the paracrine VEGF activates vascular endothelial cells to promote tumor angiogenesis and thus supports tumor cell growth in an angiogenesis-dependent manner. In this study, we found angiogenesis-independent novel crosstalk between the VEGF and the EGF pathways in the regulation of colon cancer cell proliferation. Stimulation of colon cancer cells with VEGF-A and placental growth factor (PlGF) activated VEGF receptor-1 (VEGFR-1) and increased proliferation activity in an autocrine EGF/EGF receptor (EGF-R)-dependent manner. Mechanistically, VEGFR-1 interacted with and stabilized EGF-R, leading to increased EGF-R protein levels and prolonged its expression on cell surface plasma membrane. In contrast, VEGFR-1 blockade by a neutralizing antibody and an antagonistic peptide of VEGFR-1 suppressed the complex formation of VEGFR-1 and EGF-R and decreased EGF-R expression via a lysosome-dependent pathway, resulting in the suppression of proliferation activity. Our results indicated that VEGFR-1 regulated EGF-R expression to promote proliferation activity in a cell-autonomous-dependent manner. Full article
(This article belongs to the Special Issue Molecular and Translational Research on Colorectal Cancer)
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Graphical abstract

Graphical abstract
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<p>Vascular endothelial growth factor receptor-1 (VEGFR-1) activation results in increased proliferation activity that depends on autocrine epidermal growth factor (EGF)/EGF receptor (EGF-R) pathway. (<b>A</b>) Activation of VEGFR-1 by vascular endothelial growth factor-A (VEGF-A) and placental growth factor (PlGF) stimulation. Cells were treated with VEGF-A, (PlGF) or control bovine serum albumin (BSA) for 5 min. Phosphorylated VEGFR-1 was detected by immunoprecipitation with an anti-phospho-tyrosine antibody and immunoblotting with an anti-VEGFR-1 antibody. The same lysates (10% input) were immunoblotted with an anti-VEGFR-1 antibody to normalize the amounts of each sample. The levels of β-actin are shown as a loading control. (<b>B</b>) Quantification of EdU (5-ethynyl-2’-deoxyuridine) positive cells under VEGFR-1 activating conditions. Data are indicated by means ± SD (<span class="html-italic">n</span> = 6–8). <span class="html-italic">* p</span> &lt; 0.01, statistically significant increase compared with the BSA-treated control cells. (<b>C</b>) Quantification of EdU positive cells under EGF/EGF-R inhibiting conditions. Cells were pretreated with neutralizing antibodies against EGF (anti-EGF Ab) and EGF-R (anti-EGF-R Ab), or control non-immune IgG (control) for 1 h, and then treated with VEGF-A or PlGF for 24 h. Data are indicated by means ± SD (<span class="html-italic">n</span> = 6–8).</p>
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<p>VEGFR-1 activation results in increased EGF-R expression levels. (<b>A</b>–<b>D</b>) Cells were treated with control BSA for 18 h, or with VEGF-A or PlGF for the indicated times. EGF-R (<b>A</b>) and phosphorylated EGF-R (<b>C</b>) levels were determined by immunoblot analysis. The levels of β-actin are shown as a loading control. Quantification of EGF-R levels (<b>B</b>) and phosphorylated EGF-R levels (<b>D</b>) normalized to β-actin from three independent experiments. * <span class="html-italic">p</span> &lt; 0.01, statistically significant increase compared with the BSA-treated control. (<b>E</b>) Immunofluorescent staining with cell surface EGF-R. Cells were pre-treated with control BSA for 4 h or with VEGF-A and PlGF for the indicated times. Living cells were then incubated with an anti-EGF-R antibody conjugated with FITC for 30 min at 4 degrees and fixed. Nuclei were stained with 4′,6-diamidino-2-phenylindole (DAPI). Representative fluorescent images are shown. Scale bar = 10 μm. (<b>F</b>) Expression levels of <span class="html-italic">EGF-R</span> mRNA were determined by RT-qPCR analysis. Values were normalized for the amount of <span class="html-italic">GAPDH</span> mRNA (<span class="html-italic">n</span> = 5, means ± SD).</p>
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<p>VEGFR-1 activation stabilizes EGF-R. (<b>A</b>) Cells were pretreated with control BSA, VEGF-A or PlGF for 1 h, then incubated with EGF plus cycloheximide for the indicated times. EGF-R protein levels were determined by immunoblot analysis. The levels of β-actin are shown as a loading control. (<b>B</b>) Quantification of EGF-R levels normalized to β-actin from three independent experiments. * <span class="html-italic">p</span> &lt; 0.01, statistically significant increase compared with the BSA-treated control cells at the corresponding each time point.</p>
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<p>VEGFR-1 activation increases complex formation of VEGFR-1 and EGF-R. (<b>A</b>) Cells were treated with BSA, VEGF-A or PlGF for 30 min. Cell lysates were immunoprecipitated with an anti-EGF-R antibody (IP: EGF-R) and then immunoblotted for VEGFR-1 (<b>A</b>, upper panel) and for phosphorylated VEGFR-1 (<b>A</b>, middle panel). In parallel, Western blot was performed to control for EGF-R concentration in the immunoprecipitates (<b>A</b>, lower panel). (<b>B</b>,<b>C</b>) Quantification of co-immunoprecipitated VEGFR-1 levels (<b>B</b>) and phosphorylated VEGFR-1 levels (<b>C</b>) normalized to immunoprecipitated EGF-R levels from three independent experiments. * <span class="html-italic">p</span> &lt; 0.01, statistically significant increase compared with the BSA-treated control. (<b>D</b>) Cells were treated with BSA, VEGF-A or PlGF for 30 min. Cell lysates were immunoprecipitated with an anti-VEGFR-1 antibody (IP: VEGFR-1) and then immunoblotted for EGF-R (<b>D</b>, upper panel). In parallel, Western blot was performed to control for VEGFR-1 concentration in the immunoprecipitates (<b>D</b>, lower panel). (<b>E</b>) Quantification of co-immunoprecipitated EGF-R levels normalized to immunoprecipitated VEGFR-1 levels from three independent experiments. * <span class="html-italic">p</span> &lt; 0.01, statistically significant increase compared with the BSA-treated control.</p>
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<p>VEGFR-1 blockade decreases proliferation activity. (<b>A</b>) Inhibition of VEGFR-1 by anti-VEGFR-1 neutralizing antibody (anti-R1 Ab) and VEGFR-1 antagonist (R1 antagonist). Cells were treated with anti-R1 Ab, R1 antagonist or control IgG (control) in the presence of VEGF-A for 5 min. Phosphorylated VEGFR-1 was detected as described in legend to <a href="#ijms-20-05608-f001" class="html-fig">Figure 1</a>. The same lysates (10% input) were immunoblotted with an anti-VEGFR-1 antibody to normalize the amounts of each sample. The levels of β-actin are shown as a loading control. (<b>B</b>) Quantification of EdU positive cells under VEGFR-1 inhibiting conditions. Cells were treated with anti-R1 Ab, R1 antagonist or control IgG for 24 h. Data are indicated by means ± SD (<span class="html-italic">n</span> = 6–8). <span class="html-italic">* p</span> &lt; 0.01, statistically significant decrease compared with the control cells.</p>
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<p>VEGFR-1 blockade decreases EGF-R expression. (<b>A</b>) Cells were treated with anti-R1 Ab and R1 antagonist for the indicated times, or with a control IgG for 24 h (indicated by control). EGF-R protein levels were determined by immunoblot analysis. The levels of β-actin are shown as a loading control. (<b>B</b>) Quantification of EGF-R levels normalized to β-actin from three independent experiments. * <span class="html-italic">p</span> &lt; 0.01, statistically significant decrease compared with the control cells. (<b>C</b>,<b>D</b>) Cells were transfected with the indicated siRNA for 24 h. Levels of VEGFR-1 protein (<b>C</b>, left) and EGF-R protein (<b>D</b>, left) were determined by immunoblot analysis. Quantification of VEGFR-1 levels (<b>C</b>, right) and EGF-R levels (<b>D</b>, right) normalized to β-actin from three independent experiments. <span class="html-italic">* p</span> &lt; 0.01, statistically significant decrease compared with the si-control-transfected cells. (<b>E</b>) Immunofluorescent staining with cell surface EGF-R. After cells were treated with anti-R1 Ab, R1 antagonist or control IgG for 1 h, cell surface EGF-R was stained as described in legend to <a href="#ijms-20-05608-f002" class="html-fig">Figure 2</a>E. Scale bar = 10 μm. (<b>F</b>) Cells were treated with anti-R1 Ab, R1 antagonist or control IgG for 12 h. Expression levels of <span class="html-italic">EGF-R</span> mRNA were determined by RT-qPCR analysis. Values were normalized for the amount of <span class="html-italic">GAPDH</span> mRNA (<span class="html-italic">n</span> = 5, means ± SD).</p>
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<p>EGF-R knockdown blocks VEGFR-1 activation-induced proliferation activity. (<b>A</b>) Cells were transfected with the indicated siRNA for 24 h. EGF-R protein levels were determined by immunoblot analysis. The levels of β-actin are shown as a loading control. (<b>B</b>) Quantification of EGF-R levels normalized to β-actin from three independent experiments. * <span class="html-italic">p</span> &lt; 0.01, statistically significant decrease compared with the si-control-treated cells. (<b>C</b>) Quantification of EdU positive cells under EGF-R silencing conditions. Cells were transfected with the indicated siRNA for 24 h, then treated with VEGF-A, PlGF or BSA for the additional 24 h. For a control experiment, cells were treated with only anti-R1 Ab or control IgG (indicated by control) for 24 h. Data are indicated by means ± SD (<span class="html-italic">n</span> = 6–8). <sup>#</sup> <span class="html-italic">p</span> &lt; 0.01, statistically significant decrease compared with the control. * <span class="html-italic">p</span> &lt; 0.01, statistically significant increase compared with the control.</p>
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<p>EGF-R downregulation is mediated by a lysosomal degradation pathway under VEGFR-1 inhibited conditions. (<b>A</b>) Cells were pretreated with a lysosomal inhibitor (bafilomycin A) or a proteasomal inhibitor (MG132) for 1 h, then treated with anti-R1 Ab (lanes 4 and 6) and R1 antagonist (lanes 5 and 7) for 1 h. Without bafilomycin A or MG132, cells were treated with anti-R1 Ab alone (lane 2), R1 antagonist alone (lane 3) or control IgG alone (lane 1, indicated by control) for 1 h. EGF-R protein levels were determined by immunoblot analysis. The levels of β-actin are shown as a loading control. (<b>B</b>) Quantification of EGF-R levels normalized to β-actin from three independent experiments. * <span class="html-italic">p</span> &lt; 0.01, statistically significant decrease compared with the control.</p>
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<p>VEGFR-1 blockade decreases complex formation of VEGFR-1 and EGF-R. Cells were untreated or pretreated with anti-R1 Ab, R1 antagonist or control IgG (control) for 3 min, and then stimulated with VEGF-A for 2 min. (<b>A</b>) Cell lysates were immunoprecipitated with an anti-EGF-R antibody (IP: EGF-R) and then immunoblotted for VEGFR-1 (<b>A</b>, upper panel). In parallel, Western blot was performed to control for EGF-R concentration in the immunoprecipitates (<b>A</b>, lower panel). (<b>B</b>) Quantification of co-immunoprecipitated VEGFR-1 levels normalized to immunoprecipitated EGF-R from three independent experiments. * <span class="html-italic">p</span> &lt; 0.01, statistically significant increase compared with the untreated cells. (<b>C</b>) Cell lysates were immunoprecipitated with an anti-VEGFR-1 antibody (IP: VEGFR-1) and then immunoblotted for EGF-R (<b>C</b>, upper panel). In parallel, Western blot was performed to control for VEGFR-1 concentration in the immunoprecipitates (<b>C</b>, lower panel). (<b>D</b>) Quantification of co-immunoprecipitated EGF-R levels normalized to immunoprecipitated VEGFR-1 from three independent experiments. * <span class="html-italic">p</span> &lt; 0.01, statistically significant increase compared with the untreated cells.</p>
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17 pages, 5855 KiB  
Article
The Histochemical Alterations of Mucin in Colorectal Carcinoma Quantified by Two Efficient Algorithms of Digital Image Analysis
by Aldona Kasprzak, Agnieszka Seraszek-Jaros, Joanna Jagielska, Celina Helak-Łapaj, Elżbieta Siodła, Jacek Szmeja and Elżbieta Kaczmarek
Int. J. Mol. Sci. 2019, 20(18), 4580; https://doi.org/10.3390/ijms20184580 - 16 Sep 2019
Cited by 17 | Viewed by 4331
Abstract
The practical use of knowledge on the diagnostic-prognostic role of polysaccharide components of mucins in colorectal cancer (CRC) has been difficult, due to the number of histochemical (HC) reaction types, as well as lack of standard methods of computer-assisted analysis of tissue expression [...] Read more.
The practical use of knowledge on the diagnostic-prognostic role of polysaccharide components of mucins in colorectal cancer (CRC) has been difficult, due to the number of histochemical (HC) reaction types, as well as lack of standard methods of computer-assisted analysis of tissue expression of these molecules. Using two algorithms of digital image analysis (by application of Image-Pro Premier and our originally designed program Filter HSV), we evaluated the expression of polysaccharides in tissue samples of CRC patients (n = 33), and fragments of normal colorectal tissue from the same patients (control) using periodic acid Schiff reaction (PAS) (neutral mucins) and alcian blue staining (AB) (acidic mucins). Our results indicate lower expression of the PAS+ and AB+ mucins in CRC, as compared to the control samples. The higher expression of PAS+ polysaccharides was detected in flat tumors than in protruded CRC, while higher AB+ mucins expression was a feature of mucinous CRC subtypes. Positive correlation between mutual PAS+ and AB+ expression, as well as correlations with glucose concentration (PAS+ mucins), and hemoglobin level (AB+ mucins) were observed exclusively in unchanged colorectal samples (control). Both algorithms of digital image analysis (smart segmentation and Filter HSV) work properly and can be used interchangeably in daily practice of pathologists, as useful tools of quantitative evaluation of HC reaction in both normal and cancerous tissues. Full article
(This article belongs to the Special Issue Molecular and Translational Research on Colorectal Cancer)
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Figure 1

Figure 1
<p>Illustrations with histochemical reaction in colorectal carcinoma (<b>A</b>,<b>B</b>,<b>D</b>,<b>E</b>) and normal colorectal tissue (control) (<b>C,F</b>). PAS reactivity in cytoplasm of the majority of neoplastic cells (arrow) (<b>A</b>); an intense PAS+ reaction in the extracellular mucus produced by neoplastic cells (arrow) (<b>B</b>); representative image of PAS expression in the control intestinal crypts (<b>C</b>); light blue AB staining in neoplastic cells (arrow) and in the extracellular mucus (arrow) of colorectal carcinoma specimens (<b>D</b>,<b>E</b>); dark blue AB staining in goblet cells and in the lumen of normal intestinal crypts (<b>F</b>). Hematoxylin (<b>A</b>,<b>B</b>,<b>C</b>) and safranin (<b>D</b>,<b>E</b>,<b>F</b>) counterstained.</p>
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<p>Correlation between two types of computerized methods used for quantitative evaluation of PAS+ and AB+ expression in colorectal carcinoma (<b>A</b> and <b>B</b>) and unaltered colorectal tissue samples (control) (<b>C</b> and <b>D</b>) (<span class="html-italic">p</span> &lt; 0.05 in all cases).</p>
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<p>Comparative histochemical expression of PAS+ and AB+ polysaccharides (mean ± SD) in nonmucinous and mucinous subtypes of colorectal carcinoma. PAS+—periodic acid Schiff positive reactivity; AB+—alcian blue positive reactivity, a—significance = 0.028.</p>
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<p>Correlation between mutual expression of PAS+ and AB+ polysaccharides in colorectal cancer <span class="html-italic">(p &gt;</span> 0.05) (<b>A</b>) and unaltered colorectal tissue (control) (<span class="html-italic">p</span> &lt; 0.05) (<b>B</b>); <span class="html-italic">r</span>—Spearman’s rank correlation coefficient.</p>
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<p>Kaplan-Meier survival curves for CRC patients related to tissue HC reactivity of PAS+ polysaccharides (<b>A</b>) and AB+ polysaccharides (<b>B</b>), showing that expression of both polysaccharide’s mucins in colorectal carcinoma tissue samples is not associated with survival time. High expression—above-mean tissue expression; low expression—below-mean tissue expression.</p>
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<p>Typical steps for digital image analysis by using Filter HSV program concerning expression of PAS+ mucins in control colon (<b>A</b>) and colorectal cancer tissue samples (<b>E</b>). Row image (<b>A</b>); program settings for HSV space (<b>B</b>); successive stage of segmentation of PAS+ reaction (<b>C</b>,<b>D</b>); segmented PAS reaction (<b>D</b>); raw image (<b>E</b>); segmented PAS+ reaction (<b>F</b>).</p>
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<p>Typical steps for digital image analysis by using Image-Pro Premier software concerning expression of PAS+ mucins in control colon (<b>A</b>) and colorectal cancer tissue samples (<b>E</b>). Raw image (<b>A</b>); smart segmentation to identify PAS+ reaction—class 1, tissue area without reaction—class 2, background—class 3 (<b>B</b>); classification of structures (<b>C</b>); final PAS reaction image with mask applied (green color—PAS reaction, yellow—remaining tissue, black—background) (<b>D</b>,<b>F</b>).</p>
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20 pages, 3636 KiB  
Article
SILAC-Based Quantification of TGFBR2-Regulated Protein Expression in Extracellular Vesicles of Microsatellite Unstable Colorectal Cancers
by Fabia Fricke, Malwina Michalak, Uwe Warnken, Ingrid Hausser, Martina Schnölzer, Jürgen Kopitz and Johannes Gebert
Int. J. Mol. Sci. 2019, 20(17), 4162; https://doi.org/10.3390/ijms20174162 - 26 Aug 2019
Cited by 18 | Viewed by 4862
Abstract
Microsatellite unstable (MSI) colorectal cancers (CRCs) are characterized by mutational inactivation of Transforming Growth Factor Beta Receptor Type 2 (TGFBR2). TGFBR2-deficient CRCs present altered target gene and protein expression. Such cellular alterations modulate the content of CRC-derived extracellular vesicles (EVs). EVs [...] Read more.
Microsatellite unstable (MSI) colorectal cancers (CRCs) are characterized by mutational inactivation of Transforming Growth Factor Beta Receptor Type 2 (TGFBR2). TGFBR2-deficient CRCs present altered target gene and protein expression. Such cellular alterations modulate the content of CRC-derived extracellular vesicles (EVs). EVs function as couriers of proteins, nucleic acids, and lipids in intercellular communication. At a qualitative level, we have previously shown that TGFBR2 deficiency causes overall alterations in the EV protein content. To deepen the basic understanding of altered protein dynamics, this work aimed to determine TGFBR2-dependent EV protein signatures in a quantitative manner. Using a stable isotope labeling with amino acids in cell culture (SILAC) approach for mass spectrometry-based quantification, 48 TGFBR2-regulated proteins were identified in MSI CRC-derived EVs. Overall, TGFBR2 deficiency caused upregulation of several EV proteins related to the extracellular matrix and nucleosome as well as downregulation of proteasome-associated proteins. The present study emphasizes the general overlap of proteins between EVs and their parental CRC cells but also highlights the impact of TGFBR2 deficiency on EV protein composition. From a clinical perspective, TGFBR2-regulated quantitative differences of protein expression in EVs might nominate novel biomarkers for liquid biopsy-based MSI typing in the future. Full article
(This article belongs to the Special Issue Molecular and Translational Research on Colorectal Cancer)
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<p>Transforming Growth Factor Beta Receptor Type 2 (TGFBR2)-dependent protein quantification using stable isotope labeling with amino acids in cell culture (SILAC). HCT116-TGFBR2 cells were labeled with light (<span class="html-italic">red</span>) or heavy (<span class="html-italic">blue</span>) amino acids. The mass shift induction was performed over a period of 14 days. Heavy-labeled cells were treated with dox to induce reconstituted expression of TGFBR2 (heavy state: TGFBR2-proficient, pT). Light-labeled cells were cultivated in the absence of dox and remained TGFBR2-deficient (light state: TGFBR2-deficient, dT). The SILAC experiment was performed in triplicate. Extracellular vesicles (EVs) were isolated and characterized from both conditions. Protein extracts from EVs and their parental cells were mixed separately and prepared for high-resolution mass spectrometry analysis. “Light-to-heavy” (dT/pT) protein ratios were calculated in order to identify proteins regulated in a TGFBR2-dependent manner.</p>
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<p>Identification of EVs. (<b>A</b>) Transmission electron microscopy (TEM) pictures visualized the morphology of isolated EVs. Scale bar: 100 nm. (<b>B</b>) Nanoparticle tracking analysis (NTA) revealed the size distribution and particle concentration of isolated EVs. (<b>C</b>) Western blot analysis confirmed EV-specific and cell-specific protein marker expression. Protein sizes are indicated.</p>
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<p>Correlation analysis of protein ratios between biological replicates of EVs. Log2-transformed protein ratios (dT/pT) of each biological replicate were plotted against each other and Pearson′s correlation coefficients (R) were calculated.</p>
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<p>Analysis of TGFBR2-regulated proteins in EVs. (<b>A</b>) Volcano plot shows non-regulated proteins (<span class="html-italic">grey</span>) and significantly up- (<span class="html-italic">red</span>) and downregulated (<span class="html-italic">blue</span>) proteins of EVs derived from TGFBR2-deficient cells. <span class="html-italic">X</span> axis represents log<sub>2</sub>-transformed fold change values. <span class="html-italic">Y</span> axis shows the −log<sub>10</sub> <span class="html-italic">p</span>-value adjusted for multiple comparisons; (<b>B</b>) Pie chart specifies non-regulated EV proteins (<span class="html-italic">grey</span>) and EV proteins up- (<span class="html-italic">red</span>) and downregulated (<span class="html-italic">blue</span>) by cellular TGFBR2 deficiency.</p>
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<p>Venn diagram analysis of proteins identified in HCT116-TGFBR2 cells (<span class="html-italic">yellow</span>) and EVs (<span class="html-italic">green</span>) derived thereof. Numbers of proteins per intersection are given.</p>
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<p>Validation of TGFBR2-regulated EV proteins. Representative TGFBR2-regulated EV proteins (FN1, GLUL, CTGF, CDK1) were analyzed on Western blots in at least two biological replicates. The non-regulated EV marker protein TSG101 was used as a loading control. EVs derived from HCT116-AWE17 cells served as a control for dox-related effects. Band intensities of proteins and calculated ratios are shown below each lane and were normalized to TSG101 set to 1. Protein sizes are indicated.</p>
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<p>Prediction of protein interactions between upregulated proteins and functional enrichment analysis. (<b>A</b>) Protein interaction map of upregulated proteins identified in TGFBR2-deficient EVs (generated by STRING (v11.0)). The connecting lines between protein nodes represent protein-protein interactions. Line thickness indicates the strength of data support (minimum required interaction score = 0.700, high confidence). Coloring of proteins is based on functional enrichment analysis. (<b>B</b>) and (<b>C</b>) Visible clusters of the protein-protein interaction map were assigned to enriched ontologies for Biological Processes (<b>B</b>) and Molecular Function (<b>C</b>). Graphs showing most significant enriched Gene Ontology (GO) among the upregulated EV proteins with observed protein count for each category and calculated <span class="html-italic">p</span>-value adjusted for multiple comparisons.</p>
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11 pages, 3609 KiB  
Article
The Expression Profile and Prognostic Significance of Metallothionein Genes in Colorectal Cancer
by Kuo-Chen Hung, Tsui-Chin Huang, Chia-Hsiung Cheng, Ya-Wen Cheng, Ding-Yen Lin, Jhen-Jia Fan and Kuen-Haur Lee
Int. J. Mol. Sci. 2019, 20(16), 3849; https://doi.org/10.3390/ijms20163849 - 7 Aug 2019
Cited by 16 | Viewed by 3471
Abstract
Colorectal cancer (CRC) is a heterogeneous disease resulting from the combined influence of many genetic factors. This complexity has caused the molecular characterization of CRC to remain uncharacterized, with a lack of clear gene markers associated with CRC and the prognosis of this [...] Read more.
Colorectal cancer (CRC) is a heterogeneous disease resulting from the combined influence of many genetic factors. This complexity has caused the molecular characterization of CRC to remain uncharacterized, with a lack of clear gene markers associated with CRC and the prognosis of this disease. Thus, highly sensitive tumor markers for the detection of CRC are the most essential determinants of survival. In this study, we examined the simultaneous downregulation of the mRNA levels of six metallothionein (MT) genes in CRC cell lines and public CRC datasets for the first time. In addition, we detected downregulation of these six MT mRNAs’ levels in 30 pairs of tumor (T) and adjacent non-tumor (N) CRC specimens. In order to understand the potential prognostic relevance of these six MT genes and CRC, we presented a four-gene signature to evaluate the prognosis of CRC patients. Further discovery suggested that the four-gene signature (MT1F, MT1G, MT1L, and MT1X) predicted survival better than any combination of two-, three-, four-, five-, or six-gene models. In conclusion, this study is the first to report that simultaneous downregulation of six MT mRNAs’ levels in CRC patients, and their aberrant expression together, accurately predicted CRC patients’ outcomes. Full article
(This article belongs to the Special Issue Molecular and Translational Research on Colorectal Cancer)
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<p>Metallothionein (MT) mRNA expression in the National Cancer Institute (NCI)-60 human tumor cell lines and various cancer tissues. (<b>A</b>) Relative MT gene expression profile. Bars to the right show high expression, while bars to the left show low expression relative to the expression mean. Expression values are normalized as z-scores. Data are accessible at <a href="http://discover.nci.nih.gov/cellminer" target="_blank">http://discover.nci.nih.gov/cellminer</a>. (<b>B</b>) Expressions of MT mRNA in 20 common cancers were compared with those in corresponding normal tissues (Oncomine Database). The search criteria thresholds for datasets of cancer versus normal analysis were a <span class="html-italic">p</span>-value of &lt;0.05, a fold change of &gt;1.5, and a gene rank in the top 10%. Red signifies gene overexpression in the analyses; blue represents gene underexpression.</p>
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<p>The mRNA expression of six MT genes in 30 pairs of tumor (T) and adjacent non-tumor (N) CRC specimens. The 30 NT paired tissues were used for examination of the expression of the six MT genes. Lower expression of <span class="html-italic">MT1B</span> (<b>A</b>), <span class="html-italic">MT1F</span> (<b>B</b>), <span class="html-italic">MT1G</span> (<b>C</b>), <span class="html-italic">MT1H</span> (<b>D</b>), <span class="html-italic">MT1L</span> (<b>E</b>), and <span class="html-italic">MT1X</span> (<b>F</b>) was found in tumor part specimens than in non-tumor part tissues. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>MT genes were downregulated in CRC tissues. Relative expression levels of <span class="html-italic">MT1B</span> (<b>A</b>), <span class="html-italic">MT1F</span> (<b>B</b>), <span class="html-italic">MT1G</span> (<b>C</b>), <span class="html-italic">MT1H</span> (<b>D</b>), <span class="html-italic">MT1L</span> (<b>E</b>), and <span class="html-italic">MT1X</span> (<b>F</b>) in colon/rectum normal tissue and CRC tissues by using the Oncomine database (<a href="http://www.oncomine.org/" target="_blank">http://www.oncomine.org/</a>).</p>
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<p>Expression levels of MT genes were associated with prognosis in patients with CRC. Low expression levels of <span class="html-italic">MT1B</span> (<b>A</b>), <span class="html-italic">MT1F</span> (<b>B</b>), <span class="html-italic">MT1G</span> (<b>C</b>), <span class="html-italic">MT1H</span> (<b>D</b>), <span class="html-italic">MT1L</span> (<b>E</b>), and <span class="html-italic">MT1X</span> (<b>F</b>) were correlated with shorter survival time of CRC patients. Green and red lines indicate high- and low-risk groups, respectively. <span class="html-italic">p</span> &lt; 0.05 was considered to be statistically significant.</p>
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<p>Clinical outcomes for the four-gene combination predicted survival better than the individual genes did alone in CRC patients. (<b>A</b>) The <span class="html-italic">SurvExpress</span> database was used to analyze the association of the four-gene signature with the predicted risk. (<b>B</b>) The gene expression levels of <span class="html-italic">MT1F</span>, <span class="html-italic">MT1G</span>, <span class="html-italic">MT1L</span>, and <span class="html-italic">MT1X</span> were detected in high-risk and low-risk groups. (<b>C</b>) Kaplan–Meier survival curves showed that patients with high levels of <span class="html-italic">MT1F</span>, <span class="html-italic">MT1G</span>, <span class="html-italic">MT1L</span>, and <span class="html-italic">MT1X</span> (<span class="html-italic">n</span> = 195) had significantly longer survival times than did those with low levels of <span class="html-italic">MT1F</span>, <span class="html-italic">MT1G</span>, <span class="html-italic">MT1L</span>, and <span class="html-italic">MT1X</span> (<span class="html-italic">n</span> = 155). <span class="html-italic">p</span> &lt; 0.05 was considered to be statistically significant.</p>
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Review

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22 pages, 270 KiB  
Review
Current and New Predictors for Treatment Response in Metastatic Colorectal Cancer. The Role of Circulating miRNAs as Biomarkers
by Alexandra Gherman, Loredana Balacescu, Sinziana Gheorghe-Cetean, Catalin Vlad, Ovidiu Balacescu, Alexandru Irimie and Cosmin Lisencu
Int. J. Mol. Sci. 2020, 21(6), 2089; https://doi.org/10.3390/ijms21062089 - 18 Mar 2020
Cited by 12 | Viewed by 3552
Abstract
Colorectal cancer (CRC) is the third most frequently diagnosed cancer in the world. More than half of all CRC patients will eventually develop metastases and require treatment accordingly, but few validated predictive factors for response to systemic treatments exist. In order to ascertain [...] Read more.
Colorectal cancer (CRC) is the third most frequently diagnosed cancer in the world. More than half of all CRC patients will eventually develop metastases and require treatment accordingly, but few validated predictive factors for response to systemic treatments exist. In order to ascertain which patients benefit from specific treatments, there is a strong need for new and reliable biomarkers. We conducted a comprehensive search using the PUBMED database, up to December 2019, in order to identify relevant studies on predictive biomarkers for treatment response in metastatic CRC. We will herein present the currently used and potential biomarkers for treatment response and bring up-to-date knowledge on the role of circulating microRNAs, associated with chemotherapy and targeted therapy regimens used in metastatic CRC treatment. Molecular, tumor-related, disease-related, clinical, and laboratory predictive markers for treatment response were identified, mostly proposed, with few validated. Several circulating microRNAs have already proven their role of prediction for treatment response in CRC, but future clinical studies are needed to confirm their role as biomarkers across large cohorts of patients. Full article
(This article belongs to the Special Issue Molecular and Translational Research on Colorectal Cancer)
21 pages, 482 KiB  
Review
Exosomal Non Coding RNA in LIQUID Biopsies as a Promising Biomarker for Colorectal Cancer
by Amro Baassiri, Farah Nassar, Deborah Mukherji, Ali Shamseddine, Rihab Nasr and Sally Temraz
Int. J. Mol. Sci. 2020, 21(4), 1398; https://doi.org/10.3390/ijms21041398 - 19 Feb 2020
Cited by 81 | Viewed by 7381
Abstract
Colorectal cancer (CRC) is one of the most common cancers worldwide, with a high mortality rate, especially in those that are diagnosed in late stages of the disease. The current screening blood-based markers, such as carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9), [...] Read more.
Colorectal cancer (CRC) is one of the most common cancers worldwide, with a high mortality rate, especially in those that are diagnosed in late stages of the disease. The current screening blood-based markers, such as carcinoembryonic antigen (CEA) and carbohydrate antigen 19-9 (CA19-9), have low sensitivity and specificity. Meanwhile, other modalities are either expensive or invasive. Therefore, recent research has shifted towards a minimally invasive test, namely, liquid biopsy. Exosomes are favorable molecules sought in blood samples, since they are abundant, stable in circulation, and harbor genetic information and other biomolecules that could serve as biomarkers or even therapeutic targets. Furthermore, exosomal noncoding RNAs, such as miRNAs, lncRNAs, and circRNAs, have demonstrated the diagnostic potential to detect CRC at an early stage with a higher sensitivity and specificity than CEA and CA19-9 alone. Moreover, they have prognostic potential that is TNM stage specific and could serve as predictive biomarkers for the most common chemotherapeutic drug and combination regimen in CRC, which are 5-FU and FOLFOX, respectively. Therefore, in this review, we focus on the role of these exosomal noncoding RNAs as diagnostic, prognostic, and predictive biomarkers. In addition, we discuss the advantages and challenges of exosomes as a liquid biopsy target. Full article
(This article belongs to the Special Issue Molecular and Translational Research on Colorectal Cancer)
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<p>Exosome biogenesis and role in colorectal cancer (CRC).</p>
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16 pages, 1471 KiB  
Review
Prostaglandin E2/EP Signaling in the Tumor Microenvironment of Colorectal Cancer
by Rei Mizuno, Kenji Kawada and Yoshiharu Sakai
Int. J. Mol. Sci. 2019, 20(24), 6254; https://doi.org/10.3390/ijms20246254 - 11 Dec 2019
Cited by 125 | Viewed by 10019
Abstract
The number of colorectal cancer (CRC) patients is increasing worldwide. Accumulating evidence has shown that the tumor microenvironment (TME), including macrophages, neutrophils, and fibroblasts, plays an important role in the development and progression of CRC. Although targeting the TME could be a promising [...] Read more.
The number of colorectal cancer (CRC) patients is increasing worldwide. Accumulating evidence has shown that the tumor microenvironment (TME), including macrophages, neutrophils, and fibroblasts, plays an important role in the development and progression of CRC. Although targeting the TME could be a promising therapeutic approach, the mechanisms by which inflammatory cells promote CRC tumorigenesis are not well understood. When inflammation occurs in tissues, prostaglandin E2 (PGE2) is generated from arachidonic acid by the enzyme cyclooxygenase-2 (COX-2). PGE2 regulates multiple functions in various immune cells by binding to the downstream receptors EP1, EP2, EP3, and EP4, and plays an important role in the development of CRC. The current therapies targeting PGE2 using non-steroidal anti-inflammatory drugs (NSAIDs) or COX-2 inhibitors have failed due to the global prostanoid suppression resulting in the severe adverse effects despite the fact they could prevent tumorigenesis. Therefore, therapies targeting the specific downstream molecules of PGE2 signaling could be a promising approach. This review highlights the role of each EP receptor in the TME of CRC tumorigenesis and their therapeutic potential. Full article
(This article belongs to the Special Issue Molecular and Translational Research on Colorectal Cancer)
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<p>Inflammation-related carcinogenesis. Environmental mutagens, ROS, and RNI produced by recruited immune cells can cause DNA damage, resulting in the initiation of inflammation-related carcinogenesis. Cytokines or growth factors produced by immune cells can induce epigenetic changes in tumor suppressor genes (TSGs) and promote tumor initiation. Cytokines or chemokines from immune cells also promote tumor growth and progression.</p>
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<p>PGE2/EP signaling in TME. PGE2/EP signaling in each TME component promotes tumorigenesis by (i) switching the phenotype of macrophages and neutrophils from anti-tumor to pro-tumor, (ii) accelerating the migration of macrophages, CAFs and neutrophils, (iii) promoting lymphatic endothelial sprouting and angiogenesis, and (iv) suppressing the functions of T cells or NK cells. Arrows indicate positive regulation, while T-bars indicate negative regulation.</p>
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27 pages, 1182 KiB  
Review
A Snapshot of The Tumor Microenvironment in Colorectal Cancer: The Liquid Biopsy
by Mercedes Herrera, Cristina Galindo-Pumariño, Vanesa García-Barberán and Cristina Peña
Int. J. Mol. Sci. 2019, 20(23), 6016; https://doi.org/10.3390/ijms20236016 - 29 Nov 2019
Cited by 31 | Viewed by 8875
Abstract
The molecular profile of liquid biopsies is emerging as an alternative to tissue biopsies in the clinical management of malignant diseases. In colorectal cancer, significant liquid biopsy-based biomarkers have demonstrated an ability to discriminate between asymptomatic cancer patients and healthy controls. Furthermore, this [...] Read more.
The molecular profile of liquid biopsies is emerging as an alternative to tissue biopsies in the clinical management of malignant diseases. In colorectal cancer, significant liquid biopsy-based biomarkers have demonstrated an ability to discriminate between asymptomatic cancer patients and healthy controls. Furthermore, this non-invasive approach appears to provide relevant information regarding the stratification of tumors with different prognoses and the monitoring of treatment responses. This review focuses on the tumor microenvironment components which are detected in blood samples of colorectal cancer patients and might represent potential biomarkers. Exosomes released by tumor and stromal cells play a major role in the modulation of cancer progression in the primary tumor microenvironment and in the formation of an inflammatory pre-metastatic niche. Stromal cells-derived exosomes are involved in driving mechanisms that promote tumor growth, migration, metastasis, and drug resistance, therefore representing substantial signaling mediators in the tumor-stroma interaction. Besides, recent findings of specifically packaged exosome cargo in Cancer-Associated Fibroblasts of colorectal cancer patients identify novel exosomal biomarkers with potential clinical applicability. Furthermore, additional different signals emitted from the tumor microenvironment and also detectable in the blood, such as soluble factors and non-tumoral circulating cells, arise as novel promising biomarkers for cancer diagnosis, prognosis, and treatment response prediction. The therapeutic potential of these factors is still limited, and studies are in their infancy. However, innovative strategies aiming at the inhibition of tumor progression by systemic exosome depletion, exosome-mediated circulating tumor cell capturing, and exosome-drug delivery systems are currently being studied and may provide considerable advantages in the near future. Full article
(This article belongs to the Special Issue Molecular and Translational Research on Colorectal Cancer)
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<p>Role of tumor exosomes in colorectal cancer: Tumor exosomes act at different levels on the microenvironment, enhancing tumor progression and driving an inflammatory pre-metastatic niche, although in some cases an antitumor mechanism is also induced (red text): (<b>A</b>) induction of normal fibroblasts into Cancer-Associated Fibroblasts (CAFs), increasing the expression of myofibroblast markers and remodeling the extracellular matrix, (<b>B</b>) reprogramming mesenchymal stem cells to favor tumor growth and malignant progression, (<b>C</b>) induction of apoptosis of activated CD8+ T cells, negative regulation of T-cells and phenotypic alteration of the T cells to Treg, (<b>D</b>) polarization of M1 to M2 macrophages inducing a tumor-supporting phenotype in macrophages, (<b>E</b>) enhanced migration and reactivity of natural killer cells, (<b>F</b>) promotion proliferation and permeability of endothelial cells, increasing vascular permeability and angiogenesis, (<b>G</b>) modulation of lymphangiogenesis and (<b>H</b>) induction of the pre-metastatic niche by CXCR4-stromal cell recruitment, generating an immunosuppressive microenvironment. Dotted line represents separation between primary tumor and distal metastasis. References are shown in brackets [<a href="#B38-ijms-20-06016" class="html-bibr">38</a>,<a href="#B39-ijms-20-06016" class="html-bibr">39</a>,<a href="#B40-ijms-20-06016" class="html-bibr">40</a>,<a href="#B41-ijms-20-06016" class="html-bibr">41</a>,<a href="#B42-ijms-20-06016" class="html-bibr">42</a>,<a href="#B43-ijms-20-06016" class="html-bibr">43</a>,<a href="#B44-ijms-20-06016" class="html-bibr">44</a>,<a href="#B45-ijms-20-06016" class="html-bibr">45</a>,<a href="#B46-ijms-20-06016" class="html-bibr">46</a>,<a href="#B47-ijms-20-06016" class="html-bibr">47</a>,<a href="#B48-ijms-20-06016" class="html-bibr">48</a>,<a href="#B49-ijms-20-06016" class="html-bibr">49</a>,<a href="#B50-ijms-20-06016" class="html-bibr">50</a>,<a href="#B51-ijms-20-06016" class="html-bibr">51</a>,<a href="#B52-ijms-20-06016" class="html-bibr">52</a>,<a href="#B53-ijms-20-06016" class="html-bibr">53</a>,<a href="#B54-ijms-20-06016" class="html-bibr">54</a>,<a href="#B55-ijms-20-06016" class="html-bibr">55</a>,<a href="#B56-ijms-20-06016" class="html-bibr">56</a>,<a href="#B57-ijms-20-06016" class="html-bibr">57</a>,<a href="#B58-ijms-20-06016" class="html-bibr">58</a>,<a href="#B59-ijms-20-06016" class="html-bibr">59</a>,<a href="#B60-ijms-20-06016" class="html-bibr">60</a>,<a href="#B61-ijms-20-06016" class="html-bibr">61</a>,<a href="#B62-ijms-20-06016" class="html-bibr">62</a>,<a href="#B63-ijms-20-06016" class="html-bibr">63</a>]. Created with BioRender.com.</p>
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<p>Stromal cell-derived exosomes’ effects on tumor and stromal cells in colorectal cancer: (<b>A</b>) CAF-derived exosomes promote tumor growth, migration, metastasis, and drug resistance, (<b>B</b>) mesenchymal stem cells (MSCs)-derived exosomes promote colon cancer stem cell-like traits, (<b>C</b>) Treg-derived exosomes inhibit Th1 immune response promoting immunosuppression, (<b>D</b>) M2 macrophage-derived exosomes promote tumor metastases. References are shown in brackets [<a href="#B27-ijms-20-06016" class="html-bibr">27</a>,<a href="#B103-ijms-20-06016" class="html-bibr">103</a>,<a href="#B104-ijms-20-06016" class="html-bibr">104</a>,<a href="#B105-ijms-20-06016" class="html-bibr">105</a>,<a href="#B106-ijms-20-06016" class="html-bibr">106</a>,<a href="#B107-ijms-20-06016" class="html-bibr">107</a>,<a href="#B108-ijms-20-06016" class="html-bibr">108</a>,<a href="#B109-ijms-20-06016" class="html-bibr">109</a>]. Created with BioRender.com.</p>
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16 pages, 1537 KiB  
Review
Transforming Growth Factor-? Signaling Pathway in Colorectal Cancer and Its Tumor Microenvironment
by Yoshiro Itatani, Kenji Kawada and Yoshiharu Sakai
Int. J. Mol. Sci. 2019, 20(23), 5822; https://doi.org/10.3390/ijms20235822 - 20 Nov 2019
Cited by 188 | Viewed by 14190
Abstract
Transforming growth factor-beta (TGF-β) signaling is one of the important cellular pathways that play key roles for tissue maintenance. In particular, it is important in the context of inflammation and tumorigenesis by modulating cell growth, differentiation, apoptosis, and homeostasis. TGF-β receptor type 2 [...] Read more.
Transforming growth factor-beta (TGF-β) signaling is one of the important cellular pathways that play key roles for tissue maintenance. In particular, it is important in the context of inflammation and tumorigenesis by modulating cell growth, differentiation, apoptosis, and homeostasis. TGF-β receptor type 2 (TGFBR2) mutations affected by a mismatch repair deficiency causes colorectal cancers (CRCs) with microsatellite instability, which is, however, associated with relatively better survival rates. On the other hand, loss of SMAD4, a transcription factor in the TGF-β superfamily signaling, promotes tumor progression. Loss of heterozygosity on chromosome 18 can case SMAD4-deficient CRC, which results in poorer patients’ survival. Such bidirectional phenomenon driven by TGF-β signaling insufficiency reflects the complexity of this signaling pathway in CRC. Moreover, recent understanding of CRC at the molecular level (consensus molecular subtype classification) provides deep insight into the important roles of TGF-β signaling in the tumor microenvironment. Here we focus on the TGF-β signaling in CRC and its interaction with the tumor microenvironment. We summarize the molecular mechanisms of CRC tumorigenesis and progression caused by disruption of TGF-β signaling by cancer epithelial cells and host stromal cells. Full article
(This article belongs to the Special Issue Molecular and Translational Research on Colorectal Cancer)
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<p>Schematic representation of Transforming growth factor-beta (TGF-β) superfamily signaling pathway.</p>
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<p>TGF-β alteration in colorectal cancer (CRC) cells.</p>
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<p>TGF-β activation in the tumor microenvironment.</p>
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26 pages, 2153 KiB  
Review
Long Noncoding RNA (lncRNA)-Mediated Competing Endogenous RNA Networks Provide Novel Potential Biomarkers and Therapeutic Targets for Colorectal Cancer
by Liye Wang, Kwang Bog Cho, Yan Li, Gabriel Tao, Zuoxu Xie and Bin Guo
Int. J. Mol. Sci. 2019, 20(22), 5758; https://doi.org/10.3390/ijms20225758 - 16 Nov 2019
Cited by 493 | Viewed by 15026
Abstract
Colorectal cancer (CRC) is the third most common cancer and has a high metastasis and reoccurrence rate. Long noncoding RNAs (lncRNAs) play an important role in CRC growth and metastasis. Recent studies revealed that lncRNAs participate in CRC progression by coordinating with microRNAs [...] Read more.
Colorectal cancer (CRC) is the third most common cancer and has a high metastasis and reoccurrence rate. Long noncoding RNAs (lncRNAs) play an important role in CRC growth and metastasis. Recent studies revealed that lncRNAs participate in CRC progression by coordinating with microRNAs (miRNAs) and protein-coding mRNAs. LncRNAs function as competitive endogenous RNAs (ceRNAs) by competitively occupying the shared binding sequences of miRNAs, thus sequestering the miRNAs and changing the expression of their downstream target genes. Such ceRNA networks formed by lncRNA/miRNA/mRNA interactions have been found in a broad spectrum of biological processes in CRC, including liver metastasis, epithelial to mesenchymal transition (EMT), inflammation formation, and chemo-/radioresistance. In this review, we summarize typical paradigms of lncRNA-associated ceRNA networks, which are involved in the underlying molecular mechanisms of CRC initiation and progression. We comprehensively discuss the competitive crosstalk among RNA transcripts and the novel targets for CRC prognosis and therapy. Full article
(This article belongs to the Special Issue Molecular and Translational Research on Colorectal Cancer)
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<p>Representative lncRNAs in the different stages of CRC. There are four major stages of CRC development: precancerous polys, Adenomas, Carcinoma and invasive cancer. Representative lncRNAs involved in the certain stages could be regarded as early-stage diagnostic biomarkers to evaluate CRC progression or therapeutic targets to suppress CRC metastasis.</p>
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<p>The competitive endogenous RNA (ceRNA) mechanism. (<b>A</b>) In the conventional crosstalk of RNA transcripts, in the cytoplasm, miRNAs exert the suppressive function on protein-coding mRNAs by base pairing with partial complementarity via the miRNA recognition element (MREs) mapped to the 3′UTR of mRNAs. (<b>B</b>) Under the ceRNA mechanism in cancer cells, aberrantly expressed long noncoding RNAs (lncRNAs) with MREs competitively sequestrate miRNAs and reduce the interaction between miRNA and mRNA, and thus attenuate the repression on the downstream mRNAs.</p>
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<p>The lncRNA-associated ceRNA networks affect the four common hallmarks of colorectal cancer. Representative lncRNA‒miRNA‒mRNA networks are listed, which highlighted the involvement of lncRNA-ceRNA networks in four major hallmarks of CRC: tumorigenesis, EMT formation, inflammatory process and chemo-/radioresistance.</p>
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17 pages, 940 KiB  
Review
BRAF-Mutated Colorectal Cancer: Clinical and Molecular Insights
by Francesco Caputo, Chiara Santini, Camilla Bardasi, Krisida Cerma, Andrea Casadei-Gardini, Andrea Spallanzani, Kalliopi Andrikou, Stefano Cascinu and Fabio Gelsomino
Int. J. Mol. Sci. 2019, 20(21), 5369; https://doi.org/10.3390/ijms20215369 - 28 Oct 2019
Cited by 105 | Viewed by 8979
Abstract
Colorectal cancer (CRC) is one of the leading causes of mortality and morbidity in the world. It is a heterogeneous disease, which can be classified into different subtypes, characterized by specific molecular and morphological alterations. In this context, BRAF mutations are found in [...] Read more.
Colorectal cancer (CRC) is one of the leading causes of mortality and morbidity in the world. It is a heterogeneous disease, which can be classified into different subtypes, characterized by specific molecular and morphological alterations. In this context, BRAF mutations are found in about 10% of CRC patients and define a particular subtype, characterized by a dismal prognosis, with a median survival of less than 12 months. Chemotherapy plus bevacizumab is the current standard therapy in first-line treatment of BRAF-mutated metastatic CRC (mCRC), with triplet (FOLFOXIRI) plus bevacizumab as a valid option in patients with a good performance status. BRAF inhibitors are not so effective as compared to melanoma, because of various resistance mechanisms. However, the recently published results of the BEACON trial will establish a new standard of care in this setting. This review provides insights into the molecular underpinnings underlying the resistance to standard treatment of BRAF-mutated CRCs, with a focus on their molecular heterogeneity and on the research perspectives both from a translational and a clinical point of view. Full article
(This article belongs to the Special Issue Molecular and Translational Research on Colorectal Cancer)
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<p>Mitogen-activated protein kinase (MAPK) pathway in BRAF V600E-mutated metastatic colorectal cancer (mCRC). RAS activates the RAF family proteins (ARAF, BRAF, and CRAF). Activated RAF proteins lead to phosphorylation and activation of MEK1/2 proteins, which subsequently phosphorylate and activate ERKs, leading to cell growth.</p>
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<p>Adaptive feedback signaling in BRAF V600E-mutated mCRC. (<b>A</b>) In BRAF V600E-mutated mCRC, activated BRAF V600E monomer activates the MAPK pathway (MEK and ERK), leading to cell growth. Activated ERK suppresses the upstream activation of MAPK pathway through negative feedback on TRK, such as EGFR. (<b>B</b>) BRAF inhibitors (iBRAF) lead to transient inhibition of MAPK pathway and loss of ERK-dependent negative feedback on RTK, resulting in paradoxical activation of MAPK pathway.</p>
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18 pages, 947 KiB  
Review
The Landscape of Actionable Gene Fusions in Colorectal Cancer
by Filippo Pagani, Giovanni Randon, Vincenzo Guarini, Alessandra Raimondi, Michele Prisciandaro, Riccardo Lobefaro, Maria Di Bartolomeo, Gabriella Sozzi, Filippo de Braud, Patrizia Gasparini and Filippo Pietrantonio
Int. J. Mol. Sci. 2019, 20(21), 5319; https://doi.org/10.3390/ijms20215319 - 25 Oct 2019
Cited by 38 | Viewed by 5609
Abstract
The treatment scenario of metastatic colorectal cancer (mCRC) has been rapidly enriched with new chemotherapy combinations and biological agents that lead to a remarkable improvement in patients’ outcome. Kinase gene fusions account for less than 1% of mCRC overall but are enriched in [...] Read more.
The treatment scenario of metastatic colorectal cancer (mCRC) has been rapidly enriched with new chemotherapy combinations and biological agents that lead to a remarkable improvement in patients’ outcome. Kinase gene fusions account for less than 1% of mCRC overall but are enriched in patients with high microsatellite instability, RAS/BRAF wild-type colorectal cancer. mCRC patients harboring such alterations show a poor prognosis with standard treatments that could be reversed by adopting novel therapeutic strategies. Moving forward to a positive selection of mCRC patients suitable for targeted therapy in the era of personalized medicine, actionable gene fusions, although rare, represent a peculiar opportunity to disrupt a tumor alteration to achieve therapeutic goal. Here we summarize the current knowledge on potentially actionable gene fusions in colorectal cancer available from retrospective experiences and promising preliminary results of new basket trials. Full article
(This article belongs to the Special Issue Molecular and Translational Research on Colorectal Cancer)
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<p>Genetic map of BRAF and RET and their partner genes. (<b>A</b>) Genetic and physical map of the chromosome 7, indicating (in the enlarged rectangles) the location of BRAF (in red) and some of its gene partners (in blue). (<b>B</b>) Moreover, for chromosome 10, containing RET (in red), some of the intrachromosomal genes partners are indicated in blue. Maps and gene location are derived from the website of the University of California Santa Cruz Genome Browser (<a href="http://genome.ucsc.edu/" target="_blank">http://genome.ucsc.edu/</a>), with adaptations.</p>
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28 pages, 1889 KiB  
Review
Insulin-Like Growth Factor 2 (IGF2) Signaling in Colorectal Cancer—From Basic Research to Potential Clinical Applications
by Aldona Kasprzak and Agnieszka Adamek
Int. J. Mol. Sci. 2019, 20(19), 4915; https://doi.org/10.3390/ijms20194915 - 3 Oct 2019
Cited by 55 | Viewed by 7218
Abstract
Colorectal cancer (CRC) is one of the most common cancers in men and women worldwide as well as is the leading cause of death in the western world. Almost a third of the patients has or will develop liver metastases. While genetic as [...] Read more.
Colorectal cancer (CRC) is one of the most common cancers in men and women worldwide as well as is the leading cause of death in the western world. Almost a third of the patients has or will develop liver metastases. While genetic as well as epigenetic mechanisms are important in CRC pathogenesis, the basis of the most cases of cancer is unknown. High spatial and inter-patient variability of the molecular alterations qualifies this cancer in the group of highly heterogeneous tumors, which makes it harder to elucidate the mechanisms underlying CRC progression. Determination of highly sensitive and specific early diagnosis markers and understanding the cellular and molecular mechanism(s) of cancer progression are still a challenge of the current era in oncology of solid tumors. One of the accepted risk factors for CRC development is overexpression of insulin-like growth factor 2 (IGF2), a 7.5-kDa peptide produced by liver and many other tissues. IGF2 is the first gene discovered to be parentally imprinted. Loss of imprinting (LOI) or aberrant imprinting of IGF2 could lead to IGF2 overexpression, increased cell proliferation, and CRC development. IGF2 as a mitogen is associated with increased risk of developing colorectal neoplasia. Higher serum IGF2 concentration as well as its tissue overexpression in CRC compared to control are associated with metastasis. IGF2 protein was one of the three candidates for a selective marker of CRC progression and staging. Recent research indicates dysregulation of different micro- and long non-coding RNAs (miRNAs and lncRNAs, respectively) embedded within the IGF2 gene in CRC carcinogenesis, with some of them indicated as potential diagnostic and prognostic CRC biomarkers. This review systematises the knowledge on the role of genetic and epigenetic instabilities of IGF2 gene, free (active form of IGF2) and IGF-binding protein (IGFBP) bound (inactive form), paracrine/autocrine secretion of IGF2, as well as mechanisms of inducing dysplasia in vitro and tumorigenicity in vivo. We have tried to answer which molecular changes of the IGF2 gene and its regulatory mechanisms have the most significance in initiation, progression (including liver metastasis), prognosis, and potential anti-IGF2 therapy in CRC patients. Full article
(This article belongs to the Special Issue Molecular and Translational Research on Colorectal Cancer)
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<p>Immunohistochemical (IHC) localization of insulin-like growth factor (IGF) 2 in primary colorectal cancer (pCRC), metastatic colorectal cancer (mCRC), and control colon (C) samples: (<b>A</b>) A representative IHC expression of IGF2 mainly in cytoplasm of surface epithelium of tumor-changed colon crypts, in few white blood cells (arrowhead), and in individual cells of the tumor stroma (arrow); (<b>B</b>) homogenous cytoplasmic IHC reaction in neoplastic cells lining the glandular structures of colorectal cancer (CRC); (<b>C</b>) an intense IHC reaction of IGF2 in the cytoplasm and nuclei of numerous cancer cells present in lymph node metastatic carcinoma; (<b>D</b>) very strong cytoplasmic IHC reaction of IGF2 in cancer cells of lymph node metastatic CRC of other patient; (<b>E</b>) cytoplasmic expression of IGF2 in majority of goblet cells in normal colon epithelium; and (<b>F</b>) negative IgG control. New polymer-based immunohistochemistry with 3,3′-Diaminobenzidine (DAB) was the chromogen. Hematoxylin counterstained. Objective ×40 (<a href="#ijms-20-04915-f001" class="html-fig">Figure 1</a><b>A</b>–<b>F</b>) (our unpublished data).</p>
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<p>IGF2 gene and protein-associated non-coding RNA-regulatory mechanisms and the best-known IGF2-associated signaling pathways in colorectal carcinogenesis. Legend: ⇓ regulation; ↑/↓ increase/decrease; ⊥: inhibition.</p>
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32 pages, 910 KiB  
Review
Epigenetic Biomarkers in Colorectal Cancer Patients Receiving Adjuvant or Neoadjuvant Therapy: A Systematic Review of Epidemiological Studies
by Martina Barchitta, Andrea Maugeri, Giovanni Li Destri, Guido Basile and Antonella Agodi
Int. J. Mol. Sci. 2019, 20(15), 3842; https://doi.org/10.3390/ijms20153842 - 6 Aug 2019
Cited by 21 | Viewed by 4573
Abstract
Colorectal cancer (CRC) represents the third-most common cancer worldwide and one of the main challenges for public health. Despite great strides in the application of neoadjuvant and adjuvant therapies for rectal and colon cancer patients, each of these treatments is still associated with [...] Read more.
Colorectal cancer (CRC) represents the third-most common cancer worldwide and one of the main challenges for public health. Despite great strides in the application of neoadjuvant and adjuvant therapies for rectal and colon cancer patients, each of these treatments is still associated with certain adverse effects and different response rates. Thus, there is an urgent need for identifying novel potential biomarkers that might guide personalized treatments for specific subgroups of patients. However, until now, there are no biomarkers to predict the manifestation of adverse effects and the response to treatment in CRC patients. Herein, we provide a systematic review of epidemiological studies investigating epigenetic biomarkers in CRC patients receiving neoadjuvant or adjuvant therapy, and their potential role for the prediction of outcomes and response to treatment. With this aim in mind, we identified several epigenetic markers in CRC patients who received surgery with adjuvant or neoadjuvant therapy. However, none of them currently has the robustness to be translated into the clinical setting. Thus, more efforts and further large-size prospective studies and/or trials should be encouraged to develop epigenetic biomarker panels for personalized prevention and medicine in CRC cancer. Full article
(This article belongs to the Special Issue Molecular and Translational Research on Colorectal Cancer)
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<p>PRISMA 2009 Flow Diagram of study selection.</p>
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15 pages, 1016 KiB  
Review
Vaccinations for Colorectal Cancer: Progress, Strategies, and Novel Adjuvants
by Stephen Jiang, David Good and Ming Q. Wei
Int. J. Mol. Sci. 2019, 20(14), 3403; https://doi.org/10.3390/ijms20143403 - 11 Jul 2019
Cited by 18 | Viewed by 3912
Abstract
Although cancer is a leading cause of death, significant breakthroughs have been made in its treatment in recent years. In particular, increasingly effective cancer vaccines are being developed, including some for colorectal cancer. There are also currently a variety of compounds that can [...] Read more.
Although cancer is a leading cause of death, significant breakthroughs have been made in its treatment in recent years. In particular, increasingly effective cancer vaccines are being developed, including some for colorectal cancer. There are also currently a variety of compounds that can act as adjuvants, such as signalling molecules called cytokines. Other adjuvants target and inhibit the specific mechanisms by which cancers evade the immune system. One of them is a galectin inhibitor, which targets galectins—proteins produced by cancer cells that can cause the death of immune cells. Likewise, immune checkpoint inhibitors affect immune checkpoints—natural host proteins that usually control inflammation but can be exploited by cancers to weaken the body’s defences. Equally, regulatory T cells may contribute to the progression of cancer by inhibiting the functions of other T cells. The main advantages of cancer vaccines include their low toxicity and their ability to strengthen the immune system. Nevertheless, significant limitations include their slow effects and their inability to treat cancer at times due to immunosuppression. Ultimately, ongoing trials provide hope for the development of more effective methods of immunotherapeutic inoculation that can target a greater variety of cancers. Full article
(This article belongs to the Special Issue Molecular and Translational Research on Colorectal Cancer)
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<p>(<b>A</b>) The tumour volume was measured every three days. The administration of interferon-α (IFNα) decreased the rate of the tumour volume growth in comparison with the control group. ** represents the statistical significance (<span class="html-italic">p</span> &lt; 0.01) of the difference in tumour volume between the two groups. (<b>B</b>) The tumour weight of each group was measured. The administration of IFNα significantly reduced the tumour weight in contrast with the control group (227.5 ± 36.4 vs 122.9 ± 12.5). * represents the statistical significance (<span class="html-italic">p</span> = 0.02) of the difference in tumour weight between the two groups [<a href="#B22-ijms-20-03403" class="html-bibr">22</a>].</p>
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<p>The relationship between regulatory T cells (Tregs) and circulating tumour cells (CTCs) in patients with breast cancer. The shaded region of the bars represents the percentage of individuals in the group that had CTCs. Individuals with a high number of Tregs were more likely to be CTC-positive than those that had a low number, and this result was significant (67% in contrast to 43%, <span class="html-italic">p</span> = 0.002) [<a href="#B34-ijms-20-03403" class="html-bibr">34</a>].</p>
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<p>Diprovocim (TLR1/TLR2 agonist) acts in combination with anti-PD-L1 to cause tumour cell lysis, leading to a 100% survival rate in mice. Anti-PD-L1 decreases the inhibition of immune cells to allow an unrestrained response against tumours. Diprovocim increases antigen presentation and cytokine release, increasing immune cell proliferation [<a href="#B63-ijms-20-03403" class="html-bibr">63</a>].</p>
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