Molecular and Cellular Mechanisms of Cancers: Colorectal Cancer

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Biomolecules biomolecules	4.8	9.4	2011	16.3 Days	CHF 2700
Cancers cancers	4.5	8.0	2009	16.3 Days	CHF 2900
Cells cells	5.1	9.9	2012	17.5 Days	CHF 2700
Journal of Molecular Pathology jmp	-	-	2020	25.4 Days	CHF 1000
Organoids organoids	-	-	2022	15.0 days *	CHF 1000

22 pages, 13691 KiB

Open AccessArticle

Pyrroline-5-Carboxylate Reductase-2 Promotes Colorectal Carcinogenesis by Modulating Microtubule-Associated Serine/Threonine Kinase-like/Wnt/β-Catenin Signaling

by Raju Lama Tamang, Balawant Kumar, Sagar M. Patel, Ishwor Thapa, Alshomrani Ahmad, Vikas Kumar, Rizwan Ahmad, Donald F. Becker, Dundy (Kiran) Bastola, Punita Dhawan and Amar B. Singh

Cells 2023, 12(14), 1883; https://doi.org/10.3390/cells12141883 - 18 Jul 2023

Cited by 2 | Viewed by 1941

Abstract

Background: Despite significant progress in clinical management, colorectal cancer (CRC) remains the third most common cause of cancer-related deaths. A positive association between PYCR2 (pyrroline-5-carboxylate reductase-2), a terminal enzyme of proline metabolism, and CRC aggressiveness was recently reported. However, how PYCR2 promotes colon [...] Read more.

Background: Despite significant progress in clinical management, colorectal cancer (CRC) remains the third most common cause of cancer-related deaths. A positive association between PYCR2 (pyrroline-5-carboxylate reductase-2), a terminal enzyme of proline metabolism, and CRC aggressiveness was recently reported. However, how PYCR2 promotes colon carcinogenesis remains ill understood. Methods: A comprehensive analysis was performed using publicly available cancer databases and CRC patient cohorts. Proteomics and biochemical evaluations were performed along with genetic manipulations and in vivo tumor growth assays to gain a mechanistic understanding. Results: PYCR2 expression was significantly upregulated in CRC and associated with poor patient survival, specifically among PYCR isoforms (PYCR1, 2, and 3). The genetic inhibition of PYCR2 inhibited the tumorigenic abilities of CRC cells and in vivo tumor growth. Coinciding with these observations was a significant decrease in cellular proline content. PYCR2 overexpression promoted the tumorigenic abilities of CRC cells. Proteomics (LC-MS/MS) analysis further demonstrated that PYCR2 loss of expression in CRC cells inhibits survival and cell cycle pathways. A subsequent biochemical analysis supported the causal role of PYCR2 in regulating CRC cell survival and the cell cycle, potentially by regulating the expression of MASTL, a cell-cycle-regulating protein upregulated in CRC. Further studies revealed that PYCR2 regulates Wnt/β-catenin-signaling in manners dependent on the expression of MASTL and the cancer stem cell niche. Conclusions: PYCR2 promotes MASTL/Wnt/β-catenin signaling that, in turn, promotes cancer stem cell populations and, thus, colon carcinogenesis. Taken together, our data highlight the significance of PYCR2 as a novel therapeutic target for effectively treating aggressive colon cancer. Full article

(This article belongs to the Topic Molecular and Cellular Mechanisms of Cancers: Colorectal Cancer)

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14 pages, 3484 KiB

Open AccessArticle

Loss of Tumor Suppressor C9orf9 Promotes Metastasis in Colorectal Cancer

by Erfei Chen, Fangfang Yang, Qiqi Li, Tong Li, Danni Yao, Lichao Cao and Jin Yang

Biomolecules 2023, 13(2), 312; https://doi.org/10.3390/biom13020312 - 7 Feb 2023

Cited by 2 | Viewed by 1572

Abstract

The whole genome sequencing of tumor samples identifies thousands of somatic mutations. However, the function of these genes or mutations in regulating cancer progression remains unclear. We previously performed exome sequencing in patients with colorectal cancer, and identified one splicing mutation in C9orf9 [...] Read more.

The whole genome sequencing of tumor samples identifies thousands of somatic mutations. However, the function of these genes or mutations in regulating cancer progression remains unclear. We previously performed exome sequencing in patients with colorectal cancer, and identified one splicing mutation in C9orf9. The subsequent target sequencing of C9orf9 gene based on a validation cohort of 50 samples also found two function mutations, indicating that the loss of wild-type C9orf9 may participate in the tumorigenesis of colorectal cancer. In this research, we aimed to further confirm the function of C9orf9 in the CRC phenotype. Our Q-PCR analysis of the tumor and matched normal samples found that C9orf9 was downregulated in the CRC samples. Function assays revealed that C9orf9 exerts its tumor suppressor role mainly on cancer cell migration and invasion, and its loss was essential for certain tumor-microenvironment signals to induce EMT and metastasis in vivo. RNA-sequencing showed that stable-expressing C9orf9 can inhibit the expression of several metastasis-related genes and pathways, including vascular endothelial growth factor A (VEGFA), one of the essential endothelial cell mitogens which plays a critical role in normal physiological and tumor angiogenesis. Overall, our results showed that the loss of C9orf9 contributes to the malignant phenotype of CRC. C9orf9 may serve as a novel metastasis repressor for CRC. Full article

(This article belongs to the Topic Molecular and Cellular Mechanisms of Cancers: Colorectal Cancer)

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Exome sequencing and deep sequencing identified three somatic mutations in C9orf9 gene. In-3: patient from initial exome sequencing; Ex-6, Ex-36, patients from extended validation cohort. Somatic mutations were visualized and analyzed by SnapGene v4.2. Full article ">Figure 2
Gene expression analysis of C9orf9 in multiple samples of digestive tumor patients. (A). Analysis of C9orf9 expression in five digestive tumors from TCGA and GTEx samples. * p < 0.05. T: tumor (red), N: normal (grey). (B). C9orf9 expression level in tumor and matched normal tissues from TCGA and local validation cohort. ** p < 0.01, *** p < 0.001. (C). Correlation between copy number variation (CNV) and C9orf9 expression level in TCGA cohort. **** p < 0.0001. (D). C9orf9 is downregulated in metastatic tumor samples of CRC, data from GSE41258. ** p < 0.01. Full article ">Figure 3
C9orf9 has limit effect on cell growth. (A). Overexpression of C9orf9 in SW480 and LoVo cells did not affect cell proliferation. (B). Knockdown of C9orf9 slightly promoted cell proliferation in LoVo cells, but not in SW480. (C). Cell apoptosis analysis using Annexin/PI double staining in LoVo and SW480 cells transfected with C9orf9 expression plasmid. (D). Cell apoptosis analysis using Annexin/PI double staining in LoVo and SW480 cells transfected with C9orf9-specific siRNAs. Ns, not significant, ** p < 0.01, *** p < 0.001, **** p < 0.0001. Full article ">Figure 4
C9orf9 regulates LoVo and SW480 cell migration and invasion capacity. Transwell assays (Matrigel-free) or Transwell invasion assays (coated with Matrigel) were performed, respectively, in control and C9orf9-overexpression LoVo (A) and SW480 (B) cells. (C). Cell migration and invasion assays in control and C9orf9-knockdown LoVo cells. (D). Cell migration and invasion assays in control and C9orf9-knockdown SW480 cells. * p < 0.05, ** p < 0.01, *** p < 0.001. Full article ">Figure 5
C9orf9 inhibits tumor metastasis in vivo. (A). Representative images of luciferase expression from lung metastasis of the normal saline (n = 3), LV-NC (n = 5), and LV-C9orf9 (n = 5) groups. (B). Quantification of the total flux, compared with the LV-NC group. (C). HE staining of lungs from the normal saline, LV-NC, and LV-C9orf9 groups. (D). mRNA level of metastasis-related marker N-cadherin and Vimentin. ** p < 0.01, *** p < 0.001, **** p < 0.0001. Full article ">Figure 6
C9orf9 responds to and involves in FGF, EGF, and IL6 induced EMT. (A). Q-PCR analysis of C9orf9 and EMT-related markers (E-cadherin, N-cadherin, Vimentin) in FGF (20 ng/mL), EGF (20 ng/mL), and IL6 (50 ng/mL)-stimulated SW480 cells at 0, 12, and 24 h. (B) Transwell (Matrigel-free) assays of LV-NC and LV-C9orf9 SW480 cells with or without cytokine stimulation. * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001. Full article ">Figure 7
C9orf9 regulates metastasis-related gene and pathway. (A). Volcano plot of differentially expressed genes (DEGs). (B). Top 20 KEGG pathway enrichment from DEGs. (C). GSEA plot of oxidative phosphorylation, EMT, and hypoxia gene set. (D). Q-PCR validation of angiogenesis, oxidative phosphorylation, and EMT related genes. * p < 0.05, ** p < 0.01, **** p < 0.0001. Full article ">

20 pages, 9976 KiB

Open AccessArticle

Prognostic Biomarker SPOCD1 and Its Correlation with Immune Infiltrates in Colorectal Cancer

by Lin Gan, Changjiang Yang, Long Zhao, Shan Wang, Zhidong Gao and Yingjiang Ye

Biomolecules 2023, 13(2), 209; https://doi.org/10.3390/biom13020209 - 20 Jan 2023

Cited by 2 | Viewed by 1829

Abstract

The biological role of the spen paralogue and orthologue C-terminal domain containing 1 (SPOCD1) has been investigated in human malignancies, but its function in colorectal cancer (CRC) is unclear. This study investigated the association between SPOCD1 expression and clinicopathological features of CRC cases, [...] Read more.

The biological role of the spen paralogue and orthologue C-terminal domain containing 1 (SPOCD1) has been investigated in human malignancies, but its function in colorectal cancer (CRC) is unclear. This study investigated the association between SPOCD1 expression and clinicopathological features of CRC cases, as well as its prognostic value and biological function based on large-scale databases and clinical samples. The results showed that the expression level of SPOCD1 was elevated in CRC, which was generally associated with shortened survival time and poor clinical indexes, including advanced T, N, and pathologic stages. Multivariate Cox regression analysis showed that elevated SPOCD1 expression was an independent factor for poor prognosis in CRC patients. Functional enrichment analysis of SPOCD1 and its co-expressed genes revealed that SPOCD1 could act as an oncogene by regulating gene expression in essential functions and pathways of tumorigenesis, such as extracellular matrix organization, chemokine signaling pathways, and calcium signaling pathways. In addition, immune cell infiltration results showed that SPOCD1 expression was associated with various immune cells, especially macrophages. Furthermore, our findings suggested a possible function for SPOCD1 in the polarization of macrophages from M1 to M2 in CRC. In conclusion, SPOCD1 is a promising diagnostic and prognostic marker for CRC, opening new avenues for research and treatment. Full article

(This article belongs to the Topic Molecular and Cellular Mechanisms of Cancers: Colorectal Cancer)

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15 pages, 2356 KiB

Open AccessReview

Effects of Long Non-Coding RNAs Induced by the Gut Microbiome on Regulating the Development of Colorectal Cancer

by Shiying Fan, Juan Xing, Zhengting Jiang, Zhilin Zhang, Huan Zhang, Daorong Wang and Dong Tang

Cancers 2022, 14(23), 5813; https://doi.org/10.3390/cancers14235813 - 25 Nov 2022

Cited by 8 | Viewed by 2128

Abstract

Although an imbalanced gut microbiome is closely associated with colorectal cancer (CRC), how the gut microbiome affects CRC is not known. Long non-coding RNAs (lncRNAs) can affect important cellular functions such as cell division, proliferation, and apoptosis. The abnormal expression of lncRNAs can [...] Read more.

Although an imbalanced gut microbiome is closely associated with colorectal cancer (CRC), how the gut microbiome affects CRC is not known. Long non-coding RNAs (lncRNAs) can affect important cellular functions such as cell division, proliferation, and apoptosis. The abnormal expression of lncRNAs can promote CRC cell growth, proliferation, and metastasis, mediating the effects of the gut microbiome on CRC. Generally, the gut microbiome regulates the lncRNAs expression, which subsequently impacts the host transcriptome to change the expression of downstream target molecules, ultimately resulting in the development and progression of CRC. We focused on the important role of the microbiome in CRC and their effects on CRC-related lncRNAs. We also reviewed the impact of the two main pathogenic bacteria, Fusobacterium nucleatum and enterotoxigenic Bacteroides fragilis, and metabolites of the gut microbiome, butyrate, and lipopolysaccharide, on lncRNAs. Finally, available therapies that target the gut microbiome and lncRNAs to prevent and treat CRC were proposed. Full article

(This article belongs to the Topic Molecular and Cellular Mechanisms of Cancers: Colorectal Cancer)

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Biogenesis and the mode of action of lncRNAs. (A) Signal: some lncRNAs are specifically transcribed under different TFs and signaling pathways, and they subsequently participate in specific signaling pathways as signaling molecules. (B) Decoy: a certain type of lncRNAs are transcribed and bind directly to protein targets but are inactive, thus inhibiting the function of the molecule and the signaling pathway. (C) Guidance: certain lncRNAs, when bound to proteins, can direct the localization of ribonucleoprotein complex to specific targets (colored flags), thereby regulating the transcription of downstream molecules. (D) Scaffolding: some lncRNAs act as a “central platform” to bring multiple proteins together to form ribonucleoprotein complexes, enabling the intersection and integration of information between various signaling pathways and facilitating the rapid feedback and regulation of the body or cell to external signals and stimuli. (Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>). lncRNAs: long non-coding RNAs; TFs: transcription factors. Full article ">Figure 2
F. nucleatum promotes CRC metastasis as well as enhances drug resistance by upregulating EVADR, KRT7-AS, and ENO1-IT1. Elevated EVADR directs RBP YBX1 to recruit EMT-related factors, including Snail, Slug, and Zeb1, to polyribosomes, and enhances the translation of these EMT-related factors, thereby inducing EMT, ultimately facilitating colorectal cancer metastasis. F. nucleatum infection activates the NF-κB pathway, and activated NF-κB P-p65 (activated NF-κB subunit) may upregulate KRT7-AS by increasing the transcriptional activity of KRT7-AS, then activating the downstream target of KRT7-AS, KRT7, which regulates CRC metastasis. F. nucleatum upregulates the binding efficiency of the transcription factor SP1 to the promoter region of lncRNA ENO1-IT1 to activate the transcription of lncRNA ENO1- IT1 and subsequently recruit KAT7 to the promoter of the ENO1 gene to regulate ENO1 transcription via epigenetic modulation, thereby activating CRC glycolysis, which enhances the drug resistance. (Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>). F. nucleatum: Fusobacterium nucleatum; EVADR: endogenous retroviral-associated adenocarcinoma lncRNA; KRT7-AS: Keratin7-antisense; ENO1-IT1: enolase1-intronic transcript 1; RBP: RNA-binding protein; KRT7: Keratin7; EMT: epithelial mesenchymal transition; lncRNA: long non-coding RNA. Full article ">Figure 3
ETBF induces CRC cells growth, proliferation, and metastasis by regulating BFAL1 and AERRIE. BFAL1 competes with miR-155-5p and miR-200a-3p, thus impeding the inhibitory effect of miR-155-5p and miR-200a-3p on RHEB, which upregulates the target RHEB mRNA expression. RHEB can bind directly to the mTOR complex and regulate the mTOR-signaling pathway by phosphorylating the p70 S6K, leading to the activation of the mTOR pathway, thus promoting ETBF-induced CRC cells growth. ETBF can also increase the levels of JMJD2B, and then induce the expression of lncRNA AERRIE, which in turn induces the expression of SULF1. Elevated SULF1 can activate the canonical Wnt pathway, which has been demonstrated to promote the proliferation and metastasis of CRC cells. (Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>). ETBF: Enterotoxigenic Bacteroides fragilis; BFAL1: B. fragilis-associated lncRNA1; RHEB: Ras homolog enriched in brain; S6K: S6 Kinase; JMJD2B: Jumonji domain-containing protein 2B; lncRNA: long non-coding RNA; SULF1: sulfatase 1. Full article ">Figure 4
Butyrate as the metabolite of the gut microbiome inhibits the intestinal inflammation to lower the risk of CRC by regulating lncRNA LncLy6C. Butyrate-induced LncLy6C binds to the C/EBPβ and H3K4me3, specifically encouraging the enrichment of C/EBPβ and H3K4me3 marks on the promoter region of Nr4A1, thus promoting the expression of Nr4A1. This promotes the differentiation of Ly6Chigh inflammatory monocytes into Ly6Cint/neg resident macrophages, leading to the inhibition of inflammation. As a result, the risk of developing CAC goes down. (Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>). lncRNA: long non-coding RNA; C/EBPβ: CCAAT/enhancer binding protein β; CAC: colitis-associated colorectal cancer. Full article ">Figure 5
The gut microbiome-derived LPS promotes cancer cells migration and invasion by regulating LINC00152. The bacteria-derived LPS introduces histone lactonization on the promoter of LINC00152 and reduces the binding efficiency of YY1 to LINC00152, thus upregulating the expression of LINC00152. The LPS-induced overexpression of LINC00152 was associated with cancer cells migration and invasion. (Created with <a href="http://BioRender.com" target="_blank">BioRender.com</a>). LPS: lipopolysaccharide. Full article ">

23 pages, 1144 KiB

Open AccessReview

Short Linear Motifs in Colorectal Cancer Interactome and Tumorigenesis

by Candida Fasano, Valentina Grossi, Giovanna Forte and Cristiano Simone

Cells 2022, 11(23), 3739; https://doi.org/10.3390/cells11233739 - 23 Nov 2022

Cited by 4 | Viewed by 2303

Abstract

Colorectal tumorigenesis is driven by alterations in genes and proteins responsible for cancer initiation, progression, and invasion. This multistage process is based on a dense network of protein–protein interactions (PPIs) that become dysregulated as a result of changes in various cell signaling effectors. [...] Read more.

Colorectal tumorigenesis is driven by alterations in genes and proteins responsible for cancer initiation, progression, and invasion. This multistage process is based on a dense network of protein–protein interactions (PPIs) that become dysregulated as a result of changes in various cell signaling effectors. PPIs in signaling and regulatory networks are known to be mediated by short linear motifs (SLiMs), which are conserved contiguous regions of 3–10 amino acids within interacting protein domains. SLiMs are the minimum sequences required for modulating cellular PPI networks. Thus, several in silico approaches have been developed to predict and analyze SLiM-mediated PPIs. In this review, we focus on emerging evidence supporting a crucial role for SLiMs in driver pathways that are disrupted in colorectal cancer (CRC) tumorigenesis and related PPI network alterations. As a result, SLiMs, along with short peptides, are attracting the interest of researchers to devise small molecules amenable to be used as novel anti-CRC targeted therapies. Overall, the characterization of SLiMs mediating crucial PPIs in CRC may foster the development of more specific combined pharmacological approaches. Full article

(This article belongs to the Topic Molecular and Cellular Mechanisms of Cancers: Colorectal Cancer)

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23 pages, 2249 KiB

Open AccessReview

The Role of Fusobacterium nucleatum in Colorectal Cancer Cell Proliferation and Migration

by Zihong Wu, Qiong Ma, Ying Guo and Fengming You

Cancers 2022, 14(21), 5350; https://doi.org/10.3390/cancers14215350 - 30 Oct 2022

Cited by 10 | Viewed by 3325

Abstract

Colorectal cancer (CRC) is a common cancer worldwide with poor prognosis. The presence of Fusobacterium nucleatum (Fn) in the intestinal mucosa is associated with the progression of CRC. In this review, we explore the mechanisms by which Fn contributes to proliferation and migration [...] Read more.

Colorectal cancer (CRC) is a common cancer worldwide with poor prognosis. The presence of Fusobacterium nucleatum (Fn) in the intestinal mucosa is associated with the progression of CRC. In this review, we explore the mechanisms by which Fn contributes to proliferation and migration of CRC cells from the following four aspects: induction of the epithelial–mesenchymal transition (EMT), regulation of the tumor microenvironment (TME), expression of oncogenic noncoding RNAs, and DNA damage. This review outlines the scientific basis for the use of Fn as a biomarker and therapeutic target in CRC. Full article

(This article belongs to the Topic Molecular and Cellular Mechanisms of Cancers: Colorectal Cancer)

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17 pages, 1228 KiB

Open AccessReview

Zinc Finger Proteins: Functions and Mechanisms in Colon Cancer

by Shujie Liu, Xiaonan Sima, Xingzhu Liu and Hongping Chen

Cancers 2022, 14(21), 5242; https://doi.org/10.3390/cancers14215242 - 26 Oct 2022

Cited by 10 | Viewed by 3600

Abstract

According to the global cancer burden data for 2020 issued by the World Health Organization (WHO), colorectal cancer has risen to be the third-most frequent cancer globally after breast and lung cancer. Despite advances in surgical treatment and chemoradiotherapy for colon cancer, individuals [...] Read more.

According to the global cancer burden data for 2020 issued by the World Health Organization (WHO), colorectal cancer has risen to be the third-most frequent cancer globally after breast and lung cancer. Despite advances in surgical treatment and chemoradiotherapy for colon cancer, individuals with extensive liver metastases still have depressing prognoses. Numerous studies suggest ZFPs are crucial to the development of colon cancer. The ZFP family is encoded by more than 2% of the human genome sequence and is the largest transcriptional family, all with finger-like structural domains that could combine with Zn²⁺. In this review, we summarize the functions, molecular mechanisms and recent advances of ZFPs in colon cancer. We also discuss how these proteins control the development and progression of colon cancer by regulating cell proliferation, EMT, invasion and metastasis, inflammation, apoptosis, the cell cycle, drug resistance, cancer stem cells and DNA methylation. Additionally, several investigations have demonstrated that Myeloid zinc finger 1 (MZF1) has dual functions in colon cancer, which may both promote cancer proliferation and inhibit cancer progression through apoptosis. Generally, a comprehensive understanding of the action mechanisms of ZFPs in colon cancer will not only shed light on the discovery of new diagnostic and prognosis indicators but will also facilitate the design of novel targeted therapies. Full article

(This article belongs to the Topic Molecular and Cellular Mechanisms of Cancers: Colorectal Cancer)

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The function of ZFPs in regulating the cellular biological processes of colon cancer. ZFPs play important roles in the regulation of cell proliferation, epithelial–mesenchymal transition (EMT), invasion and metastasis, inflammation, cell cycle, cancer stem cells and DNA methylation in colon cancer cells. (This figure was created with <a href="http://biorender.com" target="_blank">biorender.com</a>). Full article ">Figure 2
The possible mechanisms of ZFPs in regulating the cell cycle of colon cancer. There are various zinc finger proteins involved in cell cycle processes, such as ZFP91, ZFP278, ZFP692, Slug, KLF6-SV2, and P52-ZER6. The underlying mechanisms involve cyclin D, cyclin E, E2F, p21, p53, Bax, and MDM2. The underlying mechanism affects multiple molecules, such as cyclin D, cyclin E, E2F, p21, p53, Bax, and MDM2, eventually inducing cell progression or inhibiting cell proliferation in colon cancer. These ZFPs have great potential as novel therapeutic targets for colon cancer. (This figure was created with <a href="http://biorender.com" target="_blank">biorender.com</a>). Full article ">Figure 3
In colon cancer, MZF1 plays dual and opposite roles in different signaling pathways: (a) MZF1 transcriptionally activates the downstream target gene Axl and stimulates various signaling pathways, such as PI3K, FAK, Grb2/Ras, MEK/ERK, advancing cell proliferation, EMT transformation, invasion, and metastasis in colon cancer. (b) MZF1 transcriptionally activates the downstream target gene p55PIK; stimulates diverse signaling pathways such as PI3K/Akt and PI3K/RAC; and activates a series of downstream target genes such as CDC2, ALDH, BCL2, TWIST, SNAIL, and SLUG, promoting cell proliferation, EMT transformation, invasion, and metastasis in colon cancer. (c) MZF1 transcriptionally triggers the downstream target gene c-myc and multiple downstream target genes, such as MINA53, ID2, BCL2, and PTMA, promoting proliferation in colon cancer. (d) Sulfide sulindac sulfide induces the upregulation of MZF1. MZF1 promotes the expression of DR5 that interacts with FADD to activate caspases, promoting cell apoptosis and eventually inhibiting metastasis in colon cancer. (This figure was created with <a href="http://biorender.com" target="_blank">biorender.com</a>). Full article ">

18 pages, 4132 KiB

Open AccessArticle

CREPT Disarms the Inhibitory Activity of HDAC1 on Oncogene Expression to Promote Tumorigenesis

by Yajun Cao, Bobin Ning, Ye Tian, Tingwei Lan, Yunxiang Chu, Fangli Ren, Yinyin Wang, Qingyu Meng, Jun Li, Baoqing Jia and Zhijie Chang

Cancers 2022, 14(19), 4797; https://doi.org/10.3390/cancers14194797 - 30 Sep 2022

Cited by 2 | Viewed by 1863

Abstract

Histone deacetylases 1 (HDAC1), an enzyme that functions to remove acetyl molecules from ε-NH3 groups of lysine in histones, eliminates the histone acetylation at the promoter regions of tumor suppressor genes to block their expression during tumorigenesis. However, it remains unclear why HDAC1 [...] Read more.

Histone deacetylases 1 (HDAC1), an enzyme that functions to remove acetyl molecules from ε-NH3 groups of lysine in histones, eliminates the histone acetylation at the promoter regions of tumor suppressor genes to block their expression during tumorigenesis. However, it remains unclear why HDAC1 fails to impair oncogene expression. Here we report that HDAC1 is unable to occupy at the promoters of oncogenes but maintains its occupancy with the tumor suppressors due to its interaction with CREPT (cell cycle-related and expression-elevated protein in tumor, also named RPRD1B), an oncoprotein highly expressed in tumors. We observed that CREPT competed with HDAC1 for binding to oncogene (such as CCND1, CLDN1, VEGFA, PPARD and BMP4) promoters but not the tumor suppressor gene (such as p21 and p27) promoters by a chromatin immunoprecipitation (ChIP) qPCR experiment. Using immunoprecipitation experiments, we deciphered that CREPT specifically occupied at the oncogene promoter via TCF4, a transcription factor activated by Wnt signaling. In addition, we performed a real-time quantitative PCR (qRT-PCR) analysis on cells that stably over-expressed CREPT and/or HDAC1, and we propose that HDAC1 inhibits CREPT to activate oncogene expression under Wnt signaling activation. Our findings revealed that HDAC1 functions differentially on tumor suppressors and oncogenes due to its interaction with the oncoprotein CREPT. Full article

(This article belongs to the Topic Molecular and Cellular Mechanisms of Cancers: Colorectal Cancer)

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14 pages, 74690 KiB

Open AccessArticle

Nuclear Beclin 1 Destabilizes Retinoblastoma Protein to Promote Cell Cycle Progression and Colorectal Cancer Growth

by Yang Pan, Zhiqiang Zhao, Juan Li, Jinsong Li, Yue Luo, Weiyuxin Li, Wanbang You, Yujun Zhang, Zhonghan Li, Jian Yang, Zhi-Xiong Jim Xiao and Yang Wang

Cancers 2022, 14(19), 4735; https://doi.org/10.3390/cancers14194735 - 28 Sep 2022

Cited by 5 | Viewed by 2442

Abstract

Autophagy is elevated in colorectal cancer (CRC) and is generally associated with poor prognosis. However, the role of autophagy core-protein Beclin 1 remains controversial in CRC development. Here, we show that the expression of nuclear Beclin 1 protein is upregulated in CRC with [...] Read more.

Autophagy is elevated in colorectal cancer (CRC) and is generally associated with poor prognosis. However, the role of autophagy core-protein Beclin 1 remains controversial in CRC development. Here, we show that the expression of nuclear Beclin 1 protein is upregulated in CRC with a negative correlation to retinoblastoma (RB) protein expression. Silencing of BECN1 upregulates RB resulting in cell cycle G1 arrest and growth inhibition of CRC cells independent of p53. Furthermore, ablation of BECN1 inhibits xenograft tumor growth through elevated RB expression and reduced autophagy, while simultaneous silencing of RB1 restores tumor growth but has little effect on autophagy. Mechanistically, knockdown of BECN1 promotes the complex formation of MDM2 and MDMX, resulting in MDM2-dependent MDMX instability and RB stabilization. Our results demonstrate that nuclear Beclin 1 can promote cell cycle progression through modulation of the MDM2/X-RB pathway and suggest that Beclin 1 promotes CRC development by facilitating both cell cycle progression and autophagy. Full article

(This article belongs to the Topic Molecular and Cellular Mechanisms of Cancers: Colorectal Cancer)

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16 pages, 1749 KiB

Open AccessArticle

Teenage-Onset Colorectal Cancers in a Digenic Cancer Predisposition Syndrome Provide Clues for the Interaction between Mismatch Repair and Polymerase δ Proofreading Deficiency in Tumorigenesis

by Esther Schamschula, Miriam Kinzel, Annekatrin Wernstedt, Klaus Oberhuber, Hendrik Gottschling, Simon Schnaiter, Nicolaus Friedrichs, Sabine Merkelbach-Bruse, Johannes Zschocke, Richard Gallon and Katharina Wimmer

Biomolecules 2022, 12(10), 1350; https://doi.org/10.3390/biom12101350 - 22 Sep 2022

Cited by 12 | Viewed by 2828

Abstract

Colorectal cancer (CRC) in adolescents and young adults (AYA) is very rare. Known predisposition syndromes include Lynch syndrome (LS) due to highly penetrant MLH1 and MSH2 alleles, familial adenomatous polyposis (FAP), constitutional mismatch-repair deficiency (CMMRD), and polymerase proofreading-associated polyposis (PPAP). Yet, 60% of [...] Read more.

Colorectal cancer (CRC) in adolescents and young adults (AYA) is very rare. Known predisposition syndromes include Lynch syndrome (LS) due to highly penetrant MLH1 and MSH2 alleles, familial adenomatous polyposis (FAP), constitutional mismatch-repair deficiency (CMMRD), and polymerase proofreading-associated polyposis (PPAP). Yet, 60% of AYA-CRC cases remain unexplained. In two teenage siblings with multiple adenomas and CRC, we identified a maternally inherited heterozygous PMS2 exon 12 deletion, NM_000535.7:c.2007-786_2174+493del1447, and a paternally inherited POLD1 variant, NP_002682.2:p.Asp316Asn. Comprehensive molecular tumor analysis revealed ultra-mutation (>100 Mut/Mb) and a large contribution of COSMIC signature SBS20 in both siblings’ CRCs, confirming their predisposition to AYA-CRC results from a high propensity for somatic MMR deficiency (MMRd) compounded by a constitutional Pol δ proofreading defect. COSMIC signature SBS20 as well as SBS26 in the index patient’s CRC were associated with an early mutation burst, suggesting MMRd was an early event in tumorigenesis. The somatic second hits in PMS2 were through loss of heterozygosity (LOH) in both tumors, suggesting PPd-independent acquisition of MMRd. Taken together, these patients represent the first cases of cancer predisposition due to heterozygous variants in PMS2 and POLD1. Analysis of their CRCs supports that POLD1-mutated tumors acquire hypermutation only with concurrent MMRd. Full article

(This article belongs to the Topic Molecular and Cellular Mechanisms of Cancers: Colorectal Cancer)

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Figure 1
Immunohistochemical staining of mismatch-repair (MMR) proteins (PMS2, MLH1, MSH2 and MSH6) in the tumor of the index patient (A) and his sister (B). Loss of PMS2 expression is seen in carcinoma epithelia (red arrows) but not in tumor-infiltrating leukocytes (blue arrows; A,B). PMS2 expression is also absent or strongly reduced in the patient’s non-dysplastic crypts adjacent to carcinoma tissue (black arrows) (A). An enlarged view of the relevant areas (black box) is shown below the upper panels (A,B). In the pedigree of the family (C), the identified germline pathogenic variants (PVs), PMS2:c.2007-786_2174+493del1447 (green points) and POLD1:c.946G>A (blue triangles), are indicated. Cancers are labelled as filled quarters (see figure key). The index patient is depicted by an arrow. Full article ">Figure 2
Identification and characterization of the familial PMS2 exon 12 deletion. Copy number (CN) CN analysis of next-generation sequencing (NGS) data with the SeqNext software shows a loss of one copy of exon 12 of either the PMS2 gene or the PMS2CL pseudogene. Bars indicate the relative CN of each PMS2 exon (shown on the x-axis) in the patient compared to 12 controls. Red lines indicate thresholds for CN variant calling (A). Direct PMS2 gene-specific cDNA sequencing using a reverse primer located in exon 13 (black arrow) shows exon 12 skipping in 50% of the transcripts (B). Sequencing of the deletion-spanning gene-specific amplicon reveals an Alu-mediated 1447 bp-deletion (Δex12). These (green and purple) and other (gray) Alu elements in the introns are indicated as arrows in the schematic illustration of PMS2 exons 11 and 12 and flanking intronic sequences. The amplified region using an unspecific, i.e., not discriminating between PMS2 and PMS2CL, forward primer (black arrow, universal) and a PMS2-specific reverse primer (blue arrow) is indicated above the scheme. The shortened 1256 bp PCR amplicon generated from the patient’s DNA and the wild-type (wt) 2703 bp amplicon generated from a control (Ct) DNA (Ø: negative control) are visible in the agarose gel shown on the left below the scheme. Sanger sequencing of the deletion-spanning amplicon of the patient shows a transition from AluI (green) to AluII (purple). The last AluI-specific and the first AluII-specific nucleotides are marked in bold in the sequence shown right below the scheme. The intervening 25 bp sequence in which AluI does not differ from AluII is framed in gray (C). Full article ">Figure 3
Histogram of somatic single nucleotide variants (SNVs) detected in the patient’s (IV-1; (A)) and his sister’s (IV-2; (B)) tumor. Short tandem repeat variants are highlighted in dark turquoise. Late and early events with low and high variant allele frequencies (VAFs), respectively, are shown as framed solid. Possible artefacts are framed dashed (A,B). Pie chart of the signature contribution of all SNVs for the patient’s (IV-1 all events) and his sister’s (IV-2 all events) tumor and signature contribution of separately analyzed late and early SNV events for the patient’s tumor (IV-1 late events; IV-1 early events). For the separate analysis of late and early events, regions suspected to be affected by CN variants and/or loss of heterozygosity (LOH) events were omitted (see Methods and <a href="#app1-biomolecules-12-01350" class="html-app">Figure S3A,B</a>) (C). Full article ">

23 pages, 3866 KiB

Open AccessArticle

Anti-Human CD9 Fab Fragment Antibody Blocks the Extracellular Vesicle-Mediated Increase in Malignancy of Colon Cancer Cells

by Mark F. Santos, Germana Rappa, Simona Fontana, Jana Karbanová, Feryal Aalam, Derek Tai, Zhiyin Li, Marzia Pucci, Riccardo Alessandro, Chikao Morimoto, Denis Corbeil and Aurelio Lorico

Cells 2022, 11(16), 2474; https://doi.org/10.3390/cells11162474 - 10 Aug 2022

Cited by 10 | Viewed by 3686

Abstract

Intercellular communication between cancer cells themselves or with healthy cells in the tumor microenvironment and/or pre-metastatic sites plays an important role in cancer progression and metastasis. In addition to ligand–receptor signaling complexes, extracellular vesicles (EVs) are emerging as novel mediators of intercellular communication [...] Read more.

Intercellular communication between cancer cells themselves or with healthy cells in the tumor microenvironment and/or pre-metastatic sites plays an important role in cancer progression and metastasis. In addition to ligand–receptor signaling complexes, extracellular vesicles (EVs) are emerging as novel mediators of intercellular communication both in tissue homeostasis and in diseases such as cancer. EV-mediated transfer of molecular activities impacting morphological features and cell motility from highly metastatic SW620 cells to non-metastatic SW480 cells is a good in vitro example to illustrate the increased malignancy of colorectal cancer leading to its transformation and aggressive behavior. In an attempt to intercept the intercellular communication promoted by EVs, we recently developed a monovalent Fab fragment antibody directed against human CD9 tetraspanin and showed its effectiveness in blocking the internalization of melanoma cell-derived EVs and the nuclear transfer of their cargo proteins into recipient cells. Here, we employed the SW480/SW620 model to investigate the anti-cancer potential of the anti-CD9 Fab antibody. We first demonstrated that most EVs derived from SW620 cells contain CD9, making them potential targets. We then found that the anti-CD9 Fab antibody, but not the corresponding divalent antibody, prevented internalization of EVs from SW620 cells into SW480 cells, thereby inhibiting their phenotypic transformation, i.e., the change from a mesenchymal-like morphology to a rounded amoeboid-like shape with membrane blebbing, and thus preventing increased cell migration. Intercepting EV-mediated intercellular communication in the tumor niche with an anti-CD9 Fab antibody, combined with direct targeting of cancer cells, could lead to the development of new anti-cancer therapeutic strategies. Full article

(This article belongs to the Topic Molecular and Cellular Mechanisms of Cancers: Colorectal Cancer)

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Graphical abstract

Graphical abstract
Full article ">Figure 1
CD9 expression in SW480 and SW620 cells. (A) CD9 expression was investigated by indirect immunofluorescence labeling using anti-CD9 5H9 Abs on either intact or permeabilized SW480 and SW620 cells cultured on poly-D-lysine-coated dishes. Nuclei were stained with DAPI and samples were observed by CLSM. Composite images (top panels) or single x-y sections (middle and bottom panels) are shown. Arrowheads indicate membrane blebs, while asterisks mark cytoplasmic CD9 immunoreactivity. (B) The amount of surface CD9 antigens per cell detected with FITC-conjugated eBioSN4 Abs was estimated using a flow cytometer calibrated with fluorescent microparticles. (C) Total CD9 antigens were analyzed by immunoblotting (top panel) using 5H9 Abs and quantified (bottom panel). The samples were normalized to β-actin. Molecular mass markers (kDa) are indicated. Arrowhead indicates the protein of interest. (D,E) SW620 cells stably transfected with CD9-GFP were analyzed either by fluorescence microscopy without permeabilization (D) or immunoblotting (E). For the microscopy, nuclei were stained with DAPI and samples were observed by CLSM. A composite image (top panel) or a single x-y section (bottom panel) is displayed. Arrowheads indicate membrane blebs. For immunoblotting, the membrane was probed with 5H9 Abs. The arrow and arrowhead indicate the CD9-GFP fusion protein and the endogenous CD9, respectively. Means ± S.D. and individual values for each experiment are shown (n = 3). p values are indicated. Scale bars, 10 µm. Full article ">Figure 2
Characterization of EVs released by SW620, CD9-GFP+ SW620, and SW480 cells. (A–E) EVs were recovered from the conditioned media of SW620, CD9-GFP+ SW620, and SW480 cells by differential centrifugation, and the resulting 200,000× g pellets were analyzed by the ZetaView particle analyzer (A), immunoblotting (B,C), and dSTORM (D,E). The concentration and size of EVs derived from the indicated cells are shown (A). Note the presence of a common population of small particles (<200 nm) with a peak at 100–150 nm (pink area), and larger ones (350–500 nm, gray areas) enriched in SW480 samples. EVs, and for comparison the cells from which they were derived, were probed by immunoblotting for CD9, CD63, CD81, Alix, and Calnexin (B,C). Arrowheads and brackets indicate the endogenous proteins of interest, while the arrow points to the CD9-GFP fusion protein. Molecular mass markers (kDa) are indicated. EVs were imaged after immunolabeling of three tetraspanins using dSTORM (D). The proteins of interest (CD9, CD63, and CD81) were pseudo-colored as indicated. Small single-, double-, and triple-positive EVs were shown (D) and quantified (E). Means ± S.D. and individual values for the three experiments are shown (n > 5000 EVs per experiment). Note that small and large EVs derived from SW480 cells were also quantified (<a href="#app1-cells-11-02474" class="html-app">Supplementary Figure S2</a>). Scale bars, 50 nm. Full article ">Figure 3
Effects of CD9 Fab and divalent Abs on the internalization of SW620 cell-derived CD9-GFP+ EVs into SW480 cells. (A–D) SW480 cells and fluorescent EVs derived from CD9-GFP+ SW620 cells (1 × 109 particles) were individually pre-incubated for 30 min without (control) or with different concentrations of anti-CD9 Fab or divalent Ab as indicated before their co-incubation for 5 h in the absence or presence of Abs (protocol #1). Fixed cells were either stained with DAPI (A,B) or immunolabeled for SUN2 (C,D) before observation by CLSM. Note the presence of discrete punctate GFP signals in the cytoplasmic (A,C) or nucleoplasmic (C) compartments (asterisk and circle, respectively). Their intensity (B) or amount (D) was quantified using serial optical sections through a cell (see <a href="#app1-cells-11-02474" class="html-app">Supplementary Figure S3</a>). Means ± S.D. of individual signals from three independent experiments, as indicated by color coding, are shown (n > 15 cells per experiment). p values are indicated. Arrowheads indicate CD9-GFP signals in the nuclear envelope invagination (C). Scale bars, 5 µm. Full article ">Figure 4
Effects of CD9 Fab and divalent Ab on the pro-metastatic morphological alterations of SW480 cells exposed to SW620 cell-derived EVs. (A–C) SW480 cells and SW620 cell-derived EVs (1 × 109 particles) were individually pre-incubated for 30 min with different concentrations of anti-CD9 Fab (red) or divalent Ab (green) as indicated before their co-incubation for 5 h in the presence of Abs, as described for protocol #1. As negative and positive controls, cells were not exposed (SW480, grey) or were exposed to EVs in the absence of Ab (+ SW620 EV, blue), respectively. Afterward, fixed cells were stained with DAPI and fluorochrome-conjugated phalloidin to label nuclei and actin filaments, respectively, before observation by CLSM (A). Single sections are presented. Rounded cell morphology and membrane blebs induced by EVs are indicated by the letter R and the arrowheads, respectively. The percentage of cells with rounded morphology (B) or membrane blebs (C) was quantified. Means ± S.D. and individual values for each experiment are shown (n = 4). At least 100 cells were evaluated for each experiment. p values are indicated. Similar experiments were performed by pre-incubating only EVs or cells with Abs (<a href="#app1-cells-11-02474" class="html-app">Supplementary Figure S5</a>). Scale bars, 10 µm. Full article ">Figure 5
Effects of CD9 Fab and divalent Ab on the migration of SW480 cells exposed to SW620 cell-derived EVs. (A) The migration–wound healing assay was performed by introducing a scratch on confluent SW480 cell monolayers cultured on 12-well standard cell culture plates and incubating them for 5 h in the absence (negative control, grey) or presence (positive control, blue) of SW620 cell-derived EVs (1 × 109 particles/mL). Alternatively, after introducing a scratch on the cell monolayer, cells and EVs were individually pre-incubated for 30 min with different concentrations of anti-CD9 Fab (red) or divalent Ab (green) as indicated before their 5 h co-incubation in the presence of Abs (A). The percentage of remaining wound areas after 5 h was quantified. Baseline (100%, dashed line) refers to wound area at 0 h. (B,C) Solely EVs (B) or cells (C) were pre-incubated for 30 min with Abs prior to co-incubation with cells or EVs for 5 h. Wound area was quantified. Controls (white) are shown for comparison. Means ± S.D. from multiple scratches are shown (n = 4–13). (D–F) The migration–Transwell filter assay was performed using a Transwell chamber as illustrated (D), where SW480 (E) or SW620 (F) cells were added to the upper chamber. Cells and EVs were pre-treated, as described in panel A, using 25 µg/mL CD9 Fab or divalent Ab ((D), steps 1 and 2) before 24 h of co-incubation ((D), step 3). In the case of SW620 cells, they were not incubated with EVs (F). The amount of migrating cells recovered in the lower chamber was then quantified. Each individual value is shown. p values are indicated. n.s., not significant. Full article ">Figure 6
The lack of CD9 in SW480 cells impedes pro-metastatic morphological alterations produced by SW620 cell-derived EVs. (A) Parental (control) or CD9-knockdown (shCD9) SW480 cells were analyzed by immunoblotting for CD9 and β-actin. Molecular mass markers (kDa) are indicated. Arrowhead indicates the protein of interest. (B) SW480 cells as indicated were incubated with (+) SW620 cell-derived EVs (1 × 109 particles) or without (–) for 5 h. Afterward, fixed cells were stained with DAPI and fluorochrome-conjugated phalloidin to label nuclei and actin filaments, respectively, before observation by CLSM. The percentage of cells with rounded morphology (top panel) or membrane blebs (bottom panel) was quantified. Means ± S.D. and individual values for each experiment are shown (n = 3). At least 100 cells were evaluated for each experiment. p values are indicated. n.s., not significant. Full article ">

17 pages, 3568 KiB

Open AccessArticle

Single-Cell FISH Analysis Reveals Distinct Shifts in PKM Isoform Populations during Drug Resistance Acquisition

by Seong Ho Kim, Ji Hun Wi, HyeRan Gwak, Eun Gyeong Yang and So Yeon Kim

Biomolecules 2022, 12(8), 1082; https://doi.org/10.3390/biom12081082 - 6 Aug 2022

Cited by 2 | Viewed by 2083

Abstract

The Warburg effect, i.e., the utilization of glycolysis under aerobic conditions, is recognized as a survival advantage of cancer cells. However, how the glycolytic activity is affected during drug resistance acquisition has not been explored at single-cell resolution. Because the relative ratio of [...] Read more.

The Warburg effect, i.e., the utilization of glycolysis under aerobic conditions, is recognized as a survival advantage of cancer cells. However, how the glycolytic activity is affected during drug resistance acquisition has not been explored at single-cell resolution. Because the relative ratio of the splicing isoform of pyruvate kinase M (PKM), PKM2/PKM1, can be used to estimate glycolytic activity, we utilized a single-molecule fluorescence in situ hybridization (SM-FISH) method to simultaneously quantify the mRNA levels of PKM1 and PKM2. Treatment of HCT116 cells with gefitinib (GE) resulted in two distinct populations of cells. However, as cells developed GE resistance, the GE-sensitive population with reduced PKM2 expression disappeared, and GE-resistant cells (Res) demonstrated enhanced PKM1 expression and a tightly regulated PKM2/PKM1 ratio. Our data suggest that maintaining an appropriate PKM2 level is important for cell survival upon GE treatment, whereas increased PKM1 expression becomes crucial in GE Res. This approach demonstrates the importance of single-cell-based analysis for our understanding of cancer cell metabolic responses to drugs, which could aid in the design of treatment strategies for drug-resistant cancers. Full article

(This article belongs to the Topic Molecular and Cellular Mechanisms of Cancers: Colorectal Cancer)

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Figure 1

Figure 1
Detection of endogenous mRNAs of PKM isoforms in HCT116 cells using SM-FISH-STIC. (A) Schematic diagram of alternative splicing of PKM isoforms. PKM1 and PKM2 are produced via alternative splicing of the mutually exclusive exons 9 and 10. (B) Fluorescence images of single PKM mRNAs in HCT116 cells. PKM1 was detected with AX488 (green), and PKM2 was detected with Cy3. Images were processed as described in the Materials and Methods section and visualized using a maximum intensity projection of the entire z-axis of the cells (20 μm). Scale bar, 10 μm. (C) (Left) 2D plot of PKM isoforms in HCT116 cells. Each point corresponds to the number of mRNA molecules of each PKM isoform in individual cells. A linear regression of all the points is shown as a red line. The slope of the blue line corresponds to 1.2 (the average ratio of PKM2 to PKM1), and it passes through the average value of each PKM isoform (132, 159). (Right) Distribution of the total number of PKM isoform molecules (top) and the relative ratio of PKM2 to PKM1 (bottom) in individual cells. Full article ">Figure 2
Effects of isoform-specific knockdown on PKM mRNA expression. Cells were treated with a scrambled siRNA (Con), PKM1-specific siRNA (SiM1), or PKM2-specific siRNA (siM2). (A) RT-PCR analysis of PKM isoforms. M1 and M2 corresponds to PKM1 and PKM2, respectively. Error bars represent average ± SD (n = 3) (B) Quantitative SM-FISH-STIC analysis of PKM isoforms. The number of mRNA molecules of each PKM isoform in individual cell was counted, and the average value is presented. M1 and M2 corresponds to PKM1 and PKM2, respectively. Error bars represent average ± standard error of the mean (SEM) (n ≥ 122) (C) 2D plot of each PKM isoform in HCT116 cells. Each point corresponds to the number of PKM isoforms in individual cell. The region enclosed within the dotted lines (the average ± SD in control cells) is highlighted in blue. The green box represents cells with a high expression level of PKM2 when PKM1 was depleted (greater than the average value + SD in control cells), and the red box represents cells with a high expression level of PKM1 when PKM2 was depleted (greater than the average value + SD in control cells). (D) Distribution of PKM1 (left) and PKM2 mRNA (right) from individual cells. The black, dark green, and red lines represent the Gaussian fits of each distribution. The black arrows indicate the corresponding average value + SD of the control. (E) Determination of relative mRNA levels of regulatory proteins involved in PKM splicing determined by RT-PCR. Data are shown as average ± SD of three independent experiments.* p < 0.05, ** p < 0.01, and *** p < 0.001. Full article ">Figure 3
Effects of GE on the mRNA levels of PKM isoforms and proteins involved in PKM regulation. Cells were untreated (−) or GE-treated for 24 h (+). GE-resistant cells (GE Res) were also analyzed. (A) Evaluation of the cell viability. HCT116 cells (Con) or GE-resistant HCT116 cells (Res) were cultured in the absence or presence of 30 μM GE for 24 h, and viable cells were counted as described in the Materials and Methods section. Data are shown as average ± SD of three independent experiments. Scale bar, 200 μm. (B) The effect of GE on cell viability. HCT116 cells (Con) or GE-resistant HCT116 cells (Res) were treated with 30 μM GE for 24 h; then, the degree of EGFR expression and activation was measured by Western blotting. Untreated cells were used as a negative control. (C) RT-PCR analysis for PKM isoforms, c-Myc, hnRNPs, PTBP1, and Sp1. M1 and M2 in the graph correspond to PKM1 and PKM2, respectively. (D) Quantitative SM-FISH-STIC analysis of PKM isoforms. M1 and M2 correspond to PKM1 and PKM2, respectively. Error bars represent average ± SEM (n ≥ 162). * p < 0.05, ** p < 0.01, and *** p < 0.001. Full article ">Figure 4
Effects of GE on the distribution of PKM isoforms. Cells were untreated (GE (−)) or GE-treated for 24 h (GE (+)). GE-resistant cells (GE Res) were also analyzed. (A) 2D plot of each PKM isoform in GE (−) or GE (+). Each point corresponds to the number of mRNA molecules of each PKM isoform in individual cells. The dotted lines represent the average value ± SD of each PKM isoform in GE (−). The dark green line represents the value at which the two PKM2 Gaussian fits (shown in orange and blue in <a href="#biomolecules-12-01082-f004" class="html-fig">Figure 4</a>C) overlap. (B) 2D plot of each PKM isoform in GE (−) or GE Res. Each point corresponds to the number of mRNA molecules of each PKM isoform in individual cells. The dotted lines represent the average value ± SD of PKM isoforms from GE (−). (C) Distribution of PKM1 (left) and PKM2 mRNAs (right). The black, dark green, and red lines represent the Gaussian fit of each distribution. In the PKM2 mRNA distribution (right), the dark green line represents the sum of the two Gaussian fits (orange and blue lines) for GE (+). The black arrow highlights the overlap of the two Gaussian fits. (D) Distribution of the ratio of PKM2 to PKM1. The black, green, and red lines represent the Gaussian fits of each distribution. Full article ">Figure 5
The importance of PKM isoforms in the acquisition of drug resistance. (A) The effect of PKM isoform depletion on the response to GE. Control or GE-resistant HCT116 cells (Con or GE Res) were pretreated with the appropriate siRNAs (siM1 and siM2) for 24 h and exposed to GE (30 μM) for 24 h. The viable cells were counted in three independent experiments as described in the Materials and Methods section. Data are shown as average ± SD of three independent experiments. Scale bar, 300 μm. (B) Expression of PKM proteins upon GE treatment in nuclear and cytoplasmic fractions. Individual fractions were normalized by vinculin (cytoplasmic extract) and Lamin B1 (nuclear extract). Data are shown as average ± SD of three independent experiments. * p < 0.05, ** p< 0.01, and *** p < 0.001. Full article ">

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