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17 pages, 774 KiB  
Review
Perceived Pain in People Living with Amyotrophic Lateral Sclerosis—A Scoping Review
by Debora Rosa, Laura Ingrande, Ilaria Marcomini, Andrea Poliani, Giulia Villa, Martina Sodano and Duilio Fiorenzo Manara
Nurs. Rep. 2024, 14(4), 3023-3039; https://doi.org/10.3390/nursrep14040220 (registering DOI) - 17 Oct 2024
Abstract
(1) Background: Pain is a common symptom in patients with Amyotrophic Lateral Sclerosis (ALS). There are no evidence-based pharmacological treatments for pain in ALS; recommendations are based on guidelines for chronic non-oncological pain and clinical experience. The aim is to map the literature [...] Read more.
(1) Background: Pain is a common symptom in patients with Amyotrophic Lateral Sclerosis (ALS). There are no evidence-based pharmacological treatments for pain in ALS; recommendations are based on guidelines for chronic non-oncological pain and clinical experience. The aim is to map the literature on how people with ALS experience pain, and how this affects their daily activities and social relationships. (2) Methods: This scoping review included studies concerning patients with spinal/bulbar ALS aged ≥ 18 years who experience pain, focusing on perception, characteristics, treatment, and impact on quality of life. Temporal and linguistic criteria were applied when searching the MEDLINE, CINAHL, and SCOPUS databases. (3) Results: The management of pain in these patients is complex and involves the use of anti-inflammatory drugs, analgesics, and opioids. Pain is associated with other conditions such as depression and anxiety, which contribute to a deterioration in the quality of life. Moreover, pain may also negatively influence patient compliance with prescribed treatment regimens and the quality of care they perceive themselves to be receiving. (4) Conclusions: It is of the most importance to identify effective ways to assess and treat this issue, with health care professionals taking an active role in this process. Full article
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<p>PRISMA flow diagram.</p>
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9 pages, 518 KiB  
Review
Genitourinary Cancer Care in Low- and Middle-Income Countries: Disparities in Incidence and Access to Care
by Kanha Shete, Joshua Ghoulian, Brian Hu and Muhannad Alsyouf
Soc. Int. Urol. J. 2024, 5(5), 330-338; https://doi.org/10.3390/siuj5050052 (registering DOI) - 16 Oct 2024
Abstract
Despite the considerable global burden of urologic malignancies, Low- and middle-income countries (LMICs) often encounter significant challenges in caring for patients with urologic malignancies. Several interrelated factors impact cancer care in LMICs, which face significant challenges that hinder effective diagnosis, treatment, and management [...] Read more.
Despite the considerable global burden of urologic malignancies, Low- and middle-income countries (LMICs) often encounter significant challenges in caring for patients with urologic malignancies. Several interrelated factors impact cancer care in LMICs, which face significant challenges that hinder effective diagnosis, treatment, and management of disease. Socioeconomic and healthcare infrastructure limitations are fundamental issues leading to the disparity observed in cancer care across the globe. This review aims to evaluate the challenges and disparities in access to comprehensive urologic care in LMICs, emphasizing the impact of such global disparities on incidence rates, timely diagnoses, and access to comprehensive care as it relates to prostate, kidney, and bladder cancers. Full article
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<p>Preferred reporting items for systematic reviews and meta-analyses diagram.</p>
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8 pages, 241 KiB  
Review
Urologic Cancer Drug Costs in Low- and Middle-Income Countries
by Lan Anh S. Galloway, Brian D. Cortese and Ruchika Talwar
Soc. Int. Urol. J. 2024, 5(5), 312-319; https://doi.org/10.3390/siuj5050050 (registering DOI) - 16 Oct 2024
Abstract
All 189 World Bank member countries are classified by their capita gross national income into one of four income groups. In this review, we aim to explore the economic burden and management of urologic oncology conditions in low- and middle-income countries (LMICs), emphasizing [...] Read more.
All 189 World Bank member countries are classified by their capita gross national income into one of four income groups. In this review, we aim to explore the economic burden and management of urologic oncology conditions in low- and middle-income countries (LMICs), emphasizing disparities and challenges in treatment access. The current World Bank classification system highlights economic stratification, showing significant health outcome disparities, particularly in urologic oncology conditions including kidney, bladder, and prostate cancer. First, this review focuses on the management of advanced prostate cancer in Asian LMICs, revealing higher mortality-to-incidence ratios and a greater prevalence of metastatic disease compared to high-income countries (HICs). The prohibitive costs of novel hormonal therapies (NHTs) like abiraterone and enzalutamide limit their use and exacerbate outcome disparities. Second, we review Wilms tumor treatment with chemotherapy in African countries, noting significant price variations for adapted and non-adapted regimens across different economic settings. The cost of chemotherapy agents, particularly dactinomycin, acts as a primary driver of treatment expenses, underscoring the economic challenges in providing high-quality care. Lastly, bladder cancer treatment costs in Brazil and Middle Eastern countries are examined, highlighting how detrimental the economic burden of intravesical therapies, like mitomycin C and Bacillus Calmette–Guérin (BCG), is on treatment accessibility. Overall, this literature review emphasizes the financial strain on healthcare systems and patients, particularly in regions facing economic instability and drug shortages, and underscores the need for international cooperation and effective resource allocation to address the economic barriers to urologic care in LMICs, aiming to improve health outcomes and ensure equitable access to advanced treatments. Full article
9 pages, 280 KiB  
Review
Challenges of Urologic Oncology in Low-to-Middle-Income Countries
by Sami E. Majdalany, Mohit Butaney, Shane Tinsley, Nicholas Corsi, Sohrab Arora, Craig G. Rogers and Firas Abdollah
Soc. Int. Urol. J. 2024, 5(5), 303-311; https://doi.org/10.3390/siuj5050049 (registering DOI) - 16 Oct 2024
Abstract
We performed a literature review to identify articles regarding the state of urological cancers in low-to-middle-income countries (LMICs). The challenges that LMICs face are multifactorial and can include poor health education, inadequate screening, as well as limited access to treatment options and trained [...] Read more.
We performed a literature review to identify articles regarding the state of urological cancers in low-to-middle-income countries (LMICs). The challenges that LMICs face are multifactorial and can include poor health education, inadequate screening, as well as limited access to treatment options and trained urologists. Many of the gold standard treatments in high-income countries (HICs) are scarce in LMICs due to their poor socioeconomic status, leading to an advanced stage of disease at diagnosis and, ultimately, a higher mortality rate. These standards of care are vital components of oncological disease management; however, the current and sparse literature available from LMICs indicates that there are many obstacles delaying early diagnosis and management options in LMICs. In the era of evolving medical diagnosis and treatments, sufficient data must be gathered and understood in order to provide appropriate diagnostic and treatment options to curtail rising mortality rates and, therefore, help to alleviate the burden in LMICs. Full article
15 pages, 2614 KiB  
Communication
Neutrophil Extracellular Traps in Pediatric Inflammatory Bowel Disease: A Potential Role in Ulcerative Colitis
by Rachel Shukrun, Victoria Fidel, Szilvia Baron, Noga Unger, Yoav Ben-Shahar, Shlomi Cohen, Ronit Elhasid and Anat Yerushalmy-Feler
Int. J. Mol. Sci. 2024, 25(20), 11126; https://doi.org/10.3390/ijms252011126 - 16 Oct 2024
Abstract
Inflammatory bowel disease (IBD), encompassing Crohn’s disease (CD) and ulcerative colitis (UC), is a chronic inflammatory condition of the gut affecting both adults and children. Neutrophil extracellular traps (NETs) are structures released by activated neutrophils, potentially contributing to tissue damage in various diseases. [...] Read more.
Inflammatory bowel disease (IBD), encompassing Crohn’s disease (CD) and ulcerative colitis (UC), is a chronic inflammatory condition of the gut affecting both adults and children. Neutrophil extracellular traps (NETs) are structures released by activated neutrophils, potentially contributing to tissue damage in various diseases. This study aimed to explore the presence and role of NETs in pediatric IBD. We compared intestinal biopsies and peripheral blood from 20 pediatric IBD patients (UC and CD) to controls. Biopsy staining and techniques for neutrophil activation were used to assess neutrophil infiltration and NET formation. We also measured the enzymatic activity of key NET proteins and evaluated NET formation in UC patients in remission. Both UC and CD biopsies showed significantly higher levels of neutrophils and NETs compared to controls (p < 0.01), with UC exhibiting the strongest association. Peripheral blood neutrophils from UC patients at diagnosis displayed increased NET formation compared to controls and CD patients. Interestingly, NET formation normalized in UC patients following remission-inducing treatment. This pilot study suggests a potential role for NETs in pediatric IBD, particularly UC. These findings warrant further investigation into the mechanisms of NET involvement and the potential for targeting NET formation as a therapeutic strategy. Full article
26 pages, 8774 KiB  
Review
RNA Binding Proteins as Potential Therapeutic Targets in Colorectal Cancer
by Vikash Singh, Amandeep Singh, Alvin John Liu, Serge Y. Fuchs, Arun K. Sharma and Vladimir S. Spiegelman
Cancers 2024, 16(20), 3502; https://doi.org/10.3390/cancers16203502 - 16 Oct 2024
Abstract
RNA-binding proteins (RBPs) play critical roles in regulating post-transcriptional gene expression, managing processes such as mRNA splicing, stability, and translation. In normal intestine, RBPs maintain the tissue homeostasis, but when dysregulated, they can drive colorectal cancer (CRC) development and progression. Understanding the molecular [...] Read more.
RNA-binding proteins (RBPs) play critical roles in regulating post-transcriptional gene expression, managing processes such as mRNA splicing, stability, and translation. In normal intestine, RBPs maintain the tissue homeostasis, but when dysregulated, they can drive colorectal cancer (CRC) development and progression. Understanding the molecular mechanisms behind CRC is vital for developing novel therapeutic strategies, and RBPs are emerging as key players in this area. This review highlights the roles of several RBPs, including LIN28, IGF2BP1–3, Musashi, HuR, and CELF1, in CRC. These RBPs regulate key oncogenes and tumor suppressor genes by influencing mRNA stability and translation. While targeting RBPs poses challenges due to their complex interactions with mRNAs, recent advances in drug discovery have identified small molecule inhibitors that disrupt these interactions. These inhibitors, which target LIN28, IGF2BPs, Musashi, CELF1, and HuR, have shown promising results in preclinical studies. Their ability to modulate RBP activity presents a new therapeutic avenue for treating CRC. In conclusion, RBPs offer significant potential as therapeutic targets in CRC. Although technical challenges remain, ongoing research into the molecular mechanisms of RBPs and the development of selective, potent, and bioavailable inhibitors should lead to more effective treatments and improved outcomes in CRC. Full article
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<p>(<b>A</b>,<b>B</b>) show 3D representations of the binding and surface view of inhibitor Ln7 with RNA binding protein Ln28 (PDBID: 5UDZ). (<b>C</b>) shows 2D representations of binding interactions of Ln7. (<b>D</b>) The binding energy of inhibitors with RNA binding protein Ln28 along with interacting residues. Color code: green = carbon, white = hydrogen, blue = nitrogen, and red = oxygen.</p>
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<p>(<b>A</b>,<b>B</b>) show 3D representations of the binding and surface view of inhibitor R12–8–44–3 with RNA binding protein Musash1 (PDBID: 2RS2). (<b>C</b>) shows 2D representations of binding interactions of Musashi1. (<b>D</b>) The binding energy of inhibitors with RNA binding protein Musashi 1 along with interacting residues. Color code: green = carbon, white = hydrogen, blue = nitrogen, and red = oxygen.</p>
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<p>(<b>A</b>,<b>B</b>) show 3D representations of the binding and surface view of inhibitor R12–8–44–3 with RNA binding protein Musash1 (PDBID: 2RS2). (<b>C</b>) shows 2D representations of binding interactions of Musashi1. (<b>D</b>) The binding energy of inhibitors with RNA binding protein Musashi 1 along with interacting residues. Color code: green = carbon, white = hydrogen, blue = nitrogen, and red = oxygen.</p>
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<p>(<b>A</b>,<b>B</b>) show 3D representations of the binding and surface view of inhibitor palmatine with RNA binding protein Musash2 (PDBID: 6DBP). (<b>C</b>) shows 2D representations of binding interactions of Musashi2. (<b>D</b>) The binding energy of inhibitors with RNA binding protein Musashi 2 along with interacting residues. Color code: green = carbon, white = hydrogen, blue = nitrogen, and red = oxygen.</p>
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<p>(<b>A</b>,<b>B</b>) show 3D representations of the binding and surface view of inhibitor C11 with RNA binding protein HUR (PDBID: 4ED5). (<b>C</b>) shows 2D representations of binding interactions of HUR. (<b>D</b>) The binding energy of inhibitors with RNA binding protein HUR along with interacting residues. Color code: green = carbon, white = hydrogen, blue = nitrogen, and red = oxygen.</p>
Full article ">Figure 4 Cont.
<p>(<b>A</b>,<b>B</b>) show 3D representations of the binding and surface view of inhibitor C11 with RNA binding protein HUR (PDBID: 4ED5). (<b>C</b>) shows 2D representations of binding interactions of HUR. (<b>D</b>) The binding energy of inhibitors with RNA binding protein HUR along with interacting residues. Color code: green = carbon, white = hydrogen, blue = nitrogen, and red = oxygen.</p>
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<p>(<b>A</b>,<b>B</b>) show 3D representations of the binding and surface view of inhibitor compound27 with RNA binding protein CELFI (PDBID: 3NMR). (<b>C</b>) shows 2D representations of binding interactions of CELFI. (<b>D</b>) The binding energy of inhibitors with RNA binding protein CELFI along with interacting residues. Color code: green = carbon, white = hydrogen, blue = nitrogen, and red = oxygen.</p>
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11 pages, 584 KiB  
Article
Association of SLC19A1 Gene Polymorphisms and Its Regulatory miRNAs with Methotrexate Toxicity in Children with Acute Lymphoblastic Leukemia
by Vasiliki Karpa, Kallirhoe Kalinderi, Eleni Gavriilaki, Vasiliki Antari, Emmanuil Hatzipantelis, Theodora Katopodi, Liana Fidani and Athanasios Tragiannidis
Curr. Issues Mol. Biol. 2024, 46(10), 11537-11547; https://doi.org/10.3390/cimb46100685 (registering DOI) - 16 Oct 2024
Abstract
Methotrexate (MTX) is an anti-folate chemotherapeutic agent that is considered to be a gold standard in Acute Lymphoblastic Leukemia (ALL) therapy. Nevertheless, toxicities induced mainly due to high doses of MTX are still a challenge for clinical practice. MTX pharmacogenetics implicate various genes [...] Read more.
Methotrexate (MTX) is an anti-folate chemotherapeutic agent that is considered to be a gold standard in Acute Lymphoblastic Leukemia (ALL) therapy. Nevertheless, toxicities induced mainly due to high doses of MTX are still a challenge for clinical practice. MTX pharmacogenetics implicate various genes as predictors of MTX toxicity, especially those that participate in MTX intake like solute carrier family 19 member 1 (SLC19A1). The aim of the present study was to evaluate the association between SLC19A1 polymorphisms and its regulatory miRNAs with MTX toxicity in children with ALL. A total of 86 children with ALL were included in this study and were all genotyped for rs2838958, rs1051266 and rs1131596 SLC19A1 polymorphisms as well as the rs56292801 polymorphism of miR-5189. Patients were followed up (48, 72 and 96 h) after treatment with MTX in order to evaluate the presence of MTX-associated adverse events. Our results indicate that there is a statistically significant correlation between the rs1131596 SLC19A1 polymorphism and the development of MTX-induced hepatotoxicity (p = 0.03), but there is no significant association between any of the studied polymorphisms and mucositis or other side effects, such as nausea, emesis, diarrhea, neutropenia, skin rash and infections. In addition, when genotype TT of rs1131596 and genotype AA of rs56292801 are both present in a patient then there is a higher risk of developing severe hepatotoxicity (p = 0.0104). Full article
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<p>ROC curve: age of ALL onset versus hepatotoxicity development.</p>
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<p>Bar chart: correlation between hepatotoxicity and age group.</p>
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9 pages, 1294 KiB  
Article
ARAF Amplification in Small-Cell Lung Cancer-Transformed Tumors Following Resistance to Epidermal Growth Factor Receptor–Tyrosine Kinase Inhibitors
by Ryo Kimura, Yuta Adachi, Kentaro Hirade, Satoru Kisoda, Shogo Yanase, Noriko Shibata, Makoto Ishii, Yutaka Fujiwara, Rui Yamaguchi, Yasuko Fujita, Waki Hosoda and Hiromichi Ebi
Cancers 2024, 16(20), 3501; https://doi.org/10.3390/cancers16203501 - 16 Oct 2024
Abstract
Background/Objectives: Although tyrosine kinase inhibitors (TKIs) targeting EGFR-activating mutations significantly improved the outcome of EGFR-mutant NSCLC, resistance inevitably emerges. Despite the heterogeneity of these resistance mechanisms, many induce activation of MAPK signaling in the presence of EGFR-TKIs. While ARAF gene amplification is identified [...] Read more.
Background/Objectives: Although tyrosine kinase inhibitors (TKIs) targeting EGFR-activating mutations significantly improved the outcome of EGFR-mutant NSCLC, resistance inevitably emerges. Despite the heterogeneity of these resistance mechanisms, many induce activation of MAPK signaling in the presence of EGFR-TKIs. While ARAF gene amplification is identified as a resistance mechanism that activates MAPK signaling by directly interacting with RAS, little is known about its clinicopathologic characteristics. Methods: We conducted a single-center retrospective analysis of the presence of ARAF amplification in re-biopsied samples in patients with EGFR-mutant NSCLC resistant to EGFR-TKIs. Demographic data, treatment course, and clinical molecular testing reports were extracted from electronic medical records. ARAF amplification was determined using a gene copy number assay. RNA sequence analysis was performed in patients with ARAF amplification as well as presenting histologic transformations to small-cell lung carcinoma (SCLC). Results: ARAF amplification was identified in five of ninety-seven patients resistant to erlotinib or gefitinib, and four of forty-eight patients resistant to Osimertinib. ARAF amplification was dominantly observed in female patients with EGFR exon 19 deletion. All ARAF-amplified tumors retained their founder EGFR mutation and were absent of secondary mutations. Two cases were found where ARAF amplification correlated with a histological transformation to SCLC. Conclusions: ARAF amplification was identified in 5–8% of EGFR-TKI-resistant tumors. The possible roles of ARAF in SCLC transformation warrant further investigation. Full article
(This article belongs to the Special Issue Advances in Molecular Oncology and Therapeutics)
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<p>The distribution of <span class="html-italic">ARAF</span> copy numbers and clinicopathologic characteristics is shown for patients resistant to first– and second–generation EGFR-TKIs (<b>A</b>,<b>B</b>) and Osimertinib (<b>C</b>,<b>D</b>). <a href="#cancers-16-03501-f001" class="html-fig">Figure 1</a>B and <a href="#cancers-16-03501-f001" class="html-fig">Figure 1</a>D provide the percentages of each characteristic within their respective categories. Blue represents <span class="html-italic">ARAF</span> non-amplified cases, while orange indicates <span class="html-italic">ARAF</span> amplified cases.</p>
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<p>Cases with <span class="html-italic">ARAF</span> amplification in SCLC-transformed tumors following resistance to EGFR-TKI. (<b>A</b>) Case with SCLC transformation after resistance to erlotinib treatment. A pre-treatment sample was taken at diagnosis using transbronchial lung biopsy (TBLB), and re-biopsied samples were taken at resistance to erlotinib treatment using CT-Guided needle biopsy (CTNB). Scale bar, 100 µm for low-power field and 50 µm for high-power field in CD56. IRI, irinotecan; AMR, amrubicin; DTX, docetaxel; PEM, pemetrexed. (<b>B</b>) Case with SCLC transformation after resistance to Osimertinib treatment. A pre-treatment sample was taken after resistance to gefitinib using CTNB, and re-biopsied samples were taken at resistance to Osimertinib using ultrasound-guided needle biopsy (USNB). Scale bar, 100 µm. ETP, etoposide. (<b>C</b>) Log 2-fold change in expression of the indicated genes at the time of resistance compared to the pre-treatment biopsy samples shown in (<b>A</b>,<b>B</b>). (<b>D</b>) GSEA data for the indicated signatures. (<b>E</b>) Immunohistochemistry staining for indicated antibodies. Scale bar, 100 µm for low-power field and 50 µm for high-power field in YAP.</p>
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20 pages, 8303 KiB  
Article
Interactive Structural Analysis of KH3-4 Didomains of IGF2BPs with Preferred RNA Motif Having m6A Through Dynamics Simulation Studies
by Muhammad Fakhar, Mehreen Gul and Wenjin Li
Int. J. Mol. Sci. 2024, 25(20), 11118; https://doi.org/10.3390/ijms252011118 - 16 Oct 2024
Abstract
m6A modification is the most common internal modification of messenger RNA in eukaryotes, and the disorder of m6A can trigger cancer progression. The GGACU is considered the most frequent consensus sequence of target transcripts which have a GGAC m [...] Read more.
m6A modification is the most common internal modification of messenger RNA in eukaryotes, and the disorder of m6A can trigger cancer progression. The GGACU is considered the most frequent consensus sequence of target transcripts which have a GGAC m6A core motif. Newly identified m6A ‘readers’ insulin-like growth factor 2 mRNA-binding proteins modulate gene expression by binding to the m6A binding sites of target mRNAs, thereby affecting various cancer-related processes. The dynamic impact of the methylation at m6A within the GGAC motif on human IGF2BPs has not been investigated at the structural level. In this study, through in silico analysis, we mapped IGF2BPs binding sites for the GGm6AC RNA core motif of target mRNAs. Subsequent molecular dynamics simulation analysis at 400 ns revealed that only the KH4 domain of IGF2BP1, containing the 503GKGG506 motif and its periphery residues, was involved in the interaction with the GGm6AC backbone. Meanwhile, the methyl group of m6A is accommodated by a shallow hydrophobic cradle formed by hydrophobic residues. Interestingly, in IGF2BP2 and IGF2BP3 complexes, the RNA was observed to shift from the KH4 domain to the KH3 domain in the simulation at 400 ns, indicating a distinct dynamic behavior. This suggests a conformational stabilization upon binding, likely essential for the functional interactions involving the KH3-4 domains. These findings highlight the potential of targeting IGF2BPs’ interactions with m6A modifications for the development of novel oncological therapies. Full article
(This article belongs to the Section Molecular Informatics)
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<p>Comparative analysis of KH3-4 domains of human IGF2BPs and gallus gallus IGF2BP1. (<b>A</b>) Multiple sequence alignment of gallus gallus (GG1) IGF2BP1 and human IGF2BP1 (Hu1), IGF2BP2 (Hu2), and IGF2BP3 (Hu3)’s KH3-4 domains. The conserved motif involved in the binding is highlighted in light olive (GXXG) and green–red color (GKGG). The secondary structure is shown above the sequences. Alpha helices are indicated in black color, β-sheets in plum color, and loops in blue color. (<b>B</b>) Structural analysis of KH3-4 domains of all human IGF2BPs and gallus gallus IGF2BP1 with their respective colors. KH3 domains, linkers, and KH4 domains are represented in white, rosy brown, and dark grey colors, respectively.</p>
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<p>Binding pattern of GGm<sup>6</sup>AC RNA motif with KH3-4 domains of IGF2BPs. (<b>A</b>) Surface representation of gallus gallus KH3-4 domains (pink) with GGm<sup>6</sup>AC RNA (red). (<b>B</b>) The same complex is indicated using a ribbon for the protein and zoomed out for highlighting the binding residues with RNA motif. The human IGF2BP1,2 and 3 KH3-4 domains (<b>C</b>–<b>H</b>) are indicated in surface and ribbon representations with green, orange, and blue colors, respectively. In all complexes, the 503GKKG506 loop of KH4 (brown), GGm<sup>6</sup>AC RNA motif (red), and the binding region (yellow) are highlighted. The binding residues of KH4 domains are labeled in black color.</p>
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<p>Time-dependent analysis of MD trajectories for a 400 ns time scale to investigate the stability and deviation of apo IGF2BPs (KH3-4) and their bound states. (<b>A</b>) gallus gallus Apo_GG1 and GG1_bound are illustrated in black and pink colors, respectively. RMSD plots (<b>B</b>–<b>D</b>) for human Apo_Hu1, Apo_Hu2, Apo_Hu3 and their bound states. In all complexes, apo and bound systems are represented by black and pink colors, respectively.</p>
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<p>Comparative analysis of RMSD, Rg, and SASA of IGF2BPs (KH3-4) with GGm<sup>6</sup>AC complexes at a 400 ns MD simulation. (<b>A</b>) Root mean square deviation (RMSD) (<b>B</b>) Radius of gyration (Rg) throughout the simulation. (<b>C</b>) Solvent-accessible surface area (SASA).</p>
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<p>RMSF analysis and RMSD calculation by superimposition of Apo IGF2BPs (KH3-4) domains and their bound states with GGm<sup>6</sup>AC at 400 ns. (<b>A</b>) Comparative RMSF plots of gallus gallus Apo_GG1 and GG1_bound are illustrated in black and pink colors respectively. Similarly, RMSF plots for human Apo_Hu1, Apo_Hu2, Apo_Hu3 (<b>B</b>–<b>D</b>), and their bound states follow the same color scheme: black for apo and pink for bound. (<b>E</b>–<b>H</b>) Superimposition of 3D structures of Apo_GG1, Apo_Hu1, Apo_Hu2, and Apo_Hu3 with their respective bound complexes. Superimposed Apo and bound 3D structures are shown in purple and brown colors, respectively.</p>
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<p>Comparative analysis of RMSF and average number of hydrogen bonds of IGF2BPs with GGm<sup>6</sup>AC complexes at 400 ns MD simulation. (<b>A</b>) RMSF values of alpha carbon over the entire simulation. (<b>B</b>) Average number of hydrogen bonds over the entire simulation.</p>
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<p>Time-dependent binding dynamics of GGm<sup>6</sup>AC RNA motif at IGF2BPs KH3-4 domains. (<b>A</b>) gallus gallus GG1_bound (pink) and (<b>B</b>) human Hu1_bound (green) binding with GGm<sup>6</sup>AC (red) at 400 ns. (<b>C</b>) Hu2_bound (orange) and (<b>D</b>) Hu3_bound (blue) at 150 and 120 ns MD simulation time scales, respectively, showed an interaction with the GGm<sup>6</sup>AC (red) RNA motif. The binding region is highlighted in yellow color, and some core binding residues at the groove region are labeled in black color. The GGm<sup>6</sup>AC RNA motif is labeled in red color in all complexes.</p>
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<p>Principal component analysis 2D projection scatters plot of 400 ns MD trajectories for apo and bound IGF2BP1 (KH3-4) with GGm<sup>6</sup>AC. Panels (<b>A</b>) apo_GG1, (<b>B</b>) GG1_bound, (<b>C</b>) apo_Hu1, and (<b>D</b>) Hu1_bound represent 2 D plots.</p>
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<p>Per-residue decomposition of binding enthalpy from MD trajectories estimated by the MM/PBSA method. Binding energy decomposition at residue basis for (<b>A</b>) GG1_bound and (<b>B</b>) Hu1_bound complexes are indicated in pink and green colors, respectively.</p>
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<p>The methylation process of m<sup>6</sup>A in consensus motif of target transcripts. The modification of m<sup>6</sup>A is regulated by ‘writers’, ‘readers’, and ‘erasers’. ‘Writers’ such as METTL3, METTL14, an d WTAP regulate m<sup>6</sup>A methylation. RNA m<sup>6</sup>A demethylation is prompted by eraser proteins such as FTO and ALKBH5. IGF2BPs have a role, like other reader proteins, in reading the m<sup>6</sup>A binding sites of target mRNAs to protect mRNA from degradation and promote cancer proliferation.</p>
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13 pages, 3161 KiB  
Communication
Assessment of a Structurally Modified Alternanthera Mosaic Plant Virus as a Delivery System for Sarcoma Cells
by Daria Fayzullina, Tatiana Manukhova, Ekaterina Evtushenko, Sergey Tsibulnikov, Kirill Kirgizov, Ilya Ulasov, Nikolai Nikitin and Olga Karpova
Viruses 2024, 16(10), 1621; https://doi.org/10.3390/v16101621 - 16 Oct 2024
Abstract
The virions of plant viruses and their structurally modified particles (SP) represent valuable platforms for recombinant vaccine epitopes and antitumor agents. The possibility of modifying their surface with biological compounds makes them a tool for developing medical biotechnology applications. Here, we applied a [...] Read more.
The virions of plant viruses and their structurally modified particles (SP) represent valuable platforms for recombinant vaccine epitopes and antitumor agents. The possibility of modifying their surface with biological compounds makes them a tool for developing medical biotechnology applications. Here, we applied a new type of SP derived from virions and virus-like particles (VLP) of Alternanthera mosaic virus (AltMV) and well-studied SP from Tobacco mosaic virus (TMV). We have tested the ability of SP from AltMV (AltMV SPV) and TMV virions also as AltMV VLP to bind to and penetrate Ewing sarcoma cells. The adsorption properties of AltMV SPV and TMV SP are greater than those of the SP from AltMV VLP. Compared to normal cells, AltMV SPV adsorbed more effectively on patient-derived sarcoma cells, whereas TMV SP were more effective on the established sarcoma cells. The AltMV SPV and TMV SP were captured by all sarcoma cell lines. In the established Ewing sarcoma cell line, the effectiveness of AltMV SPV penetration was greater than that of TMV SP. The usage of structurally modified plant virus particles as a platform for drugs and delivery systems has significant potential in the development of anticancer agents. Full article
(This article belongs to the Special Issue Plant Viruses: Pirates of Cellular Pathways, 2nd Edition)
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Graphical abstract

Graphical abstract
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<p>Morphology of spherical particles obtained from (<b>A</b>) Tobacco mosaic virus virions, (<b>B</b>) Alternanthera mosaic virus (AltMV) virions, and (<b>C</b>) AltMV virus-like particles. Scale bar 500 nm. Transmission electron microscopy.</p>
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<p>Scheme of the experiment. (<b>I</b>) Virus isolation and purification; obtaining of virus-like particles (VLP); (<b>II</b>) formation of spherical particles (SP) after the heating of virions and VLP; (<b>III</b>) SP labeling with fluorescein isothiocyanate (FITC); (<b>IV</b>) plating of cells 24 h prior to treatment with SP; (<b>V</b>) incubation of cells with SP; (<b>VI</b>) adsorption of SP on the cellular membrane of sarcoma and control cells (fibroblasts); and (<b>VII</b>) the cumulative effect of adsorption (green) and uptake (yellow). Three types of SP were used in the experiments: SP from Tobacco mosaic virus, SP from Alternanthera mosaic virus (AltMV), SP from AltMV VLP.</p>
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<p>Adsorption effectiveness of fluorescein isothiocyanate-labeled spherical particles (SP) on T46, ES36, M19, and A673 cells. The data are presented as the means and standard deviations of five technical repeats. The ordinary one-way ANOVA with Dunnett’s post hoc test was used for multiple comparisons of the adsorption of each SP type with that of the control for each cell line. * <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, **** <span class="html-italic">p</span> &lt; 0.0001, ns—not significant. ES36, T46—primary patient-derived Ewing sarcoma cells, A673—established Ewing sarcoma line, M19—normal fibroblast cell line. Control—cells without the addition of SP. TMV SP—SP obtained from tobacco mosaic virus, AltMV SP<sub>V</sub>—SP obtained from alternanthera mosaic virus (AltMV) virions, AltMV SP<sub>VLP</sub>—SP obtained from AltMV virus-like particles.</p>
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<p>Comparison of the adsorption of different fluorescein isothiocyanate-labeled spherical particles (SP) by sarcoma cell lines and fibroblasts (M19). The data are presented as the mean of five technical repeats and standard deviations. The ordinary one-way ANOVA with Dunnett’s post hoc test was used for multiple comparisons of the adsorption of each SP type with that of the control for each cell line. * <span class="html-italic">p</span> &lt; 0.05, **** <span class="html-italic">p</span> &lt; 0.0001, ns—not significant. ES36, T46—primary patient-derived Ewing sarcoma cells, A673—established Ewing sarcoma line, M19—normal fibroblast cell line. Control—cells without the addition of SP. The adsorption of SP obtained from (<b>A</b>) Tobacco mosaic virus virions (TMV SP), (<b>B</b>) Alternanthera mosaic virus virions (AltMV SP<sub>V</sub>), and (<b>C</b>) AltMV virus-like particles (AltMV SP<sub>VLP</sub>) on sarcoma cell lines and fibroblasts (M19).</p>
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<p>Spherical particles (SP) cellular uptake. (<b>A</b>) Graphical representation of the cellular uptake results according to the Sony SH800 flow cytometer for SP in A673 and M19 cells. For quantitative analysis of the efficiency of SP penetration into cells, the experiment was performed in triplicates. Statistical significance was analyzed using Welch’s <span class="html-italic">t</span>-test, ** <span class="html-italic">p</span> &lt; 0.01, ns—not significant. (<b>B</b>) Distribution of cells incubated with SP according to fluorescence intensity. The events recorded by the device are plotted along the ordinate axis. The abscissa axis shows the fluorescence intensity detected in the channel. Control—cells without the addition of SP. The control is imposed on the results obtained during incubation with particles. It determines the boundary (vertical line), after which the result is considered positive. TMV SP—SP obtained from Tobacco mosaic virus, AltMV SP<sub>V</sub>—SP obtained from Alternanthera mosaic virus virions.</p>
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<p>Retention of spherical particles (SP) obtained from Alternanthera mosaic virus virions (AltMV SP<sub>V</sub>) inside primary Ewing sarcoma cells (ES36). (<b>A</b>) AltMV SP<sub>V</sub> adsorption and penetration by ES36 cells. (<b>B</b>) Negative control—ES36 without added AltMV SP<sub>V</sub>. (<b>C</b>) Scheme of the color marks distribution produced by the green signal of fluorescein isothiocyanate-labeled AltMV SP<sub>V</sub> and the red signal of rhodamine–phalloidin-colored F-actin. Colocalization of the green and red signals produces a yellow signal (yellow arrows), indicating the penetration of SP into the cell. The green signal demonstrates AltMV SP<sub>V</sub> that did not penetrate the cells and remained on the membrane and substrate surfaces (green arrow). For the confocal microscopy, the magnification was 20.</p>
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13 pages, 2068 KiB  
Article
Clinical Meaningfulness of an Algorithm-Based Service for Analyzing Treatment Response in Patients with Metastatic Cancer Using FDG PET/CT
by Manojkumar Bupathi, Benjamin Garmezy, Michael Lattanzi, Minnie Kieler, Nevein Ibrahim, Timothy G. Perk, Amy J. Weisman and Scott B. Perlman
J. Clin. Med. 2024, 13(20), 6168; https://doi.org/10.3390/jcm13206168 - 16 Oct 2024
Abstract
Background/Objectives: Determining how a patient with metastatic cancer is responding to therapy can be difficult for medical oncologists, especially with text-only radiology reports. In this investigation, we assess the clinical usefulness of a new algorithm-based analysis that provides spatial location and quantification [...] Read more.
Background/Objectives: Determining how a patient with metastatic cancer is responding to therapy can be difficult for medical oncologists, especially with text-only radiology reports. In this investigation, we assess the clinical usefulness of a new algorithm-based analysis that provides spatial location and quantification for each detected lesion region of interest (ROI) and compare it to information included in radiology reports in the United States. Methods: Treatment response radiology reports for FDG PET/CT scans were retrospectively gathered from 228 patients with metastatic cancers. Each radiology report was assessed for the presence of both qualitative and quantitative information. A subset of patients (N = 103) was further analyzed using an algorithm-based service that provides the clinician with comprehensive quantitative information, including change over time, of all detected ROI with visualization of anatomical location. For each patient, three medical oncologists from different practices independently rated the usefulness of the additional analysis overall and in four subcategories. Results: In the 228 radiology reports, quantitative information of size and uptake was provided for at least one lesion at one time point in 78% (size) and 95% (uptake) of patients. This information was reported for both analyzed time points (current scan and previous comparator) in 52% (size) and 66% (uptake) of patients. Only 7% of reports quantified the total number of lesions, and none of the reports quantified changes in all lesions for patients with more than a few lesions. In the assessment of the augmentative algorithm-based analysis, the majority of oncologists rated it as overall useful for 98% of patients (101/103). Within specific categories of use, the majority of oncologists voted to use it for making decisions regarding systemic therapy in 97% of patients, for targeted therapy decisions in 72% of patients, for spatial location information in 96% of patients, and for patient education purposes in 93% of patients. Conclusions: For patients with metastatic cancer, the algorithm-based analysis of all ROI would allow oncologists to better understand treatment response and support their work to more precisely optimize the patient’s therapy. Full article
(This article belongs to the Section Oncology)
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<p>Schematic of the augmentative algorithm-based analysis including examples of output information. First, PET/CT images are segmented into 47 anatomic structures and skeleton parts using a 3D convolutional neural network methodology described in Weisman et al. [<a href="#B25-jcm-13-06168" class="html-bibr">25</a>]. Next, lesion-ROI are detected and segmented using an anatomic structure-specific PET threshold determined using a statistically optimized regional thresholding methodology outlined in Perk et al. [<a href="#B26-jcm-13-06168" class="html-bibr">26</a>]. CT images from the two time points are then deformably registered to one another before an overlap volume-based lesion matching algorithm is applied to determine which lesions are new, disappeared, or matched across scans [<a href="#B12-jcm-13-06168" class="html-bibr">12</a>]. Finally, quantitative metrics are extracted from all individual lesion-ROI and across all lesion-ROI in the patient.</p>
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<p>Example reports for a patient with metastatic prostate cancer (full TRAQinform report is shown in the <a href="#app1-jcm-13-06168" class="html-app">Supplementary Materials</a>), displaying how the augmentative algorithm-based analysis provides quantification information on all lesions that is not included in the standard radiology report.</p>
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<p>Proportion of patients reviewed by each oncologist for which the TRAQinform IQ analysis was rated as useful across the five usefulness categories. For each category, the “majority” calculation indicates the proportion of patients for which the majority of oncologists (at least two) rated the TRAQinform IQ analysis as useful.</p>
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<p>Outputs of the TRAQinform IQ analysis for all 103 patients analyzed, displaying an example of how the augmentative algorithm-based service can provide new, important information not included in the standard radiology report. In both graphs, each bar represents the distribution of lesions in each category for a single patient.</p>
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14 pages, 692 KiB  
Article
HIPEC as Up-Front Treatment in Locally Advanced Ovarian Cancer
by Michail Karanikas, Konstantinia Kofina, Dimitrios Kyziridis, Grigorios Trypsianis, Apostolos Kalakonas and Antonios-Apostolos Tentes
Cancers 2024, 16(20), 3500; https://doi.org/10.3390/cancers16203500 - 16 Oct 2024
Abstract
Purpose: The main objective of the study is to evaluate the effect of hyperthermic intraperitoneal chemotherapy (HIPEC) in the treatment of naïve ovarian cancer women undergoing complete or near-complete cytoreduction by assessing the overall survival, the disease-specific survival, and the disease-free survival. The [...] Read more.
Purpose: The main objective of the study is to evaluate the effect of hyperthermic intraperitoneal chemotherapy (HIPEC) in the treatment of naïve ovarian cancer women undergoing complete or near-complete cytoreduction by assessing the overall survival, the disease-specific survival, and the disease-free survival. The secondary objective is the identification of prognostic indicators of survival and recurrence of these patients. Patients—Methods: Retrospective study of treatment in naïve women with locally advanced ovarian cancer treated with cytoreductive surgery (CRS) and HIPEC and compared with those who were treated with cytoreduction alone. Clinicopathologic variables were correlated to overall survival, disease-specific survival, and disease-free survival using Kaplan–Meier method, and the multivariate Cox proportional hazards regression models. Results: 5- and 10-year overall survival, disease-specific survival, and disease-free survival rates were significantly higher in patients treated with CRS and HIPEC. These patients were 67% less likely to die from any cause (adjusted hazard ratio, aHR = 0.33, p = 0.001), 75% less likely to die from cancer (aHR = 0.25, p = 0.003), and 46% less likely to develop recurrence (aHR = 0.54, p = 0.041) compared to patients treated with CRS alone. Moreover, the poor performance status (aHR = 2.96, p < 0.001), the serous carcinomas (aHR = 0.14, p = 0.007), and the morbidity (aHR = 6.87, p < 0.001) were identified as independent indicators of poor overall survival. The degree of differentiation (aHR = 8.64, p = 0.003) was identified as the independent indicator of disease-specific survival (aHR = 4.13, p = 0.002), while the extent of peritoneal carcinomatosis (aHR = 2.32, p < 0.001) as the independent indicator of disease-free survival. Conclusions: Treatment in naïve patients with locally advanced ovarian cancer undergoing CRS plus HIPEC appears to have improved overall, disease-specific, and disease-free survival. Full article
(This article belongs to the Special Issue Research on Surgical Treatment for Ovarian Cancer)
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<p>Overall survival (OS) in relation to HIPEC.</p>
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<p>Disease-specific survival (DSS) in relation to HIPEC.</p>
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<p>Disease-free survival (DFS) in relation to HIPEC.</p>
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16 pages, 30847 KiB  
Article
Notch3 and Its Clinical Importance in Ovarian Cancer
by Bimal Prasad Jit, Alisha Behera, Sahar Qazi, Khushi Mittal, Subhadip Kundu, Babul Bansal, MD Ray and Ashok Sharma
Drugs Drug Candidates 2024, 3(4), 707-722; https://doi.org/10.3390/ddc3040040 (registering DOI) - 16 Oct 2024
Abstract
Background: Ovarian cancer (OC) is the most prevalent gynecological malignancy in women, often diagnosed at an advanced stage due to the absence of specific clinical biomarkers. Notch signaling, particularly Notch3, is frequently activated in OC and contributes to its oncogenic role. Despite its [...] Read more.
Background: Ovarian cancer (OC) is the most prevalent gynecological malignancy in women, often diagnosed at an advanced stage due to the absence of specific clinical biomarkers. Notch signaling, particularly Notch3, is frequently activated in OC and contributes to its oncogenic role. Despite its known association with poor clinical outcomes, the biomarker potential of Notch3 remains inadequately explored. Methods: We investigated the biomarker potential of Notch3 in OC using multiple databases, including ONCOMINE, GEPIA, Human Protein Atlas, UALCAN, Kaplan–Meier Plotter, and LinkedOmics. We analyzed Notch3 expression levels, survival correlations, and clinicopathological parameters. Results: Notch3 expression was significantly upregulated in OC, as well as other cancers. Correlation analysis demonstrated that high Notch3 mRNA levels were associated with poor overall survival (OS) (p < 0.05) and relapse-free survival (p < 0.05) in OC patients. Human Protein Atlas data showed elevated Notch3 protein levels in OC tissues compared to healthy controls. Clinicopathological analysis indicated significant associations between Notch3 expression and patient age (p < 0.5), TP53 mutation status (p < 0.5), and cancer stage (p < 0.1). Additionally, genes such as WIZ, TET1, and CHD4 were found to be co-expressed with Notch3 in OC. Notch3 expression also correlated with immune cell infiltration in OC. Conclusions: Our bioinformatics analysis highlights Notch3 as a potential biomarker for poor prognosis in OC. However, further in vitro and in vivo studies, along with validation using larger tissue samples, are necessary to confirm its biomarker utility. Full article
(This article belongs to the Section Preclinical Research)
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<p>NOTCH3 expression level across cancers from TCGA data in TIMER2.0. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001 (Wilcoxon test). Blue: Normal tissue; Red: Tumor tissue; Purple: Metastatic tumor tissue; Grey: Groups for which statistical analysis was performed.</p>
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<p>Notch3 expression in OC: (<b>a</b>) comparative expression of Notch3 between tumor and normal tissues using GEPIA (* <span class="html-italic">p</span> &lt; 0.05) and (<b>b</b>) Human Protein Atlas data in patients with OC show IHC of Notch3 in normal, endometroid, serous, and mucinous phenotypes. OV: Ovarian dataset; num(T): Number of tumor samples; num(N): Number of normal samples. Red box: Tumor tissues; Grey box: Normal tissue.</p>
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<p>The UALCAN data shows the association of different clinical indicators with NOTCH3 expression: (<b>a</b>) tumor stage, (<b>b</b>) age, (<b>c</b>) race, (<b>d</b>) tumor grade, and (<b>e</b>) TP53 mutation.</p>
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<p>The correlation analysis of NOTCH3 expression with tumor purity and immune infiltration level in immune cells of CD4<sup>+</sup> T cells, Treg cells, B cells, neutrophils, macrophages, myeloid dendritic cells, NK cells, cancer-associated fibroblasts, endothelial cells, hematopoietic stem cells, and myeloid-derived suppressor cells in OC. <span class="html-italic">p</span> &lt; 0.05 is considered statistically significant [Blue line: linear regression; Grey area: confidence interval; each Circle: correlation between Notch3 and individual immune cell for each sample].</p>
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<p>(<b>a</b>) The overall survival status for the expression of NOTCH3 from the GEPIA database and (<b>b</b>) the disease-free survival status for the expression of NOTCH3 from the GEPIA database. Dotted line: 95% confidence interval.</p>
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<p>Coexpression analysis of NOTCH3 by LinkedOmics dataset in OC: (<b>a</b>,<b>b</b>) identification of coexpression profile of NOTCH3; (<b>c</b>) correlation analysis of NOTCH3 with WIZ expression; (<b>d</b>) correlation of NOTCH3 with TET1 expression; and (<b>e</b>) correlation of NOTCH3 with CHD4 expression Red line represents the line of regression.</p>
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<p>Expression and survival analysis of WIZ, TET1, and CHD4 in OC: (<b>a</b>) expression of WIZ using GEPIA database. Red box: Tumor tissues; Grey box: Normal tissue; (<b>b</b>) OS status for expression of WIZ using Kaplan–Meier Plotter; (<b>c</b>) PFS status of WIZ using Kaplan–Meier Plotter; (<b>d</b>) expression of TET1 using GEPIA database; (<b>e</b>) OS status of TET1 using Kaplan–Meier Plotter; (<b>f</b>) PFS of TET1 using Kaplan–Meier Plotter; (<b>g</b>) expression of CHD4 using GEPIA database; (<b>h</b>) OS status of CHD4 using Kaplan–Meier Plotter; and (<b>i</b>) PFS status of CHD4 using Kaplan–Meier Plotter.</p>
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<p>Phylogenetic relationship assessment of NOTCH3 with (<b>a</b>) positively correlated genes and (<b>b</b>) negatively correlated genes OC using Clustal W.</p>
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<p>Spearman correlation analysis of NOTCH3: (<b>a</b>) with lymphocytes, i.e., TILs (<span class="html-italic">y</span> axis), across human cancers (<span class="html-italic">x</span> axis), (<b>b</b>) MHCs (<span class="html-italic">y</span> axis) across human cancers (<span class="html-italic">x</span> axis), (<b>c</b>) and receptors (<span class="html-italic">y</span> axis) across human cancers (<span class="html-italic">x</span> axis).</p>
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<p>Spearman correlation analysis of NOTCH3 with immunomodulators across various cancer types: (<b>a</b>) Spearman correlations analysis of NOTCH3 and immune inhibitors (<span class="html-italic">y</span> axis) across human cancers (<span class="html-italic">x</span> axis), (<b>b</b>) Spearman correlations analysis of NOTCH3 and immunostimulators (<span class="html-italic">y</span> axis) across human cancers (<span class="html-italic">x</span> axis), (<b>c</b>) Spearman correlation analysis of NOTCH3 and chemokines (<span class="html-italic">y</span> axis) across human cancers (<span class="html-italic">x</span> axis).</p>
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14 pages, 1033 KiB  
Article
Characteristics and Prognosis of Patients with Advanced Hepatocellular Carcinoma Treated with Atezolizumab/Bevacizumab Combination Therapy Who Achieved Complete Response
by Teiji Kuzuya, Naoto Kawabe, Hisanori Muto, Yoshihiko Tachi, Takeshi Ukai, Yuryo Wada, Gakushi Komura, Takuji Nakano, Hiroyuki Tanaka, Kazunori Nakaoka, Eizaburo Ohno, Kohei Funasaka, Mitsuo Nagasaka, Ryoji Miyahara and Yoshiki Hirooka
Curr. Oncol. 2024, 31(10), 6218-6231; https://doi.org/10.3390/curroncol31100463 (registering DOI) - 16 Oct 2024
Abstract
Aim: To investigate the characteristics and prognosis of patients with advanced hepatocellular carcinoma (HCC) treated with atezolizumab and bevacizumab (Atz/Bev) who achieved a complete response (CR) according to the modified Response Evaluation Criteria in Solid Tumors (mRECIST). Methods: A total of 120 patients [...] Read more.
Aim: To investigate the characteristics and prognosis of patients with advanced hepatocellular carcinoma (HCC) treated with atezolizumab and bevacizumab (Atz/Bev) who achieved a complete response (CR) according to the modified Response Evaluation Criteria in Solid Tumors (mRECIST). Methods: A total of 120 patients with Eastern Cooperative Oncology Group performance status (PS) 0 or 1 and Child–Pugh A at the start of Atz/Bev treatment were included. Barcelona Clinic Liver Cancer stage C was recorded in 59 patients. Results: The CR rate with Atz/Bev alone was 15.0%. The median time to CR was 3.4 months, and the median duration of CR was 15.6 months. A significant factor associated with achieving CR with Atz/Bev alone was an AFP ratio of 0.34 or less at 3 weeks. Adding transarterial chemoembolization (TACE) in the six patients who achieved a partial response increased the overall CR rate to 20%. Among the 24 patients who achieved CR, the median progression-free survival was 19.3 months, the median overall survival was not reached, and 14 patients (58.3%) were able to discontinue Atz/Bev and achieve a drug-free status. Twelve of these patients developed progressive disease (PD), but eleven successfully received post-PD treatments and responded well. Conclusions: Achieving CR by mRECIST using Atz/Bev alone or with additional TACE can be expected to offer an extremely favorable prognosis. Full article
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<p>PFS and OS of Atz/Bev alone for all 120 patients stratified by the best antitumor response according to mRECIST. (<b>a</b>) Median PFS was 19.3 months (95%CI: 12.6 months–NR) for the CR group, 14.5 months (95%CI: 10.3–16.7) for the PR group, 6.9 months (95%CI: 4.1–9.2 months) for the SD group, and 1.4 months (95%CI: 1.4–1.4 months) for the PD + NE group. (<b>b</b>) Median OS was NR (95%CI: 21.0 months–NR) for the CR group, 24.5 months (95%CI: 21.1 months–NR) for the PR group, 26.4 months (95%CI: 12.2 months–NR) for the SD group, and 6.8 months (95%CI: 5.1–9.1 months) for the PD + NE group. PFS, progression-free survival; OS, overall survival; Atz/Bev, atezolizumab/bevacizumab; mRECIST, modified Response Evaluation Criteria in Solid Tumors; CI, confidence interval; NR, not reached; CR, complete response; PR, partial response; SD, stable disease; PD, progressive disease; NE, not evaluated; M, months.</p>
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<p>PFS and OS of Atz/Bev + additional TACE for all 120 patients in the CR and non-CR groups according to mRECIST. (<b>a</b>) Median PFS in the CR group (<span class="html-italic">n</span> = 24) was 19.3 months (95%CI: 15.4–26.4 months), significantly longer than the 6.9 months (95%CI: 3.7–9.0 months) in the non-CR group (<span class="html-italic">n</span> = 96) (<span class="html-italic">p</span> &lt; 0.0001). (<b>b</b>) Median OS in the CR group was not reached (95%CI: 21.1 months–NR), significantly longer than the 19.3 months (95%CI: 13.6–26.4 months) in the non-CR group (<span class="html-italic">p</span> &lt; 0.0001). PFS, progression-free survival; OS, overall survival; Atz/Bev, atezolizumab/bevacizumab; TACE, transarterial chemoembolization; CR, complete response; mRECIST, modified Response Evaluation Criteria in Solid Tumors; CI, confidence interval; NR, not reached; M, months.</p>
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<p>Time to CR by mRECIST and duration of CR by mRECIST in patients who achieved CR by Atz/Bev alone (<span class="html-italic">n</span> = 18). (<b>a</b>) Median time to CR by mRECIST was 3.4 months (95%CI: 3.4–4.4 months). (<b>b</b>) Duration of CR by mRECIST was 15.6 months (95%CI: 7.1 months–NR). (<b>c</b>) Median CR duration for the drug-free group (<span class="html-italic">n</span> = 10) was 23.0 months (95%CI: 5.7 months–NR months) and for the non-drug-free group (<span class="html-italic">n</span> = 8) was 15.6 months (95%CI: 6.9 months–NR), with no significant difference between the groups (<span class="html-italic">p</span> = 0.584). CR, complete response; mRECIST, modified Response Evaluation Criteria in Solid Tumors; Atz/Bev, atezolizumab/bevacizumab; CI, confidence interval.</p>
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<p>Clinical outcomes in patients who achieved CR by mRECIST—stratified by drug-free status. CR, complete response; mRECIST, modified Response Evaluation Criteria in Solid Tumors; Atz/Bev, atezolizumab/bevacizumab; PD, progressive disease; TACE, transarterial chemoembolization.</p>
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22 pages, 4378 KiB  
Article
Characterization of EpCAM-Positive and EpCAM-Negative Tumor Cells in Early-Stage Breast Cancer
by Vladimir M. Perelmuter, Evgeniya S. Grigoryeva, Vladimir V. Alifanov, Anna Yu. Kalinchuk, Elena S. Andryuhova, Olga E. Savelieva, Ivan A. Patskan, Olga D. Bragina, Evgeniy Yu. Garbukov, Mariya A. Vostrikova, Marina V. Zavyalova, Evgeny V. Denisov, Nadezhda V. Cherdyntseva and Liubov A. Tashireva
Int. J. Mol. Sci. 2024, 25(20), 11109; https://doi.org/10.3390/ijms252011109 - 16 Oct 2024
Abstract
Most studies on CTCs have focused on isolating cells that express EpCAM. In this study, we emphasize the presence of EpCAM-negative and EpCAMlow CTCs, in addition to EpCAMhigh CTCs, in early BC. We evaluated stem cell markers (CD44/CD24 and CD133) and [...] Read more.
Most studies on CTCs have focused on isolating cells that express EpCAM. In this study, we emphasize the presence of EpCAM-negative and EpCAMlow CTCs, in addition to EpCAMhigh CTCs, in early BC. We evaluated stem cell markers (CD44/CD24 and CD133) and EMT markers (N-cadherin) in each subpopulation. Our findings indicate that all stemness variants were present in both EpCAMhigh and EpCAM-negative CTCs, whereas only one variant of stemness (nonCD44+CD24−/CD133+) was observed among EpCAMlow CTCs. Nearly all EpCAMhigh CTCs were represented by CD133+ stem cells. Notably, the hybrid EMT phenotype was more prevalent among EpCAM-negative CTCs. scRNA-seq of isolated CTCs and primary tumor partially confirmed this pattern. Therefore, further investigation is imperative to elucidate the prognostic significance of EpCAM-negative and EpCAMlow CTCs. Full article
(This article belongs to the Section Molecular Oncology)
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<p>The proportion (<b>A</b>,<b>B</b>) and count (<b>C</b>) of EpCAM<sup>high</sup>, EpCAM<sup>low</sup>, and EpCAM-negative CTCs in BC patients. When determining the proportion of each CTC type, the total sum of EpCAM<sup>high</sup>, EpCAM<sup>low</sup>, and EpCAM-negative CTCs was taken as 100%.</p>
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<p>Flow cytometry analysis of EpCAM<sup>high</sup> CTCs. (<b>A</b>) Evaluation of CD44/CD24 and CD133 stem markers expression. (<b>B</b>) Evaluation of epithelial cell markers cytokeratin 7/8 (CK7/8), panCK (panCK), E-cadherin, and mesenchymal cell marker N-cadherin expression in EpCAM<sup>high</sup> CTCs.</p>
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<p>Correlation analysis between the subsets of CTCs taking into account stem features among EpCAM<sup>high</sup> (<b>A</b>), EpCAM<sup>low</sup> (<b>B</b>), and EpCAM-negative (<b>C</b>) tumor cells. Red asterisk indicates a significant difference (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Correlation analysis between the subsets of CTCs taking into account EMT features among EpCAM<sup>high</sup> (<b>A</b>), EpCAM<sup>low</sup> (<b>B</b>), and EpCAM-negative (<b>C</b>) tumor cells. Red asterisk indicates a significant difference (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>EpCAM staining of FFPE tissue slides of breast cancer patients by IHC analysis. The red arrow indicates EpCAM<sup>high</sup> expression in tumor cells, the yellow arrow points to EpCAM<sup>low</sup> expression, and the green arrow corresponds to the absence of EpCAM expression in tumor cells.</p>
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<p>The proportion of EpCAM<sup>high</sup>, EpCAM<sup>low</sup>, and EpCAM-negative tumor cells in primary tumor of BC patients. (<b>A</b>) Interpersonal heterogeneity of EpCAM<sup>high</sup>, EpCAM<sup>low</sup>, and EpCAM-negative tumor cells in each patient. (<b>B</b>) The comparison of the proportion of EpCAM<sup>high</sup>, EpCAM<sup>low</sup>, and EpCAM-negative tumor cells in primary tumor.</p>
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<p>Manual annotation of spots by <span class="html-italic">EPCAM</span> level in BC tissue. Blue color indicates <span class="html-italic">EPCAM</span>-negative spots, green color—<span class="html-italic">EPCAM</span><sup>low</sup>, and orange color—<span class="html-italic">EPCAM</span><sup>high</sup> spots.</p>
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