The dysadherin/FAK axis promotes individual cell migration in colon cancer.

Dysregulation of cancer cell motility is a key driver of invasion and metastasis. High dysadherin expression in cancer cells is correlated with invasion and metastasis. Here, we found the molecular mechanism by which dysadherin regulates the migration and invasion of colon cancer (CC). Comprehensive analysis using single-cell RNA sequencing data from CC patients revealed that high dysadherin expression in cells is linked to cell migration-related gene signatures. We confirmed that the deletion of dysadherin in tumor cells hindered local invasion and distant migration using in vivo tumor models. In this context, by performing cell morphological analysis, we found that aberrant cell migration resulted from impaired actin dynamics, focal adhesion turnover and protrusive structure formation upon dysadherin expression. Mechanistically, the activation of focal adhesion kinase (FAK) was observed in dysadherin-enriched cells. The dysadherin/FAK axis enhanced cell migration and invasion by activating the FAK downstream cascade, which includes the Rho family of small GTPases. Overall, this study illuminates the role of dysadherin in modulating cancer cell migration by forcing actin dynamics and protrusive structure formation via FAK signaling, indicating that targeting dysadherin may be a potential therapeutic strategy for CC patients.

NSUN6 mediates 5-methylcytosine modification of METTL3 and promotes colon adenocarcinoma progression.

Colon adenocarcinoma (COAD) is a common and fatal malignant tumor of digestive system with complex etiology. 5-Methylcytosine (m5C) modification of RNA by the NSUN gene family (NSUN1-NSUN7) and DNMT2 reshape cell biology and regulate tumor development. However, the expression profile, prognostic significance and function of these m5C modifiers in COAD remain largely unclear. By mining multiple integrated tumor databases, we found that NSUN1, NSUN2, NSUN5, and NSUN6 were overexpressed in COAD tumor samples relative to normal samples. Clinically, high expression of NSUN6 was significantly associated with shorter survival (including both disease-free survival and overall survival) in COAD patients. NSUN6 was further confirmed to be upregulated at both tissue and cellular levels of COAD, suggesting that NSUN6 plays a critical role in disease progression. Through comprehensive gene enrichment analysis and cell-based functional validation, it was revealed that NSUN6 promoted the cell cycle progression and cell proliferation of COAD. Mechanistically, NSUN6 upregulates the expression of oncogenic METTL3 and catalyzes its m5C modification in COAD cells. Overexpression of METTL3 significantly relieved the cell cycle inhibition of COAD caused by NSUN6 deficiency. Furthermore, NSUN6 was negatively associated with the abundance of infiltrating immune cells in COAD tumors, such as activated B cells, natural killer cells, effector memory CD8 T cells, and regulatory T cells. Importantly, pan-cancer analysis further uncovered that NSUN6 was dysregulated and heterogeneous in various tumors. Thus our findings extend the role of m5C transferase in COAD and suggest that NSUN6 is a potential biomarker and target for this malignancy.

Identification of extracellular matrix-related biomarkers in colon adenocarcinoma by bioinformatics and experimental validation.

Backgrounds: Extracellular matrix (ECM) is an important component of tumor microenvironment, and its abnormal expression promotes tumor formation, progression and metastasis. Methods: Weighted gene co-expression network analysis (WGCNA) was used to identify ECM-related hub genes based on The Cancer Genome Atlas (TCGA) colon adenocarcinoma (COAD) data. COAD clinical samples were used to verify the expression of potential biomarkers in tumor tissues, and siRNA was used to explore the role of potential biomarkers in cell proliferation and epithelial-mesenchymal transition (EMT). Results: Three potential biomarkers (LEP, NGF and PCOLCE2) related to prognosis of COAD patients were identified and used to construct ERGPI. Immunohistochemical analysis of clinical samples showed that the three potential biomarkers were highly expressed in tumor tissues of COAD patients. Knockdown of LEP, NGF or PCOLCE2 inhibited COAD cell proliferation and EMT. Dictamnine inhibited tumor cell growth by binding to these three potential biomarkers based on molecular docking and transplanted tumor model. Conclusion: The three biomarkers can provide new ideas for the diagnosis and targeted therapy of COAD patients.

Exploring the mystery of colon cancer from the perspective of molecular subtypes and treatment.

The molecular categorization of colon cancer patients remains elusive. Gene set enrichment analysis (GSEA), which investigates the dysregulated genes among tumor and normal samples, has revealed the pivotal role of epithelial-to-mesenchymal transition (EMT) in colon cancer pathogenesis. In this study, we employed multi-clustering method for grouping data, resulting in the identification of two clusters characterized by varying prognostic outcomes. These two subgroups not only displayed disparities in overall survival (OS) but also manifested variations in clinical variables, genetic mutation, and gene expression profiles. Using the nearest template prediction (NTP) method, we were able to replicate the molecular classification effectively within the original dataset and validated it across multiple independent datasets, underscoring its robust repeatability. Furthermore, we constructed two prognostic signatures tailored to each of these subgroups. Our molecular classification, centered on EMT, hold promise in offering fresh insights into the therapy strategies and prognosis assessment for colon cancer.

The Hippo pathway terminal effector TAZ/WWTR1 mediates oxaliplatin sensitivity in p53 proficient colon cancer cells.

YAP and TAZ, the Hippo pathway terminal transcriptional activators, are frequently upregulated in cancers. In tumor cells, they have been mainly associated with increased tumorigenesis controlling different aspects from cell cycle regulation, stemness, or resistance to chemotherapies. In fewer cases, they have also been shown to oppose cancer progression, including by promoting cell death through the action of the p73/YAP transcriptional complex, in particular after chemotherapeutic drug exposure. Using HCT116 cells, we show here that oxaliplatin treatment led to core Hippo pathway down-regulation and nuclear accumulation of TAZ. We further show that TAZ was required for the increased sensitivity of HCT116 cells to oxaliplatin, an effect that appeared independent of p73, but which required the nuclear relocalization of TAZ. Accordingly, Verteporfin and CA3, two drugs affecting the activity of YAP and TAZ, showed antagonistic effects with oxaliplatin in co-treatments. Importantly, using several colorectal cell lines, we show that the sensitizing action of TAZ to oxaliplatin is dependent on the p53 status of the cells. Our results support thus an early action of TAZ to sensitize cells to oxaliplatin, consistent with a model in which nuclear TAZ in the context of DNA damage and p53 activity pushes cells towards apoptosis.

Inappropriate Ordering of Multitarget Stool DNA Tests for Colon Cancer Screening.

BACKGROUND: CRC screening is recommended for adults aged 45-75. Mt-sDNA is indicated for asymptomatic individuals between the ages of 45 and 85, but not for those with rectal bleeding, iron deficiency anemia, adenomatous polyps, previous colonoscopy within 10 years, family history of CRC, positive results from CRC screening tests within the past 6 months, or age less than 45 and greater than 85. We aimed to determine the prevalence of mt-sDNA use when not indicated and factors associated with inappropriate testing. METHODS: 7,345 patients underwent mt-sDNA testing and were randomized using EMERSE. Charts for the first 500 patients were reviewed to determine whether mt-sDNA was ordered appropriately according to the USPSTF criteria. Seven patients were excluded due to having more than one inappropriate ordering for mt-sDNA. RESULTS: Of 500 patients, 22.2% had an inappropriately ordered mt-sDNA test. The most common reason for inappropriate ordering was having a previous colonoscopy done within the past 10 years. Rates of inappropriate testing significantly varied by race and the specialty of the ordering provider, with internal medicine providers ordering the most mt-sDNA tests. Rates of inappropriate testing did not significantly vary by sex or type of insurance. DISCUSSION: Our study suggests that providers may not be familiar with guidelines for the indicated use of mtsDNA, leading to inappropriate referrals and increased costs. Patients at increased CRC risk would benefit from a more sensitive procedure such as a colonoscopy. Future studies could understand the motivation to order testing outside approved indications through provider surveys and interviews.

Distinct Driver Pathway Enrichments and a High Prevalence of TSC2 Mutations in Right Colon Cancer in Chile: A Preliminary Comparative Analysis.

Colorectal cancer (CRC) is the second leading cause of cancer deaths globally. While ethnic differences in driver gene mutations have been documented, the South American population remains understudied at the genomic level, despite facing a rising burden of CRC. We analyzed tumors of 40 Chilean CRC patients (Chp) using next-generation sequencing and compared them to data from mainly Caucasian cohorts (TCGA and MSK-IMPACT). We identified 388 mutations in 96 out of 135 genes, with TP53 (45%), KRAS (30%), PIK3CA (22.5%), ATM (20%), and POLE (20%) being the most frequently mutated. TSC2 mutations were associated with right colon cancer (44.44% in RCRC vs. 6.45% in LCRC, p-value = 0.016), and overall frequency was higher compared to TCGA (p-value = 1.847 × 10-5) and MSK-IMPACT cohorts (p-value = 3.062 × 10-2). Limited sample size restricts definitive conclusions, but our data suggest potential differences in driver mutations for Chilean patients, being that the RTK-RAS oncogenic pathway is less affected and the PI3K pathway is more altered in Chp compared to TCGA (45% vs. 25.56%, respectively). The prevalence of actionable pathways and driver mutations can guide therapeutic choices, but can also impact treatment effectiveness. Thus, these findings warrant further investigation in larger Chilean cohorts to confirm these initial observations. Understanding population-specific driver mutations can guide the development of precision medicine programs for CRC patients.

Evaluating the utility of ZNF331 promoter methylation as a prognostic and predictive marker in stage III colon cancer: results from CALGB 89803 (Alliance).

While epigenomic alterations are common in colorectal cancers (CRC), few epigenomic biomarkers that risk-stratify patients have been identified. We thus sought to determine the potential of ZNF331 promoter hypermethylation (mZNF331) as a prognostic and predictive marker in colon cancer. We examined the association of mZNF331 with clinicopathologic features, relapse, survival, and treatment efficacy in patients with stage III colon cancer treated within a randomized adjuvant chemotherapy trial (CALGB/Alliance89803). Residual tumour tissue was available for genomic DNA extraction and methylation analysis for 385 patients. ZNF331 promoter methylation status was determined by bisulphite conversion and fluorescence-based real-time polymerase chain reaction. Kaplan-Meier estimator and Cox proportional hazard models were used to assess the prognostic and predictive role of mZNF331 in this well-annotated dataset, adjusting for clinicopathologic features and standard molecular markers. mZNF331 was observed in 267/385 (69.4%) evaluable cases. Histopathologic features were largely similar between patients with mZNF331 compared to unmethylated ZNF331 (unmZNFF31). There was no significant difference in disease-free or overall survival between patients with mZNF331 versus unmZNF331 colon cancers, even when adjusting for clinicopathologic features and molecular marker status. Similarly, there was no difference in disease-free or overall survival across treatment arms when stratified by ZNF331 methylation status. While ZNF331 promoter hypermethylation is frequently observed in CRC, our current study of a small subset of patients with stage III colon cancer suggests limited applicability as a prognostic marker. Larger studies may provide more insight and clarity into the applicability of mZNF331 as a prognostic and predictive marker.

Network-based elucidation of colon cancer drug resistance mechanisms by phosphoproteomic time-series analysis.

Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. Leveraging progress in proteomic technologies and network-based methodologies, we introduce Virtual Enrichment-based Signaling Protein-activity Analysis (VESPA)-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and use it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogating tumor-specific enzyme/substrate interactions accurately infers kinase and phosphatase activity, based on their substrate phosphorylation state, effectively accounting for signal crosstalk and sparse phosphoproteome coverage. The analysis elucidates time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring, experimentally confirmed by CRISPR knock-out assays, suggesting broad applicability to cancer and other diseases.

Elucidating Cancer Subtypes by Using the Relationship between DNA Methylation and Gene Expression.

Advancements in the field of next generation sequencing (NGS) have generated vast amounts of data for the same set of subjects. The challenge that arises is how to combine and reconcile results from different omics studies, such as epigenome and transcriptome, to improve the classification of disease subtypes. In this study, we introduce sCClust (sparse canonical correlation analysis with clustering), a technique to combine high-dimensional omics data using sparse canonical correlation analysis (sCCA), such that the correlation between datasets is maximized. This stage is followed by clustering the integrated data in a lower-dimensional space. We apply sCClust to gene expression and DNA methylation data for three cancer genomics datasets from the Cancer Genome Atlas (TCGA) to distinguish between underlying subtypes. We evaluate the identified subtypes using Kaplan-Meier plots and hazard ratio analysis on the three types of cancer-GBM (glioblastoma multiform), lung cancer and colon cancer. Comparison with subtypes identified by both single- and multi-omics studies implies improved clinical association. We also perform pathway over-representation analysis in order to identify up-regulated and down-regulated genes as tentative drug targets. The main goal of the paper is twofold: the integration of epigenomic and transcriptomic datasets followed by elucidating subtypes in the latent space. The significance of this study lies in the enhanced categorization of cancer data, which is crucial to precision medicine.

AEG-1 as a Novel Therapeutic Target in Colon Cancer: A Study from Silencing AEG-1 in BALB/c Mice to Large Data Analysis.

BACKGROUND: Astrocyte elevated gene-1 (AEG-1) is overexpressed in various malignancies. Exostosin-1 (EXT-1), a tumor suppressor, is an intermediate for malignant tumors. Understanding the mechanism behind the interaction between AEG-1 and EXT-1 may provide insights into colon cancer metastasis. METHODS: AOM/DSS was used to induce tumor in BALB/c mice. Using an in vivo-jetPEI transfection reagent, transient transfection of AEG-1 and EXT-1 siRNAs were achieved. Histological scoring, immunohistochemical staining, and gene expression studies were performed from excised tissues. Data from the Cancer Genomic Atlas and GEO databases were obtained to identify the expression status of AEG-1 and itsassociation with the survival. RESULTS: In BALB/c mice, the AOM+DSS treated mice developed necrotic, inflammatory and dysplastic changes in the colon with definite clinical symptoms such as loss of goblet cells, colon shortening, and collagen deposition. Administration of AEG-1 siRNA resulted in a substantial decrease in the disease activity index. Mice treated with EXT-1 siRNA showed diffusely reduced goblet cells. In vivo investigations revealed that PTCH-1 activity was influenced by upstream gene AEG-1, which in turn may affect EXT-1 activity. Data from The Cancer Genomic Atlas and GEO databases confirmed the upregulation of AEG-1 and downregulation of EXT-1 in cancer patients. CONCLUSIONS: This study revealed that AEG-1 silencing might alter EXT-1 expression indirectly through PTCH-1, influencing cell-ECM interactions, and decreasing dysplastic changes, proliferation and invasion.

Investigating gene signatures associated with immunity in colon adenocarcinoma to predict the immunotherapy effectiveness using NFM and WGCNA algorithms.

Colon adenocarcinoma (COAD), a frequently encountered and highly lethal malignancy of the digestive system, has been the focus of intensive research regarding its prognosis. The intricate immune microenvironment plays a pivotal role in the pathological progression of COAD; nevertheless, the underlying molecular mechanisms remain incompletely understood. This study aims to explore the immune gene expression patterns in COAD, construct a robust prognostic model, and delve into the molecular mechanisms and potential therapeutic targets for COAD liver metastasis, thereby providing critical support for individualized treatment strategies and prognostic evaluation. Initially, we curated a comprehensive dataset by screening 2600 immune-related genes (IRGs) from the ImmPort and InnateDB databases, successfully obtaining a rich data resource. Subsequently, the COAD patient cohort was classified using the non-negative matrix factorization (NMF) algorithm, enabling accurate categorization. Continuing on, utilizing the weighted gene co-expression network analysis (WGCNA) method, we analyzed the top 5000 genes with the smallest p-values among the differentially expressed genes (DEGs) between immune subtypes. Through this rigorous screening process, we identified the gene modules with the strongest correlation to the COAD subpopulation, and the intersection of genes in these modules with DEGs (COAD vs COAD vs Normal colon tissue) is referred to as Differentially Expressed Immune Genes Associated with COAD (DEIGRC). Employing diverse bioinformatics methodologies, we successfully developed a prognostic model (DPM) consisting of six genes derived from the DEIGRC, which was further validated across multiple independent datasets. Not only does this predictive model accurately forecast the prognosis of COAD patients, but it also provides valuable insights for formulating personalized treatment regimens. Within the constructed DPM, we observed a downregulation of CALB2 expression levels in COAD tissues, whereas NOXA1, KDF1, LARS2, GSR, and TIMP1 exhibited upregulated expression levels. These genes likely play indispensable roles in the initiation and progression of COAD and thus represent potential therapeutic targets for patient management. Furthermore, our investigation into the molecular mechanisms and therapeutic targets for COAD liver metastasis revealed associations with relevant processes such as fat digestion and absorption, cancer gene protein polysaccharides, and nitrogen metabolism. Consequently, genes including CAV1, ANXA1, CPS1, EDNRA, and GC emerge as promising candidates as therapeutic targets for COAD liver metastasis, thereby providing crucial insights for future clinical practices and drug development. In summary, this study uncovers the immune gene expression patterns in COAD, establishes a robust prognostic model, and elucidates the molecular mechanisms and potential therapeutic targets for COAD liver metastasis, thereby possessing significant theoretical and clinical implications. These findings are anticipated to offer substantial support for both the treatment and prognosis management of COAD patients.

Identification of a 5-Gene Cuproptosis Signature Predicting the Prognosis for Colon Adenocarcinoma Based on WGCNA.

Background: Colorectal cancer is a highly aggressive malignant tumor that primarily affects the digestive system. It is frequently diagnosed at an advanced stage. Cuproptosis is a copper-dependent form cell death mechanism, distinct from all other known pathways underlying cell death, tumor progression, prognosis, and immune response. Although the role of cuproptosis in colorectal cancer has been investigated over time, there is still an urgent need to explore new methods and insights to understand its potential function. Methods: The Gene Expression Omnibus and The Cancer Genome Atlas gene expression data were systematically explored to investigate the role of cuproptosis in colon adenocarcinoma. The weighted gene coexpression network analysis was used to construct a gene coexpression network and identify the critical module and cuproptosis-related genes correlated with colon adenocarcinoma prognosis. A cuproptosis-related genes prognostic signature for colon adenocarcinoma was identified and validated. To validate the identified gene signature, quantitative reverse transcription-polymerase chain reaction was performed. Cell proliferation assays were analyzed by CCK8 and cell cycle detection. In addition, reactive oxygen species assay was also analyzed. Results: Five hub cuproptosis-related genes (Dihydrolipoamide S-acetyltransferase, Cyclin-dependent kinase inhibitor 2A, ATOX1, VEGFA, and ULK1) were screened and a prognostic risk model for predicting overall survival was established based on these genes. The model was successfully tested in the validation cohort and the GEPIA database. Colon adenocarcinoma patients were categorized into high-risk and low-risk groups based on risk scores. The study revealed that patients with higher risk scores were more likely to have a poor prognosis. Moreover, Dihydrolipoamide S-acetyltransferase was a tumor suppressor gene that can induce cell death and affected the redox reactions in the colon cancer cell line. Conclusions: These findings suggest that the newly identified 5-gene signature may serve as a more reliable prognostic factor than clinical factors such as age and stage of disease. These findings offer a theoretical foundation for further investigation into potential cuproptosis-related biomarkers for predicting colon adenocarcinoma prognosis in the future.

Choice of baseline hazards in joint modeling of longitudinal and time-to-event cancer survival data.

Longitudinal time-to-event analysis is a statistical method to analyze data where covariates are measured repeatedly. In survival studies, the risk for an event is estimated using Cox-proportional hazard model or extended Cox-model for exogenous time-dependent covariates. However, these models are inappropriate for endogenous time-dependent covariates like longitudinally measured biomarkers, Carcinoembryonic Antigen (CEA). Joint models that can simultaneously model the longitudinal covariates and time-to-event data have been proposed as an alternative. The present study highlights the importance of choosing the baseline hazards to get more accurate risk estimation. The study used colon cancer patient data to illustrate and compare four different joint models which differs based on the choice of baseline hazards [piecewise-constant Gauss-Hermite (GH), piecewise-constant pseudo-adaptive GH, Weibull Accelerated Failure time model with GH & B-spline GH]. We conducted simulation study to assess the model consistency with varying sample size (N = 100, 250, 500) and censoring (20 %, 50 %, 70 %) proportions. In colon cancer patient data, based on Akaike information criteria (AIC) and Bayesian information criteria (BIC), piecewise-constant pseudo-adaptive GH was found to be the best fitted model. Despite differences in model fit, the hazards obtained from the four models were similar. The study identified composite stage as a prognostic factor for time-to-event and the longitudinal outcome, CEA as a dynamic predictor for overall survival in colon cancer patients. Based on the simulation study Piecewise-PH-aGH was found to be the best model with least AIC and BIC values, and highest coverage probability(CP). While the Bias, and RMSE for all the models showed a competitive performance. However, Piecewise-PH-aGH has shown least bias and RMSE in most of the combinations and has taken the shortest computation time, which shows its computational efficiency. This study is the first of its kind to discuss on the choice of baseline hazards.

Impact of the thyroid hormone T3 and its nuclear receptor TRα1 on colon cancer stem cell phenotypes and response to chemotherapies.

Colorectal cancers (CRCs) are highly heterogeneous and show a hierarchical organization, with cancer stem cells (CSCs) responsible for tumor development, maintenance, and drug resistance. Our previous studies showed the importance of thyroid hormone-dependent signaling on intestinal tumor development and progression through action on stem cells. These results have a translational value, given that the thyroid hormone nuclear receptor TRα1 is upregulated in human CRCs, including in the molecular subtypes associated with CSC features. We used an established spheroid model generated from the human colon adenocarcinoma cell line Caco2 to study the effects of T3 and TRα1 on spheroid formation, growth, and response to conventional chemotherapies. Our results show that T3 treatment and/or increased TRα1 expression in spheroids impaired the response to FOLFIRI and conferred a survival advantage. This was achieved by stimulating drug detoxification pathways and increasing ALDH1A1-expressing cells, including CSCs, within spheroids. These results suggest that clinical evaluation of the thyroid axis and assessing TRα1 levels in CRCs could help to select optimal therapeutic regimens for patients with CRC. Proposed mechanism of action of T3/TRα1 in colon cancer spheroids. In the control condition, TRα1 participates in maintaining homeostatic cell conditions. The presence of T3 in the culture medium activates TRα1 action on target genes, including the drug efflux pumps ABCG2 and ABCB1. In the case of chemotherapy FOLFIRI, the increased expression of ABC transcripts and proteins induced by T3 treatment is responsible for the augmented efflux of 5-FU and Irinotecan from the cancer cells. Taken together, these mechanisms contribute to the decreased efficacy of the chemotherapy and allow cells to escape the treatment. Created with BioRender.com .

Use of Deep Learning to Evaluate Tumor Microenvironmental Features for Prediction of Colon Cancer Recurrence.

Deep learning may detect biologically important signals embedded in tumor morphologic features that confer distinct prognoses. Tumor morphologic features were quantified to enhance patient risk stratification within DNA mismatch repair (MMR) groups using deep learning. Using a quantitative segmentation algorithm (QuantCRC) that identifies 15 distinct morphologic features, we analyzed 402 resected stage III colon carcinomas [191 deficient (d)-MMR; 189 proficient (p)-MMR] from participants in a phase III trial of FOLFOX-based adjuvant chemotherapy. Results were validated in an independent cohort (176 d-MMR; 1,094 p-MMR). Association of morphologic features with clinicopathologic variables, MMR, KRAS, BRAFV600E, and time-to-recurrence (TTR) was determined. Multivariable Cox proportional hazards models were developed to predict TTR. Tumor morphologic features differed significantly by MMR status. Cancers with p-MMR had more immature desmoplastic stroma. Tumors with d-MMR had increased inflammatory stroma, epithelial tumor-infiltrating lymphocytes (TIL), high-grade histology, mucin, and signet ring cells. Stromal subtype did not differ by BRAFV600E or KRAS status. In p-MMR tumors, multivariable analysis identified tumor-stroma ratio (TSR) as the strongest feature associated with TTR [HRadj 2.02; 95% confidence interval (CI), 1.14-3.57; P = 0.018; 3-year recurrence: 40.2% vs. 20.4%; Q1 vs. Q2-4]. Among d-MMR tumors, extent of inflammatory stroma (continuous HRadj 0.98; 95% CI, 0.96-0.99; P = 0.028; 3-year recurrence: 13.3% vs. 33.4%, Q4 vs. Q1) and N stage were the most robust prognostically. Association of TSR with TTR was independently validated. In conclusion, QuantCRC can quantify morphologic differences within MMR groups in routine tumor sections to determine their relative contributions to patient prognosis, and may elucidate relevant pathophysiologic mechanisms driving prognosis. SIGNIFICANCE: A deep learning algorithm can quantify tumor morphologic features that may reflect underlying mechanisms driving prognosis within MMR groups. TSR was the most robust morphologic feature associated with TTR in p-MMR colon cancers. Extent of inflammatory stroma and N stage were the strongest prognostic features in d-MMR tumors. TIL density was not independently prognostic in either MMR group.

Analyzing the Functional Roles and Immunological Features of Chemokines in COAD.

Chemokines are key proteins that regulate cell migration and immune responses and are essential for modulating the tumor microenvironment. Despite their close association with colon cancer, the expression patterns, prognosis, immunity, and specific roles of chemokines in colon cancer are still not fully understood. In this study, we investigated the mutational features, differential expression, and immunological characteristics of chemokines in colon cancer (COAD) by analyzing the Tumor Genome Atlas (TCGA) database. We clarified the biological functions of these chemokines using Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. By univariate and multivariate COX regression analyses, we developed chemokine-based prognostic risk models. In addition, using Gene Set Enrichment Analysis (GSEA) and Gene Set Variant Analysis (GSVA), we analyzed the differences in immune responses and signaling pathways among different risk groups. The results showed that the mutation rate of chemokines was low in COAD, but 25 chemokines were significantly differentially expressed. These chemokines function in several immune-related biological processes and play key roles in signaling pathways including cytokine-cytokine receptor interactions, NF-kappa B, and IL-17. Prognostic risk models based on CCL22, CXCL1, CXCL8, CXCL9, and CXCL11 performed well. GSEA and GSVA analyses showed significant differences in immune responses and signaling pathways across risk groups. In conclusion, this study reveals the potential molecular mechanisms of chemokines in COAD and proposes a new prognostic risk model based on these insights.

The RNA-binding protein RBMS3 inhibits the progression of colon cancer by regulating the stability of LIMS1 mRNA.

BACKGROUND: The RNA-binding motif single-stranded interacting protein 3 (RBMS3) is a constituent of the RNA-binding motif (RBM) protein family, which assumes a pivotal role in governing cellular biogenesis processes such as the cell cycle and apoptosis. Despite an abundance of studies elucidating RBMS3's divergent roles in the genesis and advancement of various tumors, its involvement in colon cancer remains enigmatic. METHODS: The present investigation employed data analysis from TCGA and GTEx to unveil that RBMS3 expression demonstrated a diminished presence in colon cancer tissues when juxtaposed with normal colon tissues. The effect of RBMS3 and LIM zinc finger domain 1 (LIMS1) on colon cancer was substantiated via animal models and cellular experiments. The connection between RBMS3 and LIM zinc finger domain 1 (LIMS1) was verified by molecular biology methods. RESULTS: The study conclusively ascertained that augmenting RBMS3 expression quells the proliferation, migration, and invasion of colon cancer cells. Furthermore, the inquiry unveiled a plausible mechanism through which RBMS3 impacts the expression of LIMS1 by modulating its mRNA stability. The investigation ascertained that RBMS3 inhibits the progression of colon cancer by regulating LIMS1. The inhibitory function of LIMS1 and RBMS3 is closely intertwined in colon cancer, with knocking down LIMS1 being able to rescue the inhibitory effect of RBMS3 overexpression on the functionality of colon cancer cell CONCLUSIONS: The discernments delineate RBMS3 as a novel suppressor of cancer via LIMS1, thereby bestowing fresh therapeutic possibilities and illuminating the intricacies of colon cancer.

MGCNSS: miRNA-disease association prediction with multi-layer graph convolution and distance-based negative sample selection strategy.

Identifying disease-associated microRNAs (miRNAs) could help understand the deep mechanism of diseases, which promotes the development of new medicine. Recently, network-based approaches have been widely proposed for inferring the potential associations between miRNAs and diseases. However, these approaches ignore the importance of different relations in meta-paths when learning the embeddings of miRNAs and diseases. Besides, they pay little attention to screening out reliable negative samples which is crucial for improving the prediction accuracy. In this study, we propose a novel approach named MGCNSS with the multi-layer graph convolution and high-quality negative sample selection strategy. Specifically, MGCNSS first constructs a comprehensive heterogeneous network by integrating miRNA and disease similarity networks coupled with their known association relationships. Then, we employ the multi-layer graph convolution to automatically capture the meta-path relations with different lengths in the heterogeneous network and learn the discriminative representations of miRNAs and diseases. After that, MGCNSS establishes a highly reliable negative sample set from the unlabeled sample set with the negative distance-based sample selection strategy. Finally, we train MGCNSS under an unsupervised learning manner and predict the potential associations between miRNAs and diseases. The experimental results fully demonstrate that MGCNSS outperforms all baseline methods on both balanced and imbalanced datasets. More importantly, we conduct case studies on colon neoplasms and esophageal neoplasms, further confirming the ability of MGCNSS to detect potential candidate miRNAs. The source code is publicly available on GitHub https://github.com/15136943622/MGCNSS/tree/master.