A novel Senescence-Based prognostic model unveils tumor interactions and drug resistance in colorectal cancer.

BACKGROUND: In China, CRC incidence is escalating. The main hurdles are heterogeneity and drug resistance. This research delves into cellular senescence in CRC, aiming to devise a prognostic model and pinpoint mechanisms impacting drug resistance. METHODS: Mendelian randomization (MR) analysis confirmed the association between CRC and cellular aging. The Cancer Genome Atlas (TCGA)-CRC data served as the training set, with GSE38832 and GSE39582 as validation sets. Various bioinformatics methods were employed to construct and validate a risk model. CRC cells with NADPH Oxidase 4 (NOX4) knockout were generated using CRISPR-Cas9 technology. Protein blotting and colony formation assays elucidated the role of NOX4 in CRC cell aging and drug resistance. RESULTS: A prognostic model, derived from dataset analysis, uncovered a link between high-risk groups and cancer progression. Notable differences in the tumor microenvironment were observed between risk groups. Finally, NOX4 was found to be linked with aging and drug resistance in CRC. CONCLUSION: This research presents a novel senescence-based CRC prognosis model. It identifies NOX4's role in CRC drug resistance, suggesting it is a potential treatment target.

Deciphering alternative splicing events and their therapeutic implications in colorectal Cancer.

Colorectal cancer (CRC) is one of the most common malignant tumors with complex molecular regulatory mechanisms. Alternative splicing (AS), a fundamental regulatory process of gene expression, plays an important role in the occurrence and development of CRC. This study analyzed AS Percent Spliced In (PSI) values from 49 pairs of CRC and normal samples in the TCGA SpliceSeq database. Using Lasso and SVM, AS features that can differentiate colorectal cancer from normal were screened. Univariate COX regression analysis identified prognosis-related AS events. A risk model was constructed and validated using machine learning, Kaplan-Meier analysis, and Decision Curve Analysis. The regulatory effect of protein arginine methyltransferase 5 (PRMT5) on poly(RC) binding protein 1 (PCBP1) was verified by immunoprecipitation experiments, and the effect of PCBP1 on the AS of Obscurin (OBSCN) was verified by PCR. Five AS events, including HNF4A.59461.AP and HNF4A.59462.AP, were identified, which can distinguish CRC from normal tissue. A machine learning model using 21 key AS events accurately predicted CRC prognosis. High-risk patients had significantly shorter survival times. PRMT5 was found to regulate PCBP1 function and then influence OBSCN AS, which may drive CRC progression. The study concluded that some AS events is significantly different in CRC and normal tissues, and some of these AS events are related to the prognosis of CRC. In addition, PRMT family-driven arginine modifications play an important role in CRC-specific AS events.

A novel prognostic signatures based on metastasis- and immune-related gene pairs for colorectal cancer.

Background: Metastasis remains the leading cause of mortality in patients diagnosed with colorectal cancer (CRC). The pivotal contribution of the immune microenvironment in the initiation and progression of CRC metastasis has gained significant attention. Methods: A total of 453 CRC patients from The Cancer Genome Atlas (TCGA) were included as the training set, and GSE39582, GSE17536, GSE29621, GSE71187 were included as the validation set. The single-sample gene set enrichment analysis (ssGSEA) was performed to assess the immune infiltration of patients. Least absolute shrinkage and selection operator (LASSO) regression analysis, Time-dependent receiver operating characteristic (ROC) and Kaplan-Meier analysis were used to construct and validate risk models based on R package. CTSW and FABP4-knockout CRC cells were constructed via CRISPR-Cas9 system. Western-blot and Transwell assay were utilized to explore the role of fatty acid binding protein 4 (FABP4) / cathepsin W (CTSW) in CRC metastasis and immunity. Results: Based on the normal/tumor, high-/low-immune cell infiltration, and metastatic/non-metastatic group, we identified 161 differentially expressed genes. After random assignment and LASSO regression analysis, a prognostic model containing 3 metastasis- and immune-related gene pairs was constructed and represented good prognostic prediction efficiency in the training set and 4 independent CRC cohorts. According to this model, we clustered patients and found that the high-risk group was associated with stage, T and M stage. In addition, the high-risk group also shown higher immune infiltration and high sensitivity to PARP inhibitors. Further, FABP4 and CTSW derived from the constitutive model were identified to be involved in metastasis and immunity of CRC. Conclusion: In conclusion, a validated prognosis predictive model for CRC was constructed. CTSW and FABP4 are potential targets for CRC treatment.