Development of Prognostic Features and Investigation of Therapeutic Insights Related to Non-Apoptotic Regulatory Cell Death in Colorectal Cancer.

Abstract

Colorectal cancer (CRC) is a prevalent tumor in the gastrointestinal system. Non-apoptotic regulatory cell death-related genes (NARCDs) play a critical role in tumor development and progression. This research aims to explore the predictive value of NARCDs in CRC and to elucidate their possible biological roles. Transcriptome data for CRC were obtained from the Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO). Both univariate and multivariate regression analyses, as well as Least Absolute Shrinkage and Selection Operator (LASSO) regression, were utilized to pinpoint the prognostic genes. The expression of the characterised genes in CRC cells was also examined using quantitative reverse transcription polymerase chain reaction (qRT-PCR). The prognostic ability of NARCDs features was assessed using Kaplan-Meier survival curves and receiver operating characteristic (ROC) curves. The predictive performance of the comprehensive nomogram was evaluated using calibration curves and decision curve analysis. Additionally, single-sample Gene Set Enrichment Analysis (ssGSEA) was utilized to analyze immune cell density and functional immune scores. Furthermore, the CellMiner database was applied to identify antitumor drugs that were highly correlated with the feature genes. This project developed an innovative risk model utilizing seven NARCDs characteristic genes (JMJD7-PLA2G4B, CDKN2A, PANX2, FABP4, GSDMC, NOD2, and DYNC1I1) to estimate the survival rate of CRC patients. The prognostic features were recognized as independent indicators for CRC, demonstrating satisfactory predictive efficacy in both the training and validation cohorts. The model achieved AUC values of 0.748, 0718 and 0.668 for 1-, 3- and 5-years in the TCGA training set, respectively. In the low-risk group, patients exhibited a more pronounced potential benefit from immunotherapy and showed higher levels of immune cell infiltration. Furthermore, drug sensitivity analyses indicated that individuals with reduced risk scores demonstrated greater responsiveness to pharmacological therapies. Finally, qRT-PCR results further confirmed our findings. We successfully developed a predictive feature model consisting of seven NARCDs, offering fresh insight into the prognostic evaluation of CRC patients and establishing a theoretical basis for crafting personalized treatment approaches.