A TRPM4-associated necrotic cell death signature for prognostic stratification in pancreatic cancer

Background: Sodium overload-induced necrotic cell death has been proposed as a stress-related cellular process potentially involved in tumor progression, but its relevance in pancreatic cancer remains poorly understood. TRPM4, a sodium-permeable ion channel, has been implicated in cellular stress responses; however, the prognostic significance of TRPM4 and its associated transcriptional features in pancreatic cancer has not been comprehensively evaluated. This study integrated multi-cohort transcriptomic datasets to construct a prognostic risk score model based on TRPM4-associated genes. Methods: Genes co-expressed with TRPM4 were identified in The Cancer Genome Atlas-pancreatic adenocarcinoma (TCGA-PAAD) cohort. Survival-associated genes were screened using univariate Cox regression and further refined by least absolute shrinkage and selection operator (LASSO) Cox regression to construct a TRPM4-associated prognostic risk score. An independent Gene Expression Omnibus (GEO) dataset was used for external validation. Kaplan-Meier survival analysis, multivariate Cox regression, and time-dependent receiver operating characteristic (ROC) analyses were applied. Exploratory analyses of somatic mutation patterns inferred immune infiltration, and drug sensitivity-related transcriptional features were also performed. Results: We identified 60 TRPM4-associated genes, among which seven (MSLN, RAB27B, MYEOV, ECT2, KATNAL2, SAT2, and SLC26A11) constituted a related prognostic signature. Higher risk scores were significantly associated with worse prognosis, and correlation analysis further revealed supportive evidences between this high-risk score and poor clinicopathologic features, higher somatic mutations, more immune cell infiltration, and less clinical drug sensitivity. Conclusions: This study identifies a TRPM4-associated prognostic signature that enables risk stratification in pancreatic cancer. While based on retrospective transcriptomic analyses, the proposed model provides a framework for prognostic evaluation and warrants further prospective and experimental validation.