Machine Learning-Based WGCNA Approach for Developing an Immunogenic Cell Death-Related Hub Gene Signature and Identification of AJM1 as a Prognostic Biomarker in Pancreatic Adenocarcinoma.

ABSTRACT: Background & Aims: Pancreatic adenocarcinoma (PAAD) remains a highly lethal malignancy with limited therapeutic options, primarily due to the absence of reliable prognostic biomarkers. Immunogenic cell death (ICD) plays a pivotal role in anti-tumor immunity and has potential as both a prognostic marker and a predictor of immunotherapy response. This study aimed to identify ICD-related hub genes and establish a robust prognostic gene signature for PAAD using weighted gene co-expression network analysis (WGCNA). Methods & Results: Transcriptomic and clinical data of PAAD patients were obtained from the TCGA and GEO databases. ICD enrichment scores were calculated using single-sample gene set enrichment analysis (ssGSEA), and ICD-associated gene modules were identified through W

SUBMITTER: Ma T 

PROVIDER: S-EPMC12595326 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature