A bioinformatics approach to identify a disulfidptosis-related gene signature for prognostic implication in colon adenocarcinoma

Abstract

AbstractColon adenocarcinoma (COAD) is a type of cancer that arises from the glandular epithelial cells that produce mucus in the colon. COAD is influenced by various factors, including genetics, environment and lifestyle. The outcome of COAD is determined by the tumor stage, location, molecular characteristics and treatment. Disulfidptosis is a new mode of cell death that may affect cancer development. We discovered genes associated with disulfidptosis in colon adenocarcinoma and proposed them as novel biomarkers and therapeutic targets for COAD. We analyzed the mRNA expression data and clinical information of COAD patients from The Cancer Genome Atlas (TCGA) database and Xena databases, extracted disulfidptosis-related genes from the latest reports on disulfidptosis. We used machine learning to select key features and build a signature and validated the risk model using data from the Gene Expression Omnibus (GEO) database and Human Protein Atlas (HPA). We also explored the potential biological functions and therapeutic implications of the disulfidptosis-related genes using CIBERSORTx and GDSC2 databases. We identified four disulfidptosis-related genes: TRIP6, OXSM, MYH3 and MYH4. These genes predicted COAD patient survival and modulated the tumor microenvironment, drug sensitivity and immune microenvironment. Our study reveals the importance of disulfidptosis-related genes for COAD prognosis and therapy. Immune infiltration and drug susceptibility results provide important clues for finding new personalized treatment options for COAD. These findings may facilitate personalized cancer treatment.