Development and validation of a novel glycolysis-related risk signature for predicting survival in pancreatic adenocarcinoma

Pancreatic adenocarcinoma (PAAD) is one of the leading causes cancer-related deaths worldwide. Through data mining, an increasing number biomarkers have been identified to predict survival patients with PAAD. However, ability single gene patient still insufficient. This study aimed develop a novel risk signature for predicting mRNA expression profiling was performed large PAAD cohort (n = 177) using The Cancer Genome Atlas database (TCGA). Gene set enrichment analysis (GSEA) detect whether sets showed significant differences between and adjacent normal tissues. Univariate Cox regression used analyze identify genes related overall (OS). Multivariate subsequently confirm prognostic obtain coefficients. By analyzing level selected weighted by their coefficients through linearly combining, we constructed score formula prediction. three-mRNA prediction validated Kaplan–Meier method. We demonstrated that three (KIF20A, CHST2, MET) were significantly associated OS. Based on this three-gene signature, 177 classified into high-risk low-risk groups median as cut-off value. also reliability in GSE28735 dataset from Expression Omnibus (GEO) database. Additionally, multivariate revealed had independent To best our knowledge, first glycolysis-related pancreatic adenocarcinoma. Our findings provide insight identification poor prognosis. therapeutic targets disease.