Establishing a cancer driver gene signature-based risk model for predicting the prognoses of gastric cancer patients.

ABSTRACT: Despite the high prevalence of gastric cancer (GC), molecular biomarkers that can reliably detect GC are yet to be discovered. The present study aimed to establish a robust gene signature based on cancer driver genes (CDGs) that can predict GC prognosis. Transcriptional profiles and clinical data from GC patients were analyzed using univariate Cox regression analysis and the least absolute shrinkage and selection (LASSO)-penalized Cox regression analysis to select optimal prognosis-related genes for modeling. Time-dependent receiver operating characteristic (ROC) and Kaplan-Meier analyses were done to assess the predictive power of this gene signature. A nomogram model for prediction of survival of GC patients was established using the CDG signature and clinical information, and a seven-CD

SUBMITTER: Chen J 

PROVIDER: S-EPMC8954960 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature