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

Jun Chen,Chao Zhou,Ying Liu

doi:10.18632/aging.203948

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-CDG signature was identified. Risk scores were calculated using this signature, and patients were subsequently divided into high- and low-risk groups; high-risk patients in the training and validation datasets had poorer prognoses than low-risk patients. Cox regression analysis revealed that the CDG signature is an independent prognostic factor for GC. The signature and other clinical features were used to construct a nomogram for predicting overall GC patient survival. Calibration and decision curve analysis showed that the nomogram accurately predicted survival, highlighting its clinical utility. Thus, we established a novel CDG signature and nomogram for predicting GC prognosis, which may facilitate personalized treatment of GC.

Highlights

Gastric cancer (GC) is one of the most common forms of gastrointestinal cancer and is associated with very high morbidity and mortality rates [1]
Least absolute shrinkage and selection operator (LASSO)-penalized Cox analysis was performed to narrow down the list of cancer driver genes (CDGs), and 12 genes were identified for downstream analyses (Supplementary Figure 1B)
We developed a prognostic CDG signature and a corresponding nomogram for predicting GC patient survival

Summary

Introduction

Gastric cancer (GC) is one of the most common forms of gastrointestinal cancer and is associated with very high morbidity and mortality rates [1]. It can be histologically classified into various subtypes, including adenocarcinoma, squamous cell carcinoma, adenosquamous carcinoma, and carcinoid. Gastric adenocarcinoma accounts for 80-90% of all GC cases. The incidence of GC has increased and GC cases have been associated with poor prognoses. Identifying new therapeutic targets for GC is required [3]. Over the past few decades, several studies that focused on developing molecular targeted therapies for GC and understanding their underlying molecular mechanisms have shed light on GC pathogenesis [4]. Despite the importance of accurate classification and risk stratification of GC patients in improving management decisions and prognosis predictions, reliable biomarkers to predict GC prognosis are lacking [5, 6]

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