Identification of a Chemotherapy-associated Gene Signature for a Risk Model of Prognosis in Gastric Adenocarcinoma Through Bioinformatics Analysis

Background: Over the past few years, the overall survival rate of patients with gastric adenocarcinoma who have received different chemotherapy regimens has increased. However, not all gastric cancer patients who receive chemotherapy have a longer survival. We need better predictive biomarkers. This study is to construct a new risk model of chemotherapy-associated genes in gastric adenocarcinoma (GA) for prognostication.

Methods: RNA-seq data and clinical information of GSE26901 (containing 44 chemotherapy samples and 65 patients without chemotherapy) in Gene Expression Omnibus (GEO) and stomach adenocarcinoma (STAD, containing 360 cancer tissue samples and 50 paired normal tissue samples) in The Cancer Genome Atlas (TCGA) were selected for screening differentially expressed genes (DEGs). Multivariate Cox regression was conducted to screen prognosis-associated genes and its link to patients' prognosis were screened by least absolute shrinkage and selection operator (LASSO) regression analysis. Based on the key genes, a risk scoring equation for the prognosis model was established, and constructed survival prognosis model. The model was tested for predictive ability through training set (TCGA datasets) and validation set (GSE84437). The correlations of the risk score with clinical pathological features, immune score and drug sensitivity score were evaluated.

Results: In total, 179 overlapping genes were obtained by screening DEGs. Univariate Cox analysis revealed 36 prognosis-related genes, and LASSO regression analysis revealed 8 key genes (, , , , , , and ). Kaplan-Meier (K-M) analysis uncovered a relatively short overall survival time in the high-risk group. The model was verified to possess favourable predictive ability. In addition, the nomogram model were demonstrated good predictability with area under the curve (AUC) for 1-5 years in training set were 0.78, 0.78, 0.76, 0.79 and 0.81. The high-risk group was less likely to get benefits from immunotherapy and less sensitive to cisplatin.

Conclusions: According to the results of our training set and validation set, the risk model based on the eight chemotherapy-related gene signatures predicting prognosis has certain predictive accuracy in predicting the survival of GA patients which can be a promising prognostic parameter for GA. However, its efficacy remains to be proved in clinical practice.