AUTHOR=Xu Chengfei , Liu Zilin , Yan Chuanjing , Xiao Jiangwei 

TITLE=Application of apoptosis-related genes in a multiomics-related prognostic model study of gastric cancer

JOURNAL=Frontiers in Genetics

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2022.901200

DOI=10.3389/fgene.2022.901200

ISSN=1664-8021

ABSTRACT=Gastric cancer (GC) is one of the most common tumors in the world, and apoptosis is closely associated with GC. A number of therapeutic methods have been implemented to enhance the survival of GC, but the outcomes remain unsatisfactory. Apoptosis is a highly conserved form of cell death, but its aberrant regulation also leads to a variety of major human diseases, especially tumor formation. Genetic variations in apoptotic genes may increase susceptibility to gastric cancer. Thus, it is critical to identify novel and potent tools to predict the overall survival (OS) and treatment efficacy of GC.The expression profiles and clinical characteristics of TCGA-STAD cohort and GSE15459 were downloaded from TCGA and GEO. Apoptotic genes were extracted from the GeneCards database. Apoptosis risk scores were constructed by combining Cox regression and LASSO regression. The GSE15459 and TCGA internal validation sets were used for external validation. Moreover, we explored the relationship of the apoptosis risk score with clinical characteristics, drug sensitivity, tumor microenvironment (TME) and tumor mutational burden (TMB). Finally, we used GSVA to further explore the signaling pathways associated with apoptosis risk.By performing TCGA-STAD differential analysis, we obtained 839 differentially expressed genes, which were then analyzed with Cox regression and LASSO regression to establish 23 genes associated with apoptosis risk scores. We used TCGA-STAD testing validation cohort and the GSE15459 dataset for external validation. The AUC values of the ROC curve for 2-, 3- and 5-year survival were 0.7, 0.71 and 0.71 in TCGA-STAD internal validation cohort and 0.77, 0.74, and 0.75 in the GSE15459 dataset, respectively. We constructed a nomogram by combining the apoptosis risk signature and some clinical characteristics from TCGA-STAD. Analysis of apoptosis risk scores and clinical characteristics demonstrated noticeable differences in apoptosis risk scores between survival status, sex, grade, stage and T stage. Finally, the apoptosis risk score was correlated with TME characteristics, drug sensitivity and tumor mutational burden.