Identification of a tumor microenvironment-related gene signature for predicting prognosis in patients with gastric cancer

Tumor microenvironment (TME) plays an important role in the prognosis of gastric cancer (GC). The aim of this study was to identify a TME-related gene signature and provide a basis for prognosis evaluation of GC. X-cell and cluster analyses were performed on 373 tumor samples from The Cancer Genome Atlas-Stomach Adenocarcinoma. Prognostic-related genes were screened using differential analysis. Univariate Cox analysis, LASSO regression, and multivariate Cox analysis were used to determine the candidate genes and construct the prognostic model. Independent prognostic and correlation analyses of clinical characteristics were performed. In the observational study, patients were divided into high- and low-risk groups according to the expression levels and risk coefficients of the 4 model genes. Mutation characteristics, immune cell differences, and TME differences between the high- and low-risk groups were analyzed. The expression levels of these key genes were subsequently validated using reverse transcription quantitative polymerase chain reaction and Western blotting. Two hundred and twenty-five candidate genes were obtained by differential analysis. Performed in the training set, and 4 hub genes (CTHRC1, APOD, S100A12, and ASCL2) were finally determined as prognostic biomarkers. The area under curve of the 1-, 3-, and 5-year receiver operating characteristic curves of the training set, test set, and validation set were all >0.6. There were significant differences in the frequency of some gene mutations and scores (immune score, matrix score, and ESTIMATE composite score) between the high- and low-risk groups. Reverse transcription quantitative polymerase chain reaction and Western blot analyses confirmed that CTHRC1, APOD, and S100A12 were significantly upregulated in the tumor group, whereas ASCL2 expression was significantly downregulated. We developed a TME-related gene signature that can predict the prognosis of patients with GC.