Identification of Metabolism-Related Molecular Classifications of Gastric Cancer Based on Prognosis and Immune Infiltration

Abstract

Background: This study aimed to establish new molecular classifications of gastric cancer (GC) based on metabolism-related genes. Methods: Gene expression and clinical data of stomach adenocarcinoma from The Cancer Genome Atlas (TCGA) database were downloaded for analysis. Metabolite-protein interactions were retrieved from different public databases to identify metabolism-related genes in the TCGA dataset. Differential expression was analyzed using the Limma package. ConsensusClusterPlus was used to conduct clustering analysis. Survival, clinical data, immune cell infiltration, and tumor mutation burden (TMB) were compared between the clusters. Next, gene set variation analysis (GSVA) was conducted to analyze differential hallmark pathways between clusters. Finally, the GSE66229 dataset in the Gene Expression Omnibus (GEO) database was used for validation. Results: In total, 269 metabolism-related genes were differentially expressed in GC, of which 35 genes associated with prognosis were identified. Two metabolism-related molecular clusters were established based on these 35 prognostic genes. Samples in cluster 1 showed poor survival in both the TCGA and GSE66229 datasets. This cluster contained many patients with high histologic grade, lymph node metastasis, and advanced tumor stage. Three hundred fifty-four genes were aberrantly expressed between the two clusters and were enriched in focal adhesion, leukocyte migration, and ECM (extracellular matrix)-receptor interaction. GSVA indicated that epithelial-mesenchymal transition, angiogenesis, inflammatory response, and hypoxia were markedly enriched in cluster 1. Moreover, cluster 1 showed higher immune and stromal scores and a higher abundance of infiltrating M2 (tumor-promoting phenotype) macrophages, cluster of differentiation (CD) 8-positive (CD8⁺) T cells cells, cluster of differentiation (CD) 4-positive (CD4⁺) T cells, and neutrophils, as well as lower TMB than cluster 2. Conclusions: We successfully developed two metabolism-related molecular clusters of GC, which differed in terms of clinical pathological characteristics, prognosis, immune status and mutation spectrum. This contributed to stratify GC pateints so as to develop personalized therapy.