Classification of stomach adenocarcinoma based on fatty acid metabolism-related genes frofiling

Background: Fatty acid metabolism (FAM)-related genes play a key role in the development of stomach adenocarcinoma (STAD). Although immunotherapy has led to a paradigm shift in STAD treatment, the overall response rate of immunotherapy for STAD is low due to heterogeneity of the tumor immune microenvironment (TIME). How FAM-related genes affect TIME in STAD remains unclear.

Methods: The univariate Cox regression analysis was performed to screen prognostic FAM-related genes using transcriptomic profiles of the Cancer Genome Atlas (TCGA)-STAD cohort. Next, the consensus clustering analysis was performed to divide the STAD cohort into two groups based on the 13 identified prognostic genes. Then, gene set enrichment analysis (GSEA) was carried out to identify enriched pathways in the two groups. Furthermore, we developed a prognostic signature model based on 7 selected prognostic genes, which was validated to be capable in predicting the overall survival (OS) of STAD patients using the univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression, and multivariate Cox regression analyses. Finally, the “Estimation of STromal and Immune cells in MAlignant Tumours using Expression data” (ESTIMATE) algorithm was used to evaluate the stromal, immune, and ESTIMATE scores, and tumor purity of each STAD sample.

Results: A total of 13 FAM-related genes were identified to be significantly associated with OS in STAD patients. Two molecular subtypes, which we named Group 1 and Group 2, were identified based on these FAM-related prognostic genes using the consensus clustering analysis. We showed that Group 2 was significantly correlated with poor prognosis and displayed higher programmed cell death ligand 1 (PD-L1) expressions and distinct immune cell infiltration patterns. Furthermore, using GSEA, we showed that apoptosis and HCM signaling pathways were significantly enriched in Group 2. We constructed a prognostic signature model using 7 selected FAM-related prognostic genes, which was proven to be effective for prediction of STAD (HR = 1.717, 95% CI = 1.105–1.240, p < 0.001). After classifying the patients into the high- and low-risk groups based on our model, we found that patients in the high-risk group tend to have more advanced T stages and higher tumor grades, as well as higher immune scores. We also found that the risk scores were positively correlated with the infiltration of certain immune cells, including resting dendritic cells (DCs), and M2 macrophages. We also demonstrated that elevated expression of gamma-glutamyltransferase 5 ( GGT5) is significantly associated with worse OS and disease-free survival (DFS), more advanced T stage and higher tumor grade, and increased immune cell infiltration, suggesting that STAD patients with high GGT5 expression in the tumor tissues might have a better response to immunotherapy.

Conclusion: FAM-related genes play critical roles in STAD prognosis by shaping the TIME. These genes can regulate the infiltration of various immune cells and thus are potential therapeutic targets worthy of further investigation. Furthermore, GGT5 was a promising marker for predicting immunotherapeutic response in STAD patients.