Hierarchical clustering identifies oxidative stress-related subgroups for the prediction of prognosis and immune microenvironment in gastric cancer

Abstract

Abstract Background Gastric cancer (GC) is a common digestive tract malignancy worldwide, exhibiting a high incidence of relapse and metastasis, and lack of effective therapy. Oxidative stress is involved in tumor progression and oncogenesis. Our study aimed to identify oxidative stress-related subgroups to classify GC patients and to develop oxidative stress-related genes for risk stratification in GC patients. Methods The mRNA expression profiles and corresponding clinical information of GC patients were acquired from TCGA-STAD and GSE84437 datasets. Oxidative stress-related subgroups were identified using consensus clustering analysis. GSEA and GSVA were performed to explore the underlying mechanisms, and xCell algorithm was used to assess the immune microenvironment and immune status of the GC subtypes. Prognostic risk model was constructed using TCGA-STAD dataset and validated using GSE84437 dataset. Results Two oxidative stress subtypes with significantly different survival were identified. C1 subtype exhibited activated immune signal pathway, high abundance of immune cell infiltration, high immune score, and high microenvironment score with a poor prognosis. Furthermore, an oxidative stress-related prognostic model could accurately predict the survival probability of GC patients. A nomogram containing patients’ sex, age, and the risk score could predict the survival of GC patients. Conclusion A novel classification system of GC patients was established based on oxidative stress-related signatures. This stratification could be used to accurately predict the prognosis of GC patients.