Identification of Gastric Cancer Subtypes Based on Neutrophil Extracellular Trap Related Genes

This study aimed to classify patients with gastric cancer (GC) based on neutrophil extracellular trap (NET)-related gene expression and to develop a prognostic risk score model.

Utilizing data from The Cancer Genome Atlas (TCGA)-stomach adenocarcinoma (STAD) cohort, we performed univariate Cox regression analysis followed by K-means clustering to stratify GC samples into two NET-related subtypes (C1 and C2), and then the survival analysis and immune cell infiltration analysis were performed between these subtypes. Next, to further investigate the prognostic significance of NET-related features, we developed a risk score model using LASSO Cox regression analysis, and survival analysis was then conducted.

Ten prognostic NET-related genes were identified, distinguishing two GC subtypes; compared with the C1 subtype, the C2 subtype demonstrated significantly poorer clinical outcomes. Furthermore, the C2 subtype exhibited elevated TIDE scores, along with enhanced infiltration of immunosuppressive cell populations, including regulatory T cells, macrophages, and myeloid-derived suppressor cells (MDSCs). Next, we constructed a six-gene risk score model (MPO, BST1, IL6, KCNJ15, SELP, and ELANE) derived from the aforementioned 10 NET-related genes. This risk score model may serve as an independent prognostic indicator for GC patients, with the high-risk group showing markedly reduced survival rates. Notably, the poor-prognosis C2 subtype was associated with a higher risk score compared to the C1 subtype.

NET-related genes are useful for categorizing GC samples into different subtypes and potentially playing significant roles in forecasting prognosis and guiding immunotherapy in GC. Furthermore, the six-gene risk model may aid in personalized prognosis prediction for GC patients.