Identification of a novel immune prognostic model in gastric cancer.

PURPOSE: The tumor immune microenvironment (TIME) is now considered as an important factor during gastric cancer (GC) development. This study identified a novel immune-related risk model for predicting prognosis and assessing the immune status of GC patients.

METHODS: Transcriptomic data were obtained from the TCGA database. The differential expressed immune-related genes (IRGs) were identified through the ImmPort portal. Enrichment analysis was performed to explore the potential molecular mechanism of these IRGs. By the Cox regression analysis, we constructed the immune prognostic model. Then, the association between the model and the immune microenvironment was estimated. The model was validated in the GSE84433 dataset.

RESULTS: Totally, we identified 222 differentially expressed IRGs. These IRGs were closely correlated with immune response and immune signaling pathways. Through the Cox regression analysis, we developed the immune prognostic model based on the expression of seven IRGs (CXCL3, NOX4, PROC, FAM19A4, RNASE2, IGHD2-15, CGB5). Patients were stratified into two groups according to immune-related risk scores. Survival analysis indicated that the prognosis of high-risk patients was poorer than low-risk patients. Moreover, the immune-related risk score was an independent prognostic biomarker. More importantly, we found that the infiltration level of immunosuppressive cells and the expression of inhibitory immune checkpoints were higher in high-risk patients. The immune microenvironment tended to be a suppressive status in patients with high-risk scores.

CONCLUSION: This study demonstrated that our model had predictive value for prognosis and the TIME in GC. It might be a robust tool to improve personalized patient management.