Novel insight on predicting prognosis of gastric cancer based on inflammation

The tumor microenvironment (TME) and inflammation play vital roles in the development and progression of gastric cancer (GC). However, there are no inflammation-related models that can predict the prognosis and immunotherapy response of GC patients. We aimed to establish a prognostic model based on an inflammation-related gene (IRG) signature that can predict poor clinical outcomes in GC.

We searched IRGs in The Cancer Genome Atlas (TCGA) database and identified genes differentially expressed in GC. The model was constructed using univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analysis and validated using Gene Expression Omnibus (GEO) database. Receiver operating characteristic (ROC) curve, principal component analysis (PCA), and t-distribution stochastic neighbor embedding (t-SNE) analysis were performed to evaluate model performance. Independent prognostic factor, immune infiltration, cancer stemness, immunotherapy response analysis and gene set enrichment analysis (GSEA) were performed for functional evaluation.

An inflammation-related risk model was established based on 8 genes (F2, LBP, SERPINE1, ADAMTS12, FABP4, PROC, TNFSF18, and CYSLTR1). Risk score significantly correlated with poor outcomes and independently predicted prognosis. It was also associated with immune infiltration and reflected immunotherapy response.

We established and validated an inflammation-related prognostic model that predicts immune escape and patient prognosis in GC. Our model is expected to improve clinical outcomes by facilitating clinical decision making and the development of individualized treatments.