Construction of ferroptosis and pyroptosis model to assess the prognosis of gastric cancer patients based on bioinformatics.

Background: Gastric cancer (GC) is a malignancy with a grim prognosis, ranking as the second most common cause of cancer-related deaths globally. Various investigations have demonstrated the substantial involvement of ferroptosis and pyroptosis in the advancement of tumors. Nevertheless, the precise molecular mechanisms by which distinct genes associated with ferroptosis and pyroptosis influence the tumor microenvironment (TME) in GC remain elusive. Therefore, this study aims to elucidate the role of ferroptosis and pyroptosis in GC and provide insigths for GC therapy and prognosis evaluation.

Methods: The data including gene expression, clinicopathological characteristics and survival information of GC samples from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts were collected, and the expression level of ferroptosis and pyroptosis genes (FPGs) in GC samples were analyzed. Consensus clustering analysis, Cox logistic regression, principal component analysis (PCA), and the "survival", "survminer", "limma", "ggplot2" and other packages in R were utilized to compare the differences among different groups. In the level of GC cells, cell viability experiments were conducted by Cell Counting Kit-8 (CCK-8) assay.

Results: Through the analysis of the expression level of FPGs in GC samples from the TCGA and GEO cohorts, twenty-three prognostic-related and differentially expressed FPGs were collected for further analysis. Through consensus clustering analysis, three distinct patterns of FPGs were identified and found to be correlated with clinicopathological characteristics, immune cell infiltration, and prognosis in patients with GC. Subsequently, 684 prognostic-related genes from 1,082 pattern-related differentially expressed genes (DEGs) were screened for constructing the FPG_Score system to quantify FPGs patterns in individual GC patients and predict the prognosis. The analysis indicated that GC patients with high FPG_Score exhibited improved survival rates, increased tumor mutation burden (TMB), higher microsatellite instability (MSI), and elevated programmed cell death protein ligand 1 (PD-L1) expression. These patients with high FPG_Score were more likely to benefit from immunotherapy and had a more favorable prognosis.

Conclusions: Our study innovatively provided a comprehensive analysis of FPGs in GC, and constructed the FPG_Score system for stratification of individual patients, so as to predict its benefit from immunotherapy and prognosis.