Identification the ferroptosis-related gene signature in Gastric cancer based on weighted gene co-expression network analysis (WGCNA)

Abstract Background Ferroptosis is a mode of regulated cell death that depends on iron, plays pivotal roles in regulating various biological process in human cancers. However, the role of ferroptosis in Gastric cancer (GC) remains unclear. Methods A total of 2721 differentially expressed genes (DEGs) were filtered base on The Cancer Genome Atlas (TCGA) (n = 375) dataset. Gene modules were identified based on co-expression network analysis (WGCNA). Functional analysis was performed to explore the biological function. Lasso-penalized and univariate Cox regression (UCR) analysis, survival genes were screened out to construct a prognostic model, which validated by the GSE43292 dataset. Gene set enrichment analysis (GSEA) for prognostic index was performed. Finally, the correlations of ferroptosis and immune cells were assessed through the TIMER database. Results Compared to normal specimens, 1063 highly upregulated and 1658 downregulated genes respectively and their normal counterparts in GC specimens were screened. WGCNA analysis was used and identified 7 modules, of which, blue module with the most significant enrichment result was selected. By taking intersections of blue module and differentially expressed ferroptosis-related genes (DEFRGs), we got 23 common genes. Functional analysis was performed to explore the biological function of the interested genes, and with the consequences Lasso-penalized and univariate Cox regression (UCR) analysis, survival genes were screened out to construct a prognostic model based on 3 genes (SLC1A5, ANGPTL4, and CGAS), which could play a role in predicting the survival of GC patients. UCR and multivariate Cox regression (MCR) analysis revealed that the prognostic index could be used as independent prognostic indicators and validated using another GSE84437 dataset. Notably, patients in high-risk groups had higher levels of higher mutation frequencies such as TTN and TP53.Mechanistically. Gene set enrichment analysis (GSEA) unveiled several significant and pathways involved in GC. TIMER analysis demonstrated that risk score strongly correlated with Macrophage and CD4 + T cells infiltration. In addition, high- and low-risk group illustrated different distributions in different immune status. Conclusions In this study, a novel FRGs signature was built. It could accurately predict GC prognosis and pave the new way for diagnosis and therapy strategy. May reflect the status of tumor immune microenvironment.