Abstract
Ferroptosis is a mode of regulated cell death that depends on iron and plays pivotal roles in regulating various biological processes in human cancers. However, the role of ferroptosis in gastric cancer (GC) remains unclear. In our study, a total of 2721 differentially expressed genes (DEGs) were filtered based on The Cancer Genome Atlas (TCGA) (n = 375) dataset. Weighted gene coexpression network (WGCNA) analysis was then used and identified 7 modules, of which the blue module with the most significant enrichment result was selected. By taking the intersections of the blue module and ferroptosis-related genes (FRGs), we obtained 23 common genes. Functional analysis was performed to explore the biological function of the genes of interest, and with univariate Cox regression (UCR) analysis, survival genes were screened 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 an independent prognostic indicator and validated using another GSE84437 dataset. Notably, patients in the high-risk group had higher mutation frequencies, such as TTN and TP53. TIMER analysis demonstrated that the risk score strongly correlated with macrophage and CD4+ T cell infiltration. In addition, the high- and low-risk groups illustrated different distributions of different immune statuses. Furthermore, the low-risk group had a higher immunophenoscore (IPS), which meant a better response to immune checkpoint inhibitors (ICIs). Finally, gene set enrichment analysis (GSEA) revealed several significant pathways involved in GC. In this study, a novel FRG signature was built that could predict GC prognosis and reflect the status of the tumor immune microenvironment.
Highlights
As a major public health issue globally, gastric cancer (GC) is the fourth leading cause of cancer-related death [1]
We evaluated the prognostic value of the ferroptosis-related genes (FRGs) and constructed a three-mRNA signature that could effectively predict patient survival in The Cancer Genome Atlas (TCGA) dataset and further validated this three-mRNA signature in the GEO dataset
The results revealed the receiver operating characteristic (ROC) curve area under the ROC curve (AUC) = 0:676 for validation datasets (Figure 7(d)), which is similar to the one in TCGA dataset
Summary
As a major public health issue globally, gastric cancer (GC) is the fourth leading cause of cancer-related death [1]. Because early stages of GC are usually asymptomatic, patients are diagnosed at an advanced stage, leading to poor survival [2]. Several publications have reported that natural active components alleviate multidrug resistance in cancer and inhibit the progression of multiple tumors by inducing ferroptosis [9]. These findings suggest ferroptosis as a new player that regulates tumor-suppressive function. Prognostic models for FRGs have not been constructed for the prediction of overall survival (OS) in GC patients
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