Establishment of a Prognostic Risk Score Model Based on Bioinformatics for Ferroptosis and Cuproptosis‐Related Genes in Gastric Cancer

ABSTRACTBackgroundGastric cancer (GC) stands out as one of the most prevalent forms of malignant tumors globally, characterized by a notably high mortality rate. In spite of progress in medical science and treatment alternatives, the survival rate over 5 years continues to stay under 40%. In the realm of cancer biology, ferroptosis and cuproptosis represent two distinctive forms of programmed cell death that are gaining attention for their roles in tumor progression and treatment response. Ferroptosis is characterized by its dependence on lipid peroxidation and the resultant accumulation of reactive oxygen species (ROS) which ultimately induce cellular demise. On the other hand, cuproptosis operates through a different pathway, characterized by the direct interaction of copper ions with the acylating elements of the tricarboxylic acid cycle which ultimately leads to cell death. Ferroptosis and cuproptosis, that are types of programmed cell death associated with metals, play a significant role in the onset and progression of GC.ObjectiveThis study intends to employ bioinformatics techniques to pinpoint genes that are differentially expressed genes (DEGs) linked to ferroptosis and cuproptosis that are significant for the prognosis of GC. Additionally, the study aims to develop a prognostic risk score model that will facilitate the prediction of patient outcomes based on these identified genetic factors.MethodsTranscriptome and clinical data from tissues of 412 patients with gastric adenocarcinoma and 36 adjacent non‐cancerous tissues were obtained from The Cancer Genome Atlas stomach adenocarcinoma collection (TCGA‐STAD) database. This analysis aimed to identify differentially DEGs associated with ferroptosis and cuproptosis that are linked to the prognosis of GC patients. Furthermore, gene expression data along with clinical details from 433 GC patients in the gene expression omnibus (GEO) database were combined to create a prognostic risk score model. Further investigations were carried out, encompassing pathway enrichment analysis, assessment of tumor mutation burden (TMB), evaluation of tumor immune dysfunction and exclusion (TIDE), single‐sample gene set enrichment analysis (ssGSEA), and analysis of the tumor microenvironment (TME). Validation was achieved through immunohistochemical examination of relevant gene expression in samples collected from GC patients.ResultsTwelve DEGs linked to the prognosis of GC were discovered, leading to the creation of a prognostic risk score model that incorporates four genes involved in ferroptosis (NOX4, GLS2, MYB, NNMT) alongside one gene related to cuproptosis (GCSH). Analysis of pathway enrichment revealed a notable accumulation of the DEGs within several signaling pathways associated with the extracellular matrix. In addition, examinations of immune cell infiltration demonstrated significant variations in TMB, the quantity of immune cells present within the tumor, their functional roles, and the TME when contrasting high‐risk and low‐risk groups. Findings from immunohistochemical studies showed that GCSH and GLS2 show varying levels of expression in gastric cancer tissues and have a correlation with patient prognosis.ConclusionA prognostic risk model specifically designed for gastric cancer was developed, comprising five distinct genes. This model demonstrates a strong ability to accurately forecast both the prognosis of gastric cancer patients and the effectiveness of immunotherapy treatments they may undergo.