Evaluation of Ferroptosis-Related Genes in Gastric Cancer via Bioinformatics Analysis

Reza  Maddah; Marjan  Talebi; Rouzbeh Almasi  Ghale; Fatemeh  Saadati; Saghi  Nooraei; Mahnaz  Karimi; Amir Shafie  Kafraj; Farkhondeh  Pouresmaeili

doi:10.18502/ijph.v54i8.19585

Reza Maddah National Institute of Genetic Engineering and Biotechnology (NIGEB), Tehran, Iran
Marjan Talebi Department of Pharmacognosy, School of Pharmacy, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Rouzbeh Almasi Ghale Department of Energy and Environmental Biotechnology, National Institute of Genetic Engineering and Biotechnology (NIGEB), Tehran, Iran
Fatemeh Saadati National Institute of Genetic Engineering and Biotechnology (NIGEB), Tehran, Iran
Saghi Nooraei National Institute of Genetic Engineering and Biotechnology (NIGEB), Tehran, Iran
Mahnaz Karimi National Institute of Genetic Engineering and Biotechnology (NIGEB), Tehran, Iran
Amir Shafie Kafraj National Institute of Genetic Engineering and Biotechnology (NIGEB), Tehran, Iran
Farkhondeh Pouresmaeili Men’s Health and Reproductive Health Research Center, Shahid Beheshti University of Medical Sciences Tehran, Iran

DOI: https://doi.org/10.18502/ijph.v54i8.19585

Keywords: Gastric cancer; Ferroptosis; Hub genes; Bioinformatics; Prognostic analysis

Abstract

Background: Gastric cancer (GC) ranks among the most prevalent malignancies globally, contributing significantly to both morbidity and mortality. Ferroptosis, a unique iron-dependent form of cell death, has been implicated in various cancers, including GC. This study investigated the association between ferroptosis-related genes (FRGs) and GC using bioinformatics analyses.

Methods: Differentially expressed genes (DEGs) were identified using a publicly available microarray dataset. Genes associated with ferroptosis were then extracted, and their overlap with the DEGs was assessed. To gain further insights, functional enrichment analysis was performed, followed by the prediction of microRNA (miRNA) and transcription factor (TF) interactions. Additionally, a protein-protein interaction (PPI) network was constructed. Key genes were identified using the CytoHubba extension in Cytoscape, and their prognostic value was analyzed through receiver operating characteristic (ROC) curve evaluation.

Results: Overall, 3242 DEGs were identified, of which 78 were ferroptosis-related (DEFRGs). These DEFRGs were enriched in pathways such as ferroptosis and pathways in cancer. Among them, hsa-miR-106a-5p and SP1 were identified as key miRNA and TF, respectively. The PPI network revealed five hub genes: TP53, MDM2, KRAS, IL6, and PTGS2. ROC curve analysis demonstrated that all hub genes have excellent prognostic value for GC.

Conclusion: This study highlights the critical association between GC and ferroptosis-related genes using bioinformatics tools. These findings provide insights for future investigations and the development of targeted therapies against GC.