Identification of key ferroptosis genes in hepatocellular carcinoma and type 2 diabetes mellitus through bioinformatics analysis - Jinjin Zhou; Yage Shi; Yulun Jian; Yuhan Li; Wenya Yu; Wei Mu; Yang Ge (Discover Oncology)
Abstract Ferroptosis is a programmed cell death mode associated with iron metabolism, with accumulation of intracellular lipid peroxides, which is closely related to the occurrence and development of multiple diseases, including type 2 diabetes mellitus (T2DM) and hepatocellular carcinoma (HCC). T2DM is a chronic metabolic disorder characterized by a combination of impaired insulin sensitivity and insufficient insulin production, frequently accompanied by obesity and fatty liver, which increases the risk of developing HCC. To explore the complex interactions between ferritin deposition, T2DM, and HCC, we performed bioinformatics analysis on publicly available gene expression data and identified 23 differentially expressed genes (DEGs) that are commonly expressed in both T2DM and HCC. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses revealed that these DEGs are primarily enriched in fatty acid metabolism and ferroptosis pathways. The weighted gene co-expression network analysis (WGCNA) identified 6 key genes associated with the pathogenesis of both diseases. Taking the intersection of DEGs and iron deposition-related genes, we identified ACSL4 as a key ferroptosis gene involved in the co-morbidity of T2DM and HCC. To validate the bioinformatics findings, we assessed the expression of ACSL4 using Receiver operating characteristic (ROC) curve analysis, which revealed an Area Under the Curve (AUC) of 0.886 for HCC and 0.745 for T2DM. Additionally, an insulin resistance model was established in HepG2 cells by treatment with 350 µM palmitic acid (PA), resulting in significant changes in cell morphology. Oil Red O staining showed a marked increase in lipid accumulation. RT-PCR analysis further confirmed the significant alteration in ACSL4 gene expression. In conclusion, this study is the first to integrate bioinformatics tools to investigate the potential mechanistic links between iron metabolism and the comorbidity of T2DM and HCC, uncovering a novel pathogenic pathway. These findings provide new directions for drug development and therapeutic strategies in the future.