Identifying potential ferroptosis-related biomarkers and therapeutic targets for Idiopathic Pulmonary Hypertension in the context of predictive, preventive, and personalized medicine by integrative bioinformatics approaches

Abstract

Abstract Background: Idiopathic pulmonary arterial hypertension (IPAH), a rare and devastating pulmonary vascular disorder, is characterized by cellular proliferation and vascular remodeling. Although previous studies have underscored that ferroptosis, an iron-dependent cell death process, plays an important regulatory role in pulmonary artery hypertension, its role remains understudied. Therefore, the identification of novel and dependable biomarkers will play a pivotal role in enhancing the effective management of Idiopathic Pulmonary Hypertension in the context of Predictive, Preventive, and Personalized Medicine (PPPM) Methods: Gene expression profiles were downloaded from the Gene Expression Omnibus. Differentially expressed genes (DEGs) were screened using R software and intersected with a ferroptosis database (FerrDb V1) to identify ferroptosis-related DEGs. GO and KEGG analyses were performed to explore biological functions and potential pathways. LASSO and SVM‐RFE algorithms were used to identify optimal gene biomarkers for IPAH. GSVA and GSEA were conducted to explore biological functions and potential pathways associated with these biomarkers. The CIBESORT software was employed to predict immune genes and functions. Results: Of 237 ferroptosis-related genes (FRGs), 27 differentially expressed FRGs (DE-FRGs) showed significant differences between IPAH and normal samples in GSE48149, with 15 downregulated and 12 upregulated genes. Six DE‐FRGs, including KEAP1, TNFAIP3, MEG3, NFS1, PRDX1, and BEX1, were identified as predictive diagnostic genes for IPAH. Among these DE-FRGs, PRDX1 and TNFAIP3 were the most promising diagnostic genes for IPAH and may play a corresponding role in IPAH by participating in the cell cycle, lysosomes, immune response, vascular smooth muscle contraction, and various diseases. CIBERSORT analysis revealed a positive correlation between neutrophils and TNFAIP3, whereas macrophages M0 exhibited a negative correlation with PRDX1. Conclusions: Our analysis revealed six key genes in IPAH: KEAP1, TNFAIP3, MEG3, NFS1, PRDX1, and BEX1. PRDX1 and TNFAIP3, showing consistent expression across datasets, emerge as potential biomarkers and targets for IPAH. These findings propose PRDX1 and TNFAIP3 for future use in predictive diagnostics, prevention, patient stratification, and personalized medicine for IPAH.