Bioinformatics-based identification and experimental validation of biomarkers in pulmonary arterial hypertension

Current pulmonary arterial hypertension (PAH) diagnostic approaches rely on right heart catheterization to measure the mean pulmonary artery pressure (≥ 20 mmHg), but limit early screening. Imaging techniques lack sensitivity for detecting early pulmonary pressure changes and are subject to variability, often resulting in diagnosis at an irreversible stage. The PAH pathogenesis remains incompletely understood, and improved diagnosis and treatment are urgently needed. In the present study, the Gene Expression Omnibus GSE113439 dataset underwent differential expression analysis of mRNA and Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Hub genes were identified using weighted gene co-expression network and protein-protein interaction network analyses. Immune infiltration and correlation analyses were subsequently performed. We established a PAH rat model, validated the expression of hub genes in human pulmonary arterial smooth muscle cells (hPASMCs) and lung tissues of PAH rats using qRT-PCR, and conducted sequencing on the lung tissues of PAH rat models. PAH involved 547 differentially expressed genes. GO and KEGG enrichment analyses revealed that the focal adhesion, vascular smooth muscle contraction, RNA degradation, ferroptosis, and 2-oxocarboxylic acid metabolism pathways were closely associated with PAH development (P < 0.05). PAH patients had significantly upregulated NOP58, DDX21, ABCE1, CDC5L, and HSP90AA1 expression. Memory B cells, CD8 T cells, follicular helper T cells, activated natural killer cells, monocytes, activated mast cells, and neutrophils were significantly different between PAH patients and controls. Neutrophils, macrophages, and NOP58 expression were closely associated. NOP58, DDX21, ABCE1, CDC5L, and HSP90AA1 may be novel PAH diagnostic biomarkers and therapeutic targets. Their clinical applicability should be validated in larger-sample studies to explore gene-guided personalized therapies.