Integrative transcriptomic analysis identifies immune-associated candidate genes and altered immune cell infiltration in pulmonary arterial hypertension

by Xitong Yang, Bin Zhou, Ying Yang, Yu Dong, Jifen Fu, Hong Liu, Xinhua Wu Background Pulmonary arterial hypertension (PAH) is a progressive vascular disease characterized by immune dysregulation and pulmonary vascular remodeling. This study aimed to identify immune-associated hub genes in PAH using an integrative bioinformatics framework and to validate key candidates in an experimental model.

Methods Three PAH lung transcriptomic datasets from the Gene Expression Omnibus (GEO) database were analyzed. Immune cell infiltration was estimated using single-sample gene set enrichment analysis (ssGSEA).

Differentially expressed genes (DEGs) were identified and integrated through weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) network construction. Hub genes were prioritized using multiple machine learning algorithms.

A PAH-relevant murine model (Su5416 combined with hypoxia) was used for in-vivo validation by quantitative real-time PCR. Results A total of 8 hub genes were identified through integrative screening across multiple algorithms and were validated in independent datasets.

Among these hub genes, BCLAF1 demonstrated the highest diagnostic performance. Immune infiltration analysis revealed significant alterations in T helper cell subsets in PAH.