Bioinformatics and Machine-Learning Identification of Pulmonary Hypertension Biomarkers and Candidate Therapeutic Compounds.

Abstract

This study aimed to identify pulmonary hypertension (PH)-associated molecular biomarkers and candidate small-molecule compounds using public transcriptomic data and independent validation resources. Three Gene Expression Omnibus datasets (GSE22356, GSE33463, and GSE48149) were integrated following normalization, probe annotation, and ComBat batch-effect correction. Differential expression analysis, weighted gene co-expression network analysis, functional enrichment analysis, protein-protein interaction network analysis, and three machine-learning algorithms were used to identify core feature genes. Diagnostic performance was evaluated using receiver operating characteristic curves. External validation included an independent lung-tissue cohort (GSE117261), a pulmonary artery single-cell RNA-sequencing dataset (GSE210248), and quantitative reverse transcription PCR validation in independent lung-tissue samples. Connectivity Map-based drug repositioning and molecular docking were used to screen candidate compounds. Seventy-eight differentially expressed genes were identified, and CXCL10, JUN, IFIH1, MX1, and TLR7 were selected as core feature genes. In the independent GSE117261 lung-tissue cohort, JUN showed the strongest external support, whereas replication of the other genes was variable. Quantitative reverse transcription PCR in 20 biologically independent pulmonary arterial hypertension samples and 20 control samples confirmed upregulation of all five genes. The apparent five-gene qRT-PCR model and 100 repeated stratified five-fold cross-validation analyses both yielded an area under the curve of 1.000, although the small cohort requires cautious interpretation and independent prospective validation. Single-cell analysis of GSE210248 supported altered communication between immune and structural cells and smooth muscle cell phenotypic switching. BRD-K91900765/VX-745 ranked highest in Connectivity Map screening. MAPK14/p38α, its established pharmacological target, was included as a positive-reference docking protein, whereas docking against the five biomarker-associated proteins was treated as exploratory. These findings support the five genes as candidate PH biomarkers and VX-745 as a computational drug-repositioning hypothesis requiring experimental validation.