Tao YI, Cuiwen DENG, Junsheng SUN, Qian LEI
Eurasian Journal of Medicine and Oncology - 2026;10(2):1-17
Introduction: Pulmonary hypertension (PH) presents a significant global public health challenge, underscoring the need for novel biomarkers and therapeutic strategies. Objective: This study proposes a lactylation-related diagnostic model for PH, aiming to identify potential therapeutic targets. Methods: The GSE15197 dataset was analyzed to identify differentially expressed genes (DEGs). Functional enrichment analyses, including Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and gene set enrichment analysis (GSEA), were conducted to explore underlying mechanisms. Weighted gene co-expression network analyses (WGCNA) identified two key gene modules. The intersection of significant WGCNA modules, DEGs, and lactylation-associated genes yielded candidate genes related to lactylation in PH. Machine learning methods, particularly random forest and support vector machine, were employed to identify hub genes, ultimately selecting aryl hydrocarbon receptor (AHR), polyribonucleotide nucleotidyltransferase 1 (PNPT1), and RAS p21 protein activator 1 (RASA1). These were incorporated into a diagnostic nomogram, evaluated through receiver operating characteristic curve and decision curve analyses. Immune cell infiltration was assessed using CIBERSORT and single-sample GSEA, while Enrichr was utilized to identify transcription factors and potential therapeutic agents. Molecular docking was performed to assess drug-gene binding affinities. Results: A total of 1,504 genes were upregulated and 1,931 downregulated. Functional enrichment analyses revealed clustering of DEGs in pathways associated with cellular transport, protein degradation, DNA repair, and signal transduction. WGCNA identified two critical modules comprising 1,178 genes, from which 33 candidate genes were derived. Machine learning refined this list to three hub genes (AHR, PNPT1, and RASA1), which formed the basis of a novel lactylation-related diagnostic nomogram validated in an external cohort. Immune dysregulation was evident, and friend leukemia integration 1 was recognized as a key TF. Ten potential drugs demonstrated promising binding affinity to the hub genes. Conclusion: This work introduces a lactylation-based diagnostic model for PH with strong diagnostic potential, though further clinical validation is required.
