Machine learning identifies molecular targets of Di (2-ethylhexyl) phthalate in pulmonary arterial hypertension.

Objective: This study aims to explore the potential molecular mechanisms by which di (2-ethylhexyl) phthalate (DEHP) exposure induces pulmonary arterial hypertension (PAH). Methods: We conducted differential expression analysis on multiple genomics datasets to pinpoint PAH-associated genes. Subsequently, an integrative approach combining machine learning algorithms and network toxicology was employed to examine the binding interactions between DEHP and the identified target proteins. Results: Our analysis identified 60 genes as potential targets of DEHP in PAH. Further refinement using machine learning prioritized twelve core regulatory genes: ALKBH2, AOC2,BCL2L10,CTBP2,DNM2,ERLIN2,HPS6,RABGGTA,PON2,SLC4A7,SORT1, and PDE4D. Among these, HPS6, CTBP2,RABGGTA, SORT1,ALKBH2,BCL2L10, AOC2,and PON2 were significantly downregulated, whereas SLC4A7,PDE4D, ERLIN2,and DNM2 were markedly upregulated (P < 0.05). Conclusion: These findings demonstrate that DEHP promotes PAH pathogenesis by modulating specific genes and associated pathways. The twelve core genes identified through machine learning are proposed as key regulators in this process, providing crucial insights for future mechanistic investigation into DEHP-induced PAH.