ORIGINAL RESEARCH article
Front. Bioinform.
Sec. Protein Bioinformatics
This article is part of the Research TopicAI in Protein ScienceView all 3 articles
Machine Learning Identifies Molecular Targets of Di(2-ethylhexyl) Phthalate in Pulmonary Arterial Hypertension
Provisionally accepted
- 1Nanxishan Hospital of Guangxi Zhuang Autonomous Region, Gulin, China
- 2Nanxishan Hospital of Guangxi Zhuang Autonomous Region, Guilin, China
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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.
Keywords: bioinformatics, Di(2-ethylhexyl) phthalate, machine learning, molecular targets, pulmonary arterial hypertension
Received: 23 Sep 2025; Accepted: 03 Feb 2026.
Copyright: © 2026 LI, LI and Jiang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Jijia LI
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