AccScience Publishing / EJMO / Online First / DOI: 10.36922/EJMO025190172
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ORIGINAL RESEARCH ARTICLE

Lactylation-driven diagnostic model for pulmonary hypertension: Application of serum biomarker

Tao Yi1† ,  Cuiwen Deng2† ,  Junsheng Sun2 ,  Qian Lei2*
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1 Department of Emergency Medicine, Longgang Center Hospital of Shenzhen, Shenzhen, Guangdong, China
2 Department of General Practice, Longgang Center Hospital of Shenzhen, Shenzhen, Guangdong, China
†These authors contributed equally to this work.
EJMO, 025190172 https://doi.org/10.36922/EJMO025190172
Received: 6 May 2025 | Revised: 16 June 2025 | Accepted: 4 July 2025 | Published online: 1 August 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

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.

Keywords
Pulmonary hypertension
Lactylation
Diagnostic value
Molecular docking
Immune cell infiltration
Funding
The study was supported by the Natural Science Foundation of Shenzhen Municipality (Grant ID: JCYJ20230807114505010).
Conflict of interest
The authors declare that they have no competing interests.
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Eurasian Journal of Medicine and Oncology, Electronic ISSN: 2587-196X Print ISSN: 2587-2400, Published by AccScience Publishing
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