Authors
Zhenwei Lu, Xiansheng Qiu, Liwei Zhou, Xiaodong Li, Hanwen Lu, Shuo Wang, Junfu Chen, Lifei Bian, Jianbin Lin, Wenpeng Zhao, Wujie Zhao, Xin Gao, Jinsen Zhang, Sifang Chen, Zhangyu Li, Zhanxiang Wang
Published in
Frontiers in genetics. Volume 17. Pages 1853818. Epub Jun 09, 2026.
Abstract
Moyamoya disease (MMD) is a progressive cerebrovascular disorder characterized by stenosis or occlusion of the terminal portions of the internal carotid arteries and their proximal branches, accompanied by the formation of abnormal collateral vessel networks. It represents a leading cause of ischemic and hemorrhagic stroke in both pediatric and adult populations. However, a comprehensive understanding of the molecular drivers underlying the hallmark vascular pathology of MMD remains elusive. Emerging evidence indicates that dysregulated lipid metabolism significantly contributes to MMD susceptibility and disease severity; nevertheless, its precise mechanistic roles in MMD pathogenesis have not been thoroughly investigated.
We integrated three publicly available gene expression datasets comprising MMD patients and non-MMD controls (GSE189993, GSE157628, and GSE141024). Following rigorous batch-effect correction, differential expression analysis was performed to identify differentially expressed genes (DEGs). Gene set enrichment analysis (GSEA), weighted gene co-expression network analysis (WGCNA), and machine learning approaches were then integrated to prioritize hub genes. Immune cell infiltration analysis was conducted for the identified hub genes. Subsequently, functional enrichment analysis, immune infiltration profiling, and protein-protein interaction (PPI) network construction were further performed. Validation was carried out using an independent external dataset (GSE249254) as well as in vitro experiments-including hypoxia-treated human endothelial cells and patient-derived tissue samples-to assess mRNA and protein expression levels. Finally, a Transcription Factor (TF)-miRNA-mRNA regulatory network was constructed, and potential therapeutic compounds targeting MMD were predicted via computational screening.
A total of 2,288 DEGs were identified. GSEA revealed significant enrichment of pathways related to lipid metabolism and immune responses. WGCNA identified MMD-associated co-expression modules, and integrative machine learning prioritized four hub genes: LPIN3, PPT2, ACSS1, and INPPL1. A diagnostic nomogram built upon these four genes demonstrated robust predictive performance, with an area under the curve (AUC) of 0.91. Immune infiltration analysis revealed that the abundance of B cells in the MMD patient group was significantly lower than that in the control group, with statistical significance. Notably, LPIN3 expression was significantly upregulated in MMD. It was the only hub gene whose upregulation at the mRNA level was consistently validated in both the external validation set (GSE249254) and in vitro models. Subsequent immunohistochemical (IHC) experiments further corroborated this finding at the protein level, highlighting its potential as an independent biomarker. Furthermore, leveraging the hub gene network, seven candidate compounds with potential therapeutic relevance to MMD were predicted.
This study delineates the immune-lipid metabolic transcriptomic characteristics of MMD, identifies novel molecular determinants of disease pathogenesis, and validates LPIN3 as a promising diagnostic biomarker. Collectively, these findings provide critical mechanistic insights into MMD etiology and offer a foundation for developing improved diagnostic strategies and targeted therapeutic interventions.
PMID:
42338983
Bibliographic data and abstract were imported from PubMed on 24 Jun 2026.
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