已发表论文

基于孟德尔随机化和生物信息学分析的代谢介导的 FGF5 与中风的关联

 

Authors Xu C, Xu Y, Gao L, Wang M, Wang G, Wang G 

Received 12 April 2025

Accepted for publication 18 July 2025

Published 31 July 2025 Volume 2025:18 Pages 4481—4495

DOI https://doi.org/10.2147/JMDH.S529168

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Prof. Dr. Krzysztof Laudanski

Cong Xu,1 Yonghong Xu,2 Ling Gao,3 Min Wang,1 Guangyan Wang,3 Guangming Wang1,4 

1School of Clinical Medicine, Dali University, Dali, Yunnan, 671000, People’s Republic of China; 2Department of General Surgery, Banan Hospital Affiliated to Chongqing Medical University, Banan, Chongqing, 401320, People’s Republic of China; 3Department of Clinical Laboratory, Chuxiong Yi Autonomous Prefecture People’s Hospital, Chuxiong, 675000, People’s Republic of China; 4Center of Genetic Testing, the First Affiliated Hospital of Dali University, Dali, 671000, People’s Republic of China

Correspondence: Guangming Wang, Email wgm1991@dali.edu.cn

Background: Stroke is the second leading cause of death and the third leading cause of disability worldwide. The role of fibroblast growth factor 5 (FGF5) in the occurrence and development of stroke remains unclear. We used bidirectional Mendelian randomization (MR) analysis to evaluate the mediating role of metabolites and causal association between inflammatory factors and stroke.
Methods: We analyzed the stroke dataset from the FinnGen database (v11) (cases: 43,132; Control: 297,867). Data on metabolites and inflammatory factors were obtained from the genome-wide Association Studies (GWAS) catalog of the European Institute for Bioinformatics (EBI). Using expression data of FGF5 mRNA and protein in the Comprehensive Gene Expression Database (GEO) and clinical data, expression level and clinical relevance of FGF5 in stroke were explored. The protein-protein interaction (PPI) network of FGF5-related genes was constructed, and various bioinformatics analyses (including functional enrichment, immune infiltration analysis, etc) were conducted to evaluate its functional mechanism.
Results: FGF5 was significantly associated with stroke risk (inverse variance weighting method (IVW): odds ratio (OR) = 1.052, 95% confidence interval (CI): 1.021– 1.084, P< 0.01). Mediation analysis indicated that inflammatory factors influenced stroke risk through the metabolites 1-palmitoyl-phosphoglycerol (GPG) [effect: 0.00462 (− 0.0102, 0.001); mediated effect: 9.09% (− 20.2%, 1.97%)], 1-stearoyl-2-arachidonoyl-phosphoethanolamine (GPE) [effect: 0.00274 (− 0.00212, 0.0076); mediated effect: 5.39% (4.17%, 14.9%). Among them, the mediating effect of 1-palmitoyl phosphatidylglycerol (GPG) was not significant. Furthermore, FGF5 is associated with epithelial cell proliferation, peptidyl-tyrosine phosphorylation, CD4+ primary T cells and M0 macrophages.
Conclusion: This study, by integrating multiple omics methods, such as Mendelian randomization, expression profiling analysis, and bioinformatics, has for the first time established FGF5 as a novel potential biomarker for stroke risk. Inflammatory factors can mediate the molecular pathways of stroke occurrence through metabolites such as GPE. The value of FGF5 as a novel biomarker for the diagnosis/prognosis of stroke and the new mechanism of stroke-related metabolic regulatory network provide a theoretical basis for targeted intervention of stroke.

Keywords: inflammatory factors, stroke, Mendelian randomization, mediation analysis, metabolite