Identification of potential biomarkers of myopia based on machine learning algorithms

BMC Ophthalmology

Research

Shengnan Zhang¹  Huaihua Wang¹  Chao Sun¹  Bingfang Gao²  Tao Wang³ 

[1] Department of Ophthalmology, Zibo Central Hospital, No.54, Gongqingtuan West Road, Zhangdian District, 255000, Zibo, Shandong Province, PR China;Department of Pathology, Zibo Hospital of Integrated Traditional Chinese and Western Medicine Zibo, 255000, Zibo, PR China;Sanitary Inspection Center, Zibo Center for Disease Control and Prevention, 255000, Zibo, PR China;

关键词: Myopia;    Machine learning;    Biomarkers;    Gene expression;    Diagnosis;   

DOI  :  10.1186/s12886-023-03119-5

 received in 2023-02-03, accepted in 2023-08-31,  发布年份 2023

来源: Springer

【 摘 要 】

PurposeThis study aims to identify potential myopia biomarkers using machine learning algorithms, enhancing myopia diagnosis and prognosis prediction.MethodsGSE112155 and GSE15163 datasets from the GEO database were analyzed. We used “limma” for differential expression analysis and “GO plot” and “clusterProfiler” for functional and pathway enrichment analyses. The LASSO and SVM-RFE algorithms were employed to screen myopia-related biomarkers, followed by ROC curve analysis for diagnostic performance evaluation. Single-gene GSEA enrichment analysis was executed using GSEA 4.1.0.ResultsThe functional analysis of differentially expressed genes indicated their role in carbohydrate generation and polysaccharide synthesis. We identified 23 differentially expressed genes associated with myopia, four of which were highly effective diagnostic biomarkers. Single gene GSEA results showed these genes control the ubiquitin-mediated protein hydrolysis pathway.ConclusionOur study identifies four key myopia biomarkers, providing a foundation for future clinical and experimental validation studies.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

【 图 表 】

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