基于机器学习算法的近视潜在生物标志物的鉴定。
Identification of potential biomarkers of myopia based on machine learning algorithms.
机构信息
Department of Ophthalmology, Zibo Central Hospital, No.54, Gongqingtuan West Road, Zhangdian District, Zibo, 255000, Shandong Province, PR China.
Sanitary Inspection Center, Zibo Center for Disease Control and Prevention, Zibo, 255000, PR China.
出版信息
BMC Ophthalmol. 2023 Sep 22;23(1):388. doi: 10.1186/s12886-023-03119-5.
PURPOSE
This study aims to identify potential myopia biomarkers using machine learning algorithms, enhancing myopia diagnosis and prognosis prediction.
METHODS
GSE112155 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.
RESULTS
The 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.
CONCLUSION
Our study identifies four key myopia biomarkers, providing a foundation for future clinical and experimental validation studies.
目的
本研究旨在使用机器学习算法识别潜在的近视生物标志物，以增强近视的诊断和预后预测。
方法
分析来自 GEO 数据库的 GSE112155 和 GSE15163 数据集。我们使用“limma”进行差异表达分析，使用“GO plot”和“clusterProfiler”进行功能和通路富集分析。使用 LASSO 和 SVM-RFE 算法筛选与近视相关的生物标志物，然后进行 ROC 曲线分析以评估诊断性能。使用 GSEA 4.1.0 进行单基因 GSEA 富集分析。
结果
差异表达基因的功能分析表明它们在碳水化合物生成和多糖合成中发挥作用。我们确定了 23 个与近视相关的差异表达基因，其中有 4 个是高度有效的诊断生物标志物。单基因 GSEA 结果表明这些基因控制泛素介导的蛋白质水解途径。
结论
我们的研究确定了四个关键的近视生物标志物，为未来的临床和实验验证研究提供了基础。
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