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目的：通过生物信息学与多种机器学习方法筛选非酒精性脂肪肝中与胰岛素抵抗相关的关键特征基因，并探索其潜在治疗药物。方法：本研究整合了GEO数据库中的102例非酒精性脂肪肝高通量数据和286个胰岛素抵抗相关基因，通过差异表达分析获得差异表达基因，利用加权基因共表达网络分析筛选关键模块，随后将差异表达基因，关键模块的基因与胰岛素抵抗相关基因取交集，获得候选基因，采用四种机器学习算法（LASSO,XGBoost,SVM-RFE,RF-RFE）对候选基因进行筛选，获得稳定的特征基因，并基于这些特征基因利用DSigDB数据库进行药物预测。结果：本研究筛选出4个关键特征基因（IRS2、SLC27A4、FOS和KLF11），并基于此构建了集成预测模型，该模型在测试集与独立验证集中均表现出良好的诊断能力（AUC>0.9）。基于这些特征基因预测了79个潜在治疗药物。结论：IRS2、SLC27A4、FOS和KLF11是与胰岛素抵抗相关的非酒精性脂肪肝的关键特征基因，为非酒精性脂肪肝的早期干预与机制研究提供了新的线索。
Abstract：Objective : To identify key signature genes associated with non-alcoholic fatty liver(NAFL) and insulin resistance through bioinformatics and multiple machine learning methods, and to explore their potential therapeutic drugs. Methods : In this study, high-throughput data of 102 NAFL samples from the GEO database and 286 insulin resistancerelated genes were obtained. Differential expression analysis was performed to obtain differentially expressed genes, and weighted gene co-expression network analysis was used to identify key modules. The differentially expressed genes, key module genes, and insulin resistance-related genes were then intersected to obtain candidate genes. Four machine learning algorithms(LASSO, XGBoost, SVM-RFE, and RF-RFE) were applied to identify stable feature genes. Based on these signature genes, drug prediction was performed via the DSigDB database. Results : Four key signature genes(IRS2, SLC27A4, FOS and KLF11) were identified. An integrated prediction model was constructed, which exhibited excellent diagnostic efficiency in both the test set and independent validation set(AUC>0. 9). Based on these signature genes, 79 candidate therapeutic agents were predicted. Conclusion : IRS2, SLC27A4, FOS and KLF11 are core signature genes of NAFL correlated with insulin resistance, providing new insights for early intervention and mechanistic studies of non-alcoholic fatty liver.
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基本信息:
中图分类号:R575.5;Q811.4
引用信息:
[1]李冰心,田恬.基于生物信息学与机器学习的胰岛素抵抗相关非酒精性脂肪肝关键特征基因的识别[J].赣南医科大学学报().
基金信息:
赣南医学院教育教学研究项目(Jgkt-2024-74)
2026-06-26
2026-06-26
2026-06-26