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基于机器学习识别与振荡剪切应力相关的特征基因
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Abstract:
背景：动脉粥样硬化(AS)是中风的根本原因，而振荡剪切应力或低剪切应力是动脉粥样硬化的原因之一。我们研究的目的是通过机器学习筛选出受剪切应力调控的基因，并分析其生物学功能，以帮助动脉粥样硬化的诊断和治疗。方法：从基因表达综合数据库(GEO)中检索了GSE20739、GSE16706和GSE1518数据集。首先使用limma软件包筛选差异基因，然后使用蛋白质–蛋白质相互作用(PPI)网络删除一些不相互作用的基因。利用三种机器学习算法——最小绝对收缩和选择算子(LASSO)、随机森林和支持向量机(SVM)——筛选特征基因。通过受试者工作特征(ROC)曲线评估特征基因的诊断性能。结果：筛选出四个受剪切应力调控的特征基因(ADM, RAMP2, GNA14, FABP5)，这些基因与动脉粥样硬化相关。ADM和GNA14与振荡剪切应力之间存在强相关性，ADM和FABP5之间存在基因共表达。结论：我们的研究识别了四个受剪切应力调控的基因，这些基因影响动脉粥样硬化的发生和发展，这可能有助于未来动脉粥样硬化的预防和治疗。
Background: Atherosclerosis (AS) is the root cause of stroke, while oscillatory shear stress or low shear stress is one of the reasons for atherosclerosis. The purpose of our research is to screen the genes regulated by shear stress through machine learning and analyze their biological functions to help the diagnosis and treatment of atherosclerosis. Methods: GSE20739, GSE16706 and GSE1518 datasets were retrieved from the gene expression omnibus (GEO) database. We first used the limma package to screen the differential gene, then protein-protein interaction (PPI) network was used to delete some genes that do not interact with each other. Three machine learning algorithms, Least Absolute Shrinkage and Selection Operator (LASSO), Random Forest, Support Vector Machine (SVM) were utilized for screening signature genes. Receiver operating characteristic (ROC) curves estimating the diagnostic performance of signature genes. Results: Four signature genes were screened (ADM, RAMP2, GNA14, FABP5), which regulated by shear stress. The main biological functions of characteristic genes are related to AS. There was a strong correlation between ADM and GNA14 and oscillatory shear stress. There was gene co-expression between ADM and FABP5. Conclusion: Our study identified four genes that was regulated by shear stress and affect the occurrence and development of atherosclerosis, which may be helpful for the prevention and treatment of atherosclerosis in the future.
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