心脑血管病防治2025，Vol.25Issue(6)：7-11,后插1,6.DOI:10.3969/j.issn.1009-816x.2025.06.003
机器学习筛选动脉粥样硬化不稳定斑块自噬基因及与免疫浸润特征的相关性分析
Machine learning screening of autophagy genes in atherosclerotic vulnerable plaques and association analysis with immune infiltration characteristics
摘要
Abstract
Objective To apply different machine learning menthods in screening of core autophagy genes in vulnerable plaques and investigate their association with immune infiltration.Methods Stable and unstable plaque samples from gene expression omnibus(GEO)datasets(GSE163154,GSE41571,GSE111782)were integrated,and through intersection of differentially expressed genes with the Human Autophagy Database,14 candidate genes were obtained.Core autophagy genes were screened using least absolute shrinkage and selection operator(LASSO)regression and support vector machine recursive feature elimination(SVM-RFE).Diagnostic value of core autophagy genes for unstable plaques were evaluated by receiver operating characteristic(ROC)curve analysis.A nomogram model was constructed to analyze the predictive value of core autophagy genes for unstable plaques,the predictive efficacy was verified using calibration curves and decision analysis curves.The CIBESORT algorithm was used to analyze the immune infiltration characteristics in stable plaque samples and unstable plaque samples.Correlations between core autophagy genes and different immune cells was analyzed by Pearson's method.Results Four core autophagy genes(CX3CL1,CTSD,MTMR14,and NRG1)were identified using two different machine learning methods.ROC curve analysis demonstrated their diagnostic value for unstable plaques,with area under the curve(AUC)values of 0.874,0.876,0.866,and 0.733,respectively.Calibration curve analysis indicated strong predictive performance of the nomogram model based on these core autophagy genes,showing high concordance with the ideal curve.Decision analysis curve confirmed favorable net benefits for predicting unstable plaques using the core autophagy gene nomogram model.Immune infiltration analysis revealed significant differences in immune cell composition between stable and unstable plaques and unstable plaques exhibited increased M0 macrophage infiltration(P<0.05),but with a non-significant decreasing trend in M2 macrophages.The expression level of core autophagy genes had a strong correlation with the infiltration degree of various immune cells.The expression of some core autophagy genes showed significant correlations with M1 macrophage(P<0.05).Conclusion Machine learning identified four core autophagy genes(CX3CL1,CTSD,MTMR14,and NRG1)that demonstrate significant diagnostic and predictive value for unstable plaques.The expression levels of these core autophagy genes are correlated with infiltration of macrophage.关键词
机器学习/动脉粥样硬化/自噬/免疫浸润Key words
Machine learning/Arteriosclerosis/Autophagy/Immune infiltration引用本文复制引用
蔡家琪,陈松发..机器学习筛选动脉粥样硬化不稳定斑块自噬基因及与免疫浸润特征的相关性分析[J].心脑血管病防治,2025,25(6):7-11,后插1,6.基金项目
广州市荔湾区科学计划重点项目(20240606) （20240606）
