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目的 本研究旨在通过加权基因共表达网络分析(weighteal gene co-expression network analysis, WGCNA)以及机器学习算法鉴定心力衰竭(heart failure, HF)发展过程中的关键基因。方法 从GEO数据库中获得数据集GSE141910作为训练集，GSE17800和GSE79962作为验证集。通过WGCNA以及3种机器学习算法筛选出与HF相关的关键基因。在线单细胞转录组学数据库分析关键基因在心脏中的细胞分布。免疫浸润分析关键基因与心脏免疫细胞丰度的相关性。使用ROC曲线下面积(area under curve, AUC)评估关键基因与HF的相关性。数据集GSE17800和GSE79962对筛选到的关键基因进行验证。对关键基因在主动脉功缩窄术(TAC)构建的小鼠心力衰竭模型以及血管紧张素Ⅱ(ANGⅡ)诱导的成纤维细胞转分化模型中的表达进行了qPCR验证。结果 通过WGCNA结合机器学习筛选到3个关键基因，分别是FRZB,ITIH5和SEZ6L。单细胞组学检索结果显示：FRZB,ITIH5和SEZ6L均高表达于心脏成纤维细胞。免疫浸润分析显示FRZB,ITIH5和SEZ6L的表达与成纤维细胞的数量呈正相关且在HF组中3个基因与成纤维细胞的相关性相较于CON组明显升高。ROC曲线分析显示FRZB,ITIH5和SEZ6L对HF鉴别诊断AUC分别为(0.989,0.973,1.000)。在数据集GSE17800中的验证结果显示，FRZB,ITIH5和SEZ6L在扩张性心肌病患者(dilated cardiomyopathy, DCM)组中表达明显上调，在数据集GSE79962中，FRZB,ITIH5在DCM组中明显上调。qPCR结果发现，小鼠心力衰竭模型以及ANGⅡ诱导的成纤维细胞转分化模型中FRZB,ITIH5和SEZ6L表达都明显增加。结论 在HF的发生发展中，FRZB,ITIH5和SEZ6L可能作为关键基因参与到HF发生发展中。
Abstract：Objective This study aims to identify key genes in the development process of heart failure(HF) through weighted gene co-expression network analysis(WGCNA) and machine learning algorithms. Methods Obtain dataset GSE141910 from the GEO database as the training set, and GSE17800 and GSE79962 as the validation sets. Screen out key genes related to HF through WGCNA and three machine learning algorithms. Analyze the cellular distribution of key genes in the heart through an online single-cell transcriptomics database. Analyze the correlation between key genes and cardiac immune cell through immune infiltration. Use the area under the ROC curve(AUC) to evaluate the association of key genes with HF. Verify the screened key genes through datasets GSE17800 and GSE79962. Verify the expression of key genes in the mouse heart failure model constructed by aortic constriction(TAC) and the fibroblast transdifferentiation model induced by angiotensin II(ANGⅡ) by qPCR. Results Three key genes, FRZB, ITIH5 and SEZ6L, were screened through the combination of WGCNA and machine learning. The results of single-cell omics retrieval showed that FRZB, ITIH5 and SEZ6L were all highly expressed in cardiac fibroblasts. Immune infiltration analysis showed the expression of FRZB, ITIH5 and SEZ6L were positively correlated with the number of fibroblasts, and the correlation between the three genes and fibroblasts in the HF group was significantly increased compared to the control group. ROC curve analysis showed that the AUCs of FRZB, ITIH5 and SEZ6L for HF recognition were(0.989, 0.973, 1.000), respectively. The verification results in dataset GSE17800 showed that the expressions of FRZB, ITIH5 and SEZ6L were significantly up-regulated in the dilated cardiomyopathy patient(DCM) group. In dataset GSE79962, FRZB and ITIH5 were significantly up-regulated in the DCM group. The qPCR results showed that the expressions of FRZB, ITIH5 and SEZ6L were significantly increased in both the mouse heart failure model and the fibroblast transdifferentiation model induced by ANGⅡ. Conclusion FRZB, ITIH5 and SEZ6L may serve as key genes involved in the development and progression of HF.
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基本信息:
DOI：10.13880/j.cnki.65-1174/n.2025.22.018
中图分类号:R541.6
引用信息:
[1]范诗语,马克涛,张幼怡.通过加权基因共表达网络和机器学习识别心力衰竭发展中的关键基因[J].石河子大学学报(自然科学版),2025,43(05):569-579.DOI:10.13880/j.cnki.65-1174/n.2025.22.018.
基金信息:
国家自然科学基金项目(81830009)