单细胞转录组学与机器学习综合分析揭示腹主动脉瘤潜在生物标志物

doi:10.6040/j.issn.1671-7554.0.2024.0162

摘要/Abstract

摘要： 目的利用单细胞转录组测序(single-cell RNA sequencing, scRNA)数据分析、加权基因共表达网络分析(weighted gene co-expression network analysis, WGCNA)、机器学习算法和免疫浸润分析筛选腹主动脉瘤(abdominal aortic aneurysm, AAA)潜在的生物标志物。方法下载基因表达数据库中包含AAA和正常主动脉(normal aorta control, NAC)的scRNA测序数据,经数据质量控制、降维、差异分析、细胞类型注释和拟时序分析后,筛选AAA发生过程中最早分化的细胞类型,筛选差异表达基因(differential expressed genes, DEGs),进行高维WGCNA(high dimensional WGCNA, hdWGCNA),识别与AAA相关的基因模块,并进行富集分析;下载包含AAA和NAC的常规转录组测序数据,进行差异分析、WGCNA,将scRNA样本DEGs、常规转录组DEGs和WGCNA结果进行整合,筛选与AAA病变相关的基因,并进行基因本体(gene ontology, GO)和京都基因与基因组百科全书(Kyoto Encyclopedia of Genes and Genomes, KEGG)信号通路富集分析。利用最小绝对收缩和选择算子(least absolute shrinkage and selection operator, LASSO)、支持向量机递归特征消除(support vector machine recursive feature elimination, SVM-RFE)和随机森林(random forest, RF)等机器学习方法筛选AAA的潜在生物标志物,并进行免疫浸润分析。结果 scRNA数据分析结果显示,内皮细胞是AAA发生过程中最早分化的细胞类型,共获得853个scRNA DEGs;hdWGCNA识别出与AAA相关的2个基因模块,显著富集于辅助性T细胞17细胞分化、辅助性T细胞1和2细胞分化等信号通路。常规转录组分析共获得162个DEGs;整合后获得17个AAA相关基因,显著富集于趋化因子、辅助性T细胞17细胞分化、辅助性T细胞1和2细胞分化等信号通路。机器学习算法识别出AAA的潜在生物标志物生态病毒整合位点 2B(ecotropic viral integration site 2B, EVI2B)。EVI2B在AAA样本中的表达量高于NAC样本。免疫浸润结果显示,AAA样本中幼稚B细胞、浆细胞、活化树突细胞和中性粒细胞比例高于NAC样本。EVI2B表达量与M2巨噬细胞、M1巨噬细胞、CD8 T细胞、浆细胞、辅助滤泡T细胞、M0巨噬细胞和中性粒细胞呈正相关;与静息树突细胞呈负相关。结论 AAA发病涉及多种免疫细胞和信号通路,EVI2B在AAA样本中表达显著增高,与多种免疫细胞具有相关性,可能成为AAA治疗的新靶点。

关键词: 腹主动脉瘤, 加权基因共表达网络分析, 机器学习, 生物标志物, 免疫浸润

Abstract: Objective To screen for potential biomarkers of abdominal aortic aneurysm(AAA)using single-cell RNA(scRNA)data analysis, weighted gene co-expression network analysis(WGCNA), machine learning, and immune infiltration analysis. Methods The scRNA sequencing data containing AAA and normal aorta control(NAC)in the gene expression database were downloaded and processed by data quality control, dimensionality reduction, differential analysis, and cell type annotation. Chronological analysis was proposed to screen for the earliest differentiated cell types during AAA genesis, and to screen for differentially expressed genes(DEGs). High dimensional WGCNA(hdWGCNA)was performed to identify AAA-related gene modules, and enrichment analysis was conducted. Conventional transcriptome sequencing data containing AAA and NAC was downloaded for differential analysis and WGCNA. DEGs of scRNA samples, DEGs of conventional transcriptomes and WGCNA results were integrated to screen genes associated with AAA lesions. Gene ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)signaling pathway enrichment analysis were carried out. The potential biomarkers of AAA were screened using the least absolute shrinkage and selection operator(LASSO), support vector machine recursive feature elimination(SVM-RFE), and random forest(RF)machine learning methods. The immune infiltration analysis was performed. Results The results of scRNA data analysis showed that endothelial cell was the earliest cell type to differentiate during AAA development, and a total of 853 scRNA DEGs were obtained. hdWGCNA identified 2 gene modules associated with AAA, which were significantly enriched in the signaling pathways of T helper 17 cell differentiation, and T helper 1 and 2 cell differentiation. Conventional transcriptome analysis yielded a total of 162 DEGs. Integration yielded 17 AAA-associated genes, which significantly enriched in signaling pathways such as chemokines, T helper 17 cell differentiation, and T helper 1 and 2 cell differentiation. The machine learning algorithm identified a potential biomarker for AAA, ecotropic viral integration site 2B(EVI2B). The expression of EVI2B was higher in AAA samples than in NAC samples. The immune infiltration results showed that the proportions of naive B cells, plasma cells, activated dendritic cells and neutrophils were higher in AAA samples than in NAC samples. EVI2B was positively correlated with M2 macrophages, M1 macrophages, CD8 T cells, plasma cells, helper follicular T cells, M0 macrophages, and neutrophils; and it was negatively correlated with resting dendritic cells. Conclusion AAA pathogenesis involves a variety of immune cells and signaling pathways, and EVI2B expression is significantly increased in AAA samples, correlating with a variety of immune cells, which may be a new target for AAA treatment.

Key words: Abdominal aortic aneurysm, Weighted gene co-expression network analysis, Machine learning, Biomarkers, Immune infiltration

中图分类号:

R654.4

王玉涛,孙岩. 单细胞转录组学与机器学习综合分析揭示腹主动脉瘤潜在生物标志物[J]. 山东大学学报 (医学版), 2024, 62(11): 40-53.

WANG Yutao, SUN Yan. Comprehensive analysis of single-cell transcriptomics and machine learning reveals potential biomarkers for abdominal aortic aneurysm[J]. Journal of Shandong University (Health Sciences), 2024, 62(11): 40-53.

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