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基于综合生物信息分析鉴定心房颤动相关炎症基因及其与免疫细胞浸润的关联

杨曼 赵兴安 葛芸娜 秦娟 王玺雅 陶四明

杨曼, 赵兴安, 葛芸娜, 秦娟, 王玺雅, 陶四明. 基于综合生物信息分析鉴定心房颤动相关炎症基因及其与免疫细胞浸润的关联[J]. 昆明医科大学学报, 2024, 45(3): 18-29. doi: 10.12259/j.issn.2095-610X.S20240303
引用本文: 杨曼, 赵兴安, 葛芸娜, 秦娟, 王玺雅, 陶四明. 基于综合生物信息分析鉴定心房颤动相关炎症基因及其与免疫细胞浸润的关联[J]. 昆明医科大学学报, 2024, 45(3): 18-29. doi: 10.12259/j.issn.2095-610X.S20240303
Man YANG, Xingan ZHAO, Yunna GE, Juan QIN, Xiya WANG, Siming TAO. Identification of Atrial Fibrillation-related Inflammatory Genes and Their Association with Immune Cell Infiltration Based on Comprehensive Bioinformatic Analysis[J]. Journal of Kunming Medical University, 2024, 45(3): 18-29. doi: 10.12259/j.issn.2095-610X.S20240303
Citation: Man YANG, Xingan ZHAO, Yunna GE, Juan QIN, Xiya WANG, Siming TAO. Identification of Atrial Fibrillation-related Inflammatory Genes and Their Association with Immune Cell Infiltration Based on Comprehensive Bioinformatic Analysis[J]. Journal of Kunming Medical University, 2024, 45(3): 18-29. doi: 10.12259/j.issn.2095-610X.S20240303

基于综合生物信息分析鉴定心房颤动相关炎症基因及其与免疫细胞浸润的关联

doi: 10.12259/j.issn.2095-610X.S20240303
基金项目: 云南省“高层次人才培养支持计划”入选名医专项基金资助项目(YNWR-MY-2020-024)
详细信息
    作者简介:

    杨曼(1986~),女,白族,云南大理人,医学硕士,主治医师,主要从事心血管疾病研究工作

    通讯作者:

    陶四明,E-mail:taosm6450@126.com

  • 中图分类号: Q811.4,R541.7+5

Identification of Atrial Fibrillation-related Inflammatory Genes and Their Association with Immune Cell Infiltration Based on Comprehensive Bioinformatic Analysis

  • 摘要:   目的  鉴定心房颤动 ( atrial fibrillation,AF)患者的炎症相关基因,并探讨这些基因与浸润免疫细胞在AF的发生发展过程中可能的作用和机制。  方法  通过一系列的生物信息学分析结合机器学习算法识别AF的生物标志物,使用受试者操作特性曲线(receiver operating characteristic,ROC)验证关键基因的预测及诊断价值,采用Spearman 相关分析明确关键基因与浸润免疫细胞的相关性。  结果  筛选出593个差异基因[|log2 (fold change,FC)|>1,P<0.05],7种免疫细胞亚型(P<0.05),获得190个免疫相关差异基因,识别出 3 个生物标志物(IGF1、PTGS2和PPARG),相关性分析结果显示3个标志物与浸润免疫细胞显著相关(P<0.05)。  结论  IGF1、PTGS2和PPARG是AF的炎症相关基因,推测其与免疫细胞浸润过程和途径密切相关。
  • 图  1  生物信息学分析的流程图

    DEGs:差异表达基因;WGCNA:加权基因共表达网络分析; GO:基因本体论;KEGG:京都基因和基因组百科全书;PPI:蛋白质-蛋白质相互作用;ROC:受试者操作特性曲线。

    Figure  1.  Flow diagram of the bioinformatics analysis

    图  2  AF和SR样本组间的DEGs鉴定

    A:去除批次效应后AF和SR组间的PCA图;B:前50个DEGs的热图;C:DEGs的火山图。

    Figure  2.  Identification of DEGs between AF and SR samples

    图  3  免疫细胞浸润的分布和相关性

    A:22种免疫细胞亚型的分布相对百分比;B:SR和AF组间22种免疫细胞亚型的浸润分数;C:22种免疫细胞亚型组成的相关矩阵。

    Figure  3.  Distribution and correlation of immune cell infiltration

    图  4  WGCNA分析

    A:无尺度分布网络的软阈值选择(β=10);B:WGCNA网络模块分类(mergeCutHeight=0.25);C:4种模块与7种免疫细胞亚型之间关联的热图;D:黑色模块与M2巨噬细胞之间的相关性散点图。

    Figure  4.  WGCNA analysis

    图  5  DEIRGs的GO和KEGG功能富集分析

    A:GO分析气泡图;B:KEGG分析气泡图。

    Figure  5.  GO and KEGG functional enrichment analysis of the DEIRGs

    图  6  PPI网络筛选候选基因

    A:DEGs与IRGs交集的韦恩图;B:PPI网络图;C:10个候选基因相关性及排名。

    Figure  6.  PPI network screening for candidate genes

    图  7  机器学习算法识别 AF 生物标志物

    A:LASSO;B:RF;C:SVM-RFE;D:3种预测模型ROC曲线比较。

    Figure  7.  Machine learning algorithm identifies AF biomarkers

    图  8  生物标志物的诊断及预测效能

    A:3个关键基因在AF和SR组间的差异表达箱线图;B:诊断模型的列线图;C~E:ROC曲线验证3个关键基因在外部数据集中的诊断有效性。***P<0.001。

    Figure  8.  Diagnostic and predictive efficacy of the biomarkers

    图  9  3个关键基因与22种免疫细胞亚型的相关性分析

    A:IGF1;B:PTGS2;C:PPARG。右边的数值代表P值,标红说明P<0.05;底部的数值代表相关系数,正值说明基因与该免疫细胞之间是正调控的关系,负值说明是负调控。

    Figure  9.  Correlation analysis of three key genes with 22 immune cell subtypes

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  • 收稿日期:  2023-12-12
  • 网络出版日期:  2024-03-07
  • 刊出日期:  2024-03-30

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