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Article(id=1241022944617484651, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241022939957621542, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202408176, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1723478400000, receivedDateStr=2024-08-13, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773812527088, onlineDateStr=2026-03-18, pubDate=1742832000000, pubDateStr=2025-03-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773812527088, onlineIssueDateStr=2026-03-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773812527088, creator=13701087609, updateTime=1773812527088, updator=13701087609, issue=Issue{id=1241022939957621542, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='6', pageStart='961', pageEnd='1152', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773812525976, creator=13701087609, updateTime=1773815469296, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241035285174219432, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241022939957621542, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241035285174219433, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241022939957621542, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=994, endPage=1000, ext={EN=ArticleExt(id=1241022945343099261, articleId=1241022944617484651, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Association between Hashimoto’s thyroiditis and ischaemic stroke and screening of key genes based on Mendelian randomization and bioinformatics methods, columnId=1228016567443718970, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods Advances, runingTitle=null, highlight=null, articleAbstract=
Objective

This study aimed to explore the causal relationship between Hashimoto's thyroiditis (HT) and ischemic stroke (IS) through Mendelian randomization (MR) and to identify key interacting genes using bioinformatics methods.

Methods

Single nucleotide polymorphisms (SNPs) significantly associated with exposure factors were selected as instrumental variables. The random-effects inverse-variance weighted method (IVW) was used as the main analysis method of MR analysis, and the odds ratio (OR) evaluated the causal association of HT and IS. The HT and IS datasets were downloaded from the GEO database. HT data were subjected to Weighted gene co-expression network analysis (WGCNA) and differential analysis and screened for overlapping genes between the two diseases by intersection of differential genes in IS. Hub genes were obtained by intersecting the top 20 genes in the five algorithms of the Cytoscape software cytoHubba package. LASSO regression was applied to identify key comorbidity genes.

Results

IVW results showed that HT was a risk factor for the development of IS (OR=1.087, 95% CI: 1.001-1.181, P=0.048) while IS did not increase the risk of HT (OR=1.159, 95% CI: 0.899-1.494, P=0.256). Integration analysis of HT and IS data identified 102 overlapping genes. A total of 11 Hub genes were identified by the five algorithms. LASSO regression ultimately identified SERBP1 and WDR75 as key comorbidity genes.

Conclusion

This study demonstrates that HT may be a risk factor for the development of IS through MR analysis and identified SERBP1 and WDR75 as key comorbidity genes for HT with IS using bioinformatics methods, which may be the potential therapeutic target for HT patients with IS.

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目的

利用孟德尔随机化方法（Mendelian randomization, MR）探讨桥本甲状腺炎（Hashimoto’s thyroiditis, HT）与缺血性脑卒中（Ischemic stroke, IS）的因果关系，应用生物信息学方法筛选两疾病的关键共病基因。

方法

选择与暴露因素显著相关的单核苷酸多态性（SNPs）为工具变量。随机效应逆方差加权法（IVW）为MR分析的主要方法，比值比（OR）评价因果关联。在GEO数据库下载HT与IS基因芯片数据集，对HT数据进行WGCNA及差异分析并与IS的差异基因取交集筛选两疾病重叠基因。Cytoscape软件cytoHubba包5种算法排名前20的基因相交得到Hub基因，LASSO回归进一步筛选关键共病基因。

结果

IVW结果表明，HT是IS发病的危险因素（OR=1.087, 95% CI: 1.001~1.181, P=0.048）；IS未增加HT的发病风险（OR=1.159, 95% CI: 0.899~1.494, P=0.256）。将HT与IS的GEO数据整合分析，获得102个重叠基因。5种算法获得11个Hub基因，LASSO回归确定SERBP1, WDR75为关键共病基因。

结论

本研究通过MR分析证实HT可能是IS发病的危险因素，利用生物信息学方法确定SERBP1、WDR75是HT合并IS的关键共病基因，这可能是合并IS的HT患者的潜在治疗靶点。

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徐瑞，E-mail：
, copyrightStatement=本刊刊出的所有文章不代表中华预防医学会和本刊编委会的观点，除非特别声明。, copyrightOwner=中华预防医学会和四川大学华西公共卫生学院, extLink=null, articleAbsUrl=null, sourceXml=7WtCQImg9D3WVTOeWzDPmw==, magXml=6sq7hQXwympUMtvCoNhbfw==, pdfUrl=null, pdf=CpFtJFcnyUVvRN3xPxI1mQ==, pdfFileSize=1190724, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=MJezq4Oszn4vWdkXdC4FSQ==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=A73RyFYCsmnOXUdIi0beoQ==, mapNumber=null, authorCompany=null, fund=null, authors=

张哲（1999—），男，硕士在读，研究方向：心血管系统疾病的基础与临床研究

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张哲（1999—），男，硕士在读，研究方向：心血管系统疾病的基础与临床研究

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张哲（1999—），男，硕士在读，研究方向：心血管系统疾病的基础与临床研究

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Science, 2017, 357(6352): eaan2507., articleTitle=A pathology Atlas of the human cancer transcriptome, refAbstract=null)], funds=[Fund(id=1241022956407673777, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241022944617484651, awardId=ZR2023MH356, language=CN, fundingSource=山东省自然科学基金资助项目(ZR2023MH356), fundOrder=null, country=null), Fund(id=1241022956525114303, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241022944617484651, awardId=Z-2023016, language=CN, fundingSource=山东省中医药科技项目(Z-2023016), fundOrder=null, country=null), Fund(id=1241022956629971916, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241022944617484651, awardId=2023-1-4, language=CN, fundingSource=济南市科技发展计划(2023-1-4), fundOrder=null, country=null), Fund(id=1241022956743218137, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241022944617484651, awardId=2023-YBD-1-01, language=CN, fundingSource=济南市大数据科技计划(2023-YBD-1-01), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1241022948702736861, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241022944617484651, xref=1．, ext=[AuthorCompanyExt(id=1241022948723708383, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241022944617484651, companyId=1241022948702736861, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Department of Cardiology, Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250013, China), AuthorCompanyExt(id=1241022948727902688, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241022944617484651, companyId=1241022948702736861, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1．山东第一医科大学附属中心医院心内科，山东　济南　250013)]), AuthorCompany(id=1241022948845343211, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241022944617484651, xref=2．, ext=[AuthorCompanyExt(id=1241022948853731820, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241022944617484651, companyId=1241022948845343211, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2．山东第二医科大学麻醉学院)])], figs=[ArticleFig(id=1241022953073201907, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241022944617484651, language=EN, label=Fig.1, caption=Mendelian randomization condition assumptiondiagram, figureFileSmall=Nkos6HFnq0DaUGh3qEYHag==, figureFileBig=MJezq4Oszn4vWdkXdC4FSQ==, tableContent=null), ArticleFig(id=1241022953161282301, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241022944617484651, language=CN, label=图1, caption=孟德尔随机化条件假设图, figureFileSmall=Nkos6HFnq0DaUGh3qEYHag==, figureFileBig=MJezq4Oszn4vWdkXdC4FSQ==, tableContent=null), ArticleFig(id=1241022953391969045, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241022944617484651, language=EN, label=Fig.2, caption=Plots of the WGCNA analysis, figureFileSmall=L1OEJfibk2dAZjVKIApInw==, figureFileBig=yRZcKFTdkiQ6mZKVSwQfQw==, tableContent=null), ArticleFig(id=1241022953467466527, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241022944617484651, language=CN, label=图2, caption=WGCNA分析图

注：WGCNA分析的基因模块-表型相关热图。

, figureFileSmall=L1OEJfibk2dAZjVKIApInw==, figureFileBig=yRZcKFTdkiQ6mZKVSwQfQw==, tableContent=null), ArticleFig(id=1241022953563935530, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241022944617484651, language=EN, label=Fig.3, caption=GO and KEGG enrichment plots of overlapping DEGs, figureFileSmall=hfQLfkmM481Mx/IDiniPow==, figureFileBig=dp18VYsFvVpzJHQpbQcM7w==, tableContent=null), ArticleFig(id=1241022953656210226, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241022944617484651, language=CN, label=图3, caption=重叠DEGs的GO富集与KEGG信号通路富集图

注：A：GO富集-BP图；B：GO富集-MF图；C：GO富集-CC图；D：KEGG信号通路图。

, figureFileSmall=hfQLfkmM481Mx/IDiniPow==, figureFileBig=dp18VYsFvVpzJHQpbQcM7w==, tableContent=null), ArticleFig(id=1241022953765262138, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241022944617484651, language=EN, label=Fig.4, caption=Screening and validation of critical comorbid genes, figureFileSmall=ICGsjwrp6CYG8Gmh8bHe6w==, figureFileBig=PWTvIplalCaNbhkX775J5Q==, tableContent=null), ArticleFig(id=1241022953874314054, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241022944617484651, language=CN, label=图4, caption=关键共病基因的筛选与验证

注：A：HT的LASSO回归图；B：IS的LASSO回归图；C：关键共病基因诊断IS的ROC曲线；D：验证集内关键共病基因诊断IS的ROC曲线。

, figureFileSmall=ICGsjwrp6CYG8Gmh8bHe6w==, figureFileBig=PWTvIplalCaNbhkX775J5Q==, tableContent=null), ArticleFig(id=1241022954058863445, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241022944617484651, language=EN, label=Table 1, caption=

Information on GWAS summary statistics of HT and IS

, figureFileSmall=null, figureFileBig=null, tableContent=
表型ID样本量（例）SNP数量研究对象
桥本甲状腺炎ebi-a-GCST90018855395 64024 146 037欧洲人
缺血性脑卒中finn-b-I9_STR_EXH212 77416 380 445欧洲人
每天吸烟量ieu-b-142249 75212 003 613欧洲人
每周饮酒量ieu-b-73335 39411 887 865欧洲人
), ArticleFig(id=1241022954180498269, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241022944617484651, language=CN, label=表1, caption=

HT与IS的GWAS汇总数据的简要信息

, figureFileSmall=null, figureFileBig=null, tableContent=
表型ID样本量（例）SNP数量研究对象
桥本甲状腺炎ebi-a-GCST90018855395 64024 146 037欧洲人
缺血性脑卒中finn-b-I9_STR_EXH212 77416 380 445欧洲人
每天吸烟量ieu-b-142249 75212 003 613欧洲人
每周饮酒量ieu-b-73335 39411 887 865欧洲人
), ArticleFig(id=1241022955707224938, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241022944617484651, language=EN, label=Table 2, caption=

Results of the univariate MR analysis of the relationship between HT and IS

, figureFileSmall=null, figureFileBig=null, tableContent=
方法SNP数量OR95% CIP值
Inverse variance weighted121.0871.001~1.1810.048
MR Egger121.0170.800~1.2920.896
Weighted median121.0430.965~1.1260.290
Simple mode121.0180.895~1.1580.794
Weighted mode121.0180.930~1.1130.708
), ArticleFig(id=1241022955866608500, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241022944617484651, language=CN, label=表2, caption=

HT与IS关系的正向MR分析结果

, figureFileSmall=null, figureFileBig=null, tableContent=
方法SNP数量OR95% CIP值
Inverse variance weighted121.0871.001~1.1810.048
MR Egger121.0170.800~1.2920.896
Weighted median121.0430.965~1.1260.290
Simple mode121.0180.895~1.1580.794
Weighted mode121.0180.930~1.1130.708
), ArticleFig(id=1241022955984049021, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241022944617484651, language=EN, label=Table 3, caption=

Results of the multivariate MR analysis of the relationship between HT and IS

, figureFileSmall=null, figureFileBig=null, tableContent=
暴露因素SNP数量OR95% CIP值
桥本甲状腺炎91.0851.021~1.1540.009
每天吸烟量170.9590.851~1.0820.497
每周饮酒量311.1110.748~1.6510.603
), ArticleFig(id=1241022956101489547, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241022944617484651, language=CN, label=表3, caption=

HT与IS关系的多变量MR分析结果

, figureFileSmall=null, figureFileBig=null, tableContent=
暴露因素SNP数量OR95% CIP值
桥本甲状腺炎91.0851.021~1.1540.009
每天吸烟量170.9590.851~1.0820.497
每周饮酒量311.1110.748~1.6510.603
), ArticleFig(id=1241022956193764249, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241022944617484651, language=EN, label=Table 4, caption=

Results of the sensitivity analysis

, figureFileSmall=null, figureFileBig=null, tableContent=
暴露结局方法异质性检验水平多效性检验
Q值P值截距项SEP值
桥本甲状腺炎缺血性脑卒中MR-Egger23.8030.0080.0130.0210.569
IVW24.6290.010
缺血性脑卒中桥本甲状腺炎MR-Egger68.936<0.0010.0300.0380.438
IVW72.337<0.001
), ArticleFig(id=1241022956298621859, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241022944617484651, language=CN, label=表4, caption=

敏感性分析结果

, figureFileSmall=null, figureFileBig=null, tableContent=
暴露结局方法异质性检验水平多效性检验
Q值P值截距项SEP值
桥本甲状腺炎缺血性脑卒中MR-Egger23.8030.0080.0130.0210.569
IVW24.6290.010
缺血性脑卒中桥本甲状腺炎MR-Egger68.936<0.0010.0300.0380.438
IVW72.337<0.001
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基于孟德尔随机化及生物信息学方法探究桥本甲状腺炎与缺血性脑卒中的关系及关键基因筛选
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张哲 1 , 文丛 1 , 张凯运 2 , 徐瑞 1
现代预防医学 | 流行病与统计方法 2025,52(6): 994-1000
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现代预防医学 | 流行病与统计方法 2025, 52(6): 994-1000
基于孟德尔随机化及生物信息学方法探究桥本甲状腺炎与缺血性脑卒中的关系及关键基因筛选
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张哲1, 文丛1, 张凯运2, 徐瑞1
作者信息
  • 1．山东第一医科大学附属中心医院心内科，山东　济南　250013
  • 2．山东第二医科大学麻醉学院

    • 张哲（1999—），男，硕士在读，研究方向：心血管系统疾病的基础与临床研究

通讯作者:

徐瑞，E-mail：
Association between Hashimoto’s thyroiditis and ischaemic stroke and screening of key genes based on Mendelian randomization and bioinformatics methods
Zhe ZHANG1, Cong WEN1, Kai-yun ZHANG2, Rui XU1
Affiliations
  • Department of Cardiology, Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong 250013, China
出版时间: 2025-03-25 doi: 10.20043/j.cnki.MPM.202408176
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摘要
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目的

利用孟德尔随机化方法（Mendelian randomization, MR）探讨桥本甲状腺炎（Hashimoto’s thyroiditis, HT）与缺血性脑卒中（Ischemic stroke, IS）的因果关系，应用生物信息学方法筛选两疾病的关键共病基因。

方法

选择与暴露因素显著相关的单核苷酸多态性（SNPs）为工具变量。随机效应逆方差加权法（IVW）为MR分析的主要方法，比值比（OR）评价因果关联。在GEO数据库下载HT与IS基因芯片数据集，对HT数据进行WGCNA及差异分析并与IS的差异基因取交集筛选两疾病重叠基因。Cytoscape软件cytoHubba包5种算法排名前20的基因相交得到Hub基因，LASSO回归进一步筛选关键共病基因。

结果

IVW结果表明，HT是IS发病的危险因素（OR=1.087, 95% CI: 1.001~1.181, P=0.048）；IS未增加HT的发病风险（OR=1.159, 95% CI: 0.899~1.494, P=0.256）。将HT与IS的GEO数据整合分析，获得102个重叠基因。5种算法获得11个Hub基因，LASSO回归确定SERBP1, WDR75为关键共病基因。

结论

本研究通过MR分析证实HT可能是IS发病的危险因素，利用生物信息学方法确定SERBP1、WDR75是HT合并IS的关键共病基因，这可能是合并IS的HT患者的潜在治疗靶点。

桥本甲状腺炎  /  缺血性脑卒中  /  孟德尔随机化  /  生物信息学  /  共病机制
Objective

This study aimed to explore the causal relationship between Hashimoto's thyroiditis (HT) and ischemic stroke (IS) through Mendelian randomization (MR) and to identify key interacting genes using bioinformatics methods.

Methods

Single nucleotide polymorphisms (SNPs) significantly associated with exposure factors were selected as instrumental variables. The random-effects inverse-variance weighted method (IVW) was used as the main analysis method of MR analysis, and the odds ratio (OR) evaluated the causal association of HT and IS. The HT and IS datasets were downloaded from the GEO database. HT data were subjected to Weighted gene co-expression network analysis (WGCNA) and differential analysis and screened for overlapping genes between the two diseases by intersection of differential genes in IS. Hub genes were obtained by intersecting the top 20 genes in the five algorithms of the Cytoscape software cytoHubba package. LASSO regression was applied to identify key comorbidity genes.

Results

IVW results showed that HT was a risk factor for the development of IS (OR=1.087, 95% CI: 1.001-1.181, P=0.048) while IS did not increase the risk of HT (OR=1.159, 95% CI: 0.899-1.494, P=0.256). Integration analysis of HT and IS data identified 102 overlapping genes. A total of 11 Hub genes were identified by the five algorithms. LASSO regression ultimately identified SERBP1 and WDR75 as key comorbidity genes.

Conclusion

This study demonstrates that HT may be a risk factor for the development of IS through MR analysis and identified SERBP1 and WDR75 as key comorbidity genes for HT with IS using bioinformatics methods, which may be the potential therapeutic target for HT patients with IS.

Hashimoto’s thyroiditis  /  Ischemic stroke  /  Mendelian randomization  /  Bioinformatics  /  Comorbidity mechanism
张哲, 文丛, 张凯运, 徐瑞. 基于孟德尔随机化及生物信息学方法探究桥本甲状腺炎与缺血性脑卒中的关系及关键基因筛选. 现代预防医学, 2025 , 52 (6) : 994 -1000 . DOI: 10.20043/j.cnki.MPM.202408176
Zhe ZHANG, Cong WEN, Kai-yun ZHANG, Rui XU. Association between Hashimoto’s thyroiditis and ischaemic stroke and screening of key genes based on Mendelian randomization and bioinformatics methods[J]. Modern Preventive Medicine, 2025 , 52 (6) : 994 -1000 . DOI: 10.20043/j.cnki.MPM.202408176
正文
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桥本甲状腺炎（Hashimoto’s thyroiditis, HT）又称自身免疫性甲状腺炎或慢性淋巴细胞性甲状腺炎，是自身免疫性甲状腺疾病，病理特征为甲状腺实质淋巴细胞浸润、甲状腺特异性抗体产生及甲状腺体积增大[1]。脑卒中是一种常见的急性脑血管疾病，起病迅速，进展快，全球每年新发缺血性脑卒中（Ischemic stroke, IS）患者超过1 370万人，是患者死亡和残疾的主要原因之一。IS是脑卒中最常见的类型，占所有卒中的62.4%[2]。
目前，诸多研究者发现HT患者易合并IS。研究表明，颈动脉内中膜厚度、房颤等脑血管疾病危险因素可能与HT密切相关，这提示HT可能是导致IS发生的独立影响因素，André Karch等通过队列研究发现，HT患者发生IS的风险轻度升高，加强对无症状甲状腺功能减退的HT患者的早期筛查及治疗可减少HT诊断后第一年内的卒中风险[3]。然而，另有研究发现甲状腺功能减低患者的IS发病风险并未增加[4]。不同研究的分歧表明，HT是否为IS的独立危险因素仍不明确，特别是不同HT患者表现差异大，从无症状的甲状腺功能正常状态到明显的甲减均可能影响IS的发生[5-6]。尽管先前研究发现HT与IS的发病相关，但研究多为传统流行病学研究，样本量小，易受偏倚及反向因果等因素的影响，导致HT与IS之间的因果关系存在争议，两疾病的共病机制也不明确，研究结果的可靠性有待验证。因此，亟需可以排除偏倚及反向因果等影响因素的研究方法来探究HT与IS的因果关系及共病机制，为两疾病的预防及管理提供理论支持。
孟德尔随机化（Mendelian randomization, MR）将与暴露相关的遗传变异作为工具变量来推断暴露因素与疾病的因果关系，可有效规避观察性研究的偏倚对结果的干扰[7]。生物信息学（Bioinformatics）广泛应用于探索疾病的分子机制及潜在靶点，为疾病的研究提供新的理论依据。因此，本研究旨在应用MR联合生物信息学方法探讨HT与IS的因果关联，挖掘在HT与IS中发挥作用的关键基因，为HT合并IS患者的临床诊疗提供新思路。
1　资料与方法
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1.1　两样本MR分析探究两疾病因果关联
1.1.1　数据获取
MR分析的数据来源于IEU OpenGWAS数据库[8]。HT数据由2021年Sakaue等[9]发表。IS数据来自芬兰数据库（The FinnGen Biobank）。HT诊断编码为ICD-10: E06.3，数据包含15 654例HT患者及379 986例健康受试者。IS诊断编码为ICD-10: I63.9，数据包含10 551例IS患者及202 223例健康受试者。饮酒（每周饮酒量）及吸烟（每天吸烟量）的数据来自GSCAN平台，每周饮酒量数据包括335 394例饮酒者，选取饮酒者每周饮酒的中位数作为每周饮酒量，如一位受试者报告每周饮酒1~5杯，则记录为每周2.5杯。每天吸烟量数据包含249 752例吸烟者，将受试者每天吸烟数量分为5级，1级为1~5支/天；2级为6~15支/天；3级为16~25支/天；4级为26~35支/天；5级为36+支/天，见表1。
1.1.2　工具变量筛选
工具变量（Instrumental variables，IVs）满足三大假设[10]：①关联性假设，IVs与暴露因素强相关；②独立性假设，IVs独立于影响暴露和结局的混杂因素；③排他性假设，IVs仅通过暴露因素而影响结局变量，见图1。为确定工具变量，以P<5×10-8为条件，筛选与暴露显著相关的SNPs。为保持低连锁不平衡（Linkage disequilibrium, LD），设置阈值为r2=0.001，kb=10 000。F>10为标准排除弱工具变量，减少弱工具变量偏倚[11]。
1.1.3　MR分析与敏感性分析
单变量MR分析中，以HT为暴露，IS为结局进行正向MR分析；为评估IS与HT的反向因果关联，以IS为暴露，HT为结局进行反向MR分析；随机效应逆方差加权法（Inverse-variance weighted, IVW）为主要分析方法，当遗传变异满足工具变量假设，IVW法对因果效应估计最准确[12]。MR-Egger回归法、加权中位数（Weighted median, WM）、简单模型（Simple Mode）及加权模型（Weighted Mode）法为补充。MR-Egger回归法用于敏感性分析[13]。多变量MR分析评估HT与IS的因果关联是否独立于吸烟及饮酒[14]的影响。敏感性分析中，Cochran Q检验评估异质性，P<0.05提示存在异质性，关注随机效应IVW结果[15]。MR-Egger回归法检测水平多效性，截距显著偏离0且P<0.05时存在水平多效性[13]。留一法（Leave-one-out sensitivity test）进行敏感性分析，评估单个SNP的影响[16]。
MR分析由R 4.2.2的“Two Sample MR”包（版本：0.6.3）实现，结局为二分类变量，采用比值比（Odds ratio, OR）及95%置信区间（95% confidence interval, 95% CI）评价，P<0.05认为有统计学意义；检验水准α=0.05。
1.2　生物信息学方法筛选关键共病基因
1.2.1　GEO数据获取
为探究HT合并IS的关键共病基因，在基因表达数据库（Gene Expression Omnibu, GEO）[17]中下载HT基因芯片数据集GSE138198及IS基因芯片数据集GSE58294、GSE66724，GSE66724作为IS的外部验证集。GSE138198数据集包括13例HT样本，3例Control样本；GSE58294数据集包括69例IS样本，23例Control样本；GSE66724数据集包括8例IS样本，8例Control样本。
1.2.2　HT与IS重叠基因的筛选与功能富集分析
首先利用“WGCNA”包对HT数据集进行加权基因共表达网络分析（Weighted gene co-expression network analysis, WGCNA），网络拓扑分析确定软阈值，构建共表达网络，筛选与HT高度相关的模块基因。应用“Limma”包筛选HT与IS的差异表达基因（Differentially expressed genes, DEGs）。筛选标准：P<0.05及|log2FC|>0.5。应用jvenn工具[18]将HT的DEGs及模块基因与IS的DEGs取交集获得HT与IS的重叠基因。
为观察重叠基因潜在的生物学功能，应用微生信平台[19]进行基因本体论（Gene ontology, GO）及京都基因与基因组百科全书（Kyoto encyclopedia of genes and genomes, KEGG）富集分析及可视化。GO富集包括生物过程（Biological process, BP）、分子功能（Molecular function, MF）和细胞成分（Cellular component, CC）三个类别。
1.2.3　HT与IS的关键共病基因筛选及验证
将Cytoscape 3.7.2软件Cytohubba包的5种算法（MMC、EPC、Betweenness、Colseness及Degeree）[20]中各自排名前20的基因取交集得到的Hub基因作为候选基因。利用“glmnet”包进行最小绝对收缩和选择算子（LASSO）算法筛选候选基因中对IS具有良好诊断效能的关键共病基因。绘制关键共病基因的受试者工作特征（Receiver operating characteristic, ROC）曲线并计算曲线下面积（area under the curve, AUC），AUC>0.6为标准评估关键共病基因在IS中的诊断效能[21]，在验证集GSE66724中对关键共病基因的诊断效能进行外部验证。
2　结果
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2.1　MR分析探究HT与IS的因果关系
2.1.1　MR分析结果
单变量MR分析中，F检验提示双向MR分析均不存在弱工具变量偏倚。正向MR分析IVW结果显示，HT是导致IS发病的危险因素（OR=1.087, 95% CI: 1.001~1.181, P=0.048）。5种MR分析结果的方向一致，提示HT可能促进IS的发病，见表2。反向MR分析IVW结果未发现IS增加HT的发病风险（OR=1.159, 95% CI: 0.899~1.494, P=0.256）。多变量MR分析提示，矫正每日吸烟量、每周饮酒量的影响，HT对IS的正向因果效应仍有统计学意义（OR=1.085, 95% CI: 1.021~1.154, P=0.009），见表3。
2.1.2　敏感性分析
Cochran Q检验结果提示双向MR分析均存在异质性（P<0.05），需关注随机效应IVW结果；MR-Egger回归结果提示双向MR分析均不存在水平多效性（P>0.05），见表4。“留一法”未发现双向MR分析中对因果关联有显著影响的SNPs。
2.2　生物信息学方法筛选HT与IS的关键共病基因
2.2.1　HT与IS重叠基因的筛选及富集分析结果
WGCNA分析确定软阈值为12，获得24个基因模块。black、brown4与floralwhite模块与HT高度相关，共844个模块基因，见图2。差异分析得到5 080个HT的DEGs及2414个IS的DEGs，HT的模块基因、DEGs与IS的DEGs三者取交集获得102个重叠基因。GO富集提示重叠基因主要参与细胞迁移、细胞凋亡调控、酶活性调节等过程。KEGG富集提示其参与朊病、系统性红斑狼疮、谷胱甘肽代谢、氧化磷酸化等，见图3。
2.2.2　关键共病基因的筛选及验证
将所得11个Hub基因（AIF1、CD74、CCL5、CD27、SSRP1、FCGR2A、C1QC、TSR1、WDR75、SERBP1、RPL22）作为候选基因。LASSO回归在HT与IS中分别筛选出4个与8个特征基因。HT与IS特征基因取交集得到关键共病基因SERBP1与 WDR75。SERBP1与WDR75诊断IS的AUC值为0.941及0.846。验证集中，SERBP1与WDR75诊断IS的AUC值为0.857及0.657，见图4。
3　讨论
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目前，HT合并IS的病因尚不明确，因此本研究目的是探究HT与IS的因果关联及共病机制。因传统观察性研究常受到混杂因素、反向因果关系的干扰及识别暴露与结局因素的复杂性的限制，导致很难明确HT与IS之间的因果关联。而应用MR分析可以解决观察性研究在消除混杂因素等方面的不足[7]。因此，本研究通过MR分析探讨了HT与IS的因果关联，正向MR分析结果提示HT与IS的患病风险增加有正向因果关系（OR=1.087, 95% CI: 1.001~1.181, P=0.048），反向MR分析提示IS与HT患病风险增加无关（OR=1.159, 95% CI: 0.899~1.494, P=0.256）。多变量MR分析结果提示HT对IS发病的正向因果关联独立于吸烟及饮酒的影响。
为进一步提高结果的可靠性并探究HT合并IS的共病机制及潜在生物标志物，本研究还应用生物信息学方法对HT与IS的GEO数据集进行分析。通过将HT与IS数据整合分析，共获得102个HT与IS的重叠基因。GO与KEGG富集分析发现重叠基因主要参与细胞凋亡、细胞迁移、谷胱甘肽代谢、氧化磷酸化等生物学过程。最后，通过LASSO回归获得两疾病的关键共病基因SERBP1、WDR75。ROC曲线提示，SERBP1及WDR75对IS具有良好的诊断效能。在外部验证集中，SERBP1与WDR75仍对IS表现出较好的诊断效能。SERBP1是SERPINE1 mRNA 的稳定性调节因子，参与调控mRNA转录后翻译、细胞凋亡过程。SERBP1在肿瘤中的异常高表达与不良预后密切相关。此外，SERBP1水平升高与卵巢癌、肝癌的不良预后密切相关[22-23]，其基因多态性也可增加IS发病风险[24]。因此，SERBP1有望成为诊断HT合并IS患者的新位点，但目前相关研究较少，机制尚不明确，需进一步探究。WDR75主要参与rRNA转录与加工。目前WDR75与疾病的研究有限，多局限于肿瘤性疾病。WDR75的异常高表达是肝癌、肾癌不良预后的标志[25]。尽管WDR75在HT与IS中尚未突破，根据我们的结果，WDR75可能是HT合并IS患者诊断和治疗的潜在靶点。
综上，本研究应用MR分析探究HT与IS的因果关联，结果表明HT对IS的发病有正向因果效应，而IS与HT之间不存在反向因果效应。多变量MR分析提示HT与IS的正向因果关联不受吸烟及饮酒影响，提示HT可能是导致IS发病的独立危险因素。此外，本研究还应用生物信息学方法筛选在HT合并IS中发挥作用的关键基因，探究了HT合并IS的共病机制，为HT患者发生IS的早期预防及诊疗提供思路。但本研究存在一定局限性：首先，MR分析数据来自欧洲人群，因不同人群基因表达的差异，需进一步证实结果是否适用于其他人群。其次，转录组数据来自GEO数据库，需更大样本量数据的研究支持。
基金
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  • 山东省自然科学基金资助项目(ZR2023MH356)
  • 山东省中医药科技项目(Z-2023016)
  • 济南市科技发展计划(2023-1-4)
  • 济南市大数据科技计划(2023-YBD-1-01)
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doi: 10.20043/j.cnki.MPM.202408176
  • 接收时间：2024-08-13
  • 首发时间：2026-03-18
  • 出版时间：2025-03-25
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  • 收稿日期：2024-08-13
基金
山东省自然科学基金资助项目(ZR2023MH356)
山东省中医药科技项目(Z-2023016)
济南市科技发展计划(2023-1-4)
济南市大数据科技计划(2023-YBD-1-01)
作者信息
    1．山东第一医科大学附属中心医院心内科，山东　济南　250013
    2．山东第二医科大学麻醉学院

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Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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