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Article(id=1240395016619741867, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1240395006914130733, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202503512, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1742918400000, receivedDateStr=2025-03-26, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773662817386, onlineDateStr=2026-03-16, pubDate=1753372800000, pubDateStr=2025-07-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773662817386, onlineIssueDateStr=2026-03-16, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773662817386, creator=13701087609, updateTime=1773662817386, updator=13701087609, issue=Issue{id=1240395006914130733, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='14', pageStart='2497', pageEnd='2688', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1773662815073, creator=13701087609, updateTime=1773662858015, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1240395187109810535, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1240395006914130733, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1240395187109810536, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1240395006914130733, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=2520, endPage=2525, ext={EN=ArticleExt(id=1240395018159051459, articleId=1240395016619741867, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Identification of drug targets for preeclampsia based on gene expression: a Mendelian randomization study, columnId=1228016567443718970, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods Advances, runingTitle=null, highlight=null, articleAbstract=
Objective

To identify potential drug targets related to preeclampsia and explore their public health value in precision prevention based on gene expression–associated genetic variants and Mendelian randomization.

Methods

By integrating genome-wide association studies (GWAS) and expression quantitative trait loci (eQTL) data, potential druggable genes for preeclampsia were identified using Summary data-based Mendelian Randomization (SMR), Transcriptome-Wide Association Study (TWAS), and colocalization analysis. Drug annotation and potential side effect assessments were conducted using public drug databases, and Phenome-wide Mendelian Randomisation (MR-PheWAS) was applied to evaluate potential causal associations between the identified genes and 783 disease traits.

Results

Seven potential drug targets were identified: ALDH2, BCAR1, IL27, MUTYH, PABPC1, SULT1A1 and SULT1A2. Among them, IL27 and SULT1A1 showed consistent and significant associations across multiple analyses. ALDH2 and MUTYH were identified as strong candidates with known involvement in oxidative stress and DNA repair pathways. Several genes were linked to existing drugs, suggesting potential for drug repurposing in preeclampsia.

Conclusion

Drug-target Mendelian randomization provides novel insights into the molecular mechanisms of preeclampsia and facilitates the identification of therapeutic targets. The findings offer a promising foundation for early screening, genetic risk stratification, and individualized prevention, contributing to the development of precision public health strategies for pregnancy-related disorders.

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目的

基于基因表达相关的遗传变异，采用孟德尔随机化方法识别与先兆子痫相关的潜在药物靶点，探索其在精准防控中的公共卫生价值。

方法

结合全基因组关联研究（Genome-Wide Association Study，GWAS）与表达数量性状基因位点（expression Quantitative Trait Loci，eQTL）数据，使用基于汇总数据的孟德尔随机化分析（Summary Data-based Mendelian Randomization，SMR）、全转录组关联研究（Transcriptome-Wide Association Study，TWAS）、共定位分析（Colocalization）探索潜在的药物靶点。此外，对筛选的药靶基因进行药物注释与潜在副作用评价：通过药物数据库全面总结基因靶点现有药物，以及利用全表型组孟德尔随机化研究（Phenome-wide Mendelian Randomisation，MR-PheWAS）评价药靶基因与783种疾病潜在因果关联。

结果

本研究筛选出7个与先兆子痫相关的潜在药靶基因：ALDH2、BCAR1、IL27、MUTYH、PABPC1、SULT1A1和SULT1A2。其中IL27和SULT1A1在多种分析中表现出一致性显著关联，MUTYH和ALDH2亦为功能明确的强候选基因。BCAR1、PABPC1和SULT1A2则需进一步验证其作用。

结论

药物靶向孟德尔随机化可为先兆子痫的靶基因筛选和风险预测提供有效工具，结合现有药物资源有望推动个体化精准干预策略的构建。该研究为妊娠期高危人群的早筛与分层管理提供分子依据，具有重要的公共卫生实践价值。

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刘振谧，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=V1HUcyzL8J/VhXaTUT/2Eg==, magXml=arcwqta63uI5pZD+lAK5kw==, pdfUrl=null, pdf=9OaMgTnCunC7MYgjsVV32Q==, pdfFileSize=1295504, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=IqMpBvhRyBjTNw07ueRw/w==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=OL42G6czY4/B554Qm7nehw==, mapNumber=null, authorCompany=null, fund=null, authors=

王妤珏（1999—），女，硕士在读，研究方向：妇幼相关疾病遗传流行病学

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王妤珏（1999—），女，硕士在读，研究方向：妇幼相关疾病遗传流行病学

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Data Sources Overview

, figureFileSmall=null, figureFileBig=null, tableContent=
数据来源样本量种族
eQTL
主动脉GTEx v8670欧洲人
冠状动脉205
胫动脉134
大脑杏仁核111
大脑前扣带皮层BA24区108
大脑皮层62
大脑海马体388
下丘脑151
肝脏208
肾皮质590
子宫473
全血88
胎盘Shouneng Peng149
GWAS
先兆子痫（母体基因型效应）Jaakko S. Tyrmi288 049欧洲人
中亚人
先兆子痫 PE (Finn)FinnGen R11242 332欧洲人
), ArticleFig(id=1240413548522819934, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1240395016619741867, language=CN, label=表1, caption=

数据来源概览

, figureFileSmall=null, figureFileBig=null, tableContent=
数据来源样本量种族
eQTL
主动脉GTEx v8670欧洲人
冠状动脉205
胫动脉134
大脑杏仁核111
大脑前扣带皮层BA24区108
大脑皮层62
大脑海马体388
下丘脑151
肝脏208
肾皮质590
子宫473
全血88
胎盘Shouneng Peng149
GWAS
先兆子痫（母体基因型效应）Jaakko S. Tyrmi288 049欧洲人
中亚人
先兆子痫 PE (Finn)FinnGen R11242 332欧洲人
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先兆子痫潜在药物靶点识别：基于基因表达的孟德尔随机化研究
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王妤珏 , 刘雨晴 , 盖巧玥 , 肖成汉 , 刘振谧
现代预防医学 | 流行病与统计方法 2025,52(14): 2520-2525
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现代预防医学 | 流行病与统计方法 2025, 52(14): 2520-2525
先兆子痫潜在药物靶点识别：基于基因表达的孟德尔随机化研究
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王妤珏, 刘雨晴, 盖巧玥, 肖成汉, 刘振谧
作者信息
  • 四川大学华西公共卫生学院（华西第四医院）儿少卫生与妇幼保健系，四川 成都 610041

    • 王妤珏（1999—），女，硕士在读，研究方向：妇幼相关疾病遗传流行病学

通讯作者:

刘振谧，E-mail：
Identification of drug targets for preeclampsia based on gene expression: a Mendelian randomization study
Shu-jue WANG, Yu-qing LIU, Qiao-yue GAI, Cheng-han XIAO, Zhen-mi LIU
Affiliations
  • Department of Child and Adolescent Health and Maternal and Child Health, West China School of Public Health(West China Fourth Hospital), Sichuan University, Chengdu, Sichuan 610041, China
出版时间: 2025-07-25 doi: 10.20043/j.cnki.MPM.202503512
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摘要
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目的

基于基因表达相关的遗传变异，采用孟德尔随机化方法识别与先兆子痫相关的潜在药物靶点，探索其在精准防控中的公共卫生价值。

方法

结合全基因组关联研究（Genome-Wide Association Study，GWAS）与表达数量性状基因位点（expression Quantitative Trait Loci，eQTL）数据，使用基于汇总数据的孟德尔随机化分析（Summary Data-based Mendelian Randomization，SMR）、全转录组关联研究（Transcriptome-Wide Association Study，TWAS）、共定位分析（Colocalization）探索潜在的药物靶点。此外，对筛选的药靶基因进行药物注释与潜在副作用评价：通过药物数据库全面总结基因靶点现有药物，以及利用全表型组孟德尔随机化研究（Phenome-wide Mendelian Randomisation，MR-PheWAS）评价药靶基因与783种疾病潜在因果关联。

结果

本研究筛选出7个与先兆子痫相关的潜在药靶基因：ALDH2、BCAR1、IL27、MUTYH、PABPC1、SULT1A1和SULT1A2。其中IL27和SULT1A1在多种分析中表现出一致性显著关联，MUTYH和ALDH2亦为功能明确的强候选基因。BCAR1、PABPC1和SULT1A2则需进一步验证其作用。

结论

药物靶向孟德尔随机化可为先兆子痫的靶基因筛选和风险预测提供有效工具，结合现有药物资源有望推动个体化精准干预策略的构建。该研究为妊娠期高危人群的早筛与分层管理提供分子依据，具有重要的公共卫生实践价值。

先兆子痫  /  孟德尔随机化  /  药物靶点  /  精准预防
Objective

To identify potential drug targets related to preeclampsia and explore their public health value in precision prevention based on gene expression–associated genetic variants and Mendelian randomization.

Methods

By integrating genome-wide association studies (GWAS) and expression quantitative trait loci (eQTL) data, potential druggable genes for preeclampsia were identified using Summary data-based Mendelian Randomization (SMR), Transcriptome-Wide Association Study (TWAS), and colocalization analysis. Drug annotation and potential side effect assessments were conducted using public drug databases, and Phenome-wide Mendelian Randomisation (MR-PheWAS) was applied to evaluate potential causal associations between the identified genes and 783 disease traits.

Results

Seven potential drug targets were identified: ALDH2, BCAR1, IL27, MUTYH, PABPC1, SULT1A1 and SULT1A2. Among them, IL27 and SULT1A1 showed consistent and significant associations across multiple analyses. ALDH2 and MUTYH were identified as strong candidates with known involvement in oxidative stress and DNA repair pathways. Several genes were linked to existing drugs, suggesting potential for drug repurposing in preeclampsia.

Conclusion

Drug-target Mendelian randomization provides novel insights into the molecular mechanisms of preeclampsia and facilitates the identification of therapeutic targets. The findings offer a promising foundation for early screening, genetic risk stratification, and individualized prevention, contributing to the development of precision public health strategies for pregnancy-related disorders.

Preeclampsia  /  Mendelian randomization  /  Drug targets  /  Precision prevention
王妤珏, 刘雨晴, 盖巧玥, 肖成汉, 刘振谧. 先兆子痫潜在药物靶点识别：基于基因表达的孟德尔随机化研究. 现代预防医学, 2025 , 52 (14) : 2520 -2525 . DOI: 10.20043/j.cnki.MPM.202503512
Shu-jue WANG, Yu-qing LIU, Qiao-yue GAI, Cheng-han XIAO, Zhen-mi LIU. Identification of drug targets for preeclampsia based on gene expression: a Mendelian randomization study[J]. Modern Preventive Medicine, 2025 , 52 (14) : 2520 -2525 . DOI: 10.20043/j.cnki.MPM.202503512
正文
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先兆子痫（Pre-eclampsia，PE）是妊娠期常见的高血压相关疾病，全球发病率约为5%～8%，是孕产妇和围产儿死亡、早产及低出生体重的重要原因之一[1]。我国近年来因高龄妊娠、基础疾病合并妊娠及辅助生殖增加，先兆子痫患病负担持续上升，已成为严重威胁母婴健康的公共卫生问题。临床表现多为高血压和蛋白尿，严重者可并发肝肾功能障碍、凝血功能异常甚至胎盘早剥、胎儿宫内发育受限等严重并发症，对孕产妇健康构成持续威胁。尽管病因尚未明确，研究显示先兆子痫涉及免疫失衡、血管内皮损伤、氧化应激及胎盘异常发育等多种机制[2]，导致其高度异质性和防治困难。当前防控仍以高危人群识别和经验性干预为主[3]，缺乏分子水平的风险预测手段，难以实现疾病的早期识别与精准干预[4-5]。常用的低剂量阿司匹林虽具一定效果，但存在个体差异大、起效窗口短等局限[6]；传统的营养补充[7]和降压治疗亦难满足精准防控需求。因此，探索靶向机制明确、基于分子标志物的个体化干预路径，对提升先兆子痫的防控水平具有重要意义。
药物靶向孟德尔随机化（drug-target Mendelian Randomization, d-MR）通过模拟“自然随机化”的遗传变异，可在大样本人群中揭示基因表达与疾病之间的因果关系，有效规避混杂偏倚[8]。与传统药物研发流程相比，d-MR能更高效地识别潜在药物靶点，加速药物开发，并拓展现有药物的适应症范围[9]。近年来，d-MR在妊娠相关疾病研究中显示出独特价值，有望为早期筛查与精准干预提供理论支持。
本研究整合全基因组关联研究（Genome-wide Association Studies，GWAS）与表达数量性状基因位点（Expression Quantitative Trait Locus，eQTL）数据，近年来，d-MR在妊娠相关疾病研究中显示出独特价值，有望为早期筛查与精准干预提供理论支持。
1　资料与方法
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1.1　研究设计
本文结合13个组织的eQTL数据与2个先兆子痫的GWAS数据，采用基于汇总数据的孟德尔随机化（Summary Data-based Mendelian Randomization，SMR）、联合转录组广泛关联研究（Joint Transcriptome-Wide Association Study，JOINT TWAS）、共定位分析（Colocalization），系统筛选与先兆子痫相关的候选基因。并且，利用权重基因共表达网络分析（Weighted Gene Co-expression Network Analysis，WGCNA）及中心性网络分析，对这些候选基因进行重要性排序，并识别出关键的基因模块，为后续的药物开发提供理论依据。具体如图1所示。
1.2　数据来源
本研究所使用的数据主要来自以下三个方面，详见表1:
药靶基因池：来自DGIdb 4.0数据库和Finan等人的研究，去重后共纳入5883个药靶基因，为靶点筛选提供了基础。DGIdb（Drug Gene Interaction Database）是一个广泛使用的药物-基因交互平台，整合了与药物相关的基因信息，并为靶点筛选提供了宝贵的资源[10]。Finan等人（2017）所做的研究为这些靶点提供了进一步的验证和拓展[11]。
eQTL数据：结合先兆子痫生物学机制以及数据可及性，本研究总共纳入13种组织的eQTL数据，其中12种组织数据来源于GTEx v8（Genotype-Tissue Expression, version 8）项目[12]：主动脉、冠状动脉、胫动脉、全血、大脑杏仁核、大脑前扣带皮层（BA24）、大脑皮层、海马体、下丘脑、肾皮质、肝脏、子宫以及胎盘；同时整合了来自另外一项研究的胎盘数据[13]。
GWAS数据：先兆子痫的GWAS数据来自Tyrmi等人2023年的研究和Finngen R11。Tyrmi等人[14]的研究使用了来自芬兰先兆子痫遗传学联盟（FINNPEC）、FinnGen、和爱沙尼亚生物库的样本以及Steinthorsdottir等人早期先兆子痫Meta分析的数据。此外，本研究还使用了Finngen R11数据作为验证性结局[15]。FinnGen是一个由芬兰多个研究机构联合发起的大型基因组学研究项目，旨在通过分析芬兰人群的基因组数据，探索基因与疾病之间的关系。研究仅聚焦于母体效应，未考虑胎儿的影响。在PheWAS-MR中还使用了来自英国生物银行（UK Biobank，UKB）的783种疾病的GWAS数据作为结局[16]。
1.3　先兆子痫药靶基因筛选与验证
首先，采用SMR分析，通过整合13种组织的eQTL数据和2项先兆子痫结局的GWAS数据，探讨基因表达与先兆子痫之间的因果关系。以FDR校正后的P值FDRSMR<0.05以及HEIDI检测P值>0.05作为显著标准。随后，采用联合转录组广泛关联分析（Joint Transcriptome-Wide Association Study，Joint TWAS），在同一eQTL组织与GWAS结局组合基础上，系统分析组织特异性的基因表达与先兆子痫风险之间的关联。以 FDRTWAS<0.05 作为主要筛选标准，另外在每一个“组织×GWAS 结局”组合内按照该组合下的基因模型总数（N）应用Bonferroni校正阈值（P adjust=原P×N），以0.05作为显著性判断标准；并作为敏感性分析进行报告。以两项分析共同鉴定出的基因作为显著基因进行下一步分析。为进一步验证候选基因的可信度，开展共定位分析，使用贝叶斯方法计算多种假设下的后验概率，重点关注假设PP.H4.abf（posterior probability for H4），即GWAS与eQTL信号共因果的概率。以PP.H4.abf>0.8作为显著共定位的判断标准，识别在调控与表型间具有共同遗传基础的基因。
此外，应用皮尔逊相关系数来构建基因共表达网络。通过识别与不同基因模块中的重要基因并结合中心性网络分析，能够揭示基因之间的相互作用及其在基因网络中的核心地位，进一步增强对潜在药物靶点的识别能力[17]。在筛选过程中结合了网络中心性分析，利用接近中心性（Closeness）和介数中心性（Betweenness）这两种网络中心性指标，以确保筛选出的基因具有关键的调控作用。前者衡量基因与网络中其他节点的快速交互能力，后者反映其在不同模块间的桥梁作用。参考既往研究，中心性高的基因通常在疾病调控中功能关键[18-20]，因此，筛选出接近中心性大于0.90和介数中心性大于0.70的基因，作为潜在关键靶点。
1.4　先兆子痫药靶基因注释
针对筛选出的关键基因，进一步开展药物靶点和疾病关联注释。利用DGIdb和DrugBank等药物数据库，系统整理与候选药靶基因相关的已知药物信息。同时，基于UKB的783种疾病的GWAS数据（作为结局），与关键基因对应组织的eQTL数据（作为暴露），采用PLINK进行SNP Clumping（r2<0.01、窗口范围为10 000 kb）[21-22]；并仅纳入eQTL P值<5×10-8的显著SNP作为工具变量，确保变量之间的独立性与强效性。随后应用MR-PheWAS方法，系统推断候选基因与多种疾病结局之间的潜在因果关系。
1.5　统计分析
本研究采用SMR、FUSION、PLINK2、R 4.4.1进行分析。研究使用“Mendelian-Randomization” “WGCNA”等 R 包进行分析。检验水准为双侧α=0.05；在SMR分析中，显著性判定标准为FDR校正后的P值<0.05，且HEIDI检验P值>0.05，以确保排除由连锁不平衡导致的假阳性关联。在JOINT TWAS分析中，主要采用FDR.TWAS<0.05校正；以Bonferroni校正P值<0.05作为补充报告用于敏感性分析。共定位分析以PP.H4.abf>0.8作为显著共定位的判断标准。PheWAS-MR中不同基因对疾病发生风险的影响以效应估计值（odds ratio, OR）和95%置信区间（confidence interval, CI）表示。
2　结果
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2.1　先兆子痫可药用基因筛选与验证
本研究最终筛选出了7个与先兆子痫密切相关的潜在药靶基因，包括ALDH2、BCAR1、IL27、MUTYH、PABPC1、SULT1A1和SULT1A2，其中ALDH2、IL27和SULT1A1在同一组织及不同的先兆子痫结局的分析中均表现出显著的关联性，显示其作为潜在药物靶点的可能性，具体如图2所示。在组织层面，我们对13种组织的eQTL数据进行了分析，结果发现有5个组织与先兆子痫结局存在显著的关联。这些组织包括：主动脉、3个大脑相关区域（前扣带皮层BA24、大脑皮层、下丘脑）以及肝脏。这些发现揭示了不同组织中基因表达与先兆子痫的密切关系，进一步验证了相关基因作为药物靶点的潜力。
在WGCNA分析中，本研究发现SULT1A1表现出关键作用，显示出其在基因共表达网络中的重要性；在中心性网络分析中，IL27和MUTYH则表现出较强的中心性，进一步强调了它们在先兆子痫中的潜在作用（图3）。将这些基因与不同数据来源的先兆子痫GWAS结合分析后，我们将筛选出的7个基因分为三个层次。
(1)关键基因：IL27和SULT1A1，这两者在多个分析中均表现出较为一致的显著关联，并且在不同层次的分析中均显示出较高优先级。
(2)强候选基因：MUTYH和ALDH2，虽然它们在某些分析中未表现出与关键基因相同的显著程度，但仍然具有较强的潜力作为药物靶点。
(3)弱候选基因：BCAR1、PABPC1和SULT1A2，这些基因在不同分析中表现出一定的关联性，但其作用和显著性相对较弱，需进一步验证其在先兆子痫中的潜力。
通过将这些结果与基因的功能及其在生物学过程中的作用结合，我们为潜在药物靶点的筛选提供了更为明确的理论依据，并为未来的实验验证提供了方向。
2.2　基因注释
为探索上述药靶基因在先兆子痫临床治疗中的潜在副作用，本研究以每个基因对应组织的eQTL数据作为暴露，783种疾病GWAS数据作为结局，采用PheWAS-MR方法探索基因与不同疾病之间的因果关联。PABPC1基因的工具变量由于回文序列未能参与分析。IL27在肝脏来源的eQTL数据中与多个疾病结局呈显著的正向关联。IL27与超重、肥胖及其他过度营养、肾脏和输尿管其他疾病以及静脉曲张三类疾病存在显著关联。ALDH2在主动脉来源的eQTL数据中表现出与甲状腺功能减退和心肌梗死的显著正相关，表明ALDH2的变化可能增加这些疾病的风险，尤其是在内分泌和心血管系统中。此外，SULT1A1、MUTYH、BCAR1和SULT1A2等基因与大多数疾病结局之间并未表现出显著的关联（如图4所示）。
在关键基因中，IL27和SULT1A1与多种药物相关，前者涉及抗炎药和疫苗，后者与激素替代治疗药物及抗高血压药物相关。强候选基因中，MUTYH和ALDH2与化疗药物（如顺铂、氟尿嘧啶）、酒精戒断药物（如双硫仑）以及用于帕金森病的药物（如多巴胺）密切相关。弱候选基因如BCAR1、PABPC1和SULT1A2，目前与药物的关联较少，且其中一些药物尚未获得批准。总体而言，IL27、SULT1A1、MUTYH和ALDH2是具有较强药物开发潜力的基因，而BCAR1、PABPC1和SULT1A2则需要进一步的研究验证。
3　讨论
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本研究通过药物靶向孟德尔随机化方法，筛选出7个与先兆子痫密切相关的潜在药物靶基因，重点分析了IL27、SULT1A1和ALDH2，发现其在多个组织和先兆子痫不同结局中表现出显著关联，提示在先兆子痫的发生发展中可能发挥重要作用。
IL27作为免疫调节因子，可能通过调节Th1/Th2平衡和细胞因子分泌，在多种免疫介导的疾病中发挥关键作用[23]。本研究发现IL27在多个组织中的表达与先兆子痫呈显著关联，提示其可能通过调节母体免疫反应及胎盘功能参与疾病发生，具有作为免疫干预靶点的潜力。SULT1A1编码的硫酸转移酶在激素代谢中起重要作用[24]，先兆子痫与妊娠期激素水平密切相关。SULT1A1在不同组织中表达异常，可能通过影响雌激素代谢、抗氧化应激等机制增加发病风险，具有一定的干预价值[25]。ALDH2则参与毒素代谢与氧化应激调节，其功能障碍与妊娠期多种并发症密切相关[26]。这些靶基因的变化不仅反映了疾病的分子机制，也为早期识别高风险人群提供了新的生物学基础。目前，部分与上述靶基因相关的药物在动物实验中表现出潜在疗效。例如，双硫仑作为醛脱氢酶抑制剂，可减轻氧化应激反应、缓解胎盘损伤，并显著降低sFLT-1和sEng的分泌，改善内皮功能[27]。纳曲酮具备免疫调节作用，研究表明其可减轻妊娠期炎症反应，增强胎盘免疫耐受性[28]。特戈利特作为肺动脉高压治疗药物，在动物模型中可扩张血管、改善血流灌注，表现出一定的妊娠期应用前景[29]。然而，这些疗法尚处于前期研究阶段，临床转化仍需进一步验证。
我国现行《妊娠高血压疾病诊治指南（2020）》[3]及《妊娠期高血压疾病管理专家共识（2019）》[30]建议对高危孕妇早期使用低剂量阿司匹林，并联合多种手段评估先兆子痫风险如血压监测、超声检查及PLGF、sFlt-1等生化指标等。但现有策略仍面临筛查特异性不足、风险识别窗口短等挑战，迫切需要更敏感、更前移的生物标志物以优化干预时机。研究发现的关键基因具备良好可检测性和功能特异性，未来有望纳入孕早期筛查体系，构建风险评估模型，实现前瞻性识别与精准干预。例如，IL27高表达可提示免疫激活状态，对应可探索营养调节、轻度免疫调控等辅助干预措施；SULT1A1异常者可作为激素代谢干预重点对象；ALDH2功能低下者则可加强抗氧化支持。这些策略可为早干预提供分子依据，也有助于推动个体化产前管理和公共卫生防控的融合发展。同时，动态监测基因表达变化，有望构建“早识别—早干预—动态管理”的防控模式。
尽管本研究识别了多个潜在靶基因，并初步探讨其功能，但仍存在局限：一是数据来源的局限性：本研究主要依赖公开数据库中的表达数据与工具预测结果，缺乏真实队列验证；二是缺乏实验验证：本研究为纯生物信息学分析，未进行细胞、动物或临床样本水平的实验证实，推论仍需后续实验支持；三是临床转化路径尚不明确：尽管识别出若干具有潜力的药物及靶基因，但具体的干预策略、安全性评估与人群适用性仍有待深入探索。未来应结合大规模孕期队列，验证基因表达特征和预测效能；构建多基因联合模型提升筛查效果；并探索靶向干预策略，实现从发现到应用的转化。
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doi: 10.20043/j.cnki.MPM.202503512
  • 接收时间：2025-03-26
  • 首发时间：2026-03-16
  • 出版时间：2025-07-25
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    四川大学华西公共卫生学院（华西第四医院）儿少卫生与妇幼保健系，四川 成都 610041

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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
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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