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Article(id=1315688466801869419, tenantId=1146029695717560320, journalId=1315121837170065538, issueId=1315688435411694395, articleNumber=null, orderNo=null, doi=10.16368/j.issn.1674-8999.2025.12.394, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1749744000000, receivedDateStr=2025-06-13, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1791614174794, onlineDateStr=2026-10-10, pubDate=1765296000000, pubDateStr=2025-12-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1791614174794, onlineIssueDateStr=2026-10-10, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1791614174794, creator=13701087609, updateTime=1791614174794, updator=13701087609, issue=Issue{id=1315688435411694395, tenantId=1146029695717560320, journalId=1315121837170065538, year='2025', volume='40', issue='12', pageStart='2487', pageEnd='2712', issueExtLink='null', onlineDate='null', pubDate='1765296000000', pubDateStr='2025-12-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1791614167309, creator='13701087609', updateTime=1791616084449, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1315696476567744680, tenantId=1146029695717560320, journalId=1315121837170065538, issueId=1315688435411694395, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1315696476571938985, tenantId=1146029695717560320, journalId=1315121837170065538, issueId=1315688435411694395, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=2520, endPage=2529, ext={EN=ArticleExt(id=1315688467032556141, articleId=1315688466801869419, tenantId=1146029695717560320, journalId=1315121837170065538, language=EN, title=Bioinformatics Analysis of Core Genes Associated with Cuproptosis in Alcoholic Liver Cirrhosis and Prediction of Traditional Chinese Medicine, columnId=1315688466957058668, journalTitle=Acta Chinese Medicine, columnName=Innovative Thinking and Technological Frontiers, runingTitle=null, highlight=null, articleAbstract=

Objective:To screen differentially expressed genes associated with cuproptosis in alcoholic liver cirrhosis using bioinformatics methods，explore their mechanisms of action，and identify traditional Chinese medicines with potential therapeutic effects.Methods:Datasets of patients with alcoholic liver cirrhosis meeting the criteria were selected from the GEO database.Differentially expressed genes（DEGs）were identified using software analysis，followed by KEGG pathway analysis and protein-protein interaction（PPI）network analysis to screen key genes.Random forest and SVMmodels，commonly used in machine learning，were applied to study the key genes. In addition，literature was reviewed to obtain cuproptosis-related genes（CRGs）.The DEGs and CRGs were intersected to identify core genes associated with cuproptosis，followed by GSEA enrichment analysis. Core genes retrieved from the Coremine database were used to predict therapeutic Chinese herbs.Results:By screening gene chips GSE167308 and GSE142530，278 DEGs were obtained.Through further selection via PPI and validation with GSE143318，10 key genes were identified.Literature review collected 38 CRGs，and the intersection yielded 2 cuproptosis core genes-CDKN2A（Cyclin-Dependent Kinase Inhibitor 2）and PLAT（Plasminogen Activator）.GSEA analysis indicated that the core genes are closely associated with linoleic acid metabolism，amino acid biosynthesis，arginine biosynthesis，HIF-1 signaling pathway，phenylalanine metabolism，and others. Chinese herbs such as Huci（Indian damnacanthus herb），Fengfang（Honeycomb），Diren（Melastoma dode candrum lour），Xiazhicao（Lagopsis supina），Hudilong（Lumbricus）and Qishe（Agkistrodon acutus）were identified through database search，and the attributes of the herbs including their four Qi，five Flavor，and meridian tropism were summarized. Conclusion:CDKN2A and PLAT are core genes related to cuproptosis in alcoholic cirrhosis.Prediction identified 35 Chinese herbs，which may serve as potential treatments for alcoholic cirrhosis.

, authors=Runmeng YANG, Wenliang LYU, authorsList=Runmeng YANG, Wenliang LYU, authorCompany=null, correspAuthors=Wenliang LYU, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1315688469549138567, articleId=1315688466801869419, tenantId=1146029695717560320, journalId=1315121837170065538, language=CN, title=酒精性肝硬化相关铜死亡核心基因的生物信息学分析及中药预测, columnId=1315688467116442222, journalTitle=中医学报, columnName=创新思维与技术前沿, runingTitle=null, highlight=null, articleAbstract=

目的：通过生物信息学方法筛选酒精性肝硬化相关铜死亡差异基因，探索其作用机制，并找到具有潜在治疗作用的中药。方法：在GEO数据库筛选符合条件的酒精性肝硬化患者数据集，软件分析获得差异表达基因（differentially expressed genes，DEGs），并进行KEGG分析，蛋白质相互作用（protein protein interaction，PPI）网络分析，筛选出关键基因，利用机器学习方法中的随机森林模型以及SVM模型对关键基因进行研究，同时查阅文献，获得铜死亡相关基因（cuproptosis related genes，CRGs）。对DEGs和CRGs采用交集方法得到铜死亡相关核心基因，并进行GSEA富集分析。通过在Coremine数据库检索得到的核心基因预测治疗中药。结果：筛选基因芯片GSE167308和GSE142530，得到278个DEGs，通过PPI进一步筛选以及GSE143318验证获得10个关键基因，查阅文献，收集到38个CRGs，取交集得到2个铜死亡核心基因，周期素依赖性激酶抑制因子2、纤溶酶原激活因子。GSEA分析显示核心基因与亚麻酸代谢、氨基酸生物合成、精氨酸生物合成、HIF-1信号通路、苯丙氨酸代谢等关系密切。通过检索得到虎刺、蜂房、地稔、夏至草、胡地龙、蕲蛇等中药，并总结中药的四气五味、归经功效。结论：周期素依赖性激酶抑制因子2和纤溶酶原激活因子为酒精性肝硬化相关铜死亡的核心基因，通过预测得到35种中药，可能为治疗酒精性肝硬化的潜在中药。

, authors=杨润萌, 吕文良, authorsList=杨润萌, 吕文良, authorCompany=null, correspAuthors=吕文良, authorNote=

杨润萌（1993 -），男，重庆人，博士研究生，研究方向：肝病的临床与基础研究。

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吕文良（1966-），男，教授，博士研究生导师，主要从事肝病的中西医防治研究。E-mail:
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杨润萌（1993 -），男，重庆人，博士研究生，研究方向：肝病的临床与基础研究。

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杨润萌（1993 -），男，重庆人，博士研究生，研究方向：肝病的临床与基础研究。

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注：A、B：两数据集样本的中位数和四分位数比较；D1、D2：分别为GSE167308和GSE142530芯片差异基因火山图，E1、E2：分别为GSE167308和GSE142530芯片差异程度排名靠前的基因热图。

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注：A-B:GSE167308芯片的两核心基因GSEA分析图。

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主要信号通路P值富集分数基因数量/个
ECM受体相互作用5.33 ×10-812.299715729
胆汁分泌9.06 ×10-610.425473329
糖鞘脂生物合成内酯和新内酯系列7.82 ×10-517.375788874
AGE -RAGE信号通路在糖尿病并发症中的作用1.82 ×10-47.297831326
蛋白质的消化和吸收2.14 ×10-47.085273136
丙氨酸、天冬氨酸和谷氨酸代谢2.38 ×10-413.149245634
药物代谢-细胞色素P4503.22 ×10-48.446564035
补体和凝血级联7.31 ×10-47.071541985
唾液分泌1.04 ×10-36.539275385
细胞黏附分子1.96 ×10-34.648300216
HIF-1信号通路2.02 ×10-35.631042695
黏蛋白型O-聚糖生物合成3.20 ×10-310.135876843
MAPK信号通路3.48 ×10-33.254328358
GABA能突触6.31 ×10-35.466540324
内分泌抵抗8.82 ×10-34.964511114
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KEGG富集得到的主要信号通路

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主要信号通路P值富集分数基因数量/个
ECM受体相互作用5.33 ×10-812.299715729
胆汁分泌9.06 ×10-610.425473329
糖鞘脂生物合成内酯和新内酯系列7.82 ×10-517.375788874
AGE -RAGE信号通路在糖尿病并发症中的作用1.82 ×10-47.297831326
蛋白质的消化和吸收2.14 ×10-47.085273136
丙氨酸、天冬氨酸和谷氨酸代谢2.38 ×10-413.149245634
药物代谢-细胞色素P4503.22 ×10-48.446564035
补体和凝血级联7.31 ×10-47.071541985
唾液分泌1.04 ×10-36.539275385
细胞黏附分子1.96 ×10-34.648300216
HIF-1信号通路2.02 ×10-35.631042695
黏蛋白型O-聚糖生物合成3.20 ×10-310.135876843
MAPK信号通路3.48 ×10-33.254328358
GABA能突触6.31 ×10-35.466540324
内分泌抵抗8.82 ×10-34.964511114
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核心基因名称缩写基因英文名称基因中文名称
VWFvon Willebrand fALCtor血管性血友病因子
LGALS3galectin 3半乳糖凝集素3
MMP14matrix metallopeptidase 14基质金属蛋白酶14
ITGA2integrin subunit alpha 2整合素A2
FOXP3forkhead box P3叉头框蛋白P3
CDKN2 Acyclin dependent kinase inhibitor 2A周期素依赖性激酶抑制因子2
CD24CD24 molecule热稳定抗原
FUT4fucosyltransferase 4岩藻糖基转移酶4
PLATplasminogen ALCtivator，tissue type纤溶酶原激活因子
GPC3glypican 3糖脂类蛋白3
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10个核心DEGs

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核心基因名称缩写基因英文名称基因中文名称
VWFvon Willebrand fALCtor血管性血友病因子
LGALS3galectin 3半乳糖凝集素3
MMP14matrix metallopeptidase 14基质金属蛋白酶14
ITGA2integrin subunit alpha 2整合素A2
FOXP3forkhead box P3叉头框蛋白P3
CDKN2 Acyclin dependent kinase inhibitor 2A周期素依赖性激酶抑制因子2
CD24CD24 molecule热稳定抗原
FUT4fucosyltransferase 4岩藻糖基转移酶4
PLATplasminogen ALCtivator，tissue type纤溶酶原激活因子
GPC3glypican 3糖脂类蛋白3
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核心基因名称缩写基因名称
Alpha-Linolenic ALCid metabolismα-亚麻酸代谢
Biosynthesis of amino ALCids氨基酸的生物合成
Chagas disease恰加斯病
Complement and coagulation cascades补体和凝血级联
Ferroptosis铁死亡
Leishmaniasis利什曼病
Neutrophil extrALCellular trap formation中性粒细胞外陷阱的形成
Platelet ALCtivation血小板活化
Staphylococcus aureus infection金黄色葡萄球菌感染
Arginine biosynthesis精氨酸生物合成
HIF-1 signaling pathwayHIF-1信号通路
Phenylalanine metabolism苯丙氨酸代谢
Ubiquinone and other泛醌和其他萜类-
terpenoid-quinone biosynthes醌生物合成物
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CDKN2A和PLAT的单基因GSEA分析

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核心基因名称缩写基因名称
Alpha-Linolenic ALCid metabolismα-亚麻酸代谢
Biosynthesis of amino ALCids氨基酸的生物合成
Chagas disease恰加斯病
Complement and coagulation cascades补体和凝血级联
Ferroptosis铁死亡
Leishmaniasis利什曼病
Neutrophil extrALCellular trap formation中性粒细胞外陷阱的形成
Platelet ALCtivation血小板活化
Staphylococcus aureus infection金黄色葡萄球菌感染
Arginine biosynthesis精氨酸生物合成
HIF-1 signaling pathwayHIF-1信号通路
Phenylalanine metabolism苯丙氨酸代谢
Ubiquinone and other泛醌和其他萜类-
terpenoid-quinone biosynthes醌生物合成物
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调控基因相关中药
CDKN2 A虎刺、蜂房、地稔、兰花根、阿胶、当归、枣槟榔、槟榔、白花败酱草、人参、西伯利亚苦杏仁、人参花、人参芦、人参叶、眼镜蛇、垂盆草、锡生藤、鹅掌楸根
PLAT夏至草、胡地龙、蕲蛇、水芹、黑昆布、香椿子、香椿白皮、益智、茺蔚子、益母草、丹参、山楂、华细辛、蒲黄、水蛭、两面针根、黄芩
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调控基因与相关中药

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调控基因相关中药
CDKN2 A虎刺、蜂房、地稔、兰花根、阿胶、当归、枣槟榔、槟榔、白花败酱草、人参、西伯利亚苦杏仁、人参花、人参芦、人参叶、眼镜蛇、垂盆草、锡生藤、鹅掌楸根
PLAT夏至草、胡地龙、蕲蛇、水芹、黑昆布、香椿子、香椿白皮、益智、茺蔚子、益母草、丹参、山楂、华细辛、蒲黄、水蛭、两面针根、黄芩
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酒精性肝硬化相关铜死亡核心基因的生物信息学分析及中药预测
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杨润萌 , 吕文良
中医学报 | 创新思维与技术前沿 2025,40(12): 2520-2529
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中医学报 |创新思维与技术前沿 2025 , 40 (12) : 2520 -2529
酒精性肝硬化相关铜死亡核心基因的生物信息学分析及中药预测
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杨润萌（1993 -），男，重庆人，博士研究生，研究方向：肝病的临床与基础研究。

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杨润萌（1993 -），男，重庆人，博士研究生，研究方向：肝病的临床与基础研究。

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杨润萌, 吕文良
作者信息
  • 中国中医科学院广安门医院，北京 100053
通讯作者:
吕文良（1966-），男，教授，博士研究生导师，主要从事肝病的中西医防治研究。E-mail:
作者简介:

杨润萌（1993 -），男，重庆人，博士研究生，研究方向：肝病的临床与基础研究。

Bioinformatics Analysis of Core Genes Associated with Cuproptosis in Alcoholic Liver Cirrhosis and Prediction of Traditional Chinese Medicine
Runmeng YANG, Wenliang LYU
Affiliations
  • Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing China 100053
出版时间: 2025-12-10 doi: 10.16368/j.issn.1674-8999.2025.12.394
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摘要
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目的：通过生物信息学方法筛选酒精性肝硬化相关铜死亡差异基因，探索其作用机制，并找到具有潜在治疗作用的中药。方法：在GEO数据库筛选符合条件的酒精性肝硬化患者数据集，软件分析获得差异表达基因（differentially expressed genes，DEGs），并进行KEGG分析，蛋白质相互作用（protein protein interaction，PPI）网络分析，筛选出关键基因，利用机器学习方法中的随机森林模型以及SVM模型对关键基因进行研究，同时查阅文献，获得铜死亡相关基因（cuproptosis related genes，CRGs）。对DEGs和CRGs采用交集方法得到铜死亡相关核心基因，并进行GSEA富集分析。通过在Coremine数据库检索得到的核心基因预测治疗中药。结果：筛选基因芯片GSE167308和GSE142530，得到278个DEGs，通过PPI进一步筛选以及GSE143318验证获得10个关键基因，查阅文献，收集到38个CRGs，取交集得到2个铜死亡核心基因，周期素依赖性激酶抑制因子2、纤溶酶原激活因子。GSEA分析显示核心基因与亚麻酸代谢、氨基酸生物合成、精氨酸生物合成、HIF-1信号通路、苯丙氨酸代谢等关系密切。通过检索得到虎刺、蜂房、地稔、夏至草、胡地龙、蕲蛇等中药，并总结中药的四气五味、归经功效。结论：周期素依赖性激酶抑制因子2和纤溶酶原激活因子为酒精性肝硬化相关铜死亡的核心基因，通过预测得到35种中药，可能为治疗酒精性肝硬化的潜在中药。

酒精性肝硬化  /  铜死亡  /  差异基因  /  中药预测

Objective:To screen differentially expressed genes associated with cuproptosis in alcoholic liver cirrhosis using bioinformatics methods，explore their mechanisms of action，and identify traditional Chinese medicines with potential therapeutic effects.Methods:Datasets of patients with alcoholic liver cirrhosis meeting the criteria were selected from the GEO database.Differentially expressed genes（DEGs）were identified using software analysis，followed by KEGG pathway analysis and protein-protein interaction（PPI）network analysis to screen key genes.Random forest and SVMmodels，commonly used in machine learning，were applied to study the key genes. In addition，literature was reviewed to obtain cuproptosis-related genes（CRGs）.The DEGs and CRGs were intersected to identify core genes associated with cuproptosis，followed by GSEA enrichment analysis. Core genes retrieved from the Coremine database were used to predict therapeutic Chinese herbs.Results:By screening gene chips GSE167308 and GSE142530，278 DEGs were obtained.Through further selection via PPI and validation with GSE143318，10 key genes were identified.Literature review collected 38 CRGs，and the intersection yielded 2 cuproptosis core genes-CDKN2A（Cyclin-Dependent Kinase Inhibitor 2）and PLAT（Plasminogen Activator）.GSEA analysis indicated that the core genes are closely associated with linoleic acid metabolism，amino acid biosynthesis，arginine biosynthesis，HIF-1 signaling pathway，phenylalanine metabolism，and others. Chinese herbs such as Huci（Indian damnacanthus herb），Fengfang（Honeycomb），Diren（Melastoma dode candrum lour），Xiazhicao（Lagopsis supina），Hudilong（Lumbricus）and Qishe（Agkistrodon acutus）were identified through database search，and the attributes of the herbs including their four Qi，five Flavor，and meridian tropism were summarized. Conclusion:CDKN2A and PLAT are core genes related to cuproptosis in alcoholic cirrhosis.Prediction identified 35 Chinese herbs，which may serve as potential treatments for alcoholic cirrhosis.

alcoholic cirrhosis  /  cuproptosis  /  differentially expressed gene  /  Chinese medicine prediction
杨润萌, 吕文良. 酒精性肝硬化相关铜死亡核心基因的生物信息学分析及中药预测. 中医学报, 2025 , 40 (12) : 2520 -2529 . DOI: 10.16368/j.issn.1674-8999.2025.12.394
Runmeng YANG, Wenliang LYU. Bioinformatics Analysis of Core Genes Associated with Cuproptosis in Alcoholic Liver Cirrhosis and Prediction of Traditional Chinese Medicine[J]. Acta Chinese Medicine, 2025 , 40 (12) : 2520 -2529 . DOI: 10.16368/j.issn.1674-8999.2025.12.394
正文
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酒精性肝硬化（alcoholic liver cirrhosis，ALC）是由于长期、大量饮酒以致肝脏受损，发展为肝脏纤维化、肝功能受损，最终导致肝硬化的一种疾病。酒精性肝硬化是肝硬化的常见病种，饮酒频率、饮酒量、饮酒史都与本病密切相关，因此，要做到有效预防，首要措施就是把控上述因素。2019年全球疾病负担研究显示，全球约有25%的肝硬化死亡与酒精有关[1]。除过量饮酒外，个体的遗传基因、饮食习惯、生活方式等因素也会影响ALC的发展。全球范围内酒精性肝病的发病率逐年增加，ALC成为威胁人类健康的重要疾病之一[2]，特别是在发达国家和地区，ALC发病率更是居高不下，给患者的生活质量和健康带来了巨大影响。在诊疗方面，西医对ALC的诊断主要依靠临床表现、病史及肝功能检查等，一旦确诊，治疗方法主要包括戒酒、改善生活方式、药物治疗、肝移植等措施[3]。此外，对于一些合并症如腹水、肝性脑病等，需要采取相应的治疗措施。
铜死亡是一种新兴的细胞死亡模式，近年来在细胞生物学领域引起了广泛的关注[4]。铜死亡是指细胞内铜离子的过度积累导致的细胞死亡现象。铜是一种重要的微量元素，对于维持细胞的正常功能和生物体的存活至关重要[5]，但是当铜离子在细胞内过度积累时，会引起氧化应激、线粒体损伤等一系列细胞损伤反应，最终导致细胞死亡。研究发现，铜死亡在多种疾病中都发挥了重要作用，如心血管疾病[6]、肝脏疾病[7]、肿瘤疾病[8-9]等。肝细胞中的酒精代谢会影响线粒体功能并产生大量的活性氧，导致脂质过氧化[10]，因此，ALC的发生发展与铜死亡密切相关，进一步探究铜死亡在ALC中的作用，有助于揭示疾病的发病机制，为疾病治疗提供新的思路。
1　资料与方法
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1.1　检索GEO数据库及表达基因的差异筛选
在GEO数据库中筛选相关表达谱芯片数据集，寻找ALC患者的相关信息，筛选条件为“alcoholic liver cirrhosis”，物种选择“human”。下载ALC相关数据集GSE167308，其平台为GPL20301，存有ALC肝脏标本7例，正常5例；相关数据集GSE142530，其平台为GPL11154，存有ALC肝脏标本6例，正常12例；平台为GPL18573的基因数据芯片GSE143318，纳入5例ALC肝脏标本和7例健康标本。通过GEO2R对GEO数据库获得的差异表达基因（differentially expressed genes，DEGs）进行分析。并以P＜0.05和|log2FC|≥1为临界值，判断差异是否具有统计学意义。利用在线韦恩图工具（https://bioinfogp.cnb.csic.es/tools/venny/index.html）获取数据集的共有信息，并绘制成图以便可视化分析。
1.2　功能富集分析与蛋白互相作用网络分析
利用Metascape（https://metascape.org/）分析平台对所有DEGs进行KEGG富集分析，以此寻找表达基因所映射的生物学通路和生物学功能。以P＜0.05表示差异具有统计学意义。利用STRING数据库，将先前筛选得到的差异表达基因上传至系统，以获得蛋白质相互作用（protein protein interaction，PPI）网络，并下载数据文件，以供后续的基因筛选。
1.3　核心基因筛选
将1.2项下方法获得的PPI网络数据文件导入Cytoscape3.9.1进行可视化分析，通过使用CytoHubba插件的3种不同算法（radiality、closeness、degree）计算出评分位于前25位的基因，之后将这25位基因取交集获得关键基因。
1.4　核心基因验证
利用GEO数据库下载的GSE143318数据集，对1.3项下方法获取的核心基因进行验证，要求基因表达趋势一致，通过微生信（https://www.bioinformatics.com.cn/）制作热图观测基因表达趋势。
1.5　生物标志物的评判
利用绘图软件制作ROC曲线图，求证最终筛选出的关键DEGs的AUC曲线面积，以期能够找到具有一定诊断价值的生物标志物。AUC数值越接近1，说明准确性越高，反之，若等于0.5则准确性低。
1.6　机器学习
利用R语言中机器学习相关R包（“ggplot2”“random Forest”“caret”等）对所筛选出的关键基因进行数据分析，构建RF和SVM机器学习模型。
1.7　铜死亡相关基因的GSEA富集分析
查找文献，获取铜死亡相关基因（cuproptosis related genes，CRGs）。使用R语言“clusterProfiler”等R包对核心基因进行GSEA富集分析。
1.8　潜在中药预测
通过Coremine数据库（https://coremine.com/medical），搜索关键基因并查找潜在的中药及相关成分，其中以P＜0.05作为筛选标准，将筛选得到的中药与《中华人民共和国药典》《中药大辞典》《中华本草》进行匹配，进行中药的性味归经及药物功效类别统计。
2　结果
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2.1　差异基因筛选
各样本的中位距离和四分位距离基本相同，样本分散程度较小（图1A—B），以P＜0.05、|log2FC|≥1为判断标准，差异具有统计学意义，通过利用GEO2R数据分析工具进行运算可得，GSE167308数据集有584个差异基因，GSE142530数据集有2 055个差异基因，GSE167308和GSE142530共存在278个差异基因（图1C），读取两个芯片数据并制作火山图和热图，查看差异基因的表达趋势（图1D—E）。
2.2　差异基因的KEGG分析
将上述278个差异基因通过Metascape数据库进行KEGG富集分析。KEGG分析结果显示见图2，差异基因主要参与ECM受体相互作用（ECM-receptor interALCtion），胆汁分泌（bile secretion），蛋白质的消化和吸收（protein digestion and absorption），丙氨酸、天冬氨酸和谷氨酸代谢（alanine，aspartate and glutamate metabolism），HIF-1信号通路（HIF-1 signaling pathway），MAPK信号通路（MAPK signaling pathway）等20条通路（前15条通路见表1）。
2.3　差异基因的PPI网络构建以及核心基因的筛选
将两芯片共有的278个差异基因通过STRING数据库进行分析，构建PPI网络，得到解析之后的数据导入Cytoscape软件中，绘制蛋白互作图（图3），通过cytoHubba插件中radiality、closeness、degree算法，选择三者评分最高的前25位基因（图4A—C），并和GSE143318数据集的差异基因（以P＜0.05，|log2FC|≥1）取交集，共筛选出10个核心基因（表2），分别为血管性血友病因子（VWF）、半乳糖凝集素3（LGALS3）、基质金属蛋白酶14（MMP14）、整合素A2（ITGA2）、叉头框蛋白P3（FOXP3）、周期素依赖性激酶抑制因子2（CDKN2A）、热稳定抗原（CD24）、岩藻糖基转移酶4（FUT4）、纤溶酶原激活因子（PLAT）、糖脂类蛋白3（GPC3）。
2.4　核心基因验证
基于GSE143318芯片分析10个关键基因的表达趋势，结果显示均为上调基因，表达趋势与GSE167308、GSE142530一致，绘制热图以观察基因表达情况（图5）。
2.5　ALC的生物marker评估以及相关性系数分析
将最后筛选出来的10个关键基因通过两芯片各自做受试者工作特征曲线图（receiver operating characteristic，ROC），用于评估marker在ALC的诊断价值。由结果可知，在两芯片中，10个基因的曲线下面积（area under curve，AUC）均＞0.7（图6A—B）。通过相关性系数运算，对这些基因进行分析，最终获得相关性系数分析图，如图7所示，10个关键基因之间均呈正相关趋势。
2.6　关键基因的机器学习研究
进一步筛选具有高诊断价值的关键标志物，合并两芯片对照组与疾病组数据对10个关键基因，建立2个机器学习模型进行训练，分别为随机森林、SVM研究，通过R语言处理分析，随机森林算法（图8）中CD24为10个关键基因中显著性最高的基因，SVM算法中（图9），以FUT4、CDKN2A、PLAT3个基因联合诊断疾病最佳，正确率为87.1%。
2.7　核心基因和GSEA分析
将通过筛选获得的关键基因与铜死亡相关驱动基因采用取交集的方法，最终获得两个核心基因：CDKN2A和PLAT，在芯片GSE167308中利用R语言对核心基因分别进行单基因GSEA分析，见图10，结果显示，CDKN2A与亚麻酸代谢、氨基酸生物合成、铁死亡、血小板活化、补体和凝血等关系密切，PLAT与精氨酸生物合成、HIF-1信号通路、苯丙氨酸代谢、血小板活化等关系密切，见表3。
2.8　潜在的中药预测
通过Coremine数据库对核心基因进行检索，并以P＜0.05为筛选条件，得到CDKN2A相关中药如虎刺、蜂房、地稔、阿胶、当归、人参、杏仁、五毒蛇（眼镜蛇）、垂盆草、锡生藤等共计18味中药。得到PLAT相关中药如夏至草、胡地龙、蕲蛇、香椿子、茺蔚子、益母草、丹参、山楂、细辛、蒲黄、水蛭、黄芩等共计17味中药（表4）。通过典籍查找两核心基因相关中药，并对四气五味以及归经绘图，以便可视化研究（图11，图12）。
3　讨论
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酒精性肝病已成为我国最主要的慢性肝病之一[11]，ALC已成为继病毒性肝炎肝硬化后最主要的肝硬化分型，引起了临床广泛关注，但由于致病机制尚不明确，故仍需大量的临床与实验研究以期更好的疗效。酒精性肝病患者一般多不注意自身管理，在发现肝功能异常后，仍不能严格戒酒，长期大量饮酒最终导致ALC的发生，严重影响患者的生活质量，所以进一步寻找有助于治疗ALC的分子靶点至关重要。本文通过剖析GEO在线数据库中的基因芯片，探求ALC中与疾病发生发展相关的DEGs，求证DEGs影响功能的富集通路，探索可能参与铜死亡的因子，为深入研究ALC的发病机制提供新的思路。
信号通路结果表明，差异基因主要参与细胞外间质-受体的相互作用，胆汁分泌，蛋白质消化和吸收，丙氨酸、天冬氨酸和谷氨酸代谢，HIF-1信号通路，MAPK信号通路等20条通路，通过利用Cytoscape软件3种算法解析以及利用GSE143318数据集绘制热图进行验证，结果得到10个关键基因，分别为血管性血友病因子（VWF）、半乳糖凝集素3（LGALS3）、基质金属蛋白酶14（MMP14）、整合素A2（ITGA2）、叉头框蛋白P3（FOXP3）、周期素依赖性激酶抑制因子2（CDKN2A）、热稳定抗原（CD24）、岩藻糖基转移酶4（FUT4）、纤溶酶原激活因子（PLAT）、糖脂类蛋白3（GPC3），分别利用GSE167308和GSE142530两芯片中的基因数据，绘制ROC曲线图，10个关键基因AUC均＞0.7，提示10个基因对于ALC皆具有诊断价值，图中AUC出现了数值等于1的情况，可能与芯片高表达有关，且因样本量少，仍需大样本数据验证，综合两芯片，查测10个基因具有一定的ALC诊断价值，对临床疗效评判也有一定帮助。
机器学习研究结果表明，随机森林图显示CD24最具诊断意义，作为一种细胞黏附分子[12]，其结构特点是一个糖蛋白，由一个具有N -糖基化的核心蛋白和一个糖链组成，是一个仅含31个氨基酸的短肽，在人类体内广泛表达，其编码基因位于人类染色体6上，主要存在于细胞膜，并且在多种组织和细胞类型中都有表达，包括淋巴细胞、肝细胞、神经元等。CD24的功能包括参与细胞黏附、信号传导和免疫调节等多个方面。CD24是一种肿瘤干细胞标记物[13]，能够通过调节肿瘤干细胞的自我更新和分化，参与肿瘤发生发展，其主要机制是在促进肿瘤细胞与周围基质黏附、调节上皮细胞向间叶细胞转化的同时，参与调节多条信号通路[14]，从而介导肿瘤细胞的增殖、侵袭及转移。在肝癌中，其高表达与肿瘤的生长、转移和耐药性密切相关[15]。本研究发现CD24在ALC诊断方面也有一定价值，需更多的实验研究以验证。
由本研究结果可知，CRGs筛选出CDKN2A和PLAT两个核心基因。CDKN2A是一个编码蛋白质基因，位于人类染色体9的21号位置，在人体细胞中起关键的调节作用，通过转录和翻译的过程产生相应的蛋白质，用于细胞内的功能调控，主要参与细胞周期调控和细胞增殖调节。结构包括两个重要的功能区域：p16和p14ARF[16]。p16区域主要参与细胞周期的调控，通过与CDK4和CDK6结合形成复合物，抑制活性，阻止其与细胞周期相关的蛋白质结合。p14ARF区域则主要与肿瘤抑制蛋白p53相互作用，促进细胞凋亡或细胞周期停滞。此外，CDKN2A还参与细胞凋亡和肿瘤抑制等生物学过程[17]。CDKN2A在肝病中亦发挥重要的调节作用，研究表明，CDKN2A的表达水平可能与肝癌的发生和发展密切相关[18]，在肝癌细胞中，CDKN2A的功能被抑制或发生突变，导致细胞周期失控和异常增殖，影响肝脏的病理过程，参与肝病的发生和发展。PLAT是一种相对分子质量69 000左右的丝氨酸蛋白酶，由5个不同的功能结构域组成，是一种重要的蛋白酶[19]，具有重要的生物学功能，其活性中心含有蛋白酶活性位点，在血管内能够催化纤溶酶原转化成纤溶酶，具有纤维蛋白溶解作用[20]，从而促进纤溶系统的活化，降解纤维蛋白，溶解血栓，促进血管内皮细胞的增殖和迁移，参与血管重塑和组织修复等生物学过程。在人体中，PLAT主要由内皮细胞、肝细胞和其他组织细胞产生[21]，PLAT在肝病中发挥重要的调节作用。在肝病的发生发展过程中，PLAT的表达水平可能发生改变，导致纤溶系统失衡。例如，在肝硬化中，其表达可能受到抑制，导致血栓形成和纤维化加重[22-23]，且相关文献报道，过量饮酒会改变PLAT表达量，从而降低溶酶功能[24-25]。在肝癌中，PLAT的表达可能受到调节，致使表达量明显高于正常值[23]。
机器学习模型方面结果表明，SVM显示在10个关键基因取组群诊断，以CDKN2A、PLAT、FUT4 3个基因联合诊断最具诊断意义，正确率为87.1%。细胞凋亡是一种针对衰老、受损、基因突变或病毒感染细胞的通用细胞防御机制，对维持肝脏健康也至关重要[26]。本研究中两个基因为铜死亡相关基因，因此证实细胞铜死亡方式在ALC方面同样发挥重要作用，ALC的产生，可能与CDKN2A、PLAT的高表达有关。
酒精性肝硬化属于中医“酒癖、酒鼓、酒疸”范畴[27]，病因为酒毒湿热之邪，病机以“湿”“浊”“热”为主，病位先在肝胆脾胃，后及于肾，因此治疗原则为清热利湿，兼以健脾[28]，本研究预测虎刺、蜂房、地稔、五毒蛇（眼镜蛇）、垂盆草、锡生藤等18味CDKN2A相关中药，其四气以温、平为主，五味以甘、苦、辛为主，主要归肝、脾、胃经，药物分类以活血祛风、清热利湿为主。夏至草、蕲蛇、茺蔚子、益母草、丹参、山楂、黄芩等17味PLAT相关中药，其四气以寒、温为主，五味以苦、辛、甘为主，主要归肝、肺、脾经，药物分类以清热祛风、活血化瘀为主。药物归经与功效印证了临床ALC的治疗方案，清热利湿，兼以健脾解毒的同时，改善肝脏硬度，预防肝硬化及其并发症的发生。人参[29]、当归[30]、白花败酱草[31]、垂盆草[32]、山楂[33]、黄芩[34]等药均为临床治疗肝病的常用中药，已被许多文献证实其卓越的功效，对于其他中药，皆有挖掘的价值，证明中医药仍是有待开发的巨大宝库，有待更深层的探索。
综上所述，基于数据库和生物信息学分析方法，本研究发现VWF、LGALS3、MMP14、ITGA2、FOXP3、CDKN2A、CD24、FUT4、PLAT、GPC3，这10个关键基因在ALC发生发展中发挥重要作用，并发现CDKN2A、PLAT为影响ALC和铜死亡的核心基因，除此之外，找到35味潜在中药，期待为未来开展ALC的中医临床用药和基础研究提供新的思路。

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doi: 10.16368/j.issn.1674-8999.2025.12.394
  • 接收时间：2025-06-13
  • 首发时间：2026-10-10
  • 出版时间：2025-12-10
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  • 收稿日期：2025-06-13
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    中国中医科学院广安门医院，北京 100053

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吕文良（1966-），男，教授，博士研究生导师，主要从事肝病的中西医防治研究。E-mail:
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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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