Article(id=1322926123566375888, tenantId=1146029695717560320, journalId=1255847919539208197, issueId=1322925862630339452, articleNumber=null, orderNo=null, doi=10.13193/j.issn.1673-7717.2025.07.018, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793339766636, onlineDateStr=2026-10-30, pubDate=1752076800000, pubDateStr=2025-07-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793339766636, onlineIssueDateStr=2026-10-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793339766636, creator=13701087609, updateTime=1793339766636, updator=13701087609, issue=Issue{id=1322925862630339452, tenantId=1146029695717560320, journalId=1255847919539208197, year='2025', volume='43', issue='7', pageStart='1', pageEnd='258', issueExtLink='null', onlineDate='null', pubDate='1752076800000', pubDateStr='2025-07-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1793339704424, creator='13701087609', updateTime=1793340532184, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322929334587105328, tenantId=1146029695717560320, journalId=1255847919539208197, issueId=1322925862630339452, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322929334587105329, tenantId=1146029695717560320, journalId=1255847919539208197, issueId=1322925862630339452, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=80, endPage=85, ext={EN=ArticleExt(id=1322926123784479697, articleId=1322926123566375888, tenantId=1146029695717560320, journalId=1255847919539208197, language=EN, title=Screening of Biomarkers Related to Sciatica and Endoplasmic Reticulum Stress Based on Bioinformatics and Machine Learning and Prediction of Regulating Chinese Medicine, columnId=1256183361521967297, journalTitle=Chinese Archives of Traditional Chinese Medicine, columnName=Target on National Project, runingTitle=null, highlight=null, articleAbstract=
Objective

This study aimed to screen the diagnostic biomarkers in the pathogenesis of sciatica and analyze the genes related to endoplasmic reticulum stress in the pathogenesis of sciatica.

Methods

The data of sciatica microarray was retrieved from Geo database,and the differentially expressed genes(DEGs)associated with endoplasmic reticulum stress were obtained by analyzing the intersection of DEGs and endoplasmic reticulum stress genes. The differentially expressed genes were enriched and analyzed,and the differentially expressed genes were analyzed by immune cell infiltration. The differentially expressed genes associated with immunity were further obtained by combining weighted gene coexpression network analysis. Key genes were screened by Lasso,xgboost and random forest methods to construct a diagnostic model and validate it. It objectively analyzed the correlation between endoplasmic reticulum stress related genes and diagnostic genes,and then predicted the Chinese medicine that can intervene the pathogenesis of sciatica and endoplasmic reticulum stress related genes.

Results

A total of 286 DEGs were obtained by differential analysis,which were mainly concentrated in biological processes such as cell response to reactive oxygen species,cell response to chemical stress,immune receptor activity,oxidoreductase activity,as well as peroxisomeproliferater-activated receptor(PPAR)signaling pathway,phospholipase D signaling pathway,necrotic apoptosis and other signaling pathways. Immunoinfiltration analysis showed that regulatory T cells,follicular helper T cells and memory CD4 T cells were differentially expressed,which also showed that the above three kinds of immune cells played a certain role in the pathogenesis of sciatica.A total of 83 key module genes were obtained by WGCNA combined with immune infiltration analysis. Hub genes such as ZC3H15,RPL7,RPL26l1,TPT1,RPL22l1,RPS15A,RPS3A,RPL36A,RPS24 and RPL30 were obtained by PPI analysis. Four diagnostic genes such as KLF9,scarf1,CYP27A1 and CEBPA were screened by machine learning method,and the four diagnostic genes were correlated with the expression of hub gene. Finally,36 kinds of Chinese medicine that can regulate hub gene were predicted.

Conclusion

KLF9,SCARF1,CYP27A1 and CEBPA genes have diagnostic value for sciatica. Endoplasmic reticulum stress related genes play a certain role in the pathogenesis of sciatica. Quanxie(Scorpio),Yanjingshe(cobra),Ziheche(dried human placenta),Liuhuang(sulfur),Lurong(Cervi Cornu Pantotrichum)and other Chinese medicines may be potential drugs for the prevention and treatment of sciatica.

, authors=Ye WEI1, 2, Guanghui CHEN1, 2, Xiaoling QIN1, Yingtong MENG1, Hao DENG1, Xiang QI3, Yufeng HE1, 2, authorsList=Ye WEI, Guanghui CHEN, Xiaoling QIN, Yingtong MENG, Hao DENG, Xiang QI, Yufeng HE, authorCompany=null, correspAuthors=Yufeng HE, 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=1322926126212981738, articleId=1322926123566375888, tenantId=1146029695717560320, journalId=1255847919539208197, language=CN, title=基于生物信息学与机器学习的坐骨神经痛与内质网应激相关生物标志物筛选及调控中药预测, columnId=1256183362172142382, journalTitle=中华中医药学刊, columnName=国家项目点击, runingTitle=null, highlight=null, articleAbstract=
目的

旨在筛选坐骨神经痛发病进程中的诊断性生物标志物,分析该病进程中的内质网应激相关基因。

方法

检索GEO(http://www.ncbi.nlm.nih.gov/geo)数据库获得坐骨神经痛芯片数据,分析差异表达基因(differentially expressed genes,DEGs)与内质网应激基因取交集获得内质网应激相关差异基因,对差异基因进行富集分析及免疫细胞浸润分析,结果进一步结合加权基因共表达网络分析获得免疫相关差异表达基因。基于最小绝对收缩和选择算子(least absolute shrinkage and selection operator,LASSO)、极限梯度提升算法(eXtreme gradient boosting,XGBoost)与随机森林(random forest,RF)方法筛选关键基因构建诊断性模型并进行验证。分析内质网应激相关基因与诊断性基因的相关性,然后预测能干预坐骨神经痛发病过程中与内质网应激基因相关的中药。

结果

差异分析获得286个DEGs,DEGs主要富集于细胞对活性氧的反应,细胞对化学应激的反应,免疫受体活性,氧化还原酶活性等生物学过程,以及过氧化物酶增殖物激活受体(peroxisomeproliferater-activatedreceptor,PPAR)信号通路,磷脂酶D信号通路,坏死性凋亡等信号通路。免疫浸润分析发现调节性T细胞、滤泡辅助性T细胞、记忆性CD4 T细胞差异表达,这也表明以上3种免疫细胞对坐骨神经痛发病发挥着一定作用。加权基因共表达网络分析(weighted correlation network analysis,WGCNA)结合免疫浸润分析获得83个关键模块基因,蛋白-蛋白互作(protein-protein interaction,PPI)分析得到含锌指CCCH型15(zinc finger CCCH domain-containing protein 15,ZC3H15)、核糖体蛋白L7(ribosomal protein L7,RPL7)、核糖体蛋白L26样1(RPL26L1-ribosomal protein L26 like 1,RPL26L1)、翻译调节肿瘤蛋白1(tumor protein,translationally-controlled 1,TPT1)、核糖体蛋白L22样1(ribosomal protein L22 like 1,RPL22L1)、核糖体蛋白S15A(ribosomal protein S15A,RPS15A)、核糖体蛋白S3A(ribosomal protein S3A,RPS3A)、核糖体蛋白L36A(ribosomal protein L36A,RPL36A)、核糖体蛋白S24(ribosomal protein S24,RPS24)、核糖体蛋白L30(ribosomal protein L30,RPL30)等10个Hub基因。机器学习方法筛选出KLF9、SCARF1、CYP27A1、CEBPA等4个诊断性基因,4个诊断性基因与Hub基因的表达具有相关性。最后预测得到可调控Hub基因的中药共计36味。

结论

KLF9、SCARF1、CYP27A1、CEBPA基因对坐骨神经痛具有诊断价值,内质网应激相关基因在坐骨神经痛发病中起着一定作用,预测出的全蝎、眼镜蛇、紫河车、硫磺、鹿茸等中药可能是防治坐骨神经痛的潜在药物。

, authors=韦业1, 2, 陈广辉1, 2, 覃小伶1, 蒙映彤1, 邓豪1, 祁祥3, 何育风1, 2, authorsList=韦业, 陈广辉, 覃小伶, 蒙映彤, 邓豪, 祁祥, 何育风, authorCompany=null, correspAuthors=何育风, authorNote=

韦业(1984-),男,广西柳州人,主治医师,博士在读,研究方向:针灸推拿干预痛症与亚健康的临床及基础。

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何育风(1969-),男,广西桂林人,主任医师,硕士,研究方向:针灸推拿干预痛症与亚健康的临床及基础。E-mail:。
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韦业(1984-),男,广西柳州人,主治医师,博士在读,研究方向:针灸推拿干预痛症与亚健康的临床及基础。

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韦业(1984-),男,广西柳州人,主治医师,博士在读,研究方向:针灸推拿干预痛症与亚健康的临床及基础。

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序号中药对应基因性味归经功能与主治
1硫磺ZC3H15酸,温,有毒肾、大肠经内服补火助阳通便,外用解毒杀虫疗疮
2牡荆子ZC3H15苦、辛,温肺、大肠经化湿祛痰,止咳平喘,理气止痛
3毛剪秋罗RPL7寒,甘肝、胃经安神,健脾,清热利尿
4千里香RPL7辛、微苦,温,有小毒心、肝、肺经行气活血,散瘀止痛,解毒消肿
5陈皮RPL7苦、辛,温肺、脾经理气健脾,燥湿化痰
6蛤壳RPL26L1咸,寒肺、肾、胃经清热化痰,软坚散结,制酸止痛
7牛蒡子RPL26L1辛、苦,寒肺、胃经疏散风热,宣肺透疹,解毒利咽
8海韭菜RPL26L1甘,平肺、胃、肾经清热生津,解毒利湿
9姜黄TPT1辛、苦,温脾、肝经破血行气,通经止痛
10厚朴花TPT1苦,温脾、胃经理气,化湿
11厚朴TPT1苦、辛,温脾、胃、肺、大肠经燥湿消痰,下气除满
12黄连TPT1苦,寒心、脾、胃、肝、胆、大肠经清热燥湿,泻火解毒
13水牛角TPT1苦,寒心、肝、胃经清热解毒、凉血定惊
14全蝎TPT1辛,平,有毒肝经通络止痛,息风镇痉,攻毒散结
15眼镜蛇TPT1甘、咸,温,有毒肝、肾经祛风、通络、止痛
16苦杏仁TPT1苦,微温,有小毒肺、大肠经降气止咳平喘,润肠通便
17紫河车RPL22L1甘、咸,温肺、肝、肾经温肾补精,益气养血
18鹿茸RPL22L1甘、咸,温肾、肝经补肾壮阳,益精血,强筋骨,调冲任,托疮毒
19水牛角RPS15A苦,寒心、肝、胃经清热解毒,凉血定惊
20蚕沙RPS15A甘、辛,温肝、脾、胃经祛风除湿,和胃化浊,活血通经
21蚕蛾RPS15A咸,温肝、肾经补肾壮阳,涩精,止血,解毒消肿
22白果RPS3A甘、苦、涩,平,小毒肺、肾经敛肺定喘、止带缩尿
23虎杖RPS3A苦、酸,微寒肝、胆经活血散瘀、祛风通络、清热利湿、解毒
24商陆RPS3A苦,寒脾、膀胱、小肠经逐水消肿,通利二便,解毒散结
25覆盆子RPS3A甘、酸,温肾、膀胱经益肾,固精,缩尿
26积雪草RPS3A苦、辛,寒肝、脾、肾经清热利湿,解毒消肿
27桃仁RPS3A苦、甘,平心、肝、大肠经活血祛瘀,润肠通便
28茶树根RPS3A苦,凉心、肾经活血调经,强心利尿,清热解毒
29碧桃干RPS3A酸、苦,平肺、肝经活血止血,止痛,敛汗涩精
30蓖麻子RPS3A甘、辛,平,有小毒肺、大肠经消肿拔毒,泻下通治
31郁金RPS24辛、苦,寒肝、胆、心、肺经活血止痛,行气解郁,清心凉血,利胆退黄
32蛤壳RPL36A咸,寒肺、肾、胃经清肺化痰,软坚散结,利水消肿,制酸止痛,敛疮收湿
33姜黄RPS24辛、苦,温肝、脾经活血行气,通经止痛
34紫河车RPL30甘、咸,温肺、肝、肾经温肾补精,益气养血
35鹿茸RPL30甘、咸,温肾、肝经补肾壮阳,益精血,强筋骨,调冲任,托疮毒
), ArticleFig(id=1322926132844175420, tenantId=1146029695717560320, journalId=1255847919539208197, articleId=1322926123566375888, language=CN, label=表1, caption=

坐骨神经痛发病过程中与内质网应激基因相关的中药预测

, figureFileSmall=null, figureFileBig=null, tableContent=
序号中药对应基因性味归经功能与主治
1硫磺ZC3H15酸,温,有毒肾、大肠经内服补火助阳通便,外用解毒杀虫疗疮
2牡荆子ZC3H15苦、辛,温肺、大肠经化湿祛痰,止咳平喘,理气止痛
3毛剪秋罗RPL7寒,甘肝、胃经安神,健脾,清热利尿
4千里香RPL7辛、微苦,温,有小毒心、肝、肺经行气活血,散瘀止痛,解毒消肿
5陈皮RPL7苦、辛,温肺、脾经理气健脾,燥湿化痰
6蛤壳RPL26L1咸,寒肺、肾、胃经清热化痰,软坚散结,制酸止痛
7牛蒡子RPL26L1辛、苦,寒肺、胃经疏散风热,宣肺透疹,解毒利咽
8海韭菜RPL26L1甘,平肺、胃、肾经清热生津,解毒利湿
9姜黄TPT1辛、苦,温脾、肝经破血行气,通经止痛
10厚朴花TPT1苦,温脾、胃经理气,化湿
11厚朴TPT1苦、辛,温脾、胃、肺、大肠经燥湿消痰,下气除满
12黄连TPT1苦,寒心、脾、胃、肝、胆、大肠经清热燥湿,泻火解毒
13水牛角TPT1苦,寒心、肝、胃经清热解毒、凉血定惊
14全蝎TPT1辛,平,有毒肝经通络止痛,息风镇痉,攻毒散结
15眼镜蛇TPT1甘、咸,温,有毒肝、肾经祛风、通络、止痛
16苦杏仁TPT1苦,微温,有小毒肺、大肠经降气止咳平喘,润肠通便
17紫河车RPL22L1甘、咸,温肺、肝、肾经温肾补精,益气养血
18鹿茸RPL22L1甘、咸,温肾、肝经补肾壮阳,益精血,强筋骨,调冲任,托疮毒
19水牛角RPS15A苦,寒心、肝、胃经清热解毒,凉血定惊
20蚕沙RPS15A甘、辛,温肝、脾、胃经祛风除湿,和胃化浊,活血通经
21蚕蛾RPS15A咸,温肝、肾经补肾壮阳,涩精,止血,解毒消肿
22白果RPS3A甘、苦、涩,平,小毒肺、肾经敛肺定喘、止带缩尿
23虎杖RPS3A苦、酸,微寒肝、胆经活血散瘀、祛风通络、清热利湿、解毒
24商陆RPS3A苦,寒脾、膀胱、小肠经逐水消肿,通利二便,解毒散结
25覆盆子RPS3A甘、酸,温肾、膀胱经益肾,固精,缩尿
26积雪草RPS3A苦、辛,寒肝、脾、肾经清热利湿,解毒消肿
27桃仁RPS3A苦、甘,平心、肝、大肠经活血祛瘀,润肠通便
28茶树根RPS3A苦,凉心、肾经活血调经,强心利尿,清热解毒
29碧桃干RPS3A酸、苦,平肺、肝经活血止血,止痛,敛汗涩精
30蓖麻子RPS3A甘、辛,平,有小毒肺、大肠经消肿拔毒,泻下通治
31郁金RPS24辛、苦,寒肝、胆、心、肺经活血止痛,行气解郁,清心凉血,利胆退黄
32蛤壳RPL36A咸,寒肺、肾、胃经清肺化痰,软坚散结,利水消肿,制酸止痛,敛疮收湿
33姜黄RPS24辛、苦,温肝、脾经活血行气,通经止痛
34紫河车RPL30甘、咸,温肺、肝、肾经温肾补精,益气养血
35鹿茸RPL30甘、咸,温肾、肝经补肾壮阳,益精血,强筋骨,调冲任,托疮毒
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基于生物信息学与机器学习的坐骨神经痛与内质网应激相关生物标志物筛选及调控中药预测
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韦业 1, 2 , 陈广辉 1, 2 , 覃小伶 1 , 蒙映彤 1 , 邓豪 1 , 祁祥 3 , 何育风 1, 2
中华中医药学刊 | 国家项目点击 2025,43(7): 80-85
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中华中医药学刊 |国家项目点击 2025 , 43 (7) : 80 -85
基于生物信息学与机器学习的坐骨神经痛与内质网应激相关生物标志物筛选及调控中药预测
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2.广西中医药大学第一附属医院,广西 南宁 530023, bio={"content":"

韦业(1984-),男,广西柳州人,主治医师,博士在读,研究方向:针灸推拿干预痛症与亚健康的临床及基础。

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韦业(1984-),男,广西柳州人,主治医师,博士在读,研究方向:针灸推拿干预痛症与亚健康的临床及基础。

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韦业1, 2, 陈广辉1, 2, 覃小伶1, 蒙映彤1, 邓豪1, 祁祥3, 何育风1, 2
作者信息
  • 1.广西中医药大学,广西 南宁 530001
  • 2.广西中医药大学第一附属医院,广西 南宁 530023
  • 3.河南中医药大学,河南 郑州 450000
通讯作者:
何育风(1969-),男,广西桂林人,主任医师,硕士,研究方向:针灸推拿干预痛症与亚健康的临床及基础。E-mail:。
作者简介:

韦业(1984-),男,广西柳州人,主治医师,博士在读,研究方向:针灸推拿干预痛症与亚健康的临床及基础。

Screening of Biomarkers Related to Sciatica and Endoplasmic Reticulum Stress Based on Bioinformatics and Machine Learning and Prediction of Regulating Chinese Medicine
Ye WEI1, 2, Guanghui CHEN1, 2, Xiaoling QIN1, Yingtong MENG1, Hao DENG1, Xiang QI3, Yufeng HE1, 2
Affiliations
  • 1.Guangxi University of Chinese Medicine,Nanning 530001,Guangxi,China
  • 2.The First Affiliated Hospital of Guangxi University of Chinese Medicine,Nanning 530023,Guangxi,China
  • 3.Henan University of Chinese Medicine,Zhengzhou 450000,Henan,China
出版时间: 2025-07-10 doi: 10.13193/j.issn.1673-7717.2025.07.018
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目的

旨在筛选坐骨神经痛发病进程中的诊断性生物标志物,分析该病进程中的内质网应激相关基因。

方法

检索GEO(http://www.ncbi.nlm.nih.gov/geo)数据库获得坐骨神经痛芯片数据,分析差异表达基因(differentially expressed genes,DEGs)与内质网应激基因取交集获得内质网应激相关差异基因,对差异基因进行富集分析及免疫细胞浸润分析,结果进一步结合加权基因共表达网络分析获得免疫相关差异表达基因。基于最小绝对收缩和选择算子(least absolute shrinkage and selection operator,LASSO)、极限梯度提升算法(eXtreme gradient boosting,XGBoost)与随机森林(random forest,RF)方法筛选关键基因构建诊断性模型并进行验证。分析内质网应激相关基因与诊断性基因的相关性,然后预测能干预坐骨神经痛发病过程中与内质网应激基因相关的中药。

结果

差异分析获得286个DEGs,DEGs主要富集于细胞对活性氧的反应,细胞对化学应激的反应,免疫受体活性,氧化还原酶活性等生物学过程,以及过氧化物酶增殖物激活受体(peroxisomeproliferater-activatedreceptor,PPAR)信号通路,磷脂酶D信号通路,坏死性凋亡等信号通路。免疫浸润分析发现调节性T细胞、滤泡辅助性T细胞、记忆性CD4 T细胞差异表达,这也表明以上3种免疫细胞对坐骨神经痛发病发挥着一定作用。加权基因共表达网络分析(weighted correlation network analysis,WGCNA)结合免疫浸润分析获得83个关键模块基因,蛋白-蛋白互作(protein-protein interaction,PPI)分析得到含锌指CCCH型15(zinc finger CCCH domain-containing protein 15,ZC3H15)、核糖体蛋白L7(ribosomal protein L7,RPL7)、核糖体蛋白L26样1(RPL26L1-ribosomal protein L26 like 1,RPL26L1)、翻译调节肿瘤蛋白1(tumor protein,translationally-controlled 1,TPT1)、核糖体蛋白L22样1(ribosomal protein L22 like 1,RPL22L1)、核糖体蛋白S15A(ribosomal protein S15A,RPS15A)、核糖体蛋白S3A(ribosomal protein S3A,RPS3A)、核糖体蛋白L36A(ribosomal protein L36A,RPL36A)、核糖体蛋白S24(ribosomal protein S24,RPS24)、核糖体蛋白L30(ribosomal protein L30,RPL30)等10个Hub基因。机器学习方法筛选出KLF9、SCARF1、CYP27A1、CEBPA等4个诊断性基因,4个诊断性基因与Hub基因的表达具有相关性。最后预测得到可调控Hub基因的中药共计36味。

结论

KLF9、SCARF1、CYP27A1、CEBPA基因对坐骨神经痛具有诊断价值,内质网应激相关基因在坐骨神经痛发病中起着一定作用,预测出的全蝎、眼镜蛇、紫河车、硫磺、鹿茸等中药可能是防治坐骨神经痛的潜在药物。

坐骨神经痛  /  中药  /  机器学习  /  生物信息学  /  内质网应激
Objective

This study aimed to screen the diagnostic biomarkers in the pathogenesis of sciatica and analyze the genes related to endoplasmic reticulum stress in the pathogenesis of sciatica.

Methods

The data of sciatica microarray was retrieved from Geo database,and the differentially expressed genes(DEGs)associated with endoplasmic reticulum stress were obtained by analyzing the intersection of DEGs and endoplasmic reticulum stress genes. The differentially expressed genes were enriched and analyzed,and the differentially expressed genes were analyzed by immune cell infiltration. The differentially expressed genes associated with immunity were further obtained by combining weighted gene coexpression network analysis. Key genes were screened by Lasso,xgboost and random forest methods to construct a diagnostic model and validate it. It objectively analyzed the correlation between endoplasmic reticulum stress related genes and diagnostic genes,and then predicted the Chinese medicine that can intervene the pathogenesis of sciatica and endoplasmic reticulum stress related genes.

Results

A total of 286 DEGs were obtained by differential analysis,which were mainly concentrated in biological processes such as cell response to reactive oxygen species,cell response to chemical stress,immune receptor activity,oxidoreductase activity,as well as peroxisomeproliferater-activated receptor(PPAR)signaling pathway,phospholipase D signaling pathway,necrotic apoptosis and other signaling pathways. Immunoinfiltration analysis showed that regulatory T cells,follicular helper T cells and memory CD4 T cells were differentially expressed,which also showed that the above three kinds of immune cells played a certain role in the pathogenesis of sciatica.A total of 83 key module genes were obtained by WGCNA combined with immune infiltration analysis. Hub genes such as ZC3H15,RPL7,RPL26l1,TPT1,RPL22l1,RPS15A,RPS3A,RPL36A,RPS24 and RPL30 were obtained by PPI analysis. Four diagnostic genes such as KLF9,scarf1,CYP27A1 and CEBPA were screened by machine learning method,and the four diagnostic genes were correlated with the expression of hub gene. Finally,36 kinds of Chinese medicine that can regulate hub gene were predicted.

Conclusion

KLF9,SCARF1,CYP27A1 and CEBPA genes have diagnostic value for sciatica. Endoplasmic reticulum stress related genes play a certain role in the pathogenesis of sciatica. Quanxie(Scorpio),Yanjingshe(cobra),Ziheche(dried human placenta),Liuhuang(sulfur),Lurong(Cervi Cornu Pantotrichum)and other Chinese medicines may be potential drugs for the prevention and treatment of sciatica.

sciatica  /  Chinese medicine  /  machine learning  /  bioinformatics  /  endoplasmic reticulum stress
韦业, 陈广辉, 覃小伶, 蒙映彤, 邓豪, 祁祥, 何育风. 基于生物信息学与机器学习的坐骨神经痛与内质网应激相关生物标志物筛选及调控中药预测. 中华中医药学刊, 2025 , 43 (7) : 80 -85 . DOI: 10.13193/j.issn.1673-7717.2025.07.018
Ye WEI, Guanghui CHEN, Xiaoling QIN, Yingtong MENG, Hao DENG, Xiang QI, Yufeng HE. Screening of Biomarkers Related to Sciatica and Endoplasmic Reticulum Stress Based on Bioinformatics and Machine Learning and Prediction of Regulating Chinese Medicine[J]. Chinese Archives of Traditional Chinese Medicine, 2025 , 43 (7) : 80 -85 . DOI: 10.13193/j.issn.1673-7717.2025.07.018
神经病理性疼痛(neuropathic pain,NP)是由外周或中枢神经系统疾病或躯体感觉神经系统受损引起的疼痛[1]。坐骨神经痛作为神经病理性疼痛中的一种类型,是由多种因素刺激坐骨神经而引起的一种临床综合征,临床主要表现为患者坐骨神经通路及其分布区域处出现持续或阵发的放射性疼痛、麻木[2]。研究显示,坐骨神经痛患病率约为40%,已成为全球关注的热点问题[3],并给社会和家庭带来了巨大的经济负担。然而,坐骨神经痛的发病机制仍尚未清楚,亦缺乏安全有效的防治手段。
内质网(endoplasmic reticulum,ER)作为真核生物中重要的细胞器,参与细胞稳态,包括折叠蛋白的运输,以及维持细胞内Ca2+的浓度等。研究发现,当各种状态下导致机体稳态紊乱时,大量未折叠的蛋白增加并在腔内聚集,导致内质网应激(endoplasmic reticulum stress,ERS)[4-5],并引发细胞凋亡。最近的一些研究表明神经病理性疼痛和内质网应激之间存在关联[6-8],但目前尚未有从内质网应激角度探讨坐骨神经痛发病机制的研究。因此,本研究联合生物信息学与机器学习方法筛选出坐骨神经痛发病进程中的诊断性生物标志物,构建了一个坐骨神经痛的诊断模型。研究以内质网应激为切入点,分析内质网应激相关基因与免疫细胞浸润之间的关系,探讨该疾病发展过程中潜在的生物学机制,以期为坐骨神经痛的诊断、治疗提供新思路。
本研究数据来源为GEO(http://www.ncbi.nlm.nih.gov/geo)数据库中获得的基于GPL21185平台的GSE150408基因表达谱数据集作为训练集,来自GPL570平台的GSE147383基因表达谱数据集作为验证集。训练集包含17例疾病组与17例对照组,验证集的数据疾病组与对照组分别为4例。使用“limma”包的normexp与normalizeBetweenArrays功能进行背景校正与数据归一化处理,内质网应激特征基因的获取来自GeneCards数据库(https://www.genecards.org)。
利用R软件的“limma”包对GSE150408中的差异表达基因(differentially expressed genes,DEGs)进行了鉴定,DEGs的筛选参数设置为log FC绝对值≥0.3和P<0.05。然后,利用R软件“pheatmap”与“ggplot2”包绘制DEGs的热图和火山图,将获取的内质网应激特征基因与DEGs取交集获取内质网应激相关差异基因,并绘制韦恩图。
为了揭示DEGs潜在的生物学功能和潜在机制,本研究使用R包“cluster Profiler”对DEGs进行了基因本体(gene ontology,GO)、京都基因和基因百科全书(Kyoto encyclopedia of genes and genomes,KEGG)富集分析。
免疫微环境一般由免疫细胞、炎症细胞、成纤维细胞、间充质细胞等不同的细胞因子和趋化因子组成,免疫细胞浸润分析在预测疾病病程和疾病治疗方面具有重要作用[9]。使用R语言“CIBERSORT”包对DEGs数据集进行免疫浸润分析并量化不同免疫模式个体中22种免疫细胞类型的丰度,采用Wilcoxon秩和检验评估免疫细胞比例的差异,P<0.05视为差异有统计学意义。使用软件包“ggplot2”对结果进行可视化分析。
加权基因共表达网络分析(weighted correlation network analysis,WGCNA)可用于寻找高度相关的基因聚类模块,本研究基于“WGCNA”包进行加权基因共表达网络分析[10]。首先,对研究样本进行分层聚类,检测离群值并去除异常样本。然后选择β=2为软阈值建立无尺度网络与拓扑重叠矩阵,最后计算各个模块和免疫浸润细胞之间的相关性。具有高相关系数的模块被认为是与差异浸润免疫细胞相关的候选模块,并将模块基因与内质网应激差异基因取交集获取与免疫相关的关键基因,并绘制韦恩图。
基于STRING数据库(https://www.stringdb.org)建立内质网应激与免疫相关基因的蛋白相互作用网络(protein-protein interaction network,PPI)。随后,使用Cytoscape软件(版本3.8.2)对PPI网络进行可视化。Cytohubba是Cytoscape软件中识别核心(Hub)节点的插件,它提供12种分析算法来计算蛋白质互作网络图中的Hub基因,本研究基于Cytohubba计算Hub基因。
基于机器学习的方法在差异基因中寻找疾病的诊断性基因。运用最小绝对收缩和选择算子(least absolute shrinkage and selection operator,LASSO)回归方法,通过“glmnet”包对差异基因进行过滤,通过交叉验证寻找误差最小的则为疾病特征基因,然后运用随机森林(random forest,RF)方法筛选基因,基于极限梯度提升算法(eXtreme gradient boosting,XGBoost)筛选基因,最后将筛选后的疾病特征基因进行相交,并绘制韦恩图,筛选出最终的诊断性基因。将筛选出基因构建诊断模型,然后利用校准曲线(calibration curve)来评估线形图模型的预测能力。最后,采用决策曲线分析(decision curve analysis,DCA)来评估该模型的临床效用,用受试者工作特征曲线(receiver operating characteristic curve,ROC)来评估其诊断价值。
采用Pearson相关性分析揭示诊断性基因与Hub基因的相关性,相关性检验P值过滤条件为P<0.01。
基于coremine数据库(https://www.coremine.com/medical)与本草组鉴(http://herb.ac.cn)数据库预测能干预坐骨神经痛发病过程中与内质网应激基因相关的中药,基于《中华人民共和国药典》《中华本草》查询中药的性味、归经以及功效。
研究流程见图1。通过“limma”包对规范化后的芯片进行差异分析获得286个DEGs,其中95个DEG表达下调,191个DEG表达上调,进一步绘制的火山和热图见插页ⅩⅩⅨ图2A-图2B。进一步将获取的内质网应激特征基因与DEGs取交集获取112个内质网应激相关差异基因。见插页ⅩⅩⅨ图2C。
DEGs的GO富集分析结果显示,差异基因涉及细胞对活性氧的反应,细胞对化学应激的反应,免疫受体活性,氧化还原酶活性等生物学过程;KEGG分析结果主要涉及自噬,白细胞跨内皮迁移,线粒体吞噬-动物,自然杀伤细胞介导的细胞毒性,PPAR信号通路,磷脂酶D信号通路,坏死性凋亡等信号通路。见插页ⅩⅩⅩ图3。
通过“CIBERSORT”包进行免疫浸润分析发现,疾病组和对照组之间有3种免疫细胞存在显著差异(P<0.05)。其中调节性T细胞(T.cells.regulatory.Tregs.),滤泡辅助性T细胞(T.cells.follicular.helper),记忆性CD4 T细胞(T.cells.CD4.memory.resting)在疾病组低表达,可推以上3种免疫细胞在坐骨神经痛的发病中起着一定作用。见插页ⅩⅩⅪ图4。
基于GEO数据中的GSE62646数据集进行聚类分析,进一步识别离群样本并排除。见插页ⅩⅩⅩ图5A。当R2>0.9和平均连接性较高时将软阈值β设置为10。见插页ⅩⅩⅩ图5B。使用动态剪切树法鉴定出17个目标模块。见插页ⅩⅩⅩ图5C-图5D。进一步将目标模块和免疫浸润细胞进行相关性分析发现(插页ⅩⅩⅫ图6),绿色模块与T.cells.regulatory.Tregs.高度正相关(r=0.76,p=5e-05),红色模块与T cells CD4 memory activated高度正相关(r=0.58,p=0.005),青绿色模块与T cells CD4 memory resting高度正相关(r=0.63,p=0.002),将模块基因导出获得2692个关键基因,将模块基因与内质网应激相关差异基因取交集得到83个关键基因进行下一步分析。见插页ⅩⅩⅫ图7A。
为了识别83个基因之间存在潜在的交互作用,将基因导入STRING进行PPI分析并将数据导入Cytoscape软件中,基于Cytohubba插件计算得出含锌指CCCH型15(zinc finger CCCH domain-containing protein 15,ZC3H15)、核糖体蛋白L7(ribosomal protein L7,RPL7)、核糖体蛋白L26样1(RPL26L1-ribosomal protein L26 like 1,RPL26L1)、翻译调节肿瘤蛋白1(tumor protein,translationallycontrolled 1,TPT1)、核糖体蛋白L22样1(ribosomal protein L22 like 1,RPL22L1)、核糖体蛋白S15A(ribosomal protein S15A,RPS15A)、核糖体蛋白S3A(ribosomal protein S3A,RPS3A)、核糖体蛋白L36A(ribosomal protein L36A,RPL36A)、核糖体蛋白S24(ribosomal protein S24,RPS24)、核糖体蛋白L30(ribosomal protein L30,RPL30)等10个Hub基因。见插页ⅩⅩⅫ图7B。
本研究通过3种机器算法来识别诊断性基因,其中LASSO回归分析从具有统计学意义的变量中选择10个预测基因,见插页ⅩⅩⅩⅢ图8A-图8B;Random Forest筛选出13个预测基因,见插页ⅩⅩⅩⅣ图8C-图8D;XGboost筛选出14个基因,见插页ⅩⅩⅩⅣ图9A。绘制VENN图得到4个交集基因,分别为Kruppel样因子9(Kruppel like factor9,KLF9)、清道夫受体F类成员1(scavenger receptor type F family member1,SCARF1)、细胞色素P450家族27亚家族A成员 1(cytochrome P450 family 27 subfamily A member1,CYP27A1)、CCAAT增强结合蛋白α(CCAAT/enhancer binding protein alpha,CEBPA),见插页ⅩⅩⅩⅣ图9B。分析结果发现KLF9基因在疾病组低表达,SCARF1、CYP27A1以及CEBPA基因在疾病组高表达,见插页ⅩⅩⅩⅣ-插页ⅩⅩⅩⅤ图10。进一步基于“rms”包构建坐骨神经痛的诊断列线图。见插页ⅩⅩⅩⅣ图9C,并使用校准曲线评估该模型的预测能力,校准曲线显示,真实患病风险与预测患病风险之间的差异非常小,说明列线图模型预测坐骨神经痛的发病较为准确,见插页ⅩⅩⅩⅣ图9D。ROC分析结果发现 KLF9(AUC:0.785)、SCARF1(AUC:0.851)、CYP27A1(AUC:0.806)、CEBPA(AUC:0.882)的AUC值都在0.6以上,见插页ⅩⅩⅫ图7A,验证集数据分析结果与训练集相似,见插页ⅩⅩⅫ图7B。DCA分析结果发现,KLF9、SCARF1、CYP27A1、CEBPA曲线在Risk Threshold为0~0.4时可使患者获得较大临床效益,见插页ⅩⅩⅩⅢ图7C。在验证集的分析也证实了以上结论,见插页ⅩⅩⅩⅢ图7D。这进一步证明列线图模型可以对早期坐骨神经痛患者具有较好的诊断能力。
本部分研究将机器学习筛选的4个诊断性基因与Hub基因进行相关性分析。结果显示,KLF9与TPT1、RPL30、RPS15A有显著正相关;CEBPA与TPT1、RPL30、RPS15A、RPS26L1、RPS24、RPS3A、RPS22L1的表达显著负相关;CYP27A1与TPT1、RPS15A、RPS24的表达显著负相关;SCARF1与RPL7、RPL22L1、RPS15A、RPS3A、RPL36A、ZC3H15的表达显著负相关。见插页ⅩⅩⅩⅤ图11。
本部分研究预测得到可调控Hub基因的中药共计35味。其功效以温补肾阳、通络止痛、理气活血为主,如表1所示;中药以甘、苦味药和温性药为主,归经情况以肝肾经为主。见图12。
坐骨神经痛是一种由多种原因引起坐骨神经原发性或继发性损害所导致的疼痛综合征[11]。研究发现,腰椎间盘突出症(lumbar disc herniation,LDH)是导致坐骨神经痛的主要原因之一,其发病机制一般认为腰椎间盘突出病变中突出的髓核会压迫和过度牵伸脊神经根,而神经根部位受到髓核机械性压迫,神经根常易损伤,沿神经根产生放射性疼痛[12]。这一过程中伴随着髓核局部炎症反应,可进一步激发机体免疫反应并激活小胶质细胞,同时炎性因子、趋化因子的释放与氧化应激等反应随之发生[13]。目前,内质网应激在坐骨神经痛中的调控机制尚未得到研究。因此,本研究试图阐明内质网应激相关基因在坐骨神经痛发病机制中的具体作用。
本研究对GEO数据库的GSE150408数据集进行了生物信息学方法分析,最终得出286个DEGs,进一步将获取的内质网应激特征基因与DEGs取交集获取112个内质网应激相关差异基因。GO和KEGG富集分析的结果表明,差异基因涉及细胞对活性氧的反应、细胞对化学应激的反应、免疫受体活性、氧化还原酶活性等生物学过程,以及自噬、白细胞跨内皮迁移、线粒体吞噬-动物、自然杀伤细胞介导的细胞毒性、PPAR信号通路、磷脂酶D信号通路、坏死性凋亡等信号通路。
DEGs的免疫浸润分析结果显示疾病组与对照组之间其中调节性T细胞、滤泡辅助性T细胞、记忆性CD4 T细胞在疾病组低表达,可推以上3种免疫细胞在坐骨神经痛的发病中起着一定作用。椎间盘由髓核、纤维环和软骨板三部分组成,是人体最大的无血管器官。研究认为,椎间盘独特的结构将髓核从宿主的免疫系统中分离出来,正常状态下椎间盘中细胞因子的表达对免疫细胞浸润具有抑制作用,病理状态下髓核的自身免疫反应伴随着多种下游级联反应,这可能直接导致坐骨神经痛的发生[14]。WGCNA分析将目标模块和免疫浸润细胞进行相关性分析发现,绿色模块、红色模块、青绿色模块与免疫细胞相关性较高,进一步将模块基因导出获得2692个关键基因,将模块基因与内质网应激相关差异基因取交集得到83个关键基因进行PPI分析,并基于Cytoscape软件中Cytohubba插件计算得出 ZC3H15、RPL7、RPL26L1、TPT1、RPL22L1、RPS15A、RPS3A、RPL36A、RPS24、RPL30等10个Hub基因。可以推测以上基因对坐骨神经痛发病过程中的免疫反应具有调节作用。
近年来,机器学习在临床领域得到了广泛的应用,并被认为是医疗领域的一个重要工具[15]。本研究通过LASSO、XGboost和Random Forest 3种机器算法来识别诊断性基因,最终筛选得到4个交集基因,分别为KLF9、SCARF1、CYP27A1、CEBPA。基于以上基因建立了坐骨神经痛的诊断模型,并对其诊断价值进行了验证。GSE147383数据集作为验证集的分析也证实了以上结论。ROC分析结果发现验证集AUC值都在0.6以上,验证集数据分析结果与训练集相似。但未来仍需要具体的实验验证以上标志物在坐骨神经痛发病中的分子机制。
此外,研究将机器学习筛选的4个诊断性基因与Hub基因的相关性进行分析。结果显示,KLF9、SCARF1、CYP27A1、CEBPA基因与Hub基因有显著相关性,在坐骨神经痛病变中存在潜在的调控作用。有研究发现,给予KLF9结合的诱饵可显著降低神经损伤大鼠模型机械超敏反应,这也佐证了KLF9作为治疗神经疼痛潜在靶点[16]。亦有研究发现,CYP27A1被认为是椎间盘退行性变(intervertebral disc degeneration,IDD)发生发展过程中的中枢脂质代谢相关基因,尤其是在循环中被认为是诊断IDD的候选生物标志物[17]。CEBPA基因是髓系祖细胞增殖与分化过程中一个重要的转录因子,在造血干细胞自我更新和髓样分化驱动转录程序中起着重要的作用[18]。研究发现,ERS发生时可活化转录激活因6(activating transcription factor6,ATF6)途径,活化后的ATF6进入胞核与转录因子-Y(nuclear factor-Y,NF-Y)结合从而使得钙网蛋白(Calreticulin,CRT)表达升高,CRT与CEBPA mRNA结合抑制CEBPA的表达,阻碍骨髓细胞的生长发育[19]。IDD发病过程中往往伴随着髓核细胞的衰减,而通过补充骨髓间充质干细胞改善椎间盘内减少的髓核细胞延缓IDD进展是当前研究的热点[20]。据此推测,CEBPA基因可能与椎间盘退变的发病相关。当前SCARF1与坐骨神经痛相关性鲜有研究,但研究发现了SCARF1在疾病病理学中的许多其他作用,包括在先天性和适应性免疫中的作用,但其在坐骨神经痛发病中的作用尚待实验验证[21]。
中医学认为坐骨神经痛属“腰腿痛”“痹证”范畴,其病机为肝肾亏虚、经脉不荣、复感外邪,以致经筋挛急发为本病。其病为本虚标实之证,虚实夹杂,肝肾两虚,以虚证为主,兼夹实邪[22]。本研究对内质网应激相关基因进行中药预测与筛选,结果发现预测出的药物五味以甘、苦味药为主,四气以温性药为主,归经情况以肝、肾经为主。从四气五味角度看,预测药物以苦温清热燥湿,以甘温补虚,与本病研究学者从肝、肾论治坐骨神经痛的观点一致[23-24]。因此,临床使用肝、肾两经引经药治疗腰腿经络痹阻引发的坐骨神经痛有一定可行性。临床治疗坐骨神经痛多以补肾壮骨、舒筋通络、益气活血为原则,预测得到全蝎、眼镜蛇、紫河车、硫磺、鹿茸、蚕沙、原蚕蛾、覆盆子、千里香皆有补肾阳、舒经络之效。剪秋罗、姜黄、积雪草、桃仁、茶树根、碧桃干、黄丝郁金具有活血化瘀、消肿止痛之效。本研究预测结果与临床相应,由此,以上中药可能是治疗坐骨神经痛的潜在药物[25-26]。
综上,本研究为预测坐骨神经痛发病提供了新的诊断性靶点,证实了KLF9、SCARF1、CYP27A1、CEBPA基因对坐骨神经痛具有诊断价值。研究还预测了坐骨神经痛发病过程中内质网应激与免疫浸润相关的特征基因,并基于特征基因预测了相关调控中药,这为探索坐骨神经痛的发病机制及中医药对该疾病的干预机制提供了一定的参考。然而,本研究仍存在局限性。文章基于GEO数据库进行分析,纳入的样本量有限,且预测中药治疗坐骨神经痛的药效缺乏相应的实验验证。未来的研究应当设计科学的临床试验与基础实验,验证以上诊断性生物标志物的临床适用性,中药药效以及内质网应激与免疫浸润相关的特征基因在坐骨神经痛发病过程中发挥的作用。
  • 国家自然科学基金项目(82160943)
  • 广西重点研发计划项目(桂科AB20159026)
  • 广西自然科学基金项目(2020GXNSFAA259013)
  • 广西中医药重点培育学科建设项目(GZXK-Z-20-32)
  • 广西壮族自治区中医药管理局项目(GZZC2020042)
  • 广西中医药适宜技术开发与推广项目(GZSY21-13)
  • 广西中医药大学高层次人才培育创新团队项目(2022A006)
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2025年第43卷第7期
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doi: 10.13193/j.issn.1673-7717.2025.07.018
  • 首发时间:2026-10-30
  • 出版时间:2025-07-10
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基金
国家自然科学基金项目(82160943)
广西重点研发计划项目(桂科AB20159026)
广西自然科学基金项目(2020GXNSFAA259013)
广西中医药重点培育学科建设项目(GZXK-Z-20-32)
广西壮族自治区中医药管理局项目(GZZC2020042)
广西中医药适宜技术开发与推广项目(GZSY21-13)
广西中医药大学高层次人才培育创新团队项目(2022A006)
作者信息
    1.广西中医药大学,广西 南宁 530001
    2.广西中医药大学第一附属医院,广西 南宁 530023
    3.河南中医药大学,河南 郑州 450000

通讯作者:

何育风(1969-),男,广西桂林人,主任医师,硕士,研究方向:针灸推拿干预痛症与亚健康的临床及基础。E-mail:。
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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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