Article(id=1241035549562163607, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241035543589483182, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202408288, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1724169600000, receivedDateStr=2024-08-21, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773815532340, onlineDateStr=2026-03-18, pubDate=1735056000000, pubDateStr=2024-12-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773815532340, onlineIssueDateStr=2026-03-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773815532340, creator=13701087609, updateTime=1773815532340, updator=13701087609, issue=Issue{id=1241035543589483182, tenantId=1146029695717560320, journalId=1227665162245664772, year='2024', volume='51', issue='24', pageStart='4417', pageEnd='4608', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773815530917, creator=13701087609, updateTime=1773815686426, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241036195896029478, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241035543589483182, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241036195896029479, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241035543589483182, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=4585, endPage=4590, ext={EN=ArticleExt(id=1241035549927068068, articleId=1241035549562163607, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Construction and validation of a prognostic model for breast cancer in disulfidptosis related genes, columnId=null, journalTitle=Modern Preventive Medicine, columnName=null, runingTitle=null, highlight=null, articleAbstract=
Objective

To explore the association between disulfidptosis related genes (DRGs) and the prognosis of breast cancer patients and establish a risk prognosis model and verify it, and provide new biomarkers for the prognosis of breast cancer patients.

Methods

CNV landscape was drawnin R language. DRGs of co-correlations and differences were identified.The risk score prognostic model was constructed by using univariate Cox regression analysis and Lasso-Cox regression analysis. Kaplan-Meier survival curve,ROC curve and calibration curve for the model was drawn.A nomogram prognostic prediction model was constructed by combining the clinical features.

Results

A risk prognostic model of breast cancer patients composed of 8 DRGs was constructed, and AUC of the ROC curve at 1,3, and 5 years was 0.809,0.848,0.883. DCA showed that the model could better predict breast cancer prognosis.

Conclusion

This research department has constructed a risk score model with 8 DRGs, which has a good prognostic value and can provide a new direction for the study of breast cancer prognosis.

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目的

本研究旨在探究双硫死亡相关基因(DRGs)与乳腺癌患者预后之间的关联,建立风险预后模型并进行验证,为乳腺癌患者的预后提供新的生物标志物。

方法

使用R语言对双硫死亡基因绘制CNV景观,鉴定共相关和差异的DRGs,使用单因素Cox回归分析和Lasso-Cox回归分析的方法构建风险评分预后模型,绘制Kaplan-Meier生存曲线、ROC曲线及校准曲线对该模型进行验证,随后联合临床特征构建列线图预后预测模型。

结果

构建了一个由8个DRGs组成的乳腺癌患者的风险预后模型,训练集中ROC曲线1、3、5年的AUC为0.809、0.848、0.883,DCA显示该模型能更好地预测乳腺癌的预后。

结论

本研究部构建了一个由8个DRGs搭建的风险评分模型,该模型预后价值较好,可为乳腺癌的预后研究提供新的方向。

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王萍玉,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=noATdIJsXozWHnGkUWDkmw==, magXml=A40eruqLuSXDutqZKVUJPA==, pdfUrl=null, pdf=VUyjg2sP223tK1zgZcNlmg==, pdfFileSize=2153729, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=JARcyu/WUwGVHeHdotJ1/A==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=fUAbHeItpUo+UzctZNWMdA==, mapNumber=null, authorCompany=null, fund=null, authors=

刘浩然,女,硕士研究生;

王萍玉,博士,教授,博士研究生导师

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BMC Endocrine Disorders, 2022, 22(1): 15., articleTitle=Hypoglycemia with lactic acidosis caused by a new MRPS2 gene mutation in a Chinese girl: a case report, refAbstract=null)], funds=[Fund(id=1241069990984601865, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035549562163607, awardId=No.ZR2020KH015, language=CN, fundingSource=山东省自然科学基金重点项目(No.ZR2020KH015), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1241069985334875072, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035549562163607, xref=null, ext=[AuthorCompanyExt(id=1241069985343263682, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035549562163607, companyId=1241069985334875072, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=School of Public Health, Binzhou Medical College, Yantai, Shandong 264003, China), AuthorCompanyExt(id=1241069985351652291, 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乳腺癌双硫死亡基因体细胞突变频率;B:双硫死亡基因在染色体上的位置;C:乳腺癌双硫死亡基因的CNV, figureFileSmall=IKltHPB0yYb+n550e+N+3A==, figureFileBig=JHTXxSaJ8QCPCuGiZR3QeA==, tableContent=null), ArticleFig(id=1241069989776642200, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035549562163607, language=EN, label=Fig.2, caption=Identification of the DRGs in breast cancer, figureFileSmall=oP6rwzcI52GaXQJZOYRmUw==, figureFileBig=a5O4brzTeOQevzH130OLGQ==, tableContent=null), ArticleFig(id=1241069989919248547, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035549562163607, language=CN, label=图2, caption=乳腺癌中DRGs鉴定, figureFileSmall=oP6rwzcI52GaXQJZOYRmUw==, figureFileBig=a5O4brzTeOQevzH130OLGQ==, tableContent=null), ArticleFig(id=1241069990053466289, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035549562163607, language=EN, label=Fig.3, caption=Risk model construction for breast cancer patients based on DRGs.A: LASSO regression analysis; B:λ selection diagram; C: Multivariate cox regression to the forest plot, figureFileSmall=PP4uL+3CtVvdRvxhCAOZaw==, figureFileBig=P8gL6A9RM+LI50Pj6MTwhg==, tableContent=null), ArticleFig(id=1241069990271570111, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035549562163607, language=CN, label=图3, caption=基于DRGs的乳腺癌患者构建风险模。A: LASSO回归分析;B:λ选择图;C:多因素Cox回归森林图, figureFileSmall=PP4uL+3CtVvdRvxhCAOZaw==, figureFileBig=P8gL6A9RM+LI50Pj6MTwhg==, tableContent=null), ArticleFig(id=1241069990372233416, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241035549562163607, language=EN, label=Fig.4, caption=Assessment of the predictive value of the prognostic model.A: Training set survival curve; B: Validation set survival curve; C: Total set survival curve;D: Training set ROC curve; E: Validation set ROC curve; F: Total set ROC curve, figureFileSmall=dLwXDp4/lFZKBqaR0fn3xA==, figureFileBig=iMbtbutHrdynYd0bH7anZQ==, tableContent=null), 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双硫死亡相关基因乳腺癌预后模型的构建与验证
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刘浩然 , 董雨晴 , 王萍玉
现代预防医学 | 临床与预防 2024,51(24): 4585-4590
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现代预防医学 | 临床与预防 2024, 51(24): 4585-4590
双硫死亡相关基因乳腺癌预后模型的构建与验证
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刘浩然, 董雨晴, 王萍玉
作者信息
  • 滨州医学院公共卫生学院,山东 烟台 264003
  • 刘浩然,女,硕士研究生;

    王萍玉,博士,教授,博士研究生导师

通讯作者:

王萍玉,E-mail:
Construction and validation of a prognostic model for breast cancer in disulfidptosis related genes
Hao-ran LIU, Yu-qing DONG, Ping-yu WANG
Affiliations
  • School of Public Health, Binzhou Medical College, Yantai, Shandong 264003, China
出版时间: 2024-12-25 doi: 10.20043/j.cnki.MPM.202408288
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目的

本研究旨在探究双硫死亡相关基因(DRGs)与乳腺癌患者预后之间的关联,建立风险预后模型并进行验证,为乳腺癌患者的预后提供新的生物标志物。

方法

使用R语言对双硫死亡基因绘制CNV景观,鉴定共相关和差异的DRGs,使用单因素Cox回归分析和Lasso-Cox回归分析的方法构建风险评分预后模型,绘制Kaplan-Meier生存曲线、ROC曲线及校准曲线对该模型进行验证,随后联合临床特征构建列线图预后预测模型。

结果

构建了一个由8个DRGs组成的乳腺癌患者的风险预后模型,训练集中ROC曲线1、3、5年的AUC为0.809、0.848、0.883,DCA显示该模型能更好地预测乳腺癌的预后。

结论

本研究部构建了一个由8个DRGs搭建的风险评分模型,该模型预后价值较好,可为乳腺癌的预后研究提供新的方向。

双硫死亡  /  乳腺癌  /  预后模型  /  单细胞分析  /  生物信息学
Objective

To explore the association between disulfidptosis related genes (DRGs) and the prognosis of breast cancer patients and establish a risk prognosis model and verify it, and provide new biomarkers for the prognosis of breast cancer patients.

Methods

CNV landscape was drawnin R language. DRGs of co-correlations and differences were identified.The risk score prognostic model was constructed by using univariate Cox regression analysis and Lasso-Cox regression analysis. Kaplan-Meier survival curve,ROC curve and calibration curve for the model was drawn.A nomogram prognostic prediction model was constructed by combining the clinical features.

Results

A risk prognostic model of breast cancer patients composed of 8 DRGs was constructed, and AUC of the ROC curve at 1,3, and 5 years was 0.809,0.848,0.883. DCA showed that the model could better predict breast cancer prognosis.

Conclusion

This research department has constructed a risk score model with 8 DRGs, which has a good prognostic value and can provide a new direction for the study of breast cancer prognosis.

Disulfidptosis  /  Breast cancer  /  Prognostic model  /  Single cell analysis  /  Bioinformatics
刘浩然, 董雨晴, 王萍玉. 双硫死亡相关基因乳腺癌预后模型的构建与验证. 现代预防医学, 2024 , 51 (24) : 4585 -4590 . DOI: 10.20043/j.cnki.MPM.202408288
Hao-ran LIU, Yu-qing DONG, Ping-yu WANG. Construction and validation of a prognostic model for breast cancer in disulfidptosis related genes[J]. Modern Preventive Medicine, 2024 , 51 (24) : 4585 -4590 . DOI: 10.20043/j.cnki.MPM.202408288
乳腺癌作为我国女性最常见的肿瘤之一,其发病率逐年上升[1]。乳腺癌是具有高度异质的,由于分子遗传学类型和表型、致病性突变、增殖率、侵袭的潜力和生物治疗反应过程的高度多样性,相同基因临床病理分期结果和临床病理分级结果的乳腺癌患者在疾病预后等方面往往存在明显差异,预后相关生物标志物研究的探索有助于为预测乳腺癌患者疾病的临床预后趋势提供一些新的研究思路[2]。双硫死亡(disulfidptosis)是一种全新出现的细胞程序性死亡的方式,它是不同于已知一般的细胞凋亡、自噬、焦亡等的细胞死亡形式[3]。最新研究表明[4-5],双硫死亡相关基因(disulfidptosis related gene, DRGs)在肿瘤发生及发展过程中发挥着重要作用,并且可以作为一种可靠的预后生物标志物,双硫死亡相关基因在肺腺癌、膀胱癌、甲状腺癌等癌症中已有研究,但到目前为止,双硫死亡相关基因在乳腺癌中的作用和临床意义尚不明确。
因此,本研究通过下载并处理癌症基因组图谱(The Cancer Atlas, TCGA)数据库中的乳腺癌及其癌旁组织的转录组数据和临床数据,构建一个基于DRGs乳腺癌患者的预后风险评分模型并加以验证,进而全面探索DRGs在乳腺癌中的预后意义,为乳腺癌的预后研究提供有价值的参考。
查阅文献获得31个双硫死亡基因[6-7],从TCGA数据库(https://protal.gdc.cancer.gov/repository)获取乳腺癌的RNA-seq数据和临床信息,包括112例正常样本和1105例乳腺癌样本数据集。通过“TCGAbiolinks”R包获取TCGA体细胞突变数据,下载拷贝数变异(copy number variation, CNV),并绘制CNV景观[8]
在R语言中使用“limma”包提取31个双硫死亡基因的表达量,将双硫死亡基因与mRNAs进行相关性分析,过滤条件为|R|>0.4且P<0.001,正常组与乳腺癌组之间差异表达的基因采用|logFC|>2且FDR<0.05为过滤标准[9-10]
将乳腺癌患者按1∶1的比例随机分为训练集和测试集,使用训练集构建预测模型,使用测试集和总集进行验证。通过单因素Cox回归分析筛选乳腺癌预后相关性DRGs,Lasso回归和多因素Cox回归筛选独立预后的DRGs并构建预后模型,风险评分方程:Riskscore =(回归系数*mRNA1EXP)+(回归系数*mRNA2EXP)+……+(回归系数*mRNAnEXP)[11]。根据风险评分中位数划分高风险组、低风险组,使用Kaplan-Meier生存曲线和受试者工作特征(receiver operating characteristic, ROC)曲线来评估预后模型的预测效能。
使用“rms”包将与预后相关的临床特征、风险评分进行单因素和多因素Cox回归分析,将独立预后因素用于列线图预后模型的构建[11]。采用校准曲线和决策分析曲线(decision curve analysis, DCA)来评估列线图预测模型的准确性[12]
本研究采用R 4.2.3、Perl 5.3.0、GraphPad Prism以及相关的R包对数据进行统计分析,未特别标注的检验水准为α=0.05。
从TCGA数据库中下载1105例乳腺癌患者的数据中有616例乳腺癌样本含有体细胞突变数据,对31个双硫死亡基因在616例含有体细胞突变的乳腺癌样本进行分析,结果显示,31个双硫死亡基因在乳腺癌中的突变频率相对较低,其中MYH9突变频率最高,PRDX1、NDUFA11、NUBPL、BRK1、MYL6和PDLIMI未显示突变(图1A)。从图1B可以看出CNV改变在染色体上的详细位置。CAPZB、GYS1、PDLIM1、MYH9、WASF2、CAPZB、MYH10、NDUFA11和FLNB的CNV丢失频率较高,其他的基因CNV的增益频率较高(图1C)。通过乳腺癌患者中31个双硫死亡基因的异质性表达显示,31个双硫死亡基因可能在乳腺癌的发展中起到关键作用。
通过相关性分析共得到4 275个双硫死亡相关基因(DRGs),差异分析得到9 073个DRGs,使用韦恩图将相关得到的DRGs和差异得到的DRGs取交集得到3 500个DRGs(图2),使用该结果进行后续分析。
对TCGA下载的临床样本数据进行整理,剔除生存时间<30 d和临床数据缺失的样本,将剩余的856例乳腺癌患者纳入分析,将乳腺癌患者按1∶1随机分为训练集(n=428)和测试集(n=428),训练集与测试集临床病理特征之间无统计学差异。使用单因素Cox回归分析筛选出92个具有预后价值的DRGs,随后进行Lasso回归分析(图3A、B),得到25个DRGs,随后进行多因素Cox回归分析,最终确定8个DRGs构建模型。其中DZIP1L、ACVR2A和LAMC2为保护因素,TAGLN2、JRKL、BCAR1、ZMAT3和MRPS2为危险因素(图3C),基于这8个DRGs建立的乳腺癌患者预后风险评分方程为:风险评分=(0.288*TAGLN2 EXP)+(-0.839*DZIP1L EXP)+(-0.717*ACVR2A EXP)+(0.591*JRKL EXP)+(-0.311*LAMC2 EXP)+(0.821*BCAR1 EXP)+(1.017*ZMAT3 EXP)+(0.812*MRPS2 EXP)。
通过Kaplan-Meier生存曲线分析结果可以看出,训练集、测试集和总集中低风险组的生存时间更长(均P<0.001,图4A-C)。乳腺癌患者应用预后模型预测乳腺癌患者1、3、5年生存率的曲线下面积,ROC曲线显示,在训练集、测试集和总集中的1、3、5年生存率均显示出了较高的灵敏度(图4D-F)。对模型总集进行10折交叉验证,1、3、5年最大AUC分别为0.943、0.793、0.873,3年平均AUC为0.699,5年平均AUC为0.737。
将风险评分与年龄、分期特征进行单因素Cox分析,所有因素均有统计学意义,随后展开多因素Cox分析结果显示年龄和风险评分具有统计学意义(图5A-B)。将年龄、N分期和风险评分纳入构建乳腺癌患者列线图模型(图5C),通过校准曲线可以看出列线图预测患者1、3、5年生存率曲线与实际生存率曲线吻合度较高(图5D),这表明列线图模型的准确度高、稳定性强。DCA分析显示该风险模型能够更准确地预测乳腺癌患者的预后,可为乳腺癌的预后治疗提供一定的参考(图5E)。
细胞死亡是肿瘤治疗的新热点,先前研究表明,铜死亡相关基因和铁死亡相关基因可作为乳腺癌患者预后的生物标志物[13-14]。双硫死亡作为最新被发现的一种细胞死亡形式,已被证明在肿瘤进展和癌症治疗中起着至关重要的作用,然而其在乳腺癌中的研究较为有限。本研究侧重探索新的乳腺癌可靠生物标志物,有助于临床诊断和监测肿瘤,并指导个性化的治疗策略。因此,为寻找可靠的预后生物标志物本研究开发了一个风险预后模型,对DRGs在乳腺癌患者的作用进行了探索分析。
笔者首先对文献收集的31个双硫死亡基因进行了CNV分析,表明了双硫死亡基因可能在疾病的发生和进展中起到关键作用。为了进一步探索双硫死亡在乳腺癌中的作用,构建了乳腺癌患者预后预测模型,并通过验证进一步验证预后模型的准确性。ROC曲线的AUC用于检查1年(0.809)、3年(0.848)和5年(0.883)生存率,相较于其他研究来说本模型的预测能力最高,也证明了该模型对预测患者预后的有效性[15-16]。与低风险样本相比,高风险样本的OS值显著降低,高、低风险组之间的DRGs表达水平存在显著差异(P<0.05),这表示DRGs可能是乳腺癌预后的可靠生物标志物。风险预后模型与临床特征结合的列线图模型,其预测生存率1、3、5年分别为0.990、0.937、0.883,说明了我们的风险模型较其他临床特征来说更具有预测力。
对于上述模型中的8个DRGs,笔者从既往的研究[17]中发现,DZIP1L基因功能的受损影响肾小管纤毛过渡区钙的异常运输,进而导致肾脏囊肿疾病的发展。ACVR2A可显著抑制癌组织的增殖和转移,在膀胱癌、结直肠癌等众多疾病的预后治疗中已被证实[18]。LAMC2介导的蛋白表达调控在多种良性肿瘤患者中蛋白表达量普遍高于泛癌靶旁癌组织,已被证实可作为肿瘤药物治疗中的一种重要蛋白靶点[19]。TAGLN2与调节甲状腺癌细胞的增殖、迁移、血管生成和黏附有关,通过Rap1/PI3K/AKT信号通路促进甲状腺癌细胞的侵袭[20]。JRKL在乳腺癌细胞阿霉素耐药性的发展中起关键作用,可以作为判断接受阿霉素治疗的乳腺癌患者预后或治疗靶点的重要指标[21]。BCAR1 失调已在多种人类癌症中得到广泛证明,其过表达与乳腺癌的增加相关,可作为乳腺癌预后的生物标志物[22]。ZMAT3在介导p53的肿瘤抑制作用中有重要的作用,研究表明其可抑制结直肠癌细胞的生长发展[23]。MRPS2又名线粒体核糖体蛋白S2基因,其突变可导致低血糖伴乳酸酸中毒和听力损失等疾病[24],但是目前并未发现其在癌症预后中的相关报道。
综上,本文章的研究是基于TCGA(The Cancer Genome Atlas, TCGA)数据库构建的DRGs乳腺癌的风险预后模型,通过验证证明模型有助于识别早期高风险人群、分层管理疾病风险、提供个性化的预防方案,通过该模型获得预测信息进而做到预防或缓解乳腺癌进一步发生发展。然而本研究也存在一定局限性。首先,本研究使用的数据来自公共数据库,其预测结果可能存在一定程度上的偏倚;其次,模型中筛选出的生物标志物后续的还需要开展分子生物学实验进行进一步研究其在乳腺癌中的机制作用。
  • 山东省自然科学基金重点项目(No.ZR2020KH015)
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doi: 10.20043/j.cnki.MPM.202408288
  • 接收时间:2024-08-21
  • 首发时间:2026-03-18
  • 出版时间:2024-12-25
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  • 收稿日期:2024-08-21
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山东省自然科学基金重点项目(No.ZR2020KH015)
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    滨州医学院公共卫生学院,山东 烟台 264003

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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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