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Article(id=1241102739212652677, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241102730337505325, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202404262, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1713196800000, receivedDateStr=2024-04-16, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773831551601, onlineDateStr=2026-03-18, pubDate=1721836800000, pubDateStr=2024-07-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773831551601, onlineIssueDateStr=2026-03-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773831551601, creator=13701087609, updateTime=1773831551601, updator=13701087609, issue=Issue{id=1241102730337505325, tenantId=1146029695717560320, journalId=1227665162245664772, year='2024', volume='51', 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=null, createTime=1773831549486, creator=13701087609, updateTime=1773831697291, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241103350322745680, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241102730337505325, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241103350322745681, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241102730337505325, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=2669, endPage=2674, ext={EN=ArticleExt(id=1241102739598528655, articleId=1241102739212652677, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Prognostic prediction model for patients with low-grade gliomas based on multi-omics data, columnId=1228016569138213037, journalTitle=Modern Preventive Medicine, columnName=Clinical Medicine and Prevention, runingTitle=null, highlight=null, articleAbstract=
Objective

To explore the application of integrated clustering methods in identifying low-grade glioma subtypes and prognostic prediction.

Methods

A comprehensive clustering algorithm (MOVICS), which pools ten clustering algorithms, was used to integrate the multi-omics data of LGG patients downloaded from TCGA to obtain cluster subtypes; prognostic factors of LGG were analyzed by multifactorial Cox regression. A random forest classification prediction model was constructed using mRNA data to evaluate the classification performance and externally validated with the CGGA dataset.

Results

LGG patients were clustered into two subtypes, and the difference in survival between the two groups was statistically significant (χ2=54.410, P<0.001). The results of multifactorial Cox regression analysis showed that age (HR=1.053，95%CI: 1.037-1.069), cancer grade (HR=2.733，95%CI: 1.836-4.069) and cluster typing (HR=3.210，95%CI: 2.216-4.650) were all prognostic factors for LGG, and the results of Nomogram plots, calibration curves and ROC curves indicated good predictive performance of the model. The average prediction accuracy of the ten-fold cross-validated RF model was 87.81%, and the C-indexes of the training set, the internal validation set, and the two external validation sets were 0.717, 0.721, 0.574, and 0.572, and the Brier scores were 0.044, 0.066, 0.179, and 0.128, and the differences in the survival of the two external validation datasets were all statistically significance (P<0.05).

Conclusion

The comprehensive clustering method can effectively identify LGG subtypes, which are prognostic factors for LGG patients, and has been validated in an external dataset, CGGA, which can provide an important theoretical basis for clinical personalized treatment of LGG.

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目的

探讨综合聚类方法在识别低级别胶质瘤（LGG）亚型和预后预测中的应用。

方法

采用集合了十种聚类算法的综合聚类算法（MOVICS）对TCGA下载的LGG患者多组学数据进行整合，得到聚类亚型；通过多因素Cox回归分析LGG的预后因素。使用mRNA数据构建随机森林分类预测模型来评估分类性能，用CGGA数据集进行外部验证。

结果

LGG患者经过聚类分为两型，两组生存率差异具有统计学意义(χ2=54.410，P<0.001)。多因素Cox回归分析结果表明，年龄（HR=1.053，95%CI：1.037~1.069）、癌症分级（HR=2.733，95%CI：1.836~4.069）和聚类亚型（HR=3.210，95%CI：2.216~4.650）都是LGG的预后因素，Nomogram图、校准曲线和ROC曲线结果表明模型的预测性能良好。十折交叉验证RF模型的平均预测准确率为87.81%，训练集、内部验证集和两个外部验证集的C指数分别为0.717、0.721、0.574和0.572，Brier评分分别为0.044、0.066、0.179和0.128，两个外部验证数据集的生存差异均有统计学意义（P<0.05）。

结论

综合聚类方法能够有效识别LGG亚型，其亚型是LGG患者的预后因素，并在外部数据集CGGA中得到验证，可为LGG的临床个性化治疗提供重要的理论依据。

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罗天娥，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=aiTgyvXtmV2mQqp6O1kFIw==, magXml=NQg7SiVt0D4V1j7xuAolaA==, pdfUrl=null, pdf=Ovq9MY31LmwsxvaxaL+NNg==, pdfFileSize=881351, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=fj9sWPNboMpSwL2BYiF3hw==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=O0Qa7sZhciusB/nxYZih6Q==, mapNumber=null, authorCompany=null, fund=null, authors=

刘毅蓉（1998—），女，硕士在读，研究方向：机器学习在疾病分类和预后预测中的应用

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刘毅蓉（1998—），女，硕士在读，研究方向：机器学习在疾病分类和预后预测中的应用

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刘毅蓉（1998—），女，硕士在读，研究方向：机器学习在疾病分类和预后预测中的应用

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external validation set, figureFileSmall=OdO5eSXzhU+Qhu2wYYKvrA==, figureFileBig=ezK8Hgq4wwSPF99PZ+kTdw==, tableContent=null), ArticleFig(id=1241102750809903939, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241102739212652677, language=CN, label=图6, caption=TCGA训练集、内部验证集和CGGA外部验证集LGG患者生存曲线, figureFileSmall=OdO5eSXzhU+Qhu2wYYKvrA==, figureFileBig=ezK8Hgq4wwSPF99PZ+kTdw==, tableContent=null), ArticleFig(id=1241102751036396367, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241102739212652677, language=EN, label=Table 1, caption=

Key features of the dataset situation

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数据集组学类型样本量特征数
TCGA-LGGmRNA502691
miRNA5021 524
DNA甲基化502671
CGGA1mRNA172691
CGGA2mRNA420691
), ArticleFig(id=1241102751153836888, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241102739212652677, language=CN, label=表1, caption=

数据集关键特征情况

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数据集组学类型样本量特征数
TCGA-LGGmRNA502691
miRNA5021 524
DNA甲基化502671
CGGA1mRNA172691
CGGA2mRNA420691
), ArticleFig(id=1241102752684757856, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241102739212652677, language=EN, label=Table 2, caption=

Basic information on TCGA-LGG patient typing

, figureFileSmall=null, figureFileBig=null, tableContent=
项目CS1(n=312)CS2(n=190)χ2/zP值
年龄(岁，)40.9±12.446.6±14.34.391<0.001
性别[n(%)]1.9050.168
男性179(57.4)97(51.1)
女性133(42.6)93(48.9)
癌症分级[n(%)]10.498<0.001
WHO G2168(53.8)74(38.9)
WHO G3144(46.2)116(61.1)
生存状况[n(%)]27.604<0.001
存活259(83.0)118(62.1)
死亡53(17.0)72(37.9)
), ArticleFig(id=1241102752764449638, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241102739212652677, language=CN, label=表2, caption=

TCGA-LGG患者亚型的基本资料

, figureFileSmall=null, figureFileBig=null, tableContent=
项目CS1(n=312)CS2(n=190)χ2/zP值
年龄(岁，)40.9±12.446.6±14.34.391<0.001
性别[n(%)]1.9050.168
男性179(57.4)97(51.1)
女性133(42.6)93(48.9)
癌症分级[n(%)]10.498<0.001
WHO G2168(53.8)74(38.9)
WHO G3144(46.2)116(61.1)
生存状况[n(%)]27.604<0.001
存活259(83.0)118(62.1)
死亡53(17.0)72(37.9)
), ArticleFig(id=1241102752877695854, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241102739212652677, language=EN, label=Table 3, caption=

Results of multifactorial Cox regression analysis in TCGA-LGG patients

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变量b(S.E)Waldχ2值P值HR(95% CI)
年龄0.051(0.008)46.221<0.0011.053(1.037~1.069)
癌症分级1.005(0.203)24.522<0.0012.733(1.836~4.069)
亚型1.166(0.189)38.065<0.0013.210(2.216~4.650)
), ArticleFig(id=1241102752982553456, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241102739212652677, language=CN, label=表3, caption=

TCGA-LGG患者多因素Cox回归分析结果

, figureFileSmall=null, figureFileBig=null, tableContent=
变量b(S.E)Waldχ2值P值HR(95% CI)
年龄0.051(0.008)46.221<0.0011.053(1.037~1.069)
癌症分级1.005(0.203)24.522<0.0012.733(1.836~4.069)
亚型1.166(0.189)38.065<0.0013.210(2.216~4.650)
), ArticleFig(id=1241102753091605370, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241102739212652677, language=EN, label=Table 4, caption=

Model performance for TCGA training set, internal validation set and CGGA external validation set test set

, figureFileSmall=null, figureFileBig=null, tableContent=
数据集C指数Brier评分log-rankP值
TCGA训练集(70%)0.7170.0441e-12
TCGA验证集(30%)0.7210.0665e-05
CGGA10.5740.1791e-02
CGGA20.5720.1281e-04
), ArticleFig(id=1241102753209045887, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241102739212652677, language=CN, label=表4, caption=

TCGA训练集、内部验证集和CGGA外部验证集的模型性能

, figureFileSmall=null, figureFileBig=null, tableContent=
数据集C指数Brier评分log-rankP值
TCGA训练集(70%)0.7170.0441e-12
TCGA验证集(30%)0.7210.0665e-05
CGGA10.5740.1791e-02
CGGA20.5720.1281e-04
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基于多组学数据构建低级别胶质瘤患者预后预测模型
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刘毅蓉 1 , 任月 1 , 秦阳 1 , 武舒琪 1 , 赵晋芳 1, 2 , 罗天娥 1, 2
现代预防医学 | 临床与预防 2024,51(14): 2669-2674
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现代预防医学 | 临床与预防 2024, 51(14): 2669-2674
基于多组学数据构建低级别胶质瘤患者预后预测模型
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刘毅蓉1, 任月1, 秦阳1, 武舒琪1, 赵晋芳1, 2, 罗天娥1, 2
作者信息
  • 1.山西医科大学公共卫生学院卫生统计学教研室，山西 太原 030001
  • 2.煤炭环境致病与防治教育部重点实验室

    • 刘毅蓉（1998—），女，硕士在读，研究方向：机器学习在疾病分类和预后预测中的应用

通讯作者:

罗天娥，E-mail：
Prognostic prediction model for patients with low-grade gliomas based on multi-omics data
Yi-rong LIU1, Yue REN1, Yang QIN1, Shu-qi WU1, Jin-fang ZHAO1, 2, Tian-e LUO1, 2
Affiliations
  • Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi 030001, China
出版时间: 2024-07-25 doi: 10.20043/j.cnki.MPM.202404262
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摘要
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目的

探讨综合聚类方法在识别低级别胶质瘤（LGG）亚型和预后预测中的应用。

方法

采用集合了十种聚类算法的综合聚类算法（MOVICS）对TCGA下载的LGG患者多组学数据进行整合，得到聚类亚型；通过多因素Cox回归分析LGG的预后因素。使用mRNA数据构建随机森林分类预测模型来评估分类性能，用CGGA数据集进行外部验证。

结果

LGG患者经过聚类分为两型，两组生存率差异具有统计学意义(χ2=54.410，P<0.001)。多因素Cox回归分析结果表明，年龄（HR=1.053，95%CI：1.037~1.069）、癌症分级（HR=2.733，95%CI：1.836~4.069）和聚类亚型（HR=3.210，95%CI：2.216~4.650）都是LGG的预后因素，Nomogram图、校准曲线和ROC曲线结果表明模型的预测性能良好。十折交叉验证RF模型的平均预测准确率为87.81%，训练集、内部验证集和两个外部验证集的C指数分别为0.717、0.721、0.574和0.572，Brier评分分别为0.044、0.066、0.179和0.128，两个外部验证数据集的生存差异均有统计学意义（P<0.05）。

结论

综合聚类方法能够有效识别LGG亚型，其亚型是LGG患者的预后因素，并在外部数据集CGGA中得到验证，可为LGG的临床个性化治疗提供重要的理论依据。

多组学聚类  /  低级别胶质瘤  /  预后预测  /  随机森林
Objective

To explore the application of integrated clustering methods in identifying low-grade glioma subtypes and prognostic prediction.

Methods

A comprehensive clustering algorithm (MOVICS), which pools ten clustering algorithms, was used to integrate the multi-omics data of LGG patients downloaded from TCGA to obtain cluster subtypes; prognostic factors of LGG were analyzed by multifactorial Cox regression. A random forest classification prediction model was constructed using mRNA data to evaluate the classification performance and externally validated with the CGGA dataset.

Results

LGG patients were clustered into two subtypes, and the difference in survival between the two groups was statistically significant (χ2=54.410, P<0.001). The results of multifactorial Cox regression analysis showed that age (HR=1.053，95%CI: 1.037-1.069), cancer grade (HR=2.733，95%CI: 1.836-4.069) and cluster typing (HR=3.210，95%CI: 2.216-4.650) were all prognostic factors for LGG, and the results of Nomogram plots, calibration curves and ROC curves indicated good predictive performance of the model. The average prediction accuracy of the ten-fold cross-validated RF model was 87.81%, and the C-indexes of the training set, the internal validation set, and the two external validation sets were 0.717, 0.721, 0.574, and 0.572, and the Brier scores were 0.044, 0.066, 0.179, and 0.128, and the differences in the survival of the two external validation datasets were all statistically significance (P<0.05).

Conclusion

The comprehensive clustering method can effectively identify LGG subtypes, which are prognostic factors for LGG patients, and has been validated in an external dataset, CGGA, which can provide an important theoretical basis for clinical personalized treatment of LGG.

Multi-omics clustering  /  Low-grade glioma  /  Prognosis prediction  /  Random forest
刘毅蓉, 任月, 秦阳, 武舒琪, 赵晋芳, 罗天娥. 基于多组学数据构建低级别胶质瘤患者预后预测模型. 现代预防医学, 2024 , 51 (14) : 2669 -2674 . DOI: 10.20043/j.cnki.MPM.202404262
Yi-rong LIU, Yue REN, Yang QIN, Shu-qi WU, Jin-fang ZHAO, Tian-e LUO. Prognostic prediction model for patients with low-grade gliomas based on multi-omics data[J]. Modern Preventive Medicine, 2024 , 51 (14) : 2669 -2674 . DOI: 10.20043/j.cnki.MPM.202404262
正文
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低级别胶质瘤（low-grade glioma，LGG）具有高度异变性，极易发展为最恶性的胶质母细胞瘤（Glioblastoma，GBM），对患者生存提出巨大考验[1]。由于LGG患者预后差异较大，不能仅根据组织学亚型进行预测，因此探讨更好的亚型预测方法十分关键。随着高通量测序技术与基因组芯片技术的飞速发展，获得了多种癌症患者的基因组学、转录组学和蛋白质组学等组学数据[2]，可以反映癌症的多种生物学过程[3]。聚类可以对组学数据进行整合分析，在疾病亚型、精准医疗、药物研究等方面具有非常重要的现实意义[4-5]。整合多组学数据有利于对生物医学数据进行全面深入的研究，补充单一组学中缺失或不可靠的信息[6]。由于组学数据通常具有高维度、样本量少、高噪声[7]的特点，研究中选择合适的聚类算法十分关键[8]。
MOVICS是一种综合聚类方法，通过综合iClusterBayes、Mocluster、CIMLR、IntNMF、ConsensusClustering、LRAcluster、COCA、PINSplus、SNF和NEMO十种聚类算法，得到聚类亚型。本文使用TCGA的LGG患者多组学数据库，通过MOVICS模型进行聚类，利用多因素Cox回归分析不同亚型对LGG患者预后的影响，绘制Nomogram图、校准曲线和ROC曲线评价模型的预测性能；使用mRNA数据构建随机森林模型验证MOVICS聚类性能，同时用CGGA数据进行外部验证，为LGG的临床个性化治疗提供重要的理论依据。
1　资料和方法
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1.1　数据来源与处理
从UCSCXena网站（https://xenabrowser.net）下载低级别胶质瘤（LGG）癌症患者的基因表达、DNA甲基化、miRNA数据以及临床信息数据（性别、年龄、癌症分级、生存时间和生存状态）。其中count数据经过log转化，DNA甲基化数据剔除未匹配到基因的探针以及对应多个基因名称的探针，再去除重复基因。多组学数据均剔除缺失值超过10%的基因，剩余缺失值用中位数进行插补，数据经过归一化处理。从中国脑胶质瘤基因组图谱计划CGGA（http://www.cgga.org.cn/）下载胶质瘤的两个基因表达数据以及临床信息数据（生存时间、生存状态），mRNA数据经log转化，排除临床数据中WHOIV型胶质瘤，删除缺失数据，去除批次效应。从COSMIC（https://cancer.sanger.ac.uk/）数据库获得733个泛癌相关基因，在mRNA和DNA甲基化数据中筛选泛癌相关基因。
1.2　MOVICS
MOVICS是Lu等[9]2020年提出的综合聚类方法。它的输入是多组学数据，输出是综合聚类后最优的分子亚型。MOVICS选择聚类预测指数(Cluster Prediction Index，CPI)和Gap统计量之和为最大值的聚类数作为最佳聚类数。通过综合十种聚类算法(iClusterBayes、moCluster、CIMLR、IntNMF、COCA、NEMO、ConsensusClustering、PINSPlus、SNF和LRA)，将患者分为不同的亚型，并使用共识聚类进行组合分类，以高度鲁棒性识别每个亚型。
具体来说，如果指定tmax算法，且2≤tmax≤10，则每个算法计算一个矩阵，其中n为样本个数，当样本i和j聚类在同一子类型中时，；否则。在获得指定算法的所有结果后，MOVICS计算共识矩阵，并且CMϵ[0，10]，并通过剪影评分计算各亚型之间的样本相似性[10]。
1.3　随机森林模型
随机森林(random forest，RF)是一种集成学习方法，通过构建多个决策树并取其结果的平均值或投票来进行预测，在每个决策树的训练过程中，采用了自助采样法对样本和特征进行随机选择，能够处理变量数量超过观测数量的数据集[11]。模型具有较高的稳定性和泛化能力，可以很好地处理过拟合问题[12]，有效处理缺失和有噪声的数据。
1.4　最佳聚类数指标
CPI：基于重采样的方法将数据重复划分为训练集和测试集。每次重复时，将算法应用于训练集估计系数矩阵，在测试集上使用系数矩阵估计公共基矩阵，重复多次，计算调整后的兰德指数的平均值为CPI[13]，一般选择导致CPI最大值的聚类数作为最佳聚类数。Gap统计量[14]：引入参考的测值由MonteCarlo采样的方法获得，通过计算标准差来矫正Gap统计量，一般选择Gap统计量最大值对应的聚类数为最佳聚类数。
1.5　分类性能指标
选择C指数、Brier评分和log-rankP值来评估模型的分类预测性能。C指数取值在0和1之间，该值越高概率预测的准确性越高；Brier评分取值在0和1之间，Brier分数越低概率预测的准确性越高；检验水准α=0.05。
1.6　软件实现
本研究在R 4.1.3软件中完成，去除批次效应在sva包中实现，聚类分析采用MOVICS包，随机森林预测模型构建在randomForest包中实现。
2　结果
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2.1　LGG患者多组学数据的描述
从TCGA数据库下载LGG的mRNA、miRNA和DNA甲基化数据预处理后整合得到502个患者，mRNA、DNA甲基化数据筛选泛癌相关基因并归一化。CGGA数据库的两个mRNA数据集筛选泛癌相关基因，去批次效应。所用数据集关键特征如表1所示。
2.2　综合聚类模型构建及亚型结果评价
使用MOVICS对TCGA的多组学数据进行聚类，聚类数的范围设定为[2,8]，结合不同聚类数的CPI和Gap统计量结果，当聚类数为2时CPI和Gap统计量之和达到最大值，模型较优，见图1。
最佳聚类数为2，即MOVICS将502名LGG患者分为两个亚型，CS1和CS2，基本资料如表2所示。绘制Kaplan-Meier生存曲线，见图2，两个亚型患者的生存率差异有统计学意义(χ2=54.410，P<0.001)，且CS2组生存率较低，预后较差，表明多组学数据聚类可以得到不同预后的LGG患者。
2.3　多因素Cox回归分析及Nomogram图构建
以生存时间和生存状态作为因变量，对年龄、性别（女性=0，男性=1）、癌症分级（WHO G2=1，WHO G3=2）和MOVICS聚类亚型结果（CS1=1，CS2=2）进行多因素Cox逐步回归分析，结果显示年龄、癌症分级、聚类亚型结果具有统计学意义（P<0.05），可作为低级别胶质瘤患者的预后预测因素，见表3。
基于多因素Cox回归分析的结果，对LGG患者的预后因素构建Nomogram图，预测LGG患者1、3和5年的生存率。Nomogram图的C指数为0.82。Nomogram图中所有变量Points轴的分数之和在Totalpoints轴上显示，可以直观的估算出LGG患者1、3和5年的生存率，见图3。
利用校准曲线评价Nomogram图，预测LGG患者生存率的准确性，以坐标轴原点且斜率为1的标准曲线作为参照。LGG患者的1、3和5年偏差校准曲线接近标准曲线，表明预测的LGG患者的生存率与实际观察到的结果偏差小，有很好的一致性，见图4。绘制1、3和5年的ROC曲线，LGG患者的AUC均大于0.8，表明模型有很好的预测准确性，见图5。
2.4　随机森林分类预测模型构建
利用TCGA数据库中LGG患者的mRNA数据，根据滑动窗口序贯向前特征选择法筛选出145个基因，将这145个基因的表达量以及获得的样本聚类标签整合到一起，构建一个十折交叉验证RF模型。按70%和30%的比例划分训练集和内部验证集，十折交叉验证的平均预测准确率为87.81%。使用CGGA数据库中两个LGG患者数据集进行外部验证。
训练集、内部验证集和外部验证集的C指数、Brier评分和log-rank P值结果如表4所示。训练集、内部验证集的C指数大于0.7，Brier评分小于0.1，模型性能良好；两个CGGA数据集的C指数大于0.5，Brier评分小于0.25，模型性能较好，表明使用的特征选择算法对预测LGG的亚型具有鲁棒性。绘制Kaplan-Meier生存曲线，表明RF模型能够很好地区分该LGG内部验证集和CGGA外部验证集的亚型类型，生存差异均有统计学意义（P<0.05），见图6。
3　讨论
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LGG具有高度的遗传异质性，传统组织病理学分级中同一类别的预后也存在异质性[15]，需要寻求其他分类方法；多组学聚类的方法适用于癌症等遗传驱动疾病的无监督聚类，目的是发现新的疾病亚型，对疾病的诊断和治疗意义重大[16]。本研究在LGG多组学数据的基础上通过MOVICS综合十种聚类结果确定LGG的最终分子亚型，然后通过RF构建预后模型，为临床更准确的癌症诊断和治疗提供了可能。
为了提高聚类的鲁棒性，MOVICS通过借鉴共识集成[17]的思想，对不同的聚类结果进行整合，得到的共识矩阵代表了样本的成对相似性。本文采用综合聚类方法对LGG多组学数据进行整合，最终将TCGA的502个LGG患者分为两型。多因素Cox分析结果显示年龄、癌症分级、聚类亚型具有统计学意义（P<0.05），可作为低级别胶质瘤患者的预后预测因素，预测LGG患者1年、3年和5年总生存期的ROC曲线AUC值都在0.82以上，校准曲线也接近标准曲线，提示模型拥有较高的预测能力。Gao等[18]关于过氧化物酶体相关基因特征预测LGG患者预后的结果中，风险评分和年龄是LGG患者的预后因素；Zhao等[19]研究了铜氧化酶基因预测LGG患者的生存率，结果显示风险评分、年龄和癌症分级是LGG患者的预后因素，本研究与之有相同之处。在LGG患者中，年龄越大，患者的死亡风险越高，生存率越低，预后越差；在癌症分级中，WHO G3级患者的死亡风险是WHO G2级患者2.733倍，在两亚型中，LGG患者CS2组的死亡风险是CS1组患者的3.210倍，且CS2组患者的平均年龄为46.6岁，大于CS1组的40.9岁，WHO G3级患者在CS2组的占比也多于CS1组，故CS2组患者的死亡风险更高，预后较差。这提示临床应对高龄、WHO G3级患者给予一定的重视，且使用多组学数据进行聚类发现的疾病亚型，为LGG患者的诊断和治疗提供了新思路。
构建的随机森林分类预测模型，在TCGA内部验证集表现出较好的分类性能，在两个CGGA外部验证集上，成功预测出样本的亚型标签，亚型之间的生存差异都具有统计学意义，预后差的亚型与TCGA训练集相一致，均为CS2组。通过内部和外部验证，说明该模型具有较好的分类预测性能，为LGG亚型提供了理论参考，具有一定的实际意义。
本研究也具有一定的局限性，第一，在获得聚类标签时，仅用到mRNA和DNA甲基化的泛癌基因，可能会缺失一些特有的关键基因；第二，在构建随机森林分类预测模型时，由于样本量的原因，仅使用了mRNA，今后会试图整合多个组学数据进行构建模型；第三，分类模型验证时，仅用到CGGA数据库，为了检验模型的泛化能力，接下来会寻找其它的数据集进行验证，如GEO数据库。
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  • 山西省自然科学基金(201801D121210)
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2024年第51卷第14期
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doi: 10.20043/j.cnki.MPM.202404262
  • 接收时间：2024-04-16
  • 首发时间：2026-03-18
  • 出版时间：2024-07-25
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  • 收稿日期：2024-04-16
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山西省自然科学基金(201801D121210)
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    1.山西医科大学公共卫生学院卫生统计学教研室，山西 太原 030001
    2.煤炭环境致病与防治教育部重点实验室

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