中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 上海营养与健康研究所
  3. 中国科学院上海生命科学研究院营养科学研究所
Integrative analysis of gene expression and DNA methylation through one-class logistic regression machine learning identifies stemness features in medulloblastoma

文献类型：期刊论文

作者Lian, Hao4; Han, Yi-Peng4; Zhao, Yang4; Li, Qi-Feng4; Wang, Bao-Cheng4; Wang, Jia-Jia4; Meng, Wei4; Yang, Jian4; Wang, Qin-Hua4; Mao, Wei-Wei4
刊名MOLECULAR ONCOLOGY
出版日期2019
卷号13期号:10页码:2227-2245
关键词connectivity map machine-learning methods medulloblastoma prognostic model stemness tumor immune environment
ISSN号1574-7891
DOI10.1002/1878-0261.12557
文献子类Article
英文摘要Most human cancers develop from stem and progenitor cell populations through the sequential accumulation of various genetic and epigenetic alterations. Cancer stem cells have been identified from medulloblastoma (MB), but a comprehensive understanding of MB stemness, including the interactions between the tumor immune microenvironment and MB stemness, is lacking. Here, we employed a trained stemness index model based on an existent one-class logistic regression (OCLR) machine-learning method to score MB samples; we then obtained two stemness indices, a gene expression-based stemness index (mRNAsi) and a DNA methylation-based stemness index (mDNAsi), to perform an integrated analysis of MB stemness in a cohort of primary cancer samples (n = 763). We observed an inverse trend between mRNAsi and mDNAsi for MB subgroup and metastatic status. By applying the univariable Cox regression analysis, we found that mRNAsi significantly correlated with overall survival (OS) for all MB patients, whereas mDNAsi had no significant association with OS for all MB patients. In addition, by combining the Lasso-penalized Cox regression machine-learning approach with univariate and multivariate Cox regression analyses, we identified a stemness-related gene expression signature that accurately predicted survival in patients with Sonic hedgehog (SHH) MB. Furthermore, positive correlations between mRNAsi and prognostic copy number aberrations in SHH MB, including MYCN amplifications and GLI2 amplifications, were detected. Analyses of the immune microenvironment revealed unanticipated correlations of MB stemness with infiltrating immune cells. Lastly, using the Connectivity Map, we identified potential drugs targeting the MB stemness signature. Our findings based on stemness indices might advance the development of objective diagnostic tools for quantitating MB stemness and lead to novel biomarkers that predict the survival of patients with MB or the efficacy of strategies targeting MB stem cells.
学科主题Oncology
WOS关键词TUMOR-ASSOCIATED MACROPHAGES ; LIGAND 1 EXPRESSION ; SONIC HEDGEHOG ; CELL SIGNATURE ; IMMUNE CELLS ; CANCER ; INHIBITOR ; CHILDHOOD ; PROPAGATION ; DOWNSTREAM
语种英语
WOS记录号WOS:000481752500001
出版者WILEY
版本出版稿
源URL[http://202.127.25.144/handle/331004/543]  
专题中国科学院上海生命科学研究院营养科学研究所
作者单位1.Fudan Univ, Sch Life Sci, Shanghai, Peoples R China;
2.Huamu Community Hlth Serv Ctr, Shanghai, Peoples R China,
3.Univ Chinese Acad Sci, Chinese Acad Sci, Inst Hlth Sci, Shanghai Inst Biol Sci, Shanghai, Peoples R China;
4.Shanghai Jiao Tong Univ, Sch Med, Xinhua Hosp, Dept Pediat Neurosurg, Shanghai 200092, Peoples R China;
推荐引用方式
GB/T 7714		 Lian, Hao,Han, Yi-Peng,Zhao, Yang,et al. Integrative analysis of gene expression and DNA methylation through one-class logistic regression machine learning identifies stemness features in medulloblastoma[J]. MOLECULAR ONCOLOGY,2019,13(10):2227-2245.
APA Lian, Hao.,Han, Yi-Peng.,Zhao, Yang.,Li, Qi-Feng.,Wang, Bao-Cheng.,...&,.(2019).Integrative analysis of gene expression and DNA methylation through one-class logistic regression machine learning identifies stemness features in medulloblastoma.MOLECULAR ONCOLOGY,13(10),2227-2245.
MLA Lian, Hao,et al."Integrative analysis of gene expression and DNA methylation through one-class logistic regression machine learning identifies stemness features in medulloblastoma".MOLECULAR ONCOLOGY 13.10(2019):2227-2245.

入库方式： OAI收割

来源：上海营养与健康研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。