Integrative analysis of gene expression and DNA methylation through one‐class logistic regression machine learning identifies stemness features in medulloblastoma

• Published in: Molecular oncology Vol. 13; no. 10; pp. 2227 - 2245
• Main Authors: Lian, Hao, Han, Yi‐Peng, Zhang, Yu‐Chao, Zhao, Yang, Yan, Shan, Li, Qi‐Feng, Wang, Bao‐Cheng, Wang, Jia‐Jia, Meng, Wei, Yang, Jian, Wang, Qin‐Hua, Mao, Wei‐Wei, Ma, Jie
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.10.2019; John Wiley and Sons Inc; Wiley
• Subjects: Analysis; Biomarkers; Cancer therapies; Chemotherapy; Clinical trials; connectivity map; Copy number; Datasets; Deoxyribonucleic acid; Development and progression; DNA; DNA methylation; Drug delivery; Epigenetic inheritance; Gene expression; Genes; Hedgehog protein; Immunosuppressive agents; Learning algorithms; Machine learning; machine‐learning methods; Medical prognosis; Medical research; Medicine, Experimental; Medulloblastoma; Metastases; Metastasis; Methylation; Mutation; Nervous system; Packaging; Progenitor cells; prognostic model; Radiation therapy; RNA polymerase; Stem cells; stemness; tumor immune environment; Tumors; Canada; Malta; connectivity map; medulloblastoma; prognostic model; machine-learning methods; stemness; tumor immune environment
• ISSN: 1574-7891; 1878-0261
• DOI: 10.1002/1878-0261.12557

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. Here, we employed a trained stemness index model to perform an integrated analysis of medulloblastoma (MB) stemness. By combining the Lasso‐penalized Cox regression with univariate and multivariate Cox regression analyses, we identified a stemness‐related gene expression signature. Furthermore, positive correlations between gene expression‐based stemness index and prognostic copy number aberrations 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.