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

Abstract

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.

Prognostic Value of a Stemness Index-Associated Signature in Primary Lower-Grade Glioma

“…Moreover, ESTIMATE is one of the most common algorithms for quantifying tumor purity and composition of stromal and immune cells. Hence, the concept of the corrected mRNAsi (mRNAsi/tumor purity) was adopted to reduce the interference of non-tumor tissue (Malta et al, 2018;Lian et al, 2019;Pan et al, 2019). Of note, after employing the survival analysis, the significant survival difference in OS was still observed between high-and low-score groups based on the corrected mRNAsi (mRNAsi/tumor purity), which was consistent with the results from a previous study of bladder cancer (Pan et al, 2019).…”

Malignant Evaluation and Clinical Prognostic Values of m6A RNA Methylation Regulators in Glioblastoma

“…CMap can identify biomarkers for predicting specific drug reactions, mechanisms of treatment, and ways to overcome them (65)(66)(67). CMap analysis, which is based on a limited number of treated cell lines, accurately identified a number of compounds that have been shown to have an effect on m6A of other tumor types with specificity (33,(68)(69)(70). METTL3 has been reported to promote gastric cancer angiogenesis by secreting HDGF (71).…”

Identification of Prognostic Model and Biomarkers for Cancer Stem Cell Characteristics in Glioblastoma by Network Analysis of Multi-Omics Data and Stemness Indices

“…We queried CMap using the DEGs of the mRNAsi grouping. CMap analysis accurately identified numerous compounds that have been shown to have an effect on CSCs of other tumor types with specificity (Subramanian et al, 2017;Brum et al, 2018;Malta et al, 2018;Lian H. et al, 2019). HDAC inhibitors have been reported to be potent differentiation agents in GSCs, reducing GBM growth mainly by inducing cell necrosis and growth arrest (Tung et al, 2018).…”