Integrative analysis of gene expression and DNA methylation through one-class logistic regression machine learning identifies stemness features in medulloblastoma
Integrative analysis of gene expression and DNA methylation through one-class logistic regression machine learning identifies stemness features in medulloblastoma
复制标题
通过一类逻辑回归机器学习对基因表达和 DNA 甲基化进行综合分析，识别髓母细胞瘤的干性特征
DOI：
10.1002/1878-0261.12557
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发表时间：
2019-08-18
影响因子：
6.6
通讯作者：
Ma, Jie
中科院分区：
文献类型：
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作者：
Lian, Hao;Han, Yi-Peng;Ma, Jie
大多数人类癌症是通过各种遗传和表观遗传改变的连续积累从干细胞和祖细胞群发展而来。已从髓母细胞瘤 (MB) 中鉴定出癌症干细胞，但缺乏对 MB 干细胞的全面了解，包括肿瘤免疫微环境与 MB 干细胞之间的相互作用。在这里，我们采用基于现有单类逻辑回归（OCLR）机器学习方法的经过训练的干性指数模型来对 MB 样本进行评分；然后，我们获得了两个干性指数，即基于基因表达的干性指数 (mRNAsi) 和基于 DNA 甲基化的干性指数 (mDNAsi)，以对原发性癌症样本队列 (n = 763) 中的 MB 干性进行综合分析。我们观察到 MB 亚组和转移状态的 mRNAsi 和 mDNAsi 之间存在相反的趋势。通过应用单变量 Cox 回归分析，我们发现 mRNAsi 与所有 MB 患者的总生存期 (OS) 显着相关，而 mDNAsi 与所有 MB 患者的 OS 没有显着相关性。此外，通过将 Lasso 惩罚 Cox 回归机器学习方法与单变量和多变量 Cox 回归分析相结合，我们确定了与干性相关的基因表达特征，可以准确预测 Sonic Hedgehog (SHH) MB 患者的生存率。此外，检测到 mRNAsi 与 SHH MB 中的预后拷贝数畸变（包括 MYCN 扩增和 GLI2 扩增）之间呈正相关。对免疫微环境的分析揭示了MB干性与浸润免疫细胞之间意想不到的相关性。最后，使用连接图，我们确定了针对 MB 干性特征的潜在药物。我们基于干性指数的研究结果可能会促进定量MB干性的客观诊断工具的开发，并产生新的生物标志物来预测MB患者的生存或针对MB干细胞的策略的有效性。
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.