基于机器学习的鉴定低级别胶质瘤干性亚型能够区分患者预后和药物反应。
Machine learning-based identification of lower grade glioma stemness subtypes discriminates patient prognosis and drug response.
发表日期：2023
作者：
Hongshu Zhou, Bo Chen, Liyang Zhang, Chuntao Li
来源：
Computational and Structural Biotechnology Journal
摘要：
胶质瘤干细胞（GSC）通过重新塑造肿瘤微环境来维持支持性生态位。在诊断和治疗胶质瘤方面，识别和分层患者干细胞性相关特征可能有所帮助。本研究使用机器学习方法计算了批量和单细胞RNA测序数据集中的mRNA干细胞性指数，并探讨了干细胞性与临床病理特征之间的相关性。利用多元Cox回归分析构建了胶质瘤干细胞关联评分（GSScore）。我们还建立了一种来源于胶质瘤患者的GSC细胞系，并使用胶质瘤细胞系验证了GSScore在预测化疗反应中的性能。将高和低GSScores的GSC之间的差异表达基因（DEGs）用于将低级别胶质瘤（LGG）样本划分为三个干细胞分类亚型。我们在这三个LGG干细胞性相关亚型中确定了临床病理特征的差异，包括生存率、拷贝数变异、突变、肿瘤微环境和免疫和化疗反应。使用机器学习方法，我们进一步确定了基因作为亚型预测因子，并使用CGGA数据集验证了它们的性能。在本研究中，我们确定了与LGG化疗反应相关的GSScore。通过该得分，我们还确定了LGG亚型的新分类和相关亚型预测因子，可能有助于精准治疗的发展。© 2023 The Authors.
Glioma stem cells (GSCs) remodel their tumor microenvironment to sustain a supportive niche. Identification and stratification of stemness related characteristics in patients with glioma might aid in the diagnosis and treatment of the disease. In this study, we calculated the mRNA stemness index in bulk and single-cell RNA-sequencing datasets using machine learning methods and investigated the correlation between stemness and clinicopathological characteristics. A glioma stemness-associated score (GSScore) was constructed using multivariate Cox regression analysis. We also generated a GSC cell line derived from a patient diagnosed with glioma and used glioma cell lines to validate the performance of the GSScore in predicting chemotherapeutic responses. Differentially expressed genes (DEGs) between GSCs with high and low GSScores were used to cluster lower-grade glioma (LGG) samples into three stemness subtypes. Differences in clinicopathological characteristics, including survival, copy number variations, mutations, tumor microenvironment, and immune and chemotherapeutic responses, among the three LGG stemness-associated subtypes were identified. Using machine learning methods, we further identified genes as subtype predictors and validated their performance using the CGGA datasets. In the current study, we identified a GSScore that correlated with LGG chemotherapeutic response. Through the score, we also identified a novel classification of the LGG subtype and associated subtype predictors, which might facilitate the development of precision therapy.© 2023 The Authors.