Zhou Hongshu, Chen Bo, Zhang Liyang, Li Chuntao
Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, Hunan, PR China.
Hypothalamic-pituitary Research Center, Xiangya Hospital, Central South University, Changsha, Hunan, PR China.
Comput Struct Biotechnol J. 2023 Jul 22;21:3827-3840. doi: 10.1016/j.csbj.2023.07.029. eCollection 2023.
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.
胶质瘤干细胞（GSCs）重塑其肿瘤微环境以维持支持性生态位。识别和分层胶质瘤患者中与干性相关的特征可能有助于该疾病的诊断和治疗。在本研究中，我们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干性指数，并研究了干性与临床病理特征之间的相关性。使用多变量Cox回归分析构建了胶质瘤干性相关评分（GSScore）。我们还从一名被诊断患有胶质瘤的患者中生成了一个GSC细胞系，并使用胶质瘤细胞系验证GSScore在预测化疗反应方面的性能。将高GSScore和低GSScore的GSCs之间的差异表达基因（DEGs）用于将低级别胶质瘤（LGG）样本聚类为三种干性亚型。确定了三种LGG干性相关亚型在临床病理特征方面的差异，包括生存、拷贝数变异、突变、肿瘤微环境以及免疫和化疗反应。使用机器学习方法，我们进一步将基因鉴定为亚型预测因子，并使用CGGA数据集验证了它们的性能。在当前研究中，我们确定了一个与LGG化疗反应相关的GSScore。通过该评分，我们还确定了LGG亚型的一种新分类以及相关的亚型预测因子，这可能有助于精准治疗的发展。
