神经胶质瘤是原发性恶性脑肿瘤，生存率低且对现有治疗具有高抵抗力。提高对神经胶质瘤的分子认识并公开肿瘤发生和进展的新生物标志物可能有助于找到针对此类癌症的新靶向疗法。癌症基因组图谱 (TCGA) 等公共数据库提供了有关癌症组织的分子信息的宝贵来源。机器学习工具在处理高维组学数据并从中提取相关信息方面显示出前景。在这项工作中，网络推理和聚类方法，即联合图形套索和鲁棒稀疏 K 均值聚类，被应用于 TCGA 神经胶质瘤患者的 RNA 测序数据，以识别不同类型神经胶质瘤（胶质母细胞瘤、星形细胞瘤、和少突胶质细胞瘤）并披露新的患者群体以及群体分离背后的相关基因。获得的结果表明，与胶质母细胞瘤相比，星形细胞瘤和少突胶质细胞瘤具有更多相似性，突出了胶质母细胞瘤与其他胶质瘤亚型之间的分子差异。经过对分析中指出的相关基因进行全面的文献检索后，我们确定了神经胶质瘤生物标志物的潜在候选者。鼓励对这些基因进行进一步的分子验证，以了解它们在诊断和新型疗法设计中的潜在作用。© 2023。BioMed Central Ltd.，Springer Nature 旗下公司。

Gliomas are primary malignant brain tumors with poor survival and high resistance to available treatments. Improving the molecular understanding of glioma and disclosing novel biomarkers of tumor development and progression could help to find novel targeted therapies for this type of cancer. Public databases such as The Cancer Genome Atlas (TCGA) provide an invaluable source of molecular information on cancer tissues. Machine learning tools show promise in dealing with the high dimension of omics data and extracting relevant information from it. In this work, network inference and clustering methods, namely Joint Graphical lasso and Robust Sparse K-means Clustering, were applied to RNA-sequencing data from TCGA glioma patients to identify shared and distinct gene networks among different types of glioma (glioblastoma, astrocytoma, and oligodendroglioma) and disclose new patient groups and the relevant genes behind groups' separation. The results obtained suggest that astrocytoma and oligodendroglioma have more similarities compared with glioblastoma, highlighting the molecular differences between glioblastoma and the others glioma subtypes. After a comprehensive literature search on the relevant genes pointed our from our analysis, we identified potential candidates for biomarkers of glioma. Further molecular validation of these genes is encouraged to understand their potential role in diagnosis and in the design of novel therapies.© 2023. BioMed Central Ltd., part of Springer Nature.