Tumor heterogeneity is a challenge to designing effective and targeted therapies. Glioma-type identification depends on specific molecular and histological features, which are defined by the official WHO classification CNS. These guidelines are constantly updated to support the diagnosis process, which affects all the successive clinical decisions. In this context, the search for new potential diagnostic and prognostic targets, characteristic of each glioma type, is crucial to support the development of novel therapies. Based on a TCGA glioma RNA-sequencing dataset updated according to the 2016 and 2021 WHO guidelines, we proposed a two-step variable selection approach for biomarker discovery. Our framework encompasses the graphical lasso algorithm to estimate sparse networks of genes carrying diagnostic information. These networks are then used as input for regularised Cox survival regression model, allowing the identification of a smaller subset of genes with prognostic value. In each step, the results derived from the 2016 and 2021 classes were discussed and compared. For both WHO glioma classifications, our analysis identifies potential biomarkers, characteristic of each glioma type. Yet, better results were obtained for the WHO CNS classification in 2021, thereby supporting recent efforts to include molecular data on glioma classification.

翻译：肿瘤异质性对设计有效靶向治疗构成挑战。胶质瘤类型的识别依赖于特定的分子和组织学特征，这些特征由WHO中枢神经系统官方分类定义。这些指南不断更新以支持诊断过程，从而影响所有后续临床决策。在此背景下，针对每种胶质瘤类型寻找新的潜在诊断和预后靶点，对支持新型疗法的开发至关重要。基于根据2016年和2021年WHO指南更新的TCGA胶质瘤RNA测序数据集，我们提出了一种两步变量筛选方法用于生物标志物发现。该框架包含图形套索算法，用于估计携带诊断信息的基因稀疏网络。随后将这些网络作为正则化Cox生存回归模型的输入，从而识别出具有预后价值的更小子集基因。在每个步骤中，我们对基于2016年和2021年分类所得的结果进行了讨论与比较。针对这两个WHO胶质瘤分类，我们的分析识别出了每种胶质瘤类型特有的潜在生物标志物。然而，基于2021年WHO CNS分类获得了更优的结果，这支持了近期将分子数据纳入胶质瘤分类的努力。