Table1_Cilium Expression Score Predicts Glioma Survival.XLSX

The accurate classification, prognostication, and treatment of gliomas has been hindered by an existing cellular, genomic, and transcriptomic heterogeneity within individual tumors and their microenvironments. Traditional clustering is limited in its ability to distinguish heterogeneity in gliomas because the clusters are required to be exclusive and exhaustive. In contrast, biclustering can identify groups of co-regulated genes with respect to a subset of samples and vice versa. In this study, we analyzed 1,798 normal and tumor brain samples using an unsupervised biclustering approach. We identified co-regulated gene expression profiles that were linked to proximally located brain regions and detected upregulated genes in subsets of gliomas, associated with their histologic grade and clinical outcome. In particular, we present a cilium-associated signature that when upregulated in tumors is predictive of poor survival. We also introduce a risk score based on expression of 12 cilium-associated genes which is reproducibly informative of survival independent of other prognostic biomarkers. These results highlight the role of cilia in development and progression of gliomas and suggest potential therapeutic vulnerabilities for these highly aggressive tumors.

脑胶质瘤的精确分类、预测及治疗受到个体肿瘤及其微环境中存在的细胞、基因组及转录组异质性的制约。传统聚类方法在区分胶质瘤异质性方面的能力有限，因为聚类要求具有排他性和完备性。相比之下，双聚类能够识别与样本子集相关的共调控基因群体，反之亦然。在本研究中，我们采用无监督双聚类方法分析了1,798个正常和肿瘤脑样本。我们确定了与邻近脑区相关的共调控基因表达谱，并在胶质瘤的子集中检测到与组织学分级和临床预后相关的上调基因。特别是，我们提出了一种与纤毛相关的特征，当肿瘤中上调时，可预测不良的生存率。此外，我们引入了一种基于12个纤毛相关基因表达的风险评估模型，该模型可重复地提供独立于其他预后生物标志物的生存信息。这些结果突出了纤毛在胶质瘤发展和进展中的作用，并提出了针对这些高度侵袭性肿瘤的潜在治疗弱点。