Vieira Francisca G, Bispo Regina, Lopes Marta B
Center for Mathematics and Applications (NOVA Math), NOVA School of Science and Technology, Caparica, Portugal.
Department of Mathematics, NOVA School of Science and Technology, Caparica, Portugal.
Bioinform Biol Insights. 2024 May 27;18:11779322241249563. doi: 10.1177/11779322241249563. eCollection 2024.
Glioma is currently one of the most prevalent types of primary brain cancer. Given its high level of heterogeneity along with the complex biological molecular markers, many efforts have been made to accurately classify the type of glioma in each patient, which, in turn, is critical to improve early diagnosis and increase survival. Nonetheless, as a result of the fast-growing technological advances in high-throughput sequencing and evolving molecular understanding of glioma biology, its classification has been recently subject to significant alterations. In this study, we integrate multiple glioma omics modalities (including mRNA, DNA methylation, and miRNA) from The Cancer Genome Atlas (TCGA), while using the revised glioma reclassified labels, with a supervised method based on sparse canonical correlation analysis (DIABLO) to discriminate between glioma types. We were able to find a set of highly correlated features distinguishing glioblastoma from lower-grade gliomas (LGGs) that were mainly associated with the disruption of receptor tyrosine kinases signaling pathways and extracellular matrix organization and remodeling. Concurrently, the discrimination of the LGG types was characterized primarily by features involved in ubiquitination and DNA transcription processes. Furthermore, we could identify several novel glioma biomarkers likely helpful in both diagnosis and prognosis of the patients, including the genes , and . Collectively, this comprehensive approach not only allowed a highly accurate discrimination of the different TCGA glioma patients but also presented a step forward in advancing our comprehension of the underlying molecular mechanisms driving glioma heterogeneity. Ultimately, our study also revealed novel candidate biomarkers that might constitute potential therapeutic targets, marking a significant stride toward personalized and more effective treatment strategies for patients with glioma.
胶质瘤是目前最常见的原发性脑癌类型之一。鉴于其高度的异质性以及复杂的生物分子标志物，人们已经做出了许多努力来准确分类每位患者的胶质瘤类型，而这反过来对于改善早期诊断和提高生存率至关重要。尽管如此，由于高通量测序技术的快速发展以及对胶质瘤生物学分子理解的不断演变，其分类最近发生了重大变化。在本研究中，我们整合了来自癌症基因组图谱（TCGA）的多种胶质瘤组学模式（包括mRNA、DNA甲基化和miRNA），同时使用修订后的胶质瘤重新分类标签，采用基于稀疏典型相关分析（DIABLO）的监督方法来区分胶质瘤类型。我们能够找到一组高度相关的特征，将胶质母细胞瘤与低级别胶质瘤（LGGs）区分开来，这些特征主要与受体酪氨酸激酶信号通路的破坏以及细胞外基质的组织和重塑有关。同时，LGG类型的区分主要由参与泛素化和DNA转录过程的特征来表征。此外，我们可以识别出几种可能有助于患者诊断和预后的新型胶质瘤生物标志物，包括基因 、 和 。总的来说，这种综合方法不仅能够对不同的TCGA胶质瘤患者进行高度准确的区分，而且在推进我们对驱动胶质瘤异质性的潜在分子机制的理解方面又迈出了一步。最终，我们的研究还揭示了可能构成潜在治疗靶点的新型候选生物标志物，这标志着朝着为胶质瘤患者制定个性化和更有效治疗策略迈出了重要一步。
