Relational Graph Convolutional Networks for Glioblastoma Biomarker Discovery via ceRNA and Copy Number Variation Analysis
Glioblastoma (GBM) is a highly aggressive brain tumor with an extremely poor five-year survival rate of 6.9%, largely attributable to the lack of reliable biomarkers. While competing endogenous RNA (ceRNA) and copy number variation (CNV) analyses each offer unique biomarker-identification potential, current approaches neglect the integration of multiple regulatory mechanisms for biomarker detection. To address this limitation, we applied relational graph convolutional networks (RGCNs) to ceRNA and CNV knowledge graphs through a novel late-fusion ensemble architecture. The proposed architecture outperformed baseline models and identified five novel biomarkers, including hsa-miR-196a and hsa-miR-224. Kaplan–Meier survival analysis and Cox regression indicated that the identified genes hold significant prognostic and diagnostic power, and the early stratification of the Kaplan–Meier curves indicates their potential for patient survival prediction. The results illustrate that a late-fusion RGCN ensemble effectively captures complex gene interactions, overcoming limitations of existing models and providing a framework for future biomarker discovery. The novel biomarkers serve as prospective targets for future GBM therapeutic development and candidates for non-invasive diagnostic assays.