Integrative Multi-Omics Prognostic Modeling of Glioma Recurrence Using Variational Autoencoder and Similarity Network Fusion

Gliomas represent the most prevalent form of malignant brain tumors, with glioblastoma (GBM) classified as the most aggressive subtype and lower-grade gliomas (LGGs) showing a high recurrence rate despite better survival outcomes. Traditional prognostic methods rely on clinical and molecular markers, yet they often fail to leverage the predictive potential of integrative multiomics data. Hence, early and accurate identification of glioma recurrence is critical for optimizing treatment strategies and improving patient outcomes. To address this challenge, we developed a deep learning-based approach that integrates multiomics data with clinical features to enhance recurrence risk stratification. Multiomics data, including mRNA, miRNA, DNA methylation, and CNV profiles from 512 LGG and 350 GBM patients in The Cancer Genome Atlas (TCGA), were processed using the variational autoencoder for nonlinear feature extraction, followed by similarity network fusion to capture cross-omics relationships. Recurrence-guided feature selection identified a robust biomarker panel that effectively categorized patients into high-and low-risk recurrence groups, with Kaplan-Meier survival analysis demonstrating a notable significant difference with p-value less than 0.001. Integration of clinical factors (age, tumor grade, IDH mutation status) further improved predictive performance, yielding a C-index of 0.695 (p < 2e-16) for LGG and 0.619 (p = 3.346e-11) for GBM. Together, these findings establish a multiomics-based predictive model that enables refined glioma recurrence risk assessment, offering personalized treatment strategies for LGG and GBM patients.