Deep learning algorithm reveals two prognostic subtypes in patients with gliomas.

Background: Gliomas are highly complex and heterogeneous tumors, rendering prognosis prediction challenging. The advent of deep learning algorithms and the accessibility of multi-omic data represent a new approach for the identification of survival-sensitive subtypes. Herein, an autoencoder-based approach was used to identify two survival-sensitive subtypes using RNA sequencing (RNA-seq) and DNA methylation (DNAm) data from The Cancer Genome Atlas (TCGA) dataset. The subtypes were used as labels to build a support vector machine model with cross-validation. We validated the robustness of the model on Chinese Glioma Genome Atlas (CGGA) dataset. DNAm-driven genes were identified by integrating DNAm and gene expression profiling analyses using the R MethylMix package and carried out for further enrichment analysis.

Results: For TCGA dataset, the model produced a high C-index (0.92 ± 0.02), low brier score (0.16 ± 0.02), and significant log-rank p value (p < 0.0001). The model also had a decent performance for CGGA dataset (CGGA DNAm: C-index of 0.70, brier score of 0.21; CGGA RNA-seq: C-index of 0.79, brier score of 0.18). Moreover, we identified 389 DNAm-driven genes of survival-sensitive subtypes, which were significantly enriched in the glutathione metabolism pathway.

Conclusions: Our study identified two survival-sensitive subtypes of glioma and provided insights into the molecular mechanisms underlying glioma development; thus, potentially providing a new target for the prognostic prediction of gliomas and supporting personalized treatment strategies.