Integration of Single-Cell RNA and Bulk RNA Sequencing Reveals Cellular Heterogeneity and Identifies Survival-Associated Regulatory Networks in Glioblastoma

Abstract

Glioblastoma is a highly aggressive and devastating brain malignancy with dismal prognosis and extremely limited therapeutic options. Identification of prognostic biomarkers and therapeutic targets from multi-omics data is critical for improving patient outcomes. In this study, we investigated the clinical significance of cellular heterogeneity and super-enhancer-driven regulatory networks, which are critically implicated in glioblastoma progression and treatment resistance. We first performed scRNA-seq to dissect tumour microenvironment heterogeneity, identifying 16 distinct cell clusters, including astrocytes, macrophages, and CD8+ T cells. CellChat analysis revealed key intercellular signalling pathways, with astrocytes and macrophages acting as central communication hubs. To integrate bulk RNA sequencing data, we applied the Scissor algorithm to identify survival-associated cell states. By combining single-cell and bulk transcriptomic data, we uncovered 642 survival-related genes, including QKI and RBM47, which robustly predicted patient survival and immunotherapy response. Furthermore, WGCNA analysis identified seven co-expression modules and super enhancer-regulated networks orchestrated by transcription factors (RFX2, RFX4) and hub genes (NEAT1, CFLAR). These networks stratified patients into high- and low-risk groups with significant survival differences. Collectively, our findings elucidate the intricate interplay between cellular heterogeneity and super enhancer-driven gene regulation in glioblastoma, providing a translational framework for targeting oncogenic hubs and modulating microenvironment interactions.