Integrative transcriptomic immune scoring and network topology reveal ME13-driven spatial heterogeneity in glioblastoma

Glioblastoma (GBM) is one of the most aggressive and heterogeneous variants of brain cancer; it is associated with intricate connections between pathways of proliferative signaling and modulation of the immune system. The paper shall clarify on how to explain immune-relevant transcriptional programs in GBM by an integrative gene co-expression network analysis. Powered by normalized expression data of 17,362 genes in the GSE16011 dataset, Weighted Gene Co-expression Network Analysis (WGCNA) was used to build functional coherent modules, and ME13 turned out to be the one most significant to the infiltration of immune cells (r = 0.82, p < 0.001). The gene-gene interaction network showed five crucial central hub genes, including GIMAP4, CD84, KRT23, KLF9, and RCBTB2, which are correlated with the FOXP3 and CD8A expression, showing their possible regulatory functions in the dynamic process of the cytotoxic T-cell and Treg. To put the pattern of spatial activation into context, overlaying and GSVA scoring of Grad-CAM revealed a wide variety in the immune landscape within glioblastoma tissues. This multimodal combination illustrated the presence of ME13 in IL-17 and the p53 signaling pathway, as well as its value for immune stratification. The results not only provide the molecular framework for interpreting immune heterogeneity in GBM but also reveal the potential of hub genes as therapeutic targets and for diagnostic profiling in the future. Although some constraints will emerge due to the absence of experimental verification, the study paves the way for translation through multi-omics platforms and spatial immunogenomics.