BACKGROUND: This study employs a comprehensive approach using Genome-Wide Association Studies (GWAS), protein-protein interaction networks, gene co-expression networks, gene interaction networks, and centrality analysis to explore genetic and network interactions related to glioma and Alzheimer's disease. METHODS: Through detailed analysis of glioma single-cell data, we found that gene expression patterns are closely related to cell types and states. Principal Component Analysis (PCA) and dimensionality reduction techniques like UMAP and t-SNE reveal cell population heterogeneity and potential subgroups. This research also involved building machine learning models to classify glioma and assessing their performances, as well as a model that can best classify each type.. RESULTS: We investigated these cell interaction networks along with NRG signaling networks for glioma to discern cell-cell communication and signaling events. The SPP1 signaling pathway and gene expression analysis further triage the specific genes mediating the interactive role in glioma cells. CONCLUSION: This study presented a comprehensive view of gene expression, cell cell interactions and signaling networks in glioma, which might be a crucial piece to understand glioma complexity and usher in new therapeutic strategies across medical divisions.