Dissecting glioblastoma risk signatures in the tumor immune microenvironment based on multi-dimensional transcriptomics.

Glioblastoma (GBM) is characterized by pronounced tumor heterogeneity and a complex immune microenvironment, contributing to poor patient survival outcomes. In this study, we comprehensively dissected the tumor microenvironment (TME) and uncovered potential molecular mechanisms by integrating single-cell, bulk, and spatial transcriptomic data. Hallmarks of malignancy and cell cycle regulatory pathways were consistently enriched across these modalities, promoting tumor cell proliferation and progression. Using a machine learning algorithm, we identified seven hallmark-related prognostic signatures (HMsig), namely AEBP1, ASF1A, PRPS1, DCC, OPHN1, IL13RA2, and HDAC5-whose predictive importance was validated through SHAP analysis. Ligand-receptor (LR) interaction analysis further revealed that interactions involving OPHN1 were associated with poorer prognosis. Along the pseudotime trajectory of T cell differentiation, immune checkpoint genes (ICGs) LAG3, PDCD1, and HAVCR2 were substantially upregulated. Notably, synergistic transcriptional regulation between tumor-related HMsig genes and ICGs in T cells was identified as a key factor influencing patient survival. Spatial transcriptomic analysis demonstrated the existence of synergistic gene interactions, deciphering the immunomodulatory functions of GBM biomarkers within the TME.