Integrating multiomics to elucidate the role of chromatin remodeling in glioma and the antitumor mechanisms and therapeutic potential of targeting LMNA.

Background: Glioma is a highly aggressive central nervous system malignancy characterized by poor clinical outcomes. While chromatin remodeling is fundamentally linked to tumorigenesis, its precise roles and underlying mechanisms in glioma remain elusive. Consequently, developing robust prognostic tools and identifying viable therapeutic targets are critical priorities. Methods: Leveraging RNA-seq profiles from the CGGA, GTEx, and TCGA cohorts, we identified chromatin remodeling-related genes (CRRGs) differentially expressed between glioma and normal brain tissues. We stratified patients into distinct molecular subtypes using consensus clustering and engineered a prognostic signature by systematically screening 117 machine-learning algorithm combinations. The expression patterns of core signature genes were cross-validated using single-cell RNA sequencing, spatial transcriptomics, and proteomic data. Finally, we interrogated the functional role of LMNA through Results: Consensus clustering segregated gliomas into two CRRG-based subtypes, with Subgroup 2 patients experiencing significantly better prognoses. We pinpointed 28 glioma-specific DECRRGs and derived an optimal SuperPC-based model that robustly stratified patients into high- and low-risk cohorts. LMNA, a core model gene markedly upregulated in glioma and correlated with poor survival, was advanced for experimental validation. Functionally, LMNA depletion severely impaired tumor cell proliferation and invasion Conclusion: The CRRG-based prognostic signature developed herein demonstrates robust clinical utility for risk stratification. Crucially, our findings highlight LMNA as a potential prognostic biomarker and a promising therapeutic target in glioma.