Integrative Transcriptomic Analysis Reveals Synaptic Pathway-Enriched Prognostic Gene Signature in Glioblastoma

Objective: This study aimed to elucidate the transcriptomic landscape of glioblastoma (GBM) by integrating multiple datasets to identify prognostically significant gene signatures and investigate the involvement of synaptic pathways. Materials and Methods: Four transcriptomic datasets were analyzed using the limma pipeline for differential gene expression analysis with empirical Bayes moderation to identify differentially expressed genes (DEGs). Functional enrichment analyses were conducted via Gene Ontology and KEGG. A prognostic model was constructed using LASSO-Cox regression on the CGGA mRNAseq_693 cohort (n=693) and validated on the CGGA_325 cohort (n=325). Multivariate Cox proportional hazards regression assessed the model’s independent prognostic value. External validation of gene expression was performed using TCGA (n=163) and GTEx (n=207) datasets. Results: A total of 108 consistently dysregulated DEGs were identified, enriched in synaptic vesicle cycling, neuronal projection, and chloride transport pathways. A robust ten-gene prognostic signature (SPRY1, CD58, RCC1, E2F7, BUB1, FAM46A, TYMS, NEDD9, CHST14, REPS2) effectively stratified patients, with time-dependent ROC AUCs of 0.718, 0.755, and 0.757 at 1-, 3-, and 5-year survival points. Multivariate Cox analysis confirmed the model’s independent prognostic value, further refined by a nomogram with AUCs of 0.8, 0.898, and 0.906. Differential expression of all ten genes was validated externally. Conclusion: This study reveals a previously underexplored synaptic pathway-related gene signature with strong prognostic relevance for GBM. The ten-gene model offers a clinically applicable tool for risk stratification and highlights neuron-tumor synaptic interactions as critical drivers of tumor progression, providing a foundation for future therapeutic strategies.