Identifying Differential Expression Genes and Prognostic Signature Based on Subventricular Zone Involved Glioblastoma - Yuan, Qing; Zuo, Fu-Xing; Cai, Hong-Qing; Qian, Hai-Peng; Wan, Jing-Hai (Frontiers in Genetics, 2022)
Background: Studies have suggested that glioblastoma (GBM) cells originate from the subventricular zone (SVZ) and that GBM contact with the SVZ correlated with worse prognosis and higher recurrence. However, research on differentially expressed genes (DEGs) between GBM and the SVZ is lacking. Methods: We performed deep RNA sequencing on seven SVZ-involved GBMs and paired tumor-free SVZ tissues. DEGs and enrichment were assessed. We obtained GBM patient expression profiles and clinical data from the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) databases. The least absolute shrinkage and selection operator Cox regression model was utilized to construct a multigene signature in the CGGA cohort. GBM patient data from TCGA cohort were used for validation. Results: We identified 137 (97 up- and 40 down-regulated) DEGs between GBM and healthy SVZ samples. Enrichment analysis revealed that DEGs were mainly enriched in immune-related terms, including humoral immune response regulation, T cell differentiation, and response to tumor necrosis factor, and the MAPK, cAMP, PPAR, PI3K-Akt, and NF-κb signaling pathways. An eight-gene (BCAT1, HPX, NNMT, TBX5, RAB42, TNFRSF19, C16orf86, and TRPC5) signature was constructed. GBM patients were stratified into two risk groups. High-risk patients showed significantly reduced overall survival compared with low-risk patients. Univariate and multivariate regression analyses indicated that the risk score level represented an independent prognostic factor. High risk score of GBM patients negatively correlated with 1p19q codeletion and IDH1 mutation. Immune infiltration analysis further showed that the high risk score was negatively correlated with activated NK cell and monocyte counts, but positively correlated with macrophage and activated dendritic cell counts and higher PD-L1 mRNA expression. Conclusion: Here, a novel gene signature based on DEGs between GBM and healthy SVZ was developed for determining GBM patient prognosis. Targeting these genes may be a therapeutic strategy for GBM.