Abstract

BACKGROUND: The survival and therapeutic outcome vary greatly among glioblastoma (GBM) patients. Treatment resistance, including resistance to temozolomide (TMZ) and radiotherapy, is a great obstacle for these therapies. In this study, we aimed to evaluate the predictive value of SEC61G on survival and therapeutic response in GBM patients.

MATERIAL AND METHODS: Survival analyses were performed to assess the correlation between SEC61G expression and survival of GBM patients from the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) datasets. Univariate and multivariate Cox proportional hazard regression analysis was introduced to determine prognostic factors with independent impact power. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) were conducted to illustrate possible biological functions of SEC61G.

RESULTS: High expression of SEC61G was significantly correlated with poor prognosis in all GBM patients. High expression of SEC61G was also associated with poor outcome in those who received TMZ treatment or radiotherapy in TCGA GBM cohort. Univariate and multivariate Cox proportional hazards regression demonstrated that SEC61G was an independent prognostic factor affecting the prognosis and therapeutic outcome. The combination of age, SEC61G expression, and MGMT promoter methylation in survival analysis could provide better outcome assessment. Finally, a strong correlation between SEC61G expression and Notch pathway was observed in GSEA and GSVA, which suggested a possible mechanism that SEC61G affected survival and TMZ resistance.

CONCLUSIONS: SEC61G expression may be a potential prognostic marker of poor survival, and a predictor of poor outcome to TMZ treatment and radiotherapy in GBM patients.

Keywords: Drug Resistance, Glioblastoma, Prognosis, Radiotherapy, Adult, Aged, Aged, 80 and over, Antineoplastic Agents, Alkylating, Biomarkers, Tumor, Brain Neoplasms, Cohort Studies, Databases, Genetic, Drug Resistance, Neoplasm, Middle Aged, Proportional Hazards Models, SEC Translocation Channels, Survival Analysis, temozolomide, Treatment Outcome