Background: Molecular charactersitcs are essential for the classification and grading of gliomas. However, majority of current understanding is based on public databases that might not accurately reflect the Asian population. Here, we studied the mutation landscape of Chinese glioma patients in hope to provide new insights for glioma prognosis and treatment. Methods: Tissue samples from 112 glioma patients underwent next-generation sequencing targeting 425 cancer-relevant genes. Gene mutations and copy number variations were investigated for their prognostic effect using overall survival data. Pathway-based survival analysis was peformed using top ten predefined oncogenic pathways. Results: We identified similar prevalence of currently established molecular diagnostic markers of glioma, including TP53 (33%), EGFR(26%), TERT (24%), PTEN (21%), ATRX (14%), BRAF (13%) and IDH1/2 (6%). Among all genetic abberations with more than 5% occurrence rate, four mutations and four copy number gains were significantly associated with poor overall survival (univariate, P < 0.05). Of these, TERT mutations (hazard ratio [HR], 3.14; 95% confidence interval [CI], 1.31 to 7.49; P = 0.01) and EGFR amplification (HR, 2.67; 95% CI, 1.20 to 5.95; P = 0.02) remained significant after adjusting for clinical parameters. Similarly, PIK3CA mutations, which was also frequently mutated in glioma but not used for clinical classification, were found to correlate with poor prognosis (HR, 2.61; 95% CI, 1.19 to 5.74; P = 0.02). Additionally, we have also identified MCL1 amplification as a potential novel biomarker for glioma (HR, 2.73; 95% CI, 1.47 to 5.07; P < 0.001), which was seldom reported in the TCGA database and might possibly be ancestral specific giving its high prevelance in our cohort (found in 32% patients). Pathway analyses revealed significantly worse prognosis with abnormal PI3K (HR, 1.81; 95% CI, 1.12 to 2.95; P = 0.02) and cell cycle pathways (HR, 2.04; 95% CI, 1.2 to 3.47; P < 0.001), both of which stayed meaningful after adjusting for clinical factors. Conclusions: In this study, we discovered PIK3CA mutations and MCL1 amplification as novel prognostic markers of glioma. We also demonstrated shorter survival with abnormal PI3K and cell cycle pathways that provided an intergrative understanding of glioma.