BackgroundlncRNA MIR17HG was upregulated in glioma, and participated in promoting proliferation, migration and invasion of glioma. However, the role of MIR17HG polymorphisms in the occurrence and prognosis of glioma is still unclear.MethodsIn the study, 592 glioma patients and 502 control subjects were recruited. Agena MassARRAY platform was used to detect the genotype of MIR17HG polymorphisms. Logistic regression analysis was used to evaluate the relationship between MIR17HG single nucleotide polymorphisms (SNPs) and glioma risk by odds ratio (OR) and 95% confidence intervals (CIs). Kaplan–Meier curves, Cox hazards models were performed for assessing the role of these SNPs in glioma prognosis by hazard ratios (HR) and 95% CIs.ResultsWe found that rs7318578 (OR = 2.25, p = 3.18 × 10− 5) was significantly associated with glioma susceptibility in the overall participants. In the subgroup with age < 40 years, rs17735387 (OR = 1.53, p = 9.05 × 10− 3) and rs7336610 (OR = 1.35, p = 0.016) were related to the higher glioma susceptibility. More importantly, rs17735387 (HR = 0.82, log-rank p = 0.026) were associated with the longer survival of glioma patients. The GA genotype of rs17735387 had a better overall survival (HR = 0.75, log-rank p = 0.013) and progression free survival (HR = 0.73, log-rank p = 0.032) in patients with I-II glioma. We also found that rs72640334 was related to the poor prognosis (HR = 1.49, Log-rank p = 0.035) in female patients. In the subgroup of patients with age ≥ 40 years, rs17735387 was associated with a better prognosis (HR = 0.036, Log-rank p = 0.002).ConclusionOur study firstly reported that MIR17HG rs7318578 was a risk factor for glioma susceptibility and rs17735387 was associated with the longer survival of glioma among Chinese Han population, which might help to enhance the understanding of MIR17HG gene in gliomagenesis. In subsequent studies, we will continue to collect samples and follow up to further validate our findings and further explore the function of these MIR17HG SNPs in glioma in a larger sample size.