Genetic variants in telomere-maintenance genes are associated with ovarian cancer risk and outcome

Abstract

Most ovarian cancer patients present at an advanced stage with poor prognosis. Telomeres play a critical role in protecting chromosomes stability. The associations of genetic variants in telomere maintenance genes and ovarian cancer risk and outcome are unclear. We genotyped 137 single nucleotide polymorphisms (SNPs) in telomere-maintenance genes in 417 ovarian cancer cases and 417 matched healthy controls to evaluate their associations with cancer risk, survival and therapeutic response. False discovery rate Q-value was calculated to account for multiple testing. Eleven SNPs from two genes showed nominally significant associations with the risks of ovarian cancer. The most significant SNP was TEP1: rs2228026 with participants carrying at least one variant allele exhibiting a 3.28-fold (95% CI: 1.72-6.29; P < 0.001, Q = 0.028) increased ovarian cancer risk, which remained significant after multiple testing adjusting. There was also suggested evidence for the associations of SNPs with outcome, although none of the associations had a Q < 0.05. Seven SNPs from two genes showed associations with ovarian cancer survival (P < 0.05). The strongest association was found in TNKS gene (rs10093972, hazard ratio = 1.88; 95% CI: 1.20-2.92; P = 0.006, Q = 0.076). Five SNPs from four genes showed suggestive associations with therapeutic response (P < 0.05). In a survival tree analysis, TEP1:rs10143407 was the primary factor contributing to overall survival. Unfavourable genotype analysis showed a cumulative effect of significant SNPs on ovarian cancer risk, survival and therapeutic response. Genetic variations in telomere-maintenance genes may be associated with ovarian cancer risk and outcome.

Keywords: cancer risk; ovarian cancer; single nucleotide polymorphism; survival; telomere maintenance; therapeutic response.

Figure 1

Kaplan–Meier curve of ovarian cancer patient with different unfavourable genotype. Patients were classified into three different groups based on the number of unfavourable genotypes (UFG) in each patient that was identified from cumulative effect analysis. The median survival time (MST) of each group was compared by the log‐rank test.

Figure 2

High‐order interactions of SNPs on modulating the overall survival of ovarian cancer patients. (A) Survival tree using a recursive partitioning method implemented in the STREE program to split the patients into nodes with different risks of death based on the distinct genotype combinations; (B) Kaplan–Meier curve of ovarian cancer patients with distinct genotype combinations. The terminal nodes identified in A were classified into three risk groups (low, medium and high) based on the HR of the terminal nodes. The median survival time (MST) of each group was compared by the log‐rank test.