Background: The fibroblast growth factor (FGF) and FGF receptor (FGFR) axis plays a critical role in tumorigenesis, but little is known of its influence in ovarian cancer. We sought to determine the association of genetic variants in the FGF pathway with risk, therapeutic response, and survival of patients with ovarian cancer.
Methods: We matched 339 non-Hispanic white ovarian cancer cases with 349 healthy controls and genotyped them for 183 single-nucleotide polymorphisms (SNPs) from 24 FGF (fibroblast growth factor) and FGFR (fibroblast growth factor receptor) genes. Genetic associations for the main effect, gene-gene interactions, and the cumulative effect were determined.
Results: Multiple SNPs in the FGF-FGFR axis were associated with an increased risk of ovarian cancer. In particular, FGF1 [fibroblast growth factor 1 (acidic)] SNP rs7727832 showed the most significant association with ovarian cancer (odds ratio, 2.27; 95% CI, 1.31-3.95). Ten SNPs were associated with a reduced risk of ovarian cancer. FGF18 (fibroblast growth factor 18) SNP rs3806929, FGF7 (fibroblast growth factor 7) SNP rs9920722, FGF23 (fibroblast growth factor 23) SNP rs12812339, and FGF5 (fibroblast growth factor 5) SNP rs3733336 were significantly associated with a favorable treatment response, with a reduction of risk of nonresponse of 40% to 60%. Eleven SNPs were significantly associated with overall survival. Of these SNPs, FGF23 rs7961824 was the most significantly associated with improved prognosis (hazard ratio, 0.55; 95% CI, 0.39-0.78) and was associated with significantly longer survival durations, compared with individuals with the common genotype at this locus (58.1 months vs. 38.0 months, P = 0.005). Survival tree analysis revealed FGF2 rs167428 as the primary factor contributing to overall survival.
Conclusions: Significant associations of genetic variants in the FGF pathway were associated with ovarian cancer risk, therapeutic response, and survival. The discovery of multiple SNPs in the FGF-FGFR pathway provides a molecular approach for risk assessment, monitoring therapeutic response, and prognosis.