Prognostic factors in early-onset epithelial ovarian cancer: a population-based study

Obstet Gynecol. 2000 Jan;95(1):119-27. doi: 10.1016/s0029-7844(99)00535-9.


Objective: To investigate the clinical prognostic factors that influence ovarian cancer survival in women with early-onset epithelial ovarian cancer using population-based data.

Methods: Subjects in the current study were from a population-based series of 197 patients with invasive ovarian cancer and 60 patients with ovarian cancer of low malignant potential who were identified from the Cancer and Steroid Hormone study. All subjects were between 20 and 54 years of age at diagnosis for ovarian cancer. Epidemiologic data were obtained from each participant. Immunohistochemical staining was performed to assess p53 expression in paraffin-embedded ovarian cancers. Univariate and multivariate analyses for survival were conducted using the proportional hazards model to test the prognostic significance of several clinicopathologic factors among subjects.

Results: Among women with invasive tumors, the proportional hazards model revealed that advanced stage at diagnosis [hazard ratio = 4.1, 95% confidence interval (CI) = 2.5, 6.6], age at diagnosis 46-54 (hazard ratio = 2.0, 95% CI = 1.3, 3.0), and overexpression of p53 (hazard ratio = 1.5, 95% CI = 1.1, 2.3) were significantly associated with decreased survival.

Conclusion: These results provide evidence that stage, age, and p53 overexpression are independent predictors of decreased survival in women with invasive ovarian cancer diagnosed younger than age 55. Further investigation of the effect of age at diagnosis on the relationship between p53 overexpression and ovarian cancer survival is warranted.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Adult
  • Age of Onset
  • Female
  • Gene Expression
  • Genes, p53 / physiology
  • Humans
  • Middle Aged
  • Ovarian Neoplasms / metabolism
  • Ovarian Neoplasms / mortality*
  • Ovarian Neoplasms / pathology
  • Prognosis
  • Proportional Hazards Models
  • Survival Analysis