Endometrial cancer risk prediction including serum-based biomarkers: results from the EPIC cohort

Renée T. Fortner*, Anika Hüsing, Tilman Kühn, Meric Konar, Kim Overvad, Anne Tjønneland, Louise Hansen, Marie Christine Boutron-Ruault, Gianluca Severi, Agnès Fournier, Heiner Boeing, Antonia Trichopoulou, Vasiliki Benetou, Philippos Orfanos, Giovanna Masala, Claudia Agnoli, Amalia Mattiello, Rosario Tumino, Carlotta Sacerdote, H. Bas Bueno-de-MesquitaPetra H M Peeters, Elisabete Weiderpass, Inger T. Gram, Oxana Gavrilyuk, J. Ramón Quirós, José Maria Huerta, Eva Ardanaz, Nerea Larrañaga, Leila Lujan-Barroso, Emilio Sánchez-Cantalejo, Salma Tunå Butt, Signe Borgquist, Annika Idahl, Eva Lundin, Kay Tee Khaw, Naomi E. Allen, Sabina Rinaldi, Laure Dossus, Marc Gunter, Melissa A. Merritt, Ioanna Tzoulaki, Elio Riboli, Rudolf Kaaks

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Endometrial cancer risk prediction models including lifestyle, anthropometric and reproductive factors have limited discrimination. Adding biomarker data to these models may improve predictive capacity; to our knowledge, this has not been investigated for endometrial cancer. Using a nested case–control study within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, we investigated the improvement in discrimination gained by adding serum biomarker concentrations to risk estimates derived from an existing risk prediction model based on epidemiologic factors. Serum concentrations of sex steroid hormones, metabolic markers, growth factors, adipokines and cytokines were evaluated in a step-wise backward selection process; biomarkers were retained at p 

Original languageEnglish
Pages (from-to)1317-1323
Number of pages7
JournalInternational Journal of Cancer
Volume140
Issue number6
DOIs
Publication statusPublished - 15 Mar 2017

Keywords

  • adipokines
  • cytokines
  • endometrial cancer
  • growth factors
  • inflammatory markers
  • lipids
  • metabolic markers
  • prospective cohort
  • risk prediction
  • sex steroids

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