Please use this identifier to cite or link to this item:
Title: Endometrial cancer risk prediction including serum-based biomarkers: results from the EPIC cohort
Author: Fortner, Renée T.
Hüsing, Anika
Kühn, Tilman
Konar, Meric
Overvad, Kim
Tjønneland, Anne
Hansen, Louise
Boutron-Ruault, Marie-Christine
Severi, Gianluca
Fournier, Agnès
Boeing, Heiner
Trichopoulou, Antonia
Benetou, Vassiliki
Orfanos, Philippos
Masala, Giovanna
Agnoli, Claudia
Mattiello, Amalia
Tumino, Rosario
Sacerdote, Carlotta
Bueno de Mesquita, H. Bas
Peeters, Petra H. M.
Weiderpass, Elisabete
Gram, Inger T.
Gavrilyuk, Oxana
Quirós, J. Ramón
Huerta Castaño, José María
Ardanaz, Eva
Larrañaga, Nerea
Luján Barroso, Leila
Sánchez Cantalejo, Emilio
Tunå Butt, Salma
Borgquist, Signe
Idahl, Annika
Lundin, Eva
Khaw, Kay-Tee
Allen, Naomi E.
Rinaldi, Sabina
Dossus, Laure
Gunter, Marc J.
Merritt, Melissa A.
Keywords: Càncer d'endometri
Marcadors bioquímics
Endometrial cancer
Biochemical markers
Issue Date: 15-Mar-2017
Publisher: Wiley
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 < 0.157 indicating improvement in the Akaike information criterion (AIC). Improvement in discrimination was assessed using the C-statistic for all biomarkers alone, and change in C-statistic from addition of biomarkers to preexisting absolute risk estimates. We used internal validation with bootstrapping (1000-fold) to adjust for over-fitting. Adiponectin, estrone, interleukin-1 receptor antagonist, tumor necrosis factor-alpha and triglycerides were select-ed into the model. After accounting for over-fitting, discrimination was improved by 2.0 percentage points when all evaluated biomarkers were included and 1.7 percentage points in the model including the selected biomarkers. Models including etiologic markers on independent pathways and genetic markers may further improve discrimination.
Note: Versió postprint del document publicat a:
It is part of: International Journal of Cancer, 2017, vol. 140, num. 6, p. 1317-1323
Related resource:
ISSN: 0020-7136
Appears in Collections:Articles publicats en revistes (Infermeria de Salut Pública, Salut mental i Maternoinfantil)
Articles publicats en revistes (Institut d'lnvestigació Biomèdica de Bellvitge (IDIBELL))

Files in This Item:
File Description SizeFormat 
677162.pdf400.81 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.