| Literature DB >> 27935083 |
Renée T Fortner1, Anika Hüsing1, Tilman Kühn1, Meric Konar1,2, Kim Overvad3, Anne Tjønneland4, Louise Hansen4, Marie-Christine Boutron-Ruault5,6,7, Gianluca Severi5,6,7,8, Agnès Fournier5,6,7, Heiner Boeing9, Antonia Trichopoulou10,11, Vasiliki Benetou10,11, Philippos Orfanos10,11, Giovanna Masala12, Claudia Agnoli13, Amalia Mattiello14, Rosario Tumino15, Carlotta Sacerdote16, H B As Bueno-de-Mesquita17,18,19, Petra H M Peeters20,21, Elisabete Weiderpass22,23,24,25, Inger T Gram22, Oxana Gavrilyuk22,26, J Ramón Quirós27, José Maria Huerta28,29, Eva Ardanaz29,30,31, Nerea Larrañaga29,32, Leila Lujan-Barroso33, Emilio Sánchez-Cantalejo29,34, Salma Tunå Butt35, Signe Borgquist36, Annika Idahl37,38, Eva Lundin39, Kay-Tee Khaw40, Naomi E Allen41, Sabina Rinaldi42, Laure Dossus42, Marc Gunter42, Melissa A Merritt43, Ioanna Tzoulaki43, Elio Riboli43, Rudolf Kaaks1.
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 selected 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.Entities:
Keywords: adipokines; cytokines; endometrial cancer; growth factors; inflammatory markers; lipids; metabolic markers; prospective cohort; risk prediction; sex steroids
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Year: 2017 PMID： 27935083 DOI： 10.1002/ijc.30560
Source DB: PubMed Journal: Int J Cancer ISSN： 0020-7136 Impact factor: 7.396