Endometrial cancer risk prediction including serum-based biomarkers: results from the EPIC cohort
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- 作者：René ; e T. Fortner ; Anika Hü ; sing ; Tilman Kü ; hn ; Meric Konar ; Kim Overvad ; Anne Tjø ; nneland ; Louise Hansen ; Marie-Christine Boutron-Ruault ; Gianluca Severi ; Agnè ; s Fournier ; Heiner Boeing ; Antonia Trichopoulou ; Vasiliki Benetou ; Philippos Orfanos ; Giovanna Masala ; Claudia Agnoli ; Amalia Mattiello ; Rosario Tumino ; Carlotta Sacerdote ; H.B(as) Bueno-de-Mesquita ; Petra H.M. Peeters ; Elisabete Weiderpass ; Inger T. Gram ; Oxana Gavrilyuk ; J. Ramó ; n Quiró ; s ; José ; Maria Huerta ; Eva Ardanaz ; Nerea Larrañ ; aga ; Leila Lujan-Barroso ; Emilio Sá ; nchez-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 and Rudolf Kaaks
- 刊名：International Journal of Cancer
- 出版年：2017
- 出版时间：15 March 2017
- 年：2017
- 卷：140
- 期：6
- 页码：1317-1323
- 全文大小：170K
- ISSN：1097-0215
文摘
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.