Wang Xuemin, Dossus Laure, Gunter Marc J, Crosbie Emma J, Ong Jue-Sheng, Glubb Dylan M, O'Mara Tracy A
Cancer Research Program, QIMR Berghofer Medical Research Institute, Brisbane QLD 4059, Australia.
Nutrition and Metabolism Branch, International Agency for Research on Cancer (IARC/WHO), 69007 Lyon, France.
medRxiv. 2025 Feb 21:2025.02.19.25322538. doi: 10.1101/2025.02.19.25322538.
Obesity is a major risk factor for endometrial cancer, but it is unknown whether it impacts the association between genetic risk and endometrial cancer. We incorporated polygenic risk score and epidemiological risk factors in the prediction of and investigated associations of BMI and polygenic risk score with endometrial cancer risk.
We generated polygenic risk score for endometrial cancer in 129,829 unrelated female participants of European ancestry (including 956 incident cases with endometrial cancer) in the UK Biobank and predicted endometrial cancer using endometrial cancer polygenic risk score and established epidemiological risk factors, including BMI. We evaluated the performance of endometrial cancer prediction models by odds ratios and area under the receiver operating characteristic curves (AUCs) to using logistic regression. Individual and joint associations of BMI and polygenic risk score with endometrial cancer were assessed using Cox proportional hazards models.
An integrated model incorporating both polygenic risk score and epidemiological risk factors achieved a modest, but statistically significant, improvement in predicting endometrial cancer status compared with the model that included epidemiologic risk factors alone (AUC = 0.74 versus 0.73; = 3.98 × 10). Obese participants (BMI ≥ 30 kg/m) in the top polygenic risk tertile had the highest endometrial cancer risk. We observed independent effects of genetic risk and BMI on endometrial cancer risk.
Integrating polygenic risk score with epidemiological risk factors may offer insights into population stratification for endometrial cancer susceptibility. Higher endometrial cancer polygenic risk is associated with endometrial cancer, irrespective of BMI.
肥胖是子宫内膜癌的主要危险因素，但肥胖是否会影响遗传风险与子宫内膜癌之间的关联尚不清楚。我们将多基因风险评分和流行病学危险因素纳入子宫内膜癌风险预测中，并研究了体重指数（BMI）和多基因风险评分与子宫内膜癌风险的关联。
我们在英国生物银行中为129829名无亲缘关系的欧洲血统女性参与者（包括956例子宫内膜癌新发病例）生成了子宫内膜癌多基因风险评分，并使用子宫内膜癌多基因风险评分和包括BMI在内的既定流行病学危险因素预测子宫内膜癌。我们通过比值比和受试者工作特征曲线下面积（AUC），采用逻辑回归评估子宫内膜癌预测模型的性能。使用Cox比例风险模型评估BMI和多基因风险评分与子宫内膜癌的个体和联合关联。
与仅包含流行病学危险因素的模型相比，整合多基因风险评分和流行病学危险因素的综合模型在预测子宫内膜癌状态方面有适度但具有统计学意义的改善（AUC = 0.74对0.73；P = 3.98×10）。处于多基因风险最高三分位数的肥胖参与者（BMI≥30 kg/m²）患子宫内膜癌的风险最高。我们观察到遗传风险和BMI对子宫内膜癌风险有独立影响。
将多基因风险评分与流行病学危险因素相结合，可能为子宫内膜癌易感性的人群分层提供见解。无论BMI如何，较高的子宫内膜癌多基因风险都与子宫内膜癌相关。
