Xu Wei, Mesa-Eguiagaray Ines, Kirkpatrick Theresa, Devlin Jennifer, Brogan Stephanie, Turner Patricia, Macdonald Chloe, Thornton Michelle, Zhang Xiaomeng, He Yazhou, Li Xue, Timofeeva Maria, Farrington Susan, Din Farhat, Dunlop Malcolm, Theodoratou Evropi
Centre for Global Health, Usher Institute, University of Edinburgh, Edinburgh EH8 9AG, UK.
Colon Cancer Genetics Group, Medical Research Council Human Genetics Unit, Medical Research Council, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK.
J Pers Med. 2023 Jun 29;13(7):1065. doi: 10.3390/jpm13071065.
We aimed to develop and validate prediction models incorporating demographics, clinical features, and a weighted genetic risk score (wGRS) for individual prediction of colorectal cancer (CRC) risk in patients with gastroenterological symptoms. Prediction models were developed with internal validation [CRC Cases: n = 1686/Controls: n = 963]. Candidate predictors included age, sex, BMI, wGRS, family history, and symptoms (changes in bowel habits, rectal bleeding, weight loss, anaemia, abdominal pain). The baseline model included all the non-genetic predictors. Models A (baseline model + wGRS) and B (baseline model) were developed based on LASSO regression to select predictors. Models C (baseline model + wGRS) and D (baseline model) were built using all variables. Models' calibration and discrimination were evaluated through the Hosmer-Lemeshow test (calibration curves were plotted) and C-statistics (corrected based on 1000 bootstrapping). The models' prediction performance was: model A (corrected C-statistic = 0.765); model B (corrected C-statistic = 0.753); model C (corrected C-statistic = 0.764); and model D (corrected C-statistic = 0.752). Models A and C, that integrated wGRS with demographic and clinical predictors, had a statistically significant improved prediction performance. Our findings suggest that future application of genetic predictors holds significant promise, which could enhance CRC risk prediction. Therefore, further investigation through model external validation and clinical impact is merited.
我们旨在开发并验证包含人口统计学、临床特征和加权遗传风险评分（wGRS）的预测模型，用于对有胃肠症状的患者进行个体结直肠癌（CRC）风险预测。通过内部验证开发预测模型[CRC病例：n = 1686/对照：n = 963]。候选预测因素包括年龄、性别、体重指数、wGRS、家族史和症状（排便习惯改变、直肠出血、体重减轻、贫血、腹痛）。基线模型包括所有非遗传预测因素。基于LASSO回归开发模型A（基线模型+wGRS）和模型B（基线模型）以选择预测因素。使用所有变量构建模型C（基线模型+wGRS）和模型D（基线模型）。通过Hosmer-Lemeshow检验（绘制校准曲线）和C统计量（基于100次自助抽样校正）评估模型的校准和区分度。模型的预测性能为：模型A（校正C统计量=0.765）；模型B（校正C统计量=0.753）；模型C（校正C统计量=0.764）；模型D（校正C统计量=0.752）。将wGRS与人口统计学和临床预测因素相结合的模型A和C具有统计学上显著改善的预测性能。我们的研究结果表明，遗传预测因素的未来应用具有重大前景，这可能会提高CRC风险预测。因此，值得通过模型外部验证和临床影响进行进一步研究。
