He Cai-Yun, Chen Le-Zong, Wang Zi-Xian, Sun Li-Ping, Peng Jun-Jie, Wu Min-Qing, Wang Tong-Min, Li Ya-Qi, Yang Xin-Hua, Zhou Da-Lei, Ye Zu-Lu, Ma Jiang-Jun, Li Xi-Zhao, Zhang Pei-Fen, Ju Huai-Qiang, Mo Hai-Yu, Zhang Zi-Chen, Zeng Zhao-Lei, Shao Jian-Yong, Jia Wei-Hua, Cai San-Jun, Yuan Yuan, Xu Rui-Hua
Sun Yat-sen University Cancer Center, Sun Yat-sen University, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China; Department of Molecular Diagnostics, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China.
Sun Yat-sen University Cancer Center, Sun Yat-sen University, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou 510060, China; Department of Medical Oncology, Sun Yat-Sen University Cancer Center, Guangzhou 510060, China.
Genomics. 2021 May;113(3):867-873. doi: 10.1016/j.ygeno.2021.01.025. Epub 2021 Feb 2.
The efficacy of susceptible variants derived from genome-wide association studies (GWAs) optimizing discriminatory accuracy of colorectal cancer (CRC) in Chinese remains unclear. In the present validation study, we assessed 75 recently identified variants from GWAs. A risk predictive model combining 19 variants using the least absolute shrinkage and selection operator (LASSO) statistics offered certain clinical advantages. This model demonstrated an area under the receiver operating characteristic (AUC) of 0.61 during training analysis and yielded robust AUCs from 0.59 to 0.61 during validation analysis in three independent centers. The individuals carrying the highest quartile of risk score revealed over 2-fold risks of CRC (ranging from 2.12 to 2.90) compared with those who presented the lowest quartile of risk score. This genetic model offered the possibility of partitioning risk within the average risk population, which might serve as a first step toward developing individualized CRC prevention strategies in China.
源自全基因组关联研究（GWAs）的易感变异体在中国优化结直肠癌（CRC）鉴别准确性方面的疗效尚不清楚。在本验证研究中，我们评估了最近从GWAs中鉴定出的75个变异体。使用最小绝对收缩和选择算子（LASSO）统计方法组合19个变异体的风险预测模型具有一定的临床优势。该模型在训练分析期间的受试者工作特征曲线下面积（AUC）为0.61，在三个独立中心的验证分析期间产生了0.59至0.61的稳健AUC。与风险评分最低四分位数的个体相比，风险评分最高四分位数的个体患CRC的风险高出2倍以上（范围为2.12至2.90）。这种遗传模型为在平均风险人群中划分风险提供了可能性，这可能是在中国制定个性化CRC预防策略的第一步。
