Liu Zhenqiu, Yuan Huangbo, Suo Chen, Zhao Renjia, Jin Li, Zhang Xuehong, Zhang Tiejun, Chen Xingdong
State Key Laboratory of Genetic Engineering, Human Phenome Institute, and School of Life Sciences, Fudan University, Shanghai, China.
Fudan University Taizhou Institute of Health Sciences, Taizhou, China.
EClinicalMedicine. 2024 Aug 22;75:102796. doi: 10.1016/j.eclinm.2024.102796. eCollection 2024 Sep.
The precise associations between common clinical biomarkers and hepatocellular carcinoma (HCC) risk remain unclear but hold valuable insights for HCC risk stratification and prediction.
We examined the linear and nonlinear associations between the baseline levels of 32 circulating biomarkers and HCC risk in the England cohort of UK Biobank (UKBB) (n = 397,702). The participants were enrolled between 2006 and 2010 and followed up to 31st October 2022. The primary outcome is incident HCC cases. We then employed random survival forests (RSF) to select the top ten most informative biomarkers, considering their association with HCC, and developed a point-based risk score to predict HCC. The performance of the risk score was evaluated in three validation sets including UKBB Scotland and Wales cohort (n = 52,721), UKBB non-White-British cohort (n = 29,315), and the Taizhou Longitudinal Study in China (n = 17,269).
Twenty-five biomarkers were significantly associated with HCC risk, either linearly or nonlinearly. Based on the RSF model selected biomarkers, our point-based risk score showed a concordance index of 0.866 in the England cohort and varied between 0.814 and 0.849 in the three validation sets. HCC incidence rates ranged from 0.95 to 30.82 per 100,000 from the lowest to the highest quintiles of the risk score in the England cohort. Individuals in the highest risk quintile had a 32-73 times greater risk of HCC compared to those in the lowest quintile. Moreover, over 70% of HCC cases were detected in individuals within the top risk score quintile across all cohorts.
Our simple risk score enables the identification of high-risk individuals of HCC in the general population. However, including some biomarkers, such as insulin-like growth factor 1, not routinely measured in clinical practice may increase the model's complexity, highlighting the need for more accessible biomarkers that can maintain or improve the predictive accuracy of the risk score.
This work was supported by the National Natural Science Foundation of China (grant numbers: 82204125) and the Science and Technology Support Program of Taizhou (TS202224).
常见临床生物标志物与肝细胞癌（HCC）风险之间的确切关联尚不清楚，但对HCC风险分层和预测具有重要意义。
我们在英国生物银行（UKBB）的英格兰队列（n = 397,702）中研究了32种循环生物标志物的基线水平与HCC风险之间的线性和非线性关联。参与者于2006年至2010年入组，并随访至2022年10月31日。主要结局是HCC发病病例。然后，我们采用随机生存森林（RSF）来选择与HCC关联最强的十种最具信息性的生物标志物，并开发了一种基于点数的风险评分来预测HCC。在包括UKBB苏格兰和威尔士队列（n = 52,721）、UKBB非白人英国队列（n = 29,315）和中国泰州纵向研究（n = 17,269）的三个验证集中评估了风险评分性能。
25种生物标志物与HCC风险存在显著的线性或非线性关联。基于RSF模型选择的生物标志物，我们的基于点数的风险评分在英格兰队列中的一致性指数为0.866，在三个验证集中介于0.814和0.849之间。在英格兰队列中，风险评分从最低五分位数到最高五分位数，HCC发病率范围为每10万人0.95至30.82例。风险评分最高五分位数的个体患HCC的风险是最低五分位数个体的3
