肝细胞癌
肝硬化
生物标志物
医学
接收机工作特性
内科学
曲线下面积
胃肠病学
前瞻性队列研究
癌症
血清淀粉样蛋白A
肿瘤科
生物
生物化学
炎症
作者
Hashem B. El‐Serag,Fasiha Kanwal,Jing Ning,Helen Powell,Saira Khaderi,Amit G. Singal,Sumeet K. Asrani,Jorge A. Marrero,Christopher I. Amos,Aaron P. Thrift,Michelle Luster,Abeer Alsarraj,Luis Olivares,Darlene G. Skapura,Jenny Deng,Emad A. Salem,Omar Najjar,Yu Xin,Hao T. Duong,Michael E. Scheurer,Christie M. Ballantyne,Salma Kaochar
出处
期刊:Gut
[BMJ]
日期:2024-02-16
卷期号:: gutjnl-332034
标识
DOI:10.1136/gutjnl-2024-332034
摘要
Inflammatory and metabolic biomarkers have been associated with hepatocellular cancer (HCC) risk in phases I and II biomarker studies. We developed and internally validated a robust metabolic biomarker panel predictive of HCC in a longitudinal phase III study.We used data and banked serum from a prospective cohort of 2266 adult patients with cirrhosis who were followed until the development of HCC (n=126). We custom designed a FirePlex immunoassay to measure baseline serum levels of 39 biomarkers and established a set of biomarkers with the highest discriminatory ability for HCC. We performed bootstrapping to evaluate the predictive performance using C-index and time-dependent area under the receiver operating characteristic curve (AUROC). We quantified the incremental predictive value of the biomarker panel when added to previously validated clinical models.We identified a nine-biomarker panel (P9) with a C-index of 0.67 (95% CI 0.66 to 0.67), including insulin growth factor-1, interleukin-10, transforming growth factor β1, adipsin, fetuin-A, interleukin-1 β, macrophage stimulating protein α chain, serum amyloid A and TNF-α. Adding P9 to our clinical model with 10 factors including AFP improved AUROC at 1 and 2 years by 4.8% and 2.7%, respectively. Adding P9 to aMAP score improved AUROC at 1 and 2 years by 14.2% and 7.6%, respectively. Adding AFP L-3 or DCP did not change the predictive ability of the P9 model.We identified a panel of nine serum biomarkers that is independently associated with developing HCC in cirrhosis and that improved the predictive ability of risk stratification models containing clinical factors.
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