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三种生物标志物的纵向监测预测肝癌根治性切除术后复发。

Longitudinal surveillance of three biomarkers to predict recurrence of hepatocellular carcinoma after radical resection.

机构信息

Department of Hepatopancreatobiliary Surgery, Ningbo Medical Centre Lihuili Hospital, Ningbo University.

Department of Hepatopancreatobiliary Surgery, The First Affiliated Hospital of Ningbo University, Ningbo, China.

出版信息

Eur J Gastroenterol Hepatol. 2023 Oct 1;35(10):1178-1185. doi: 10.1097/MEG.0000000000002610. Epub 2023 Jul 11.

DOI:10.1097/MEG.0000000000002610
PMID:37577836
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10756703/
Abstract

BACKGROUND

Radical resection is a curative treatment for patients with hepatocellular carcinoma (HCC), but the incidence of recurrence remains high. We aimed to explore the performance of predicting HCC recurrence by longitudinal surveillance of the protein induced by vitamin K absence (PIVKA-II), alpha- fetoprotein (AFP), and lectin-reactive AFP (AFP-L3) during postoperative follow-up.

METHODS

Patients who underwent radical resection for HCC at the Ningbo Medical Centre Lihuili Hospital between January 2015 and December 2020 were included. All enrolled patients regularly monitor PIVKA-II, AFP, AFP-L3 every 3 months during postoperative follow-up. The surveillance performance of PIVKA-II, AFP, AFP-L3 during follow-up for the prediction of HCC recurrence was compared in patients. The generalized estimation equation (GEE) was used to analyze the trends of the tumor biomarkers and interactions with time. Area under the receiver operator characteristic (AUROC) curves, the optimal cut-off value, the sensitivity and specificity were calculated to evaluate the performance of the three biomarkers. The recurrence-free survival (RFS) and overall survival (OS) of patients with any of the elevated biomarkers was analyzed by Kaplan-Meier curves and the log-rank test. Multivariate logistic regression models were used to analyze potential risk factors for recurrence.

RESULTS

The GEE analysis indicated that PIVKA-II, AFP, AFP-L3 in the recurrence patients were higher than the no recurrence patients during follow-up, PIVKA-II and AFP showed increasing trends from 6 months before recurrence. In predicting recurrence, the AUROCs for PIVKA-II, AFP, AFP-L3 and their combination were 0.885, 0.754, 0.781 and 0.885 respectively, the optimal cut-off value for PIVKA-II, AFP, AFP-L3 was 29.5 mAU/ml, 10.7 ng/L, 1.5% respectively. The sensitivity in predicting recurrence for PIVKA-II, AFP, AFP-L3 and combination were 75.0, 54.7, 57.8 and 79.7% respectively. The RFS and the OS of patients with any of the biomarkers elevated during the follow-up was significantly shorter than that without elevated biomarkers ( P  < 0.001). Multivariate analysis showed that any of the biomarkers elevated was the independent risk factor of recurrence.

CONCLUSION

Longitudinal surveillance of PIVKA-II, AFP and AFP-L3 can effectively predict recurrence of HCC after operation.

摘要

背景

根治性切除术是治疗肝细胞癌 (HCC) 患者的一种有治愈可能的治疗方法,但复发率仍然很高。我们旨在通过在术后随访期间对维生素 K 缺乏诱导蛋白 (PIVKA-II)、甲胎蛋白 (AFP) 和凝集素反应性 AFP (AFP-L3) 的纵向监测,探讨其对 HCC 复发预测的性能。

方法

纳入 2015 年 1 月至 2020 年 12 月期间在宁波医疗中心李惠利医院接受根治性 HCC 切除术的患者。所有入组患者在术后随访期间每 3 个月定期监测 PIVKA-II、AFP、AFP-L3。比较患者在随访期间 PIVKA-II、AFP、AFP-L3 对 HCC 复发预测的监测性能。采用广义估计方程 (GEE) 分析肿瘤标志物的趋势及其与时间的相互作用。计算受试者工作特征 (ROC) 曲线下面积 (AUROC)、最佳截断值、灵敏度和特异性,以评估三种生物标志物的性能。通过 Kaplan-Meier 曲线和对数秩检验分析任何标志物升高患者的无复发生存率 (RFS) 和总生存率 (OS)。采用多变量逻辑回归模型分析复发的潜在危险因素。

结果

GEE 分析表明,在随访期间,复发患者的 PIVKA-II、AFP、AFP-L3 高于无复发患者,PIVKA-II 和 AFP 从复发前 6 个月开始呈上升趋势。在预测复发方面,PIVKA-II、AFP、AFP-L3 及其组合的 AUROCs 分别为 0.885、0.754、0.781 和 0.885,PIVKA-II、AFP、AFP-L3 的最佳截断值分别为 29.5 mAU/ml、10.7 ng/L 和 1.5%。PIVKA-II、AFP、AFP-L3 及联合预测复发的灵敏度分别为 75.0%、54.7%、57.8%和 79.7%。在随访期间任何标志物升高的患者的 RFS 和 OS 明显短于无标志物升高的患者 ( P  < 0.001)。多变量分析表明,任何标志物升高均为复发的独立危险因素。

结论

PIVKA-II、AFP 和 AFP-L3 的纵向监测可有效预测 HCC 术后复发。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18d8/10756703/15f7dedbc89a/ejgh-35-1178-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18d8/10756703/0a58a5745096/ejgh-35-1178-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18d8/10756703/5f6b66c9dcf2/ejgh-35-1178-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18d8/10756703/91242d67c237/ejgh-35-1178-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18d8/10756703/15f7dedbc89a/ejgh-35-1178-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18d8/10756703/0a58a5745096/ejgh-35-1178-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18d8/10756703/5f6b66c9dcf2/ejgh-35-1178-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18d8/10756703/91242d67c237/ejgh-35-1178-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/18d8/10756703/15f7dedbc89a/ejgh-35-1178-g004.jpg

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