Prediction of early recurrence and response to adjuvant Sorafenib for hepatocellular carcinoma after resection

Background Early recurrence of hepatocellular carcinoma (HCC) is a major obstacle to improving the prognosis, and no widely accepted adjuvant therapy guideline for patients post-liver resection is available. Currently, all available methods and biomarkers are insufficient to accurately predict post-...

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Autores principales:	Liming Zheng, Xi Gu, Guojun Zheng, Xin Li, Meifang He, Longgen Liu, Xike Zhou
Formato:	article
Lenguaje:	EN
Publicado:	PeerJ Inc. 2021
Materias:	Hepatocellular carcinoma Early recurrence Adjuvant sorafenib Medicine R
Acceso en línea:	https://doaj.org/article/3e08f6c374c74528b081541cbed5fc68
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spelling	oai:doaj.org-article:3e08f6c374c74528b081541cbed5fc682021-11-28T15:05:25ZPrediction of early recurrence and response to adjuvant Sorafenib for hepatocellular carcinoma after resection10.7717/peerj.125542167-8359https://doaj.org/article/3e08f6c374c74528b081541cbed5fc682021-11-01T00:00:00Zhttps://peerj.com/articles/12554.pdfhttps://peerj.com/articles/12554/https://doaj.org/toc/2167-8359Background Early recurrence of hepatocellular carcinoma (HCC) is a major obstacle to improving the prognosis, and no widely accepted adjuvant therapy guideline for patients post-liver resection is available. Currently, all available methods and biomarkers are insufficient to accurately predict post-operation HCC patients’ risk of early recurrence and their response to adjuvant therapy. Methods In this study, we downloaded four gene expression datasets (GSE14520, GSE54236, GSE87630, and GSE109211) from the Gene Expression Omnibus database and identified 34 common differentially expressed genes associated with HCC dysregulation and response to adjuvant sorafenib. Then, we constructed a novel 11-messenger RNA predictive model by using ROC curves analysis, univariate Cox regression analysis, and LASSO Cox regression analysis. Furthermore, we validated the predictive values of the risk model in GSE14520 and TCGA-LIHC cohorts by using Kaplan–Meier survival analysis, multivariable Cox regression analysis, and decision curve analysis, respectively. Results The risk score model could identify patients with a high risk of HCC recurrence at the early stage and could predict the response of patients to adjuvant sorafenib. Patients with a high risk score had a worse recurrence rate in training cohorts (2-year: p < 0.0001, hazard ratio (HR): 4.658, confidence interval 95% CI [2.895–7.495]; 5-year: p < 0.0001, HR: 3.251, 95% CI [2.155–4.904]) and external validation cohorts (2-year: p < 0.001, HR: 3.65, 95% CI [2.001–6.658]; 5-year: p < 0.001, HR: 3.156, 95% CI [1.78–5.596]). The AUC values of the risk score model for predicting tumor early recurrence were 0.746 and 0.618, and that of the risk score model for predicting the response to adjuvant sorafenib were 0.722 and 0.708 in the different cohort, respectively. Multivariable Cox regression analysis and decision curve analysis also showed that the risk score model was superior to and independent of other clinicopathologic characteristics. Moreover, the risk score model had excellent abilities to predict the overall survival and HCC recurrence of patients with the same tumor stage category. Conclusions Our risk model is a reliable and superior predictive tool. With this model, we could optimize the risk stratification based on early tumor recurrence and could evaluate the response of patients to adjuvant sorafenib after liver resection.Liming ZhengXi GuGuojun ZhengXin LiMeifang HeLonggen LiuXike ZhouPeerJ Inc.articleHepatocellular carcinomaEarly recurrenceAdjuvant sorafenibMedicineRENPeerJ, Vol 9, p e12554 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Hepatocellular carcinoma Early recurrence Adjuvant sorafenib Medicine R
spellingShingle	Hepatocellular carcinoma Early recurrence Adjuvant sorafenib Medicine R Liming Zheng Xi Gu Guojun Zheng Xin Li Meifang He Longgen Liu Xike Zhou Prediction of early recurrence and response to adjuvant Sorafenib for hepatocellular carcinoma after resection
description	Background Early recurrence of hepatocellular carcinoma (HCC) is a major obstacle to improving the prognosis, and no widely accepted adjuvant therapy guideline for patients post-liver resection is available. Currently, all available methods and biomarkers are insufficient to accurately predict post-operation HCC patients’ risk of early recurrence and their response to adjuvant therapy. Methods In this study, we downloaded four gene expression datasets (GSE14520, GSE54236, GSE87630, and GSE109211) from the Gene Expression Omnibus database and identified 34 common differentially expressed genes associated with HCC dysregulation and response to adjuvant sorafenib. Then, we constructed a novel 11-messenger RNA predictive model by using ROC curves analysis, univariate Cox regression analysis, and LASSO Cox regression analysis. Furthermore, we validated the predictive values of the risk model in GSE14520 and TCGA-LIHC cohorts by using Kaplan–Meier survival analysis, multivariable Cox regression analysis, and decision curve analysis, respectively. Results The risk score model could identify patients with a high risk of HCC recurrence at the early stage and could predict the response of patients to adjuvant sorafenib. Patients with a high risk score had a worse recurrence rate in training cohorts (2-year: p < 0.0001, hazard ratio (HR): 4.658, confidence interval 95% CI [2.895–7.495]; 5-year: p < 0.0001, HR: 3.251, 95% CI [2.155–4.904]) and external validation cohorts (2-year: p < 0.001, HR: 3.65, 95% CI [2.001–6.658]; 5-year: p < 0.001, HR: 3.156, 95% CI [1.78–5.596]). The AUC values of the risk score model for predicting tumor early recurrence were 0.746 and 0.618, and that of the risk score model for predicting the response to adjuvant sorafenib were 0.722 and 0.708 in the different cohort, respectively. Multivariable Cox regression analysis and decision curve analysis also showed that the risk score model was superior to and independent of other clinicopathologic characteristics. Moreover, the risk score model had excellent abilities to predict the overall survival and HCC recurrence of patients with the same tumor stage category. Conclusions Our risk model is a reliable and superior predictive tool. With this model, we could optimize the risk stratification based on early tumor recurrence and could evaluate the response of patients to adjuvant sorafenib after liver resection.
format	article
author	Liming Zheng Xi Gu Guojun Zheng Xin Li Meifang He Longgen Liu Xike Zhou
author_facet	Liming Zheng Xi Gu Guojun Zheng Xin Li Meifang He Longgen Liu Xike Zhou
author_sort	Liming Zheng
title	Prediction of early recurrence and response to adjuvant Sorafenib for hepatocellular carcinoma after resection
title_short	Prediction of early recurrence and response to adjuvant Sorafenib for hepatocellular carcinoma after resection
title_full	Prediction of early recurrence and response to adjuvant Sorafenib for hepatocellular carcinoma after resection
title_fullStr	Prediction of early recurrence and response to adjuvant Sorafenib for hepatocellular carcinoma after resection
title_full_unstemmed	Prediction of early recurrence and response to adjuvant Sorafenib for hepatocellular carcinoma after resection
title_sort	prediction of early recurrence and response to adjuvant sorafenib for hepatocellular carcinoma after resection
publisher	PeerJ Inc.
publishDate	2021
url	https://doaj.org/article/3e08f6c374c74528b081541cbed5fc68
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_version_	1718407828241121280

Prediction of early recurrence and response to adjuvant Sorafenib for hepatocellular carcinoma after resection

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