Dataset Information

MicroRNAs for the Prediction of Early Response to Sorafenib Treatment in Human Hepatocellular Carcinoma.

ABSTRACT:

Background

Several studies suggest the role of circulating microRNAs (miRNAs) as biomarkers of hepatocellular carcinoma (HCC). However, the serum miRNA profile associated with the response to sorafenib remains to be elucidated. The aim of this study was to clarify the specific miRNAs in serum that could predict the early response of HCC to sorafenib treatment.

Summary

Analyzing the sera from 16 HCC patients, we selected five miRNAs that showed differences in serum levels between patients with and without tumor responses among 179 known secretory miRNAs by using locked nucleic acid probe-based quantitative PCR. Through further analysis using a validation cohort that included 53 HCC patients who underwent sorafenib treatment and 8 healthy control subjects, we found that miR-181a-5p and miR-339-5p showed significant differences in serum levels among patients with partial response (PR), stable disease (SD), and progressive disease (PD), where PR patients showed the highest and PD the lowest levels. We also analyzed the factors associated with disease control (DC; PR or SD) 3 months after the initiation of sorafenib treatment; patients with DC showed a significantly higher level of serum miR-181a-5p than non-DC patients or healthy control subjects (p = 0.0349 and 0.0180 for DC vs. non-DC and control vs. non-DC by Tukey-Kramer test, respectively). We further conducted multivariate analysis among HCC patients with Barcelona Clinic Liver Cancer stage C using extrahepatic metastasis, serum decarboxyprothrombin, and miR-181a-5p levels as covariables; serum miR-181a-5p was the only independent factor for achieving DC (p = 0.0092, odds ratio 0.139, and 95% confidence interval 0.011-0.658). In addition, miR-181a-5p level was also the only independent factor affecting overall survival (p = 0.0194, hazard ratio 0.267, and 95% confidence interval 0.070-0.818).

Key messages

A high serum level of miR-181a-5p before treatment is associated with DC after the initiation of sorafenib.

SUBMITTER: Nishida N

PROVIDER: S-EPMC5340220 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

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Publications

MicroRNAs for the Prediction of Early Response to Sorafenib Treatment in Human Hepatocellular Carcinoma.

Nishida Naoshi N Arizumi Tadaaki T Hagiwara Satoru S Ida Hiroshi H Sakurai Toshiharu T Kudo Masatoshi M

Liver cancer 20161217 2

<h4>Background</h4>Several studies suggest the role of circulating microRNAs (miRNAs) as biomarkers of hepatocellular carcinoma (HCC). However, the serum miRNA profile associated with the response to sorafenib remains to be elucidated. The aim of this study was to clarify the specific miRNAs in serum that could predict the early response of HCC to sorafenib treatment.<h4>Summary</h4>Analyzing the sera from 16 HCC patients, we selected five miRNAs that showed differences in serum levels between p ...[more]

PMID: 28275578

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Project description:BackgroundEarly 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.MethodsIn 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.ResultsThe 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.ConclusionsOur 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.

Project description:Advanced hepatocellular carcinoma (HCC) remains a highly lethal malignancy, although several systemic therapeutic options are available, including sorafenib (SFN), which has been one of the standard treatment agents for almost a decade. As early prediction of response to SFN remains challenging, biomarkers that enable early prediction using a clinically feasible method are needed. Here, we report that the serum secretory form of clusterin (sCLU) protein and its related predictive index are potential beneficial biomarkers for early prediction of SFN response. Using high-throughput screening and subsequent multivariate analysis in the derivation cohort, we found that changes in the concentrations of CLU, vascular cell adhesion molecule-1 (VCAM1), and α-fetoprotein were significantly associated with response to SFN. Furthermore, we confirmed that an increase in CLU serum level 1 month after treatment initiation was significantly associated with shorter progression-free survival. In addition, "NR-index," which comprises these proteins, was evaluated as a tool for accurately predicting the efficacy of SFN and confirmed in the validation cohort. We also established SFN-resistant HepG2 cells (HepG2-SR) and found that sCLU significantly increased in HepG2-SR cells compared with normal HepG2 cells, and confirmed that HepG2-SR cells treated with SFN were resistant to apoptosis. The mechanism underlying activation of sCLU expression in acquired SFN resistance involves aberrant signaling and expression of Akt, mammalian target of rapamycin (mTOR), and a nutrient-related transcription factor, sterol regulatory element binding protein 1c (SREBP-1c). Furthermore, the PI3K and mTOR inhibitor BEZ235 markedly decreased sCLU expression in HepG2-SR cells. Conclusion: These results suggest that measurement of sCLU serum levels and the sCLU-related NR-index are promising clinical tools for the early prediction of SFN response in HCC. Additionally, sCLU-overexpressing HCC might be susceptible to mTOR inhibition.

Dataset Information

MicroRNAs for the Prediction of Early Response to Sorafenib Treatment in Human Hepatocellular Carcinoma.

Background

Summary

Key messages

Publications

MicroRNAs for the Prediction of Early Response to Sorafenib Treatment in Human Hepatocellular Carcinoma.

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