Document Type
Article
Publication Date
7-6-2020
Abstract
BACKGROUND AND OBJECTIVES: In hepatocellular carcinoma (HCC) patients, microvascular invasion (MVI) is associated with worse outcomes regardless of treatment. No single reliable preoperative factor exists to predict MVI. The aim of the work described here was to develop a new MVI- based mRNA biomarker to differentiate between high and low risk patients.
METHODS: Using The Cancer Genome Atlas (TCGA) database, we collected data from 315 HCC patients, including mRNA expression and complete clinical data. We generated a seven-mRNA signature to predict patient outcomes. The mRNA signature was validated using the GSE36376 cohort. Finally, we tested the formula in our own 53 HCC patients using qPCR for the seven mRNAs and analyzing the computed tomography (CT) features.
RESULTS: This seven-mRNA signature significantly correlated with length of recurrence-free survival (RFS) and overall survival (OS) for both the training and validation groups. RFS and OS were briefer in high risk versus low risk patients. A Kaplan-Meier analysis also indicated that survival time was significantly shortened in the high risk group versus the low risk group. Time-dependent receiver operating characteristic analysis demonstrated good predictive performance for the seven-mRNA signature. The mRNA signature also acts as an independent factor according to a Multivariate analysis. Our results are consistent with the seven-mRNA formula risk score.
CONCLUSION: Our research showed a novel seven-mRNA biomarker based on MVI predicting RFS and OS in HCC patients. This mRNA signature can stratify patients into subgroups based on their risk of recurrence to help guide individualized treatment and precision management in HCC.
Recommended Citation
Du, Binbin; Wang, Fang; Jarad, Beers; Wang, Zhihui; and Zhang, Yanzhou, "A novel signature based on microvascular invasion predicts the recurrence of HCC." (2020). Department of Medicine Faculty Papers. Paper 270.
https://jdc.jefferson.edu/medfp/270
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
PubMed ID
32631357
Comments
This article has been peer reviewed. It was published in: Journal of Translational Medicine.
Volume 18, Issue 1, 6 July 2020, Article number 272.
The published version is available at DOI: 10.1186/s12967-020-02432-7
Copyright © 2020 The Author(s).
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