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A Seven-Gene Signature to Predict Prognosis of Patients With Hepatocellular Carcinoma
Purpose: Hepatocellular carcinoma (HCC) is one of the most prevalent malignant diseases worldwide and has a poor prognosis. Gene-based prognostic models have been reported to predict the overall survival of patients with HCC. Unfortunately, most of the genes used in earlier prognostic models lack pr...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8481951/ https://www.ncbi.nlm.nih.gov/pubmed/34603388 http://dx.doi.org/10.3389/fgene.2021.728476 |
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author | Wang, Junli Zhang, Qi Shi, Fukang Yadav, Dipesh Kumar Hong, Zhengtao Wang, Jianing Liang, Tingbo Bai, Xueli |
author_facet | Wang, Junli Zhang, Qi Shi, Fukang Yadav, Dipesh Kumar Hong, Zhengtao Wang, Jianing Liang, Tingbo Bai, Xueli |
author_sort | Wang, Junli |
collection | PubMed |
description | Purpose: Hepatocellular carcinoma (HCC) is one of the most prevalent malignant diseases worldwide and has a poor prognosis. Gene-based prognostic models have been reported to predict the overall survival of patients with HCC. Unfortunately, most of the genes used in earlier prognostic models lack prospective validation and, thus, cannot be used in clinical practice. Methods: Candidate genes were selected from GEPIA (Gene Expression Profiling Interactive Analysis), and their associations with patients’ survival were confirmed by RT-PCR using cDNA tissue microarrays established from patients with HCC after radical resection. A multivariate Cox proportion model was used to calculate the coefficient of corresponding gene. The expression of seven genes of interest (MKI67, AR, PLG, DNASE1L3, PTTG1, PPP1R1A, and TTR) with two reference genes was defined to calculate a risk score which determined groups of different risks. Results: Our risk scoring efficiently classified patients (n = 129) with HCC into a low-, intermediate-, and high-risk group. The three groups showed meaningful distinction of 3-year overall survival rate, i.e., 88.9, 74.5, and 20.6% for the low-, intermediate-, and high-risk group, respectively. The prognostic prediction model of risk scores was subsequently verified using an independent prospective cohort (n = 77) and showed high accuracy. Conclusion: Our seven-gene signature model performed excellent long-term prediction power and provided crucially guiding therapy for patients who are not a candidate for surgery. |
format | Online Article Text |
id | pubmed-8481951 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-84819512021-10-01 A Seven-Gene Signature to Predict Prognosis of Patients With Hepatocellular Carcinoma Wang, Junli Zhang, Qi Shi, Fukang Yadav, Dipesh Kumar Hong, Zhengtao Wang, Jianing Liang, Tingbo Bai, Xueli Front Genet Genetics Purpose: Hepatocellular carcinoma (HCC) is one of the most prevalent malignant diseases worldwide and has a poor prognosis. Gene-based prognostic models have been reported to predict the overall survival of patients with HCC. Unfortunately, most of the genes used in earlier prognostic models lack prospective validation and, thus, cannot be used in clinical practice. Methods: Candidate genes were selected from GEPIA (Gene Expression Profiling Interactive Analysis), and their associations with patients’ survival were confirmed by RT-PCR using cDNA tissue microarrays established from patients with HCC after radical resection. A multivariate Cox proportion model was used to calculate the coefficient of corresponding gene. The expression of seven genes of interest (MKI67, AR, PLG, DNASE1L3, PTTG1, PPP1R1A, and TTR) with two reference genes was defined to calculate a risk score which determined groups of different risks. Results: Our risk scoring efficiently classified patients (n = 129) with HCC into a low-, intermediate-, and high-risk group. The three groups showed meaningful distinction of 3-year overall survival rate, i.e., 88.9, 74.5, and 20.6% for the low-, intermediate-, and high-risk group, respectively. The prognostic prediction model of risk scores was subsequently verified using an independent prospective cohort (n = 77) and showed high accuracy. Conclusion: Our seven-gene signature model performed excellent long-term prediction power and provided crucially guiding therapy for patients who are not a candidate for surgery. Frontiers Media S.A. 2021-09-16 /pmc/articles/PMC8481951/ /pubmed/34603388 http://dx.doi.org/10.3389/fgene.2021.728476 Text en Copyright © 2021 Wang, Zhang, Shi, Yadav, Hong, Wang, Liang and Bai. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Genetics Wang, Junli Zhang, Qi Shi, Fukang Yadav, Dipesh Kumar Hong, Zhengtao Wang, Jianing Liang, Tingbo Bai, Xueli A Seven-Gene Signature to Predict Prognosis of Patients With Hepatocellular Carcinoma |
title | A Seven-Gene Signature to Predict Prognosis of Patients With Hepatocellular Carcinoma |
title_full | A Seven-Gene Signature to Predict Prognosis of Patients With Hepatocellular Carcinoma |
title_fullStr | A Seven-Gene Signature to Predict Prognosis of Patients With Hepatocellular Carcinoma |
title_full_unstemmed | A Seven-Gene Signature to Predict Prognosis of Patients With Hepatocellular Carcinoma |
title_short | A Seven-Gene Signature to Predict Prognosis of Patients With Hepatocellular Carcinoma |
title_sort | seven-gene signature to predict prognosis of patients with hepatocellular carcinoma |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8481951/ https://www.ncbi.nlm.nih.gov/pubmed/34603388 http://dx.doi.org/10.3389/fgene.2021.728476 |
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