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A genomic-clinical nomogram predicting recurrence-free survival for patients diagnosed with hepatocellular carcinoma
Liver resection surgery is the most commonly used treatment strategy for patients diagnosed with hepatocellular carcinoma (HCC). However, there is still a chance for recurrence in these patients despite the survival benefits of this procedure. This study aimed to explore recurrence-related genes (RR...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6825747/ https://www.ncbi.nlm.nih.gov/pubmed/31687273 http://dx.doi.org/10.7717/peerj.7942 |
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author | Kong, Junjie Wang, Tao Shen, Shu Zhang, Zifei Yang, Xianwei Wang, Wentao |
author_facet | Kong, Junjie Wang, Tao Shen, Shu Zhang, Zifei Yang, Xianwei Wang, Wentao |
author_sort | Kong, Junjie |
collection | PubMed |
description | Liver resection surgery is the most commonly used treatment strategy for patients diagnosed with hepatocellular carcinoma (HCC). However, there is still a chance for recurrence in these patients despite the survival benefits of this procedure. This study aimed to explore recurrence-related genes (RRGs) and establish a genomic-clinical nomogram for predicting postoperative recurrence in HCC patients. A total of 123 differently expressed genes and three RRGs (PZP, SPP2, and PRC1) were identified from online databases via Cox regression and LASSO logistic regression analyses and a gene-based risk model containing RRGs was then established. The Harrell’s concordance index (C-index), receiver operating characteristic (ROC) curves and calibration curves showed that the model performed well. Finally, a genomic-clinical nomogram incorporating the gene-based risk model, AJCC staging system, and Eastern Cooperative Oncology Group performance status was constructed to predict the 1-, 2-, and 3-year recurrence-free survival rates (RFS) for HCC patients. The C-index, ROC analysis, and decision curve analysis were good indicators of the nomogram’s performance. In conclusion, we identified three reliable RRGs associated with the recurrence of cancer and constructed a nomogram that performed well in predicting RFS for HCC patients. These findings could enrich our understanding of the mechanisms for HCC recurrence, help surgeons predict patients’ prognosis, and promote HCC treatment. |
format | Online Article Text |
id | pubmed-6825747 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-68257472019-11-04 A genomic-clinical nomogram predicting recurrence-free survival for patients diagnosed with hepatocellular carcinoma Kong, Junjie Wang, Tao Shen, Shu Zhang, Zifei Yang, Xianwei Wang, Wentao PeerJ Bioinformatics Liver resection surgery is the most commonly used treatment strategy for patients diagnosed with hepatocellular carcinoma (HCC). However, there is still a chance for recurrence in these patients despite the survival benefits of this procedure. This study aimed to explore recurrence-related genes (RRGs) and establish a genomic-clinical nomogram for predicting postoperative recurrence in HCC patients. A total of 123 differently expressed genes and three RRGs (PZP, SPP2, and PRC1) were identified from online databases via Cox regression and LASSO logistic regression analyses and a gene-based risk model containing RRGs was then established. The Harrell’s concordance index (C-index), receiver operating characteristic (ROC) curves and calibration curves showed that the model performed well. Finally, a genomic-clinical nomogram incorporating the gene-based risk model, AJCC staging system, and Eastern Cooperative Oncology Group performance status was constructed to predict the 1-, 2-, and 3-year recurrence-free survival rates (RFS) for HCC patients. The C-index, ROC analysis, and decision curve analysis were good indicators of the nomogram’s performance. In conclusion, we identified three reliable RRGs associated with the recurrence of cancer and constructed a nomogram that performed well in predicting RFS for HCC patients. These findings could enrich our understanding of the mechanisms for HCC recurrence, help surgeons predict patients’ prognosis, and promote HCC treatment. PeerJ Inc. 2019-10-31 /pmc/articles/PMC6825747/ /pubmed/31687273 http://dx.doi.org/10.7717/peerj.7942 Text en © 2019 Kong et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Bioinformatics Kong, Junjie Wang, Tao Shen, Shu Zhang, Zifei Yang, Xianwei Wang, Wentao A genomic-clinical nomogram predicting recurrence-free survival for patients diagnosed with hepatocellular carcinoma |
title | A genomic-clinical nomogram predicting recurrence-free survival for patients diagnosed with hepatocellular carcinoma |
title_full | A genomic-clinical nomogram predicting recurrence-free survival for patients diagnosed with hepatocellular carcinoma |
title_fullStr | A genomic-clinical nomogram predicting recurrence-free survival for patients diagnosed with hepatocellular carcinoma |
title_full_unstemmed | A genomic-clinical nomogram predicting recurrence-free survival for patients diagnosed with hepatocellular carcinoma |
title_short | A genomic-clinical nomogram predicting recurrence-free survival for patients diagnosed with hepatocellular carcinoma |
title_sort | genomic-clinical nomogram predicting recurrence-free survival for patients diagnosed with hepatocellular carcinoma |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6825747/ https://www.ncbi.nlm.nih.gov/pubmed/31687273 http://dx.doi.org/10.7717/peerj.7942 |
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