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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2019-10-01
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| Series: | PeerJ |
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| Online Access: | https://peerj.com/articles/7942.pdf |
| _version_ | 1797417812865581056 |
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| author | Junjie Kong Tao Wang Shu Shen Zifei Zhang Xianwei Yang Wentao Wang |
| author_facet | Junjie Kong Tao Wang Shu Shen Zifei Zhang Xianwei Yang Wentao Wang |
| author_sort | Junjie Kong |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T06:24:07Z |
| format | Article |
| id | doaj.art-aeaee3b649284e4d84bbef2de4f4a82a |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T06:24:07Z |
| publishDate | 2019-10-01 |
| publisher | PeerJ Inc. |
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| spelling | doaj.art-aeaee3b649284e4d84bbef2de4f4a82a2023-12-03T11:29:47ZengPeerJ Inc.PeerJ2167-83592019-10-017e794210.7717/peerj.7942A genomic-clinical nomogram predicting recurrence-free survival for patients diagnosed with hepatocellular carcinomaJunjie KongTao WangShu ShenZifei ZhangXianwei YangWentao WangLiver 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.https://peerj.com/articles/7942.pdfHepatocellular carcinomaRecurrenceBioinformaticsNomogram |
| spellingShingle | Junjie Kong Tao Wang Shu Shen Zifei Zhang Xianwei Yang Wentao Wang A genomic-clinical nomogram predicting recurrence-free survival for patients diagnosed with hepatocellular carcinoma PeerJ Hepatocellular carcinoma Recurrence Bioinformatics Nomogram |
| 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 | Hepatocellular carcinoma Recurrence Bioinformatics Nomogram |
| url | https://peerj.com/articles/7942.pdf |
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