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A novel five‐gene signature predicts overall survival of patients with hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common public health challenges, worldwide. Because of molecular complexity and tumor heterogeneity, there are no effective predictive models for prognosis of HCC. This underlines the unmet need for accurate prognostic models for HCC. Analysis of GSE...

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Autores principales: Wang, Zhigang, Pan, Leyu, Guo, Deliang, Luo, Xiaofeng, Tang, Jie, Yang, Weihua, Zhang, Yuxian, Luo, Anni, Gu, Yang, Pan, Yuxuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8178492/
https://www.ncbi.nlm.nih.gov/pubmed/33934539
http://dx.doi.org/10.1002/cam4.3900
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author Wang, Zhigang
Pan, Leyu
Guo, Deliang
Luo, Xiaofeng
Tang, Jie
Yang, Weihua
Zhang, Yuxian
Luo, Anni
Gu, Yang
Pan, Yuxuan
author_facet Wang, Zhigang
Pan, Leyu
Guo, Deliang
Luo, Xiaofeng
Tang, Jie
Yang, Weihua
Zhang, Yuxian
Luo, Anni
Gu, Yang
Pan, Yuxuan
author_sort Wang, Zhigang
collection PubMed
description Hepatocellular carcinoma (HCC) is one of the most common public health challenges, worldwide. Because of molecular complexity and tumor heterogeneity, there are no effective predictive models for prognosis of HCC. This underlines the unmet need for accurate prognostic models for HCC. Analysis of GSE14520 data from gene omnibus (GEO) database identified multiple differentially expressed mRNAs (DEMs) between HCC and normal tissues. After randomly stratifying the patients into the training and testing groups, we performed univariate, lasso, and multivariable Cox regression analyses to delineate the prognostic gene signature in training set. We then used Kaplan–Meier plot, time‐dependent receiver operating characteristic (ROC), multivariable Cox regression analysis of clinical information, nomogram, and decision curve analysis (DCA) to evaluate the predictive and overall survival value of a novel five‐gene signature (CNIH4, SOX4, SPP1, SORBS2, and CCL19) within and across sets, separately and combined. We also validated the prognostic value of the five‐gene signature using The Cancer Genome Atlas—Liver Hepatocellular Carcinoma (TCGA‐LIHC), GSE54236 and International Cancer Genome Consortium (ICGC) sets. Multivariable Cox regression analysis revealed that the five‐gene signature and tumor node metastasis (TNM) stage were independent prognostic factors for overall survival of HCC patients in GSE14520 and TCGA‐LIHC. Combining TNM stage clinical pathological parameters and nomogram greatly improved the prognosis prediction of HCC. Further gene set enrichment analysis (GSEA) revealed enrichment of KEGG pathways related to cell cycle in the high‐risk group and histidine metabolism in the low‐risk group. Finally, all these five mRNAs are overexpressed between 12 pairs of HCC and adjacent normal tissues by quantitative real‐time PCR validation. In brief, a five‐gene prognostic signature and a nomogram were identified and constructed, respectively, and further validated for their HCC prognostic value. The five‐gene risk score together with TNM stage models could aid in rationalizing customized therapies in HCC patients.
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spelling pubmed-81784922021-06-15 A novel five‐gene signature predicts overall survival of patients with hepatocellular carcinoma Wang, Zhigang Pan, Leyu Guo, Deliang Luo, Xiaofeng Tang, Jie Yang, Weihua Zhang, Yuxian Luo, Anni Gu, Yang Pan, Yuxuan Cancer Med Bioinfomatics Hepatocellular carcinoma (HCC) is one of the most common public health challenges, worldwide. Because of molecular complexity and tumor heterogeneity, there are no effective predictive models for prognosis of HCC. This underlines the unmet need for accurate prognostic models for HCC. Analysis of GSE14520 data from gene omnibus (GEO) database identified multiple differentially expressed mRNAs (DEMs) between HCC and normal tissues. After randomly stratifying the patients into the training and testing groups, we performed univariate, lasso, and multivariable Cox regression analyses to delineate the prognostic gene signature in training set. We then used Kaplan–Meier plot, time‐dependent receiver operating characteristic (ROC), multivariable Cox regression analysis of clinical information, nomogram, and decision curve analysis (DCA) to evaluate the predictive and overall survival value of a novel five‐gene signature (CNIH4, SOX4, SPP1, SORBS2, and CCL19) within and across sets, separately and combined. We also validated the prognostic value of the five‐gene signature using The Cancer Genome Atlas—Liver Hepatocellular Carcinoma (TCGA‐LIHC), GSE54236 and International Cancer Genome Consortium (ICGC) sets. Multivariable Cox regression analysis revealed that the five‐gene signature and tumor node metastasis (TNM) stage were independent prognostic factors for overall survival of HCC patients in GSE14520 and TCGA‐LIHC. Combining TNM stage clinical pathological parameters and nomogram greatly improved the prognosis prediction of HCC. Further gene set enrichment analysis (GSEA) revealed enrichment of KEGG pathways related to cell cycle in the high‐risk group and histidine metabolism in the low‐risk group. Finally, all these five mRNAs are overexpressed between 12 pairs of HCC and adjacent normal tissues by quantitative real‐time PCR validation. In brief, a five‐gene prognostic signature and a nomogram were identified and constructed, respectively, and further validated for their HCC prognostic value. The five‐gene risk score together with TNM stage models could aid in rationalizing customized therapies in HCC patients. John Wiley and Sons Inc. 2021-05-02 /pmc/articles/PMC8178492/ /pubmed/33934539 http://dx.doi.org/10.1002/cam4.3900 Text en © 2021 The Authors. Cancer Medicine published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Bioinfomatics
Wang, Zhigang
Pan, Leyu
Guo, Deliang
Luo, Xiaofeng
Tang, Jie
Yang, Weihua
Zhang, Yuxian
Luo, Anni
Gu, Yang
Pan, Yuxuan
A novel five‐gene signature predicts overall survival of patients with hepatocellular carcinoma
title A novel five‐gene signature predicts overall survival of patients with hepatocellular carcinoma
title_full A novel five‐gene signature predicts overall survival of patients with hepatocellular carcinoma
title_fullStr A novel five‐gene signature predicts overall survival of patients with hepatocellular carcinoma
title_full_unstemmed A novel five‐gene signature predicts overall survival of patients with hepatocellular carcinoma
title_short A novel five‐gene signature predicts overall survival of patients with hepatocellular carcinoma
title_sort novel five‐gene signature predicts overall survival of patients with hepatocellular carcinoma
topic Bioinfomatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8178492/
https://www.ncbi.nlm.nih.gov/pubmed/33934539
http://dx.doi.org/10.1002/cam4.3900
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