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A novel prognostic models for identifying the risk of hepatocellular carcinoma based on epithelial-mesenchymal transition-associated genes

Several epithelial-mesenchymal transition (EMT)-associated genes (EAGs) have been confirmed to correlate with the prognosis of hepatocellular carcinoma (HCC) patients. Herein, we explored the value of EAGs in the prognosis of HCC relying on data from The Cancer Genome Atlas (TCGA) database. A total...

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Autores principales: Xiong, Chen, Wang, Guifu, Bai, Dousheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Taylor & Francis 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8291854/
https://www.ncbi.nlm.nih.gov/pubmed/32951492
http://dx.doi.org/10.1080/21655979.2020.1822715
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author Xiong, Chen
Wang, Guifu
Bai, Dousheng
author_facet Xiong, Chen
Wang, Guifu
Bai, Dousheng
author_sort Xiong, Chen
collection PubMed
description Several epithelial-mesenchymal transition (EMT)-associated genes (EAGs) have been confirmed to correlate with the prognosis of hepatocellular carcinoma (HCC) patients. Herein, we explored the value of EAGs in the prognosis of HCC relying on data from The Cancer Genome Atlas (TCGA) database. A total of 200 EMT-associated genes were downloaded from the Gene set enrichment analysis (GSEA) website. Moreover, 96 differentially expressed EAGs were identified. Using Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, we forecasted the potential molecular mechanisms of EAGs. To identify prognostic EAGs, Cox regression was used in developing a prognostic risk model. Then, the Kaplan-Meier and receiver operating characteristic (ROC) curves were plotted to validate the prognostic significance of the model. A total of 5 prognostic correlated EAGs (P3H1, SPP1, MMP1, LGALS1, and ITGB5) were screened via Cox regression, which provided the basis for developing a novel prognostic risk model. Based on the risk model, patients were subdivided into high-risk and low-risk groups. The overall survival of the low-risk group was better compared to the high-risk group (P < 0.00001). The ROC curve of the risk model showed a higher AUC (Area under Curve) (AUC = 0.723) compared to other clinical features (AUC ≤ 0.511). A nomogram based on this model was constructed to predict the 1-year, 2-year, and 3-year overall survival rates (OS) of patients. Conclusively, we developed a novel HCC prognostic risk model based on the expression of EAGs, which help advance the prognostic management of HCC patients. Abbreviations: HCC: hepatocellular carcinoma; TCGA: The Cancer Genome Atlas; EMT: epithelial-mesenchymal transition; EAGs: EMT-associated genes; GSEA: gene set enrichment analysis; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; PPI: protein-protein interaction; TF: transcription factor; ROC: receiver operating characteristic; K-M: Kaplan-Meier; AUC: the area under the ROC curve; FDR: false discovery rate; TNM: Tumor size/lymph nodes/distance metastasis
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spelling pubmed-82918542021-09-01 A novel prognostic models for identifying the risk of hepatocellular carcinoma based on epithelial-mesenchymal transition-associated genes Xiong, Chen Wang, Guifu Bai, Dousheng Bioengineered Research Paper Several epithelial-mesenchymal transition (EMT)-associated genes (EAGs) have been confirmed to correlate with the prognosis of hepatocellular carcinoma (HCC) patients. Herein, we explored the value of EAGs in the prognosis of HCC relying on data from The Cancer Genome Atlas (TCGA) database. A total of 200 EMT-associated genes were downloaded from the Gene set enrichment analysis (GSEA) website. Moreover, 96 differentially expressed EAGs were identified. Using Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, we forecasted the potential molecular mechanisms of EAGs. To identify prognostic EAGs, Cox regression was used in developing a prognostic risk model. Then, the Kaplan-Meier and receiver operating characteristic (ROC) curves were plotted to validate the prognostic significance of the model. A total of 5 prognostic correlated EAGs (P3H1, SPP1, MMP1, LGALS1, and ITGB5) were screened via Cox regression, which provided the basis for developing a novel prognostic risk model. Based on the risk model, patients were subdivided into high-risk and low-risk groups. The overall survival of the low-risk group was better compared to the high-risk group (P < 0.00001). The ROC curve of the risk model showed a higher AUC (Area under Curve) (AUC = 0.723) compared to other clinical features (AUC ≤ 0.511). A nomogram based on this model was constructed to predict the 1-year, 2-year, and 3-year overall survival rates (OS) of patients. Conclusively, we developed a novel HCC prognostic risk model based on the expression of EAGs, which help advance the prognostic management of HCC patients. Abbreviations: HCC: hepatocellular carcinoma; TCGA: The Cancer Genome Atlas; EMT: epithelial-mesenchymal transition; EAGs: EMT-associated genes; GSEA: gene set enrichment analysis; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; PPI: protein-protein interaction; TF: transcription factor; ROC: receiver operating characteristic; K-M: Kaplan-Meier; AUC: the area under the ROC curve; FDR: false discovery rate; TNM: Tumor size/lymph nodes/distance metastasis Taylor & Francis 2020-09-20 /pmc/articles/PMC8291854/ /pubmed/32951492 http://dx.doi.org/10.1080/21655979.2020.1822715 Text en © 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Paper
Xiong, Chen
Wang, Guifu
Bai, Dousheng
A novel prognostic models for identifying the risk of hepatocellular carcinoma based on epithelial-mesenchymal transition-associated genes
title A novel prognostic models for identifying the risk of hepatocellular carcinoma based on epithelial-mesenchymal transition-associated genes
title_full A novel prognostic models for identifying the risk of hepatocellular carcinoma based on epithelial-mesenchymal transition-associated genes
title_fullStr A novel prognostic models for identifying the risk of hepatocellular carcinoma based on epithelial-mesenchymal transition-associated genes
title_full_unstemmed A novel prognostic models for identifying the risk of hepatocellular carcinoma based on epithelial-mesenchymal transition-associated genes
title_short A novel prognostic models for identifying the risk of hepatocellular carcinoma based on epithelial-mesenchymal transition-associated genes
title_sort novel prognostic models for identifying the risk of hepatocellular carcinoma based on epithelial-mesenchymal transition-associated genes
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8291854/
https://www.ncbi.nlm.nih.gov/pubmed/32951492
http://dx.doi.org/10.1080/21655979.2020.1822715
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