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Identification and clinical validation of EMT-associated prognostic features based on hepatocellular carcinoma

BACKGROUND: The aim of this study was to construct a model based on the prognostic features associated with epithelial–mesenchymal transition (EMT) to explore the various mechanisms and therapeutic strategies available for the treatment of metastasis and invasion by hepatocellular carcinoma (HCC) ce...

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Autores principales: Xu, Dafeng, Wang, Yu, Wu, Jincai, Lin, Shixun, Chen, Yonghai, Zheng, Jinfang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8613962/
https://www.ncbi.nlm.nih.gov/pubmed/34819088
http://dx.doi.org/10.1186/s12935-021-02326-8
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author Xu, Dafeng
Wang, Yu
Wu, Jincai
Lin, Shixun
Chen, Yonghai
Zheng, Jinfang
author_facet Xu, Dafeng
Wang, Yu
Wu, Jincai
Lin, Shixun
Chen, Yonghai
Zheng, Jinfang
author_sort Xu, Dafeng
collection PubMed
description BACKGROUND: The aim of this study was to construct a model based on the prognostic features associated with epithelial–mesenchymal transition (EMT) to explore the various mechanisms and therapeutic strategies available for the treatment of metastasis and invasion by hepatocellular carcinoma (HCC) cells. METHODS: EMT-associated genes were identified, and their molecular subtypes were determined by consistent clustering analysis. The differentially expressed genes (DEGs) among the molecular subtypes were ascertained using the limma package and they were subjected to functional enrichment analysis. The immune cell scores of the molecular subtypes were evaluated using ESTIMATE, MCPcounter, and GSCA packages of R. A multi-gene prognostic model was constructed using lasso regression, and the immunotherapeutic effects of the model were analyzed using the Imvigor210 cohort. In addition, immunohistochemical analysis was performed on a cohort of HCC tissue to validate gene expression. RESULTS: Based on the 59 EMT-associated genes identified, the 365—liver hepatocellular carcinoma (LIHC) samples were divided into two subtypes, C1 and C2. The C1 subtype mostly showed poor prognosis, had higher immune scores compared to the C2 subtype, and showed greater correlation with pathways of tumor progression. A four-gene signature construct was fabricated based on the 1130 DEGs among the subtypes. The construct was highly robust and showed stable predictive efficacy when validated using datasets from different platforms (HCCDB18 and GSE14520). Additionally, compared to currently existing models, our model demonstrated better performance. The results of the immunotherapy cohort showed that patients in the low-risk group have a better immune response, leading to a better patient’s prognosis. Immunohistochemical analysis revealed that the expression levels of the FTCD, PON1, and TMEM45A were significantly over-expressed in 41 normal samples compared to HCC samples, while that of the G6PD was significantly over-expressed in cancerous tissues. CONCLUSIONS: The four-gene signature construct fabricated based on the EMT-associated genes provides valuable information to further study the pathogenesis and clinical management of HCC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12935-021-02326-8.
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spelling pubmed-86139622021-11-29 Identification and clinical validation of EMT-associated prognostic features based on hepatocellular carcinoma Xu, Dafeng Wang, Yu Wu, Jincai Lin, Shixun Chen, Yonghai Zheng, Jinfang Cancer Cell Int Primary Research BACKGROUND: The aim of this study was to construct a model based on the prognostic features associated with epithelial–mesenchymal transition (EMT) to explore the various mechanisms and therapeutic strategies available for the treatment of metastasis and invasion by hepatocellular carcinoma (HCC) cells. METHODS: EMT-associated genes were identified, and their molecular subtypes were determined by consistent clustering analysis. The differentially expressed genes (DEGs) among the molecular subtypes were ascertained using the limma package and they were subjected to functional enrichment analysis. The immune cell scores of the molecular subtypes were evaluated using ESTIMATE, MCPcounter, and GSCA packages of R. A multi-gene prognostic model was constructed using lasso regression, and the immunotherapeutic effects of the model were analyzed using the Imvigor210 cohort. In addition, immunohistochemical analysis was performed on a cohort of HCC tissue to validate gene expression. RESULTS: Based on the 59 EMT-associated genes identified, the 365—liver hepatocellular carcinoma (LIHC) samples were divided into two subtypes, C1 and C2. The C1 subtype mostly showed poor prognosis, had higher immune scores compared to the C2 subtype, and showed greater correlation with pathways of tumor progression. A four-gene signature construct was fabricated based on the 1130 DEGs among the subtypes. The construct was highly robust and showed stable predictive efficacy when validated using datasets from different platforms (HCCDB18 and GSE14520). Additionally, compared to currently existing models, our model demonstrated better performance. The results of the immunotherapy cohort showed that patients in the low-risk group have a better immune response, leading to a better patient’s prognosis. Immunohistochemical analysis revealed that the expression levels of the FTCD, PON1, and TMEM45A were significantly over-expressed in 41 normal samples compared to HCC samples, while that of the G6PD was significantly over-expressed in cancerous tissues. CONCLUSIONS: The four-gene signature construct fabricated based on the EMT-associated genes provides valuable information to further study the pathogenesis and clinical management of HCC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12935-021-02326-8. BioMed Central 2021-11-24 /pmc/articles/PMC8613962/ /pubmed/34819088 http://dx.doi.org/10.1186/s12935-021-02326-8 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Primary Research
Xu, Dafeng
Wang, Yu
Wu, Jincai
Lin, Shixun
Chen, Yonghai
Zheng, Jinfang
Identification and clinical validation of EMT-associated prognostic features based on hepatocellular carcinoma
title Identification and clinical validation of EMT-associated prognostic features based on hepatocellular carcinoma
title_full Identification and clinical validation of EMT-associated prognostic features based on hepatocellular carcinoma
title_fullStr Identification and clinical validation of EMT-associated prognostic features based on hepatocellular carcinoma
title_full_unstemmed Identification and clinical validation of EMT-associated prognostic features based on hepatocellular carcinoma
title_short Identification and clinical validation of EMT-associated prognostic features based on hepatocellular carcinoma
title_sort identification and clinical validation of emt-associated prognostic features based on hepatocellular carcinoma
topic Primary Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8613962/
https://www.ncbi.nlm.nih.gov/pubmed/34819088
http://dx.doi.org/10.1186/s12935-021-02326-8
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