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A stemness-based eleven-gene signature correlates with the clinical outcome of hepatocellular carcinoma

BACKGROUND: Cumulative evidences have been implicated cancer stem cells in the tumor environment of hepatocellular carcinoma (HCC) cells, whereas the biological functions and prognostic significance of stemness related genes (SRGs) in HCC is still unclear. METHODS: Molecular subtypes were identified...

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Autores principales: Hong, Liang, Zhou, Yu, Xie, Xiangbang, Wu, Wanrui, Shi, Changsheng, Lin, Heping, Shi, Zhenjing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8214273/
https://www.ncbi.nlm.nih.gov/pubmed/34147074
http://dx.doi.org/10.1186/s12885-021-08351-0
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author Hong, Liang
Zhou, Yu
Xie, Xiangbang
Wu, Wanrui
Shi, Changsheng
Lin, Heping
Shi, Zhenjing
author_facet Hong, Liang
Zhou, Yu
Xie, Xiangbang
Wu, Wanrui
Shi, Changsheng
Lin, Heping
Shi, Zhenjing
author_sort Hong, Liang
collection PubMed
description BACKGROUND: Cumulative evidences have been implicated cancer stem cells in the tumor environment of hepatocellular carcinoma (HCC) cells, whereas the biological functions and prognostic significance of stemness related genes (SRGs) in HCC is still unclear. METHODS: Molecular subtypes were identified by cumulative distribution function (CDF) clustering on 207 prognostic SRGs. The overall survival (OS) predictive gene signature was developed, internally and externally validated based on HCC datasets including The Cancer Genome Atlas (TCGA), GEO and ICGC datasets. Hub genes were identified in molecular subtypes by protein-protein interaction (PPI) network analysis, and then enrolled for determination of prognostic genes. Univariate, LASSO and multivariate Cox regression analyses were performed to assess prognostic genes and construct the prognostic gene signature. Time-dependent receiver operating characteristic (ROC) curve, Kaplan-Meier curve and nomogram were used to assess the performance of the gene signature. RESULTS: We identified four molecular subtypes, among which the C2 subtype showed the highest SRGs expression levels and proportions of immune cells, whereas the worst OS; the C1 subtype showed the lowest SRGs expression levels and was associated with most favorable OS. Next, we identified 11 prognostic genes (CDX2, PON1, ADH4, RBP2, LCAT, GAL, LPA, CYP19A1, GAST, SST and UGT1A8) and then constructed a prognostic 11-gene module and validated its robustness in all three datasets. Moreover, by univariate and multivariate Cox regression, we confirmed the independent prognostic ability of the 11-gene module for patients with HCC. In addition, calibration analysis plots indicated the excellent predictive performance of the prognostic nomogram constructed based on the 11-gene signature. CONCLUSIONS: Findings in the present study shed new light on the role of stemness related genes within HCC, and the established 11-SRG signature can be utilized as a novel prognostic marker for survival prognostication in patients with HCC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-021-08351-0.
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spelling pubmed-82142732021-06-23 A stemness-based eleven-gene signature correlates with the clinical outcome of hepatocellular carcinoma Hong, Liang Zhou, Yu Xie, Xiangbang Wu, Wanrui Shi, Changsheng Lin, Heping Shi, Zhenjing BMC Cancer Research Article BACKGROUND: Cumulative evidences have been implicated cancer stem cells in the tumor environment of hepatocellular carcinoma (HCC) cells, whereas the biological functions and prognostic significance of stemness related genes (SRGs) in HCC is still unclear. METHODS: Molecular subtypes were identified by cumulative distribution function (CDF) clustering on 207 prognostic SRGs. The overall survival (OS) predictive gene signature was developed, internally and externally validated based on HCC datasets including The Cancer Genome Atlas (TCGA), GEO and ICGC datasets. Hub genes were identified in molecular subtypes by protein-protein interaction (PPI) network analysis, and then enrolled for determination of prognostic genes. Univariate, LASSO and multivariate Cox regression analyses were performed to assess prognostic genes and construct the prognostic gene signature. Time-dependent receiver operating characteristic (ROC) curve, Kaplan-Meier curve and nomogram were used to assess the performance of the gene signature. RESULTS: We identified four molecular subtypes, among which the C2 subtype showed the highest SRGs expression levels and proportions of immune cells, whereas the worst OS; the C1 subtype showed the lowest SRGs expression levels and was associated with most favorable OS. Next, we identified 11 prognostic genes (CDX2, PON1, ADH4, RBP2, LCAT, GAL, LPA, CYP19A1, GAST, SST and UGT1A8) and then constructed a prognostic 11-gene module and validated its robustness in all three datasets. Moreover, by univariate and multivariate Cox regression, we confirmed the independent prognostic ability of the 11-gene module for patients with HCC. In addition, calibration analysis plots indicated the excellent predictive performance of the prognostic nomogram constructed based on the 11-gene signature. CONCLUSIONS: Findings in the present study shed new light on the role of stemness related genes within HCC, and the established 11-SRG signature can be utilized as a novel prognostic marker for survival prognostication in patients with HCC. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-021-08351-0. BioMed Central 2021-06-19 /pmc/articles/PMC8214273/ /pubmed/34147074 http://dx.doi.org/10.1186/s12885-021-08351-0 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 Research Article
Hong, Liang
Zhou, Yu
Xie, Xiangbang
Wu, Wanrui
Shi, Changsheng
Lin, Heping
Shi, Zhenjing
A stemness-based eleven-gene signature correlates with the clinical outcome of hepatocellular carcinoma
title A stemness-based eleven-gene signature correlates with the clinical outcome of hepatocellular carcinoma
title_full A stemness-based eleven-gene signature correlates with the clinical outcome of hepatocellular carcinoma
title_fullStr A stemness-based eleven-gene signature correlates with the clinical outcome of hepatocellular carcinoma
title_full_unstemmed A stemness-based eleven-gene signature correlates with the clinical outcome of hepatocellular carcinoma
title_short A stemness-based eleven-gene signature correlates with the clinical outcome of hepatocellular carcinoma
title_sort stemness-based eleven-gene signature correlates with the clinical outcome of hepatocellular carcinoma
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8214273/
https://www.ncbi.nlm.nih.gov/pubmed/34147074
http://dx.doi.org/10.1186/s12885-021-08351-0
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