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Identification of Prognostic Genes in the Tumor Microenvironment of Hepatocellular Carcinoma

Background: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world. The efficacy of immunotherapy usually depends on the interaction of immunomodulation in the tumor microenvironment (TME). This study aimed to explore the potential stromal-immune score-based prognosti...

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Autores principales: Xiang, Shixin, Li, Jing, Shen, Jing, Zhao, Yueshui, Wu, Xu, Li, Mingxing, Yang, Xiao, Kaboli, Parham Jabbarzadeh, Du, Fukuan, Zheng, Yuan, Wen, Qinglian, Cho, Chi Hin, Yi, Tao, Xiao, Zhangang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8059369/
https://www.ncbi.nlm.nih.gov/pubmed/33897701
http://dx.doi.org/10.3389/fimmu.2021.653836
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author Xiang, Shixin
Li, Jing
Shen, Jing
Zhao, Yueshui
Wu, Xu
Li, Mingxing
Yang, Xiao
Kaboli, Parham Jabbarzadeh
Du, Fukuan
Zheng, Yuan
Wen, Qinglian
Cho, Chi Hin
Yi, Tao
Xiao, Zhangang
author_facet Xiang, Shixin
Li, Jing
Shen, Jing
Zhao, Yueshui
Wu, Xu
Li, Mingxing
Yang, Xiao
Kaboli, Parham Jabbarzadeh
Du, Fukuan
Zheng, Yuan
Wen, Qinglian
Cho, Chi Hin
Yi, Tao
Xiao, Zhangang
author_sort Xiang, Shixin
collection PubMed
description Background: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world. The efficacy of immunotherapy usually depends on the interaction of immunomodulation in the tumor microenvironment (TME). This study aimed to explore the potential stromal-immune score-based prognostic genes related to immunotherapy in HCC through bioinformatics analysis. Methods: ESTIMATE algorithm was applied to calculate the immune/stromal/Estimate scores and tumor purity of HCC using the Cancer Genome Atlas (TCGA) transcriptome data. Functional enrichment analysis of differentially expressed genes (DEGs) was analyzed by the Database for Annotation, Visualization, and Integrated Discovery database (DAVID). Univariate and multivariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were performed for prognostic gene screening. The expression and prognostic value of these genes were further verified by KM-plotter database and the Human Protein Atlas (HPA) database. The correlation of the selected genes and the immune cell infiltration were analyzed by single sample gene set enrichment analysis (ssGSEA) algorithm and Tumor Immune Estimation Resource (TIMER). Results: Data analysis revealed that higher immune/stromal/Estimate scores were significantly associated with better survival benefits in HCC within 7 years, while the tumor purity showed a reverse trend. DEGs based on both immune and stromal scores primarily affected the cytokine–cytokine receptor interaction signaling pathway. Among the DEGs, three genes (CASKIN1, EMR3, and GBP5) were found most significantly associated with survival. Moreover, the expression levels of CASKIN1, EMR3, and GBP5 genes were significantly correlated with immune/stromal/Estimate scores or tumor purity and multiple immune cell infiltration. Among them, GBP5 genes were highly related to immune infiltration. Conclusion: This study identified three key genes which were related to the TME and had prognostic significance in HCC, which may be promising markers for predicting immunotherapy outcomes.
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spelling pubmed-80593692021-04-22 Identification of Prognostic Genes in the Tumor Microenvironment of Hepatocellular Carcinoma Xiang, Shixin Li, Jing Shen, Jing Zhao, Yueshui Wu, Xu Li, Mingxing Yang, Xiao Kaboli, Parham Jabbarzadeh Du, Fukuan Zheng, Yuan Wen, Qinglian Cho, Chi Hin Yi, Tao Xiao, Zhangang Front Immunol Immunology Background: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world. The efficacy of immunotherapy usually depends on the interaction of immunomodulation in the tumor microenvironment (TME). This study aimed to explore the potential stromal-immune score-based prognostic genes related to immunotherapy in HCC through bioinformatics analysis. Methods: ESTIMATE algorithm was applied to calculate the immune/stromal/Estimate scores and tumor purity of HCC using the Cancer Genome Atlas (TCGA) transcriptome data. Functional enrichment analysis of differentially expressed genes (DEGs) was analyzed by the Database for Annotation, Visualization, and Integrated Discovery database (DAVID). Univariate and multivariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were performed for prognostic gene screening. The expression and prognostic value of these genes were further verified by KM-plotter database and the Human Protein Atlas (HPA) database. The correlation of the selected genes and the immune cell infiltration were analyzed by single sample gene set enrichment analysis (ssGSEA) algorithm and Tumor Immune Estimation Resource (TIMER). Results: Data analysis revealed that higher immune/stromal/Estimate scores were significantly associated with better survival benefits in HCC within 7 years, while the tumor purity showed a reverse trend. DEGs based on both immune and stromal scores primarily affected the cytokine–cytokine receptor interaction signaling pathway. Among the DEGs, three genes (CASKIN1, EMR3, and GBP5) were found most significantly associated with survival. Moreover, the expression levels of CASKIN1, EMR3, and GBP5 genes were significantly correlated with immune/stromal/Estimate scores or tumor purity and multiple immune cell infiltration. Among them, GBP5 genes were highly related to immune infiltration. Conclusion: This study identified three key genes which were related to the TME and had prognostic significance in HCC, which may be promising markers for predicting immunotherapy outcomes. Frontiers Media S.A. 2021-04-07 /pmc/articles/PMC8059369/ /pubmed/33897701 http://dx.doi.org/10.3389/fimmu.2021.653836 Text en Copyright © 2021 Xiang, Li, Shen, Zhao, Wu, Li, Yang, Kaboli, Du, Zheng, Wen, Cho, Yi and Xiao. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Immunology
Xiang, Shixin
Li, Jing
Shen, Jing
Zhao, Yueshui
Wu, Xu
Li, Mingxing
Yang, Xiao
Kaboli, Parham Jabbarzadeh
Du, Fukuan
Zheng, Yuan
Wen, Qinglian
Cho, Chi Hin
Yi, Tao
Xiao, Zhangang
Identification of Prognostic Genes in the Tumor Microenvironment of Hepatocellular Carcinoma
title Identification of Prognostic Genes in the Tumor Microenvironment of Hepatocellular Carcinoma
title_full Identification of Prognostic Genes in the Tumor Microenvironment of Hepatocellular Carcinoma
title_fullStr Identification of Prognostic Genes in the Tumor Microenvironment of Hepatocellular Carcinoma
title_full_unstemmed Identification of Prognostic Genes in the Tumor Microenvironment of Hepatocellular Carcinoma
title_short Identification of Prognostic Genes in the Tumor Microenvironment of Hepatocellular Carcinoma
title_sort identification of prognostic genes in the tumor microenvironment of hepatocellular carcinoma
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8059369/
https://www.ncbi.nlm.nih.gov/pubmed/33897701
http://dx.doi.org/10.3389/fimmu.2021.653836
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