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Identification of Hub Genes and Immune Infiltration in Non-alcoholic Fatty Liver Disease -Related Hepatocellular Carcinoma by Bioinformatics Analysis

BACKGROUND: Non-alcoholic fatty liver disease has been a significant risk factor for hepatocellular carcinoma. In the study, we aimed to identify the key genes associated with the transition from non-alcoholic fatty liver disease to hepatocellular carcinoma through bioinformatics analysis. METHODS:...

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Autores principales: Liu, Xu, Wang, Yan, Li, Tao, Qu, Yundong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Turkish Society of Gastroenterology 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10210691/
https://www.ncbi.nlm.nih.gov/pubmed/37089050
http://dx.doi.org/10.5152/tjg.2023.22590
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author Liu, Xu
Wang, Yan
Li, Tao
Qu, Yundong
author_facet Liu, Xu
Wang, Yan
Li, Tao
Qu, Yundong
author_sort Liu, Xu
collection PubMed
description BACKGROUND: Non-alcoholic fatty liver disease has been a significant risk factor for hepatocellular carcinoma. In the study, we aimed to identify the key genes associated with the transition from non-alcoholic fatty liver disease to hepatocellular carcinoma through bioinformatics analysis. METHODS: The GSE164760 dataset was used for identifying differentially expressed genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were performed to explore the potential function of the differentially expressed genes. Subsequently, the protein–protein interaction network was constructed to select hub genes, and the immune cell infiltration was analyzed. Finally, the receiver operating characteristic analysis was performed to assess the diagnostic ability of the crucial genes. RESULTS: A total of 156 differentially expressed genes were identified. Gene Ontology enrichment analysis indicated that differentially expressed genes were strongly associated with cellular hormone metabolic process, response to xenobiotic stimulus, collagen-containing extracellular matrix, detoxification, and regulation of growth. In the protein–protein interaction network, ESR1, CAT, CXCL8, CD4, SPP1, CYP2E1, CYP3A4, UGT2B7, GSTA1 and THBS1 were selected as the hub genes. Immune infiltration analysis demonstrated that M0 macrophages, plasma cells, CD8+T cell and M2 macrophages were significantly changed in tumor tissues. Finally, we verified the hub gene expression and selected CD4, UGT2B7, and CYP3A4 as the potential diagnostic biomarkers. CONCLUSION: CD4, UGT2B7, and CYP3A4 were selected as the potential diagnostic biomarkers of non-alcoholic fatty liver disease–hepatocellular carcinoma.
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spelling pubmed-102106912023-05-26 Identification of Hub Genes and Immune Infiltration in Non-alcoholic Fatty Liver Disease -Related Hepatocellular Carcinoma by Bioinformatics Analysis Liu, Xu Wang, Yan Li, Tao Qu, Yundong Turk J Gastroenterol Original Article BACKGROUND: Non-alcoholic fatty liver disease has been a significant risk factor for hepatocellular carcinoma. In the study, we aimed to identify the key genes associated with the transition from non-alcoholic fatty liver disease to hepatocellular carcinoma through bioinformatics analysis. METHODS: The GSE164760 dataset was used for identifying differentially expressed genes. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were performed to explore the potential function of the differentially expressed genes. Subsequently, the protein–protein interaction network was constructed to select hub genes, and the immune cell infiltration was analyzed. Finally, the receiver operating characteristic analysis was performed to assess the diagnostic ability of the crucial genes. RESULTS: A total of 156 differentially expressed genes were identified. Gene Ontology enrichment analysis indicated that differentially expressed genes were strongly associated with cellular hormone metabolic process, response to xenobiotic stimulus, collagen-containing extracellular matrix, detoxification, and regulation of growth. In the protein–protein interaction network, ESR1, CAT, CXCL8, CD4, SPP1, CYP2E1, CYP3A4, UGT2B7, GSTA1 and THBS1 were selected as the hub genes. Immune infiltration analysis demonstrated that M0 macrophages, plasma cells, CD8+T cell and M2 macrophages were significantly changed in tumor tissues. Finally, we verified the hub gene expression and selected CD4, UGT2B7, and CYP3A4 as the potential diagnostic biomarkers. CONCLUSION: CD4, UGT2B7, and CYP3A4 were selected as the potential diagnostic biomarkers of non-alcoholic fatty liver disease–hepatocellular carcinoma. Turkish Society of Gastroenterology 2023-04-01 /pmc/articles/PMC10210691/ /pubmed/37089050 http://dx.doi.org/10.5152/tjg.2023.22590 Text en 2023 authors https://creativecommons.org/licenses/by/4.0/ Content of this journal is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. (https://creativecommons.org/licenses/by/4.0/)
spellingShingle Original Article
Liu, Xu
Wang, Yan
Li, Tao
Qu, Yundong
Identification of Hub Genes and Immune Infiltration in Non-alcoholic Fatty Liver Disease -Related Hepatocellular Carcinoma by Bioinformatics Analysis
title Identification of Hub Genes and Immune Infiltration in Non-alcoholic Fatty Liver Disease -Related Hepatocellular Carcinoma by Bioinformatics Analysis
title_full Identification of Hub Genes and Immune Infiltration in Non-alcoholic Fatty Liver Disease -Related Hepatocellular Carcinoma by Bioinformatics Analysis
title_fullStr Identification of Hub Genes and Immune Infiltration in Non-alcoholic Fatty Liver Disease -Related Hepatocellular Carcinoma by Bioinformatics Analysis
title_full_unstemmed Identification of Hub Genes and Immune Infiltration in Non-alcoholic Fatty Liver Disease -Related Hepatocellular Carcinoma by Bioinformatics Analysis
title_short Identification of Hub Genes and Immune Infiltration in Non-alcoholic Fatty Liver Disease -Related Hepatocellular Carcinoma by Bioinformatics Analysis
title_sort identification of hub genes and immune infiltration in non-alcoholic fatty liver disease -related hepatocellular carcinoma by bioinformatics analysis
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10210691/
https://www.ncbi.nlm.nih.gov/pubmed/37089050
http://dx.doi.org/10.5152/tjg.2023.22590
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