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Bioinformatics Analysis of Differentially Expressed Rhythm Genes in Liver Hepatocellular Carcinoma
Liver Hepatocellular Carcinoma (LIHC), a malignant tumor with high incidence and mortality, is one of the most common cancers in the world. Multiple studies have found that the aberrant expression of rhythm genes is closely related to the occurrence of LIHC. This study aimed to use bioinformatics an...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8211427/ https://www.ncbi.nlm.nih.gov/pubmed/34149816 http://dx.doi.org/10.3389/fgene.2021.680528 |
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author | Liu, Huaifeng Gao, Yu Hu, Shangshang Fan, Zhengran Wang, Xianggang Li, Shujing |
author_facet | Liu, Huaifeng Gao, Yu Hu, Shangshang Fan, Zhengran Wang, Xianggang Li, Shujing |
author_sort | Liu, Huaifeng |
collection | PubMed |
description | Liver Hepatocellular Carcinoma (LIHC), a malignant tumor with high incidence and mortality, is one of the most common cancers in the world. Multiple studies have found that the aberrant expression of rhythm genes is closely related to the occurrence of LIHC. This study aimed to use bioinformatics analysis to identify differentially expressed rhythm genes (DERGs) in LIHC. A total of 563 DERGs were found in LIHC, including 265 downregulated genes and 298 upregulated genes. KEGG pathway enrichment and GO analyses showed that DERGs were significantly enriched in rhythmic and metabolic processes. Survival analysis revealed that high expression levels of CNK1D, CSNK1E, and NPAS2 were significantly associated with the low survival rate in LIHC patients. Through cell experiment verification, the mRNA expression levels of CSNK1D, CSNK1E, and NPAS2 were found to be strongly upregulated, which was consistent with the bioinformatics analysis of LIHC patient samples. A total of 23 nodes and 135 edges were involved in the protein–protein interaction network of CSNK1D, CSNK1E, and NPAS2 genes. Clinical correlation analyses revealed that CSNK1D, CSNK1E, and NPAS2 expression levels were high-risk factors and independently connected with the overall survival rate in LIHC patients. In conclusion, the identification of these DERGs contributes to the exploration of the molecular mechanisms of LIHC occurrence and development and may be used as diagnostic and prognostic biomarkers and molecular targets for chronotherapy in LIHC patients in the future. |
format | Online Article Text |
id | pubmed-8211427 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-82114272021-06-18 Bioinformatics Analysis of Differentially Expressed Rhythm Genes in Liver Hepatocellular Carcinoma Liu, Huaifeng Gao, Yu Hu, Shangshang Fan, Zhengran Wang, Xianggang Li, Shujing Front Genet Genetics Liver Hepatocellular Carcinoma (LIHC), a malignant tumor with high incidence and mortality, is one of the most common cancers in the world. Multiple studies have found that the aberrant expression of rhythm genes is closely related to the occurrence of LIHC. This study aimed to use bioinformatics analysis to identify differentially expressed rhythm genes (DERGs) in LIHC. A total of 563 DERGs were found in LIHC, including 265 downregulated genes and 298 upregulated genes. KEGG pathway enrichment and GO analyses showed that DERGs were significantly enriched in rhythmic and metabolic processes. Survival analysis revealed that high expression levels of CNK1D, CSNK1E, and NPAS2 were significantly associated with the low survival rate in LIHC patients. Through cell experiment verification, the mRNA expression levels of CSNK1D, CSNK1E, and NPAS2 were found to be strongly upregulated, which was consistent with the bioinformatics analysis of LIHC patient samples. A total of 23 nodes and 135 edges were involved in the protein–protein interaction network of CSNK1D, CSNK1E, and NPAS2 genes. Clinical correlation analyses revealed that CSNK1D, CSNK1E, and NPAS2 expression levels were high-risk factors and independently connected with the overall survival rate in LIHC patients. In conclusion, the identification of these DERGs contributes to the exploration of the molecular mechanisms of LIHC occurrence and development and may be used as diagnostic and prognostic biomarkers and molecular targets for chronotherapy in LIHC patients in the future. Frontiers Media S.A. 2021-06-03 /pmc/articles/PMC8211427/ /pubmed/34149816 http://dx.doi.org/10.3389/fgene.2021.680528 Text en Copyright © 2021 Liu, Gao, Hu, Fan, Wang and Li. 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 | Genetics Liu, Huaifeng Gao, Yu Hu, Shangshang Fan, Zhengran Wang, Xianggang Li, Shujing Bioinformatics Analysis of Differentially Expressed Rhythm Genes in Liver Hepatocellular Carcinoma |
title | Bioinformatics Analysis of Differentially Expressed Rhythm Genes in Liver Hepatocellular Carcinoma |
title_full | Bioinformatics Analysis of Differentially Expressed Rhythm Genes in Liver Hepatocellular Carcinoma |
title_fullStr | Bioinformatics Analysis of Differentially Expressed Rhythm Genes in Liver Hepatocellular Carcinoma |
title_full_unstemmed | Bioinformatics Analysis of Differentially Expressed Rhythm Genes in Liver Hepatocellular Carcinoma |
title_short | Bioinformatics Analysis of Differentially Expressed Rhythm Genes in Liver Hepatocellular Carcinoma |
title_sort | bioinformatics analysis of differentially expressed rhythm genes in liver hepatocellular carcinoma |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8211427/ https://www.ncbi.nlm.nih.gov/pubmed/34149816 http://dx.doi.org/10.3389/fgene.2021.680528 |
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