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Identification of Novel Characteristics in TP53-Mutant Hepatocellular Carcinoma Using Bioinformatics
Background: TP53 mutations are the most frequent mutations in hepatocellular carcinoma (HCC) and affect the occurrence and development of this cancer type. Therefore, it is essential to clarify the function and mechanism of TP53 mutations in HCC. Methods: We performed a sequence of bioinformatic ana...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9149291/ https://www.ncbi.nlm.nih.gov/pubmed/35651938 http://dx.doi.org/10.3389/fgene.2022.874805 |
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author | Yang, Yang Qu, Yajuan Li, Zhaopeng Tan, Zhiyong Lei, Youming Bai, Song |
author_facet | Yang, Yang Qu, Yajuan Li, Zhaopeng Tan, Zhiyong Lei, Youming Bai, Song |
author_sort | Yang, Yang |
collection | PubMed |
description | Background: TP53 mutations are the most frequent mutations in hepatocellular carcinoma (HCC) and affect the occurrence and development of this cancer type. Therefore, it is essential to clarify the function and mechanism of TP53 mutations in HCC. Methods: We performed a sequence of bioinformatic analyses to elucidate the characteristics of TP53 mutations in HCC. We downloaded the data of hepatocellular carcinoma from The Cancer Genome Atlas database and used different R packages for serial analyses, including gene mutation analysis, copy number variation analysis, analysis of the tumor mutational burden and microsatellite instability, differential gene expression analysis, and functional enrichment analysis of TP53 mutations, and performed gene set enrichment analysis. We established a protein-protein interaction network using the STRING online database and used the Cytoscape software for network visualization, and hub gene screening. In addition, we performed anticancer drug sensitivity analysis using data from the Genomics of Drug Sensitivity in Cancer. Immune infiltration and prognosis analyses were also performed. Results: Missense mutations accounted for a great proportion of HCC mutations, the frequency of single nucleotide polymorphisms was high, and C > T was the most common form of single nucleotide variations. TP53 had a mutation rate of 30% and was the most commonly mutated gene in HCC. In the TP53 mutant group, the tumor mutational burden (p < 0.001), drug sensitivity (p < 0.05), ESTIMATE score (p = 0.038), and stromal score (p < 0.001) dramatically decreased. The Cytoscape software screened ten hub genes, including CT45A1, XAGE1B, CT55, GAGE2A, PASD1, MAGEA4, CTAG2, MAGEA10, MAGEC1, and SAGE1. The prognostic model showed a poor prognosis in the TP53 mutation group compared with that in the wild-type group (overall survival, p = 0.023). Univariate and multivariate cox regression analyses revealed that TP53 mutation was an independent risk factor for the prognosis of HCC patients (p <0.05). The constructed prognostic model had a favorable forecast value for the prognosis of HCC patients at 1 and 3 years (1-year AUC = 0.752, 3-years AUC = 0.702). Conclusion: This study further deepened our understanding of TP53-mutated HCC, provided new insights into a precise individualized therapy for HCC, and has particular significance for prognosis prediction. |
format | Online Article Text |
id | pubmed-9149291 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-91492912022-05-31 Identification of Novel Characteristics in TP53-Mutant Hepatocellular Carcinoma Using Bioinformatics Yang, Yang Qu, Yajuan Li, Zhaopeng Tan, Zhiyong Lei, Youming Bai, Song Front Genet Genetics Background: TP53 mutations are the most frequent mutations in hepatocellular carcinoma (HCC) and affect the occurrence and development of this cancer type. Therefore, it is essential to clarify the function and mechanism of TP53 mutations in HCC. Methods: We performed a sequence of bioinformatic analyses to elucidate the characteristics of TP53 mutations in HCC. We downloaded the data of hepatocellular carcinoma from The Cancer Genome Atlas database and used different R packages for serial analyses, including gene mutation analysis, copy number variation analysis, analysis of the tumor mutational burden and microsatellite instability, differential gene expression analysis, and functional enrichment analysis of TP53 mutations, and performed gene set enrichment analysis. We established a protein-protein interaction network using the STRING online database and used the Cytoscape software for network visualization, and hub gene screening. In addition, we performed anticancer drug sensitivity analysis using data from the Genomics of Drug Sensitivity in Cancer. Immune infiltration and prognosis analyses were also performed. Results: Missense mutations accounted for a great proportion of HCC mutations, the frequency of single nucleotide polymorphisms was high, and C > T was the most common form of single nucleotide variations. TP53 had a mutation rate of 30% and was the most commonly mutated gene in HCC. In the TP53 mutant group, the tumor mutational burden (p < 0.001), drug sensitivity (p < 0.05), ESTIMATE score (p = 0.038), and stromal score (p < 0.001) dramatically decreased. The Cytoscape software screened ten hub genes, including CT45A1, XAGE1B, CT55, GAGE2A, PASD1, MAGEA4, CTAG2, MAGEA10, MAGEC1, and SAGE1. The prognostic model showed a poor prognosis in the TP53 mutation group compared with that in the wild-type group (overall survival, p = 0.023). Univariate and multivariate cox regression analyses revealed that TP53 mutation was an independent risk factor for the prognosis of HCC patients (p <0.05). The constructed prognostic model had a favorable forecast value for the prognosis of HCC patients at 1 and 3 years (1-year AUC = 0.752, 3-years AUC = 0.702). Conclusion: This study further deepened our understanding of TP53-mutated HCC, provided new insights into a precise individualized therapy for HCC, and has particular significance for prognosis prediction. Frontiers Media S.A. 2022-05-16 /pmc/articles/PMC9149291/ /pubmed/35651938 http://dx.doi.org/10.3389/fgene.2022.874805 Text en Copyright © 2022 Yang, Qu, Li, Tan, Lei and Bai. 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 Yang, Yang Qu, Yajuan Li, Zhaopeng Tan, Zhiyong Lei, Youming Bai, Song Identification of Novel Characteristics in TP53-Mutant Hepatocellular Carcinoma Using Bioinformatics |
title | Identification of Novel Characteristics in TP53-Mutant Hepatocellular Carcinoma Using Bioinformatics |
title_full | Identification of Novel Characteristics in TP53-Mutant Hepatocellular Carcinoma Using Bioinformatics |
title_fullStr | Identification of Novel Characteristics in TP53-Mutant Hepatocellular Carcinoma Using Bioinformatics |
title_full_unstemmed | Identification of Novel Characteristics in TP53-Mutant Hepatocellular Carcinoma Using Bioinformatics |
title_short | Identification of Novel Characteristics in TP53-Mutant Hepatocellular Carcinoma Using Bioinformatics |
title_sort | identification of novel characteristics in tp53-mutant hepatocellular carcinoma using bioinformatics |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9149291/ https://www.ncbi.nlm.nih.gov/pubmed/35651938 http://dx.doi.org/10.3389/fgene.2022.874805 |
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