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Identification of Crucial Genes Associated With Immune Cell Infiltration in Hepatocellular Carcinoma by Weighted Gene Co-expression Network Analysis
The dreadful prognosis of hepatocellular carcinoma (HCC) is primarily due to the low early diagnosis rate, rapid progression, and high recurrence rate. Valuable prognostic biomarkers are urgently needed for HCC. In this study, microarray data were downloaded from GSE14520, GSE22058, International Ca...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2020
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7193721/ https://www.ncbi.nlm.nih.gov/pubmed/32391055 http://dx.doi.org/10.3389/fgene.2020.00342 |
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author | Wang, Dengchuan Liu, Jun Liu, Shengshuo Li, Wenli |
author_facet | Wang, Dengchuan Liu, Jun Liu, Shengshuo Li, Wenli |
author_sort | Wang, Dengchuan |
collection | PubMed |
description | The dreadful prognosis of hepatocellular carcinoma (HCC) is primarily due to the low early diagnosis rate, rapid progression, and high recurrence rate. Valuable prognostic biomarkers are urgently needed for HCC. In this study, microarray data were downloaded from GSE14520, GSE22058, International Cancer Genome Consortium (ICGC), and The Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) were identified among GSE14520, GSE22058, and ICGC databases. Weighted gene co-expression network analysis (WGCNA) was used to establish gene co-expression modules of DEGs, and genes of key modules were examined to identify hub genes using univariate Cox regression in the ICGC cohort. Expression levels and time-dependent receiver operating characteristic (ROC) and area under the curve (AUC) were determined to estimate the prognostic competence of the hub genes. These hub genes were also validated in the Gene Expression Profiling Interactive Analysis (GEPIA) and TCGA databases. TIMER algorithm and GSCALite database were applied to analyze the association of the hub genes with immunocytotic infiltration and their pathway enrichment. Altogether, 276 DEGs were identified and WGCNA described a unique and significantly DEGs-associated co-expression module containing 148 genes, with 10 hub genes selected by univariate Cox regression in the ICGC cohort (BIRC5, FOXM1, CENPA, KIF4A, DTYMK, PRC1, IGF2BP3, KIF2C, TRIP13, and TPX2). Most of the genes were validated in the GEPIA databases, except IGF2BP3. The results of multivariate Cox regression analysis indicated that the abovementioned hub genes are all independent predictors of HCC. The 10 genes were also confirmed to be associated with immune cell infiltration using the TIMER algorithm. Moreover, four-gene signature was developed, including BIRC5, CENPA, FOXM1, DTYMK. These hub genes and the model demonstrated a strong prognostic capability and are likely to be a therapeutic target for HCC. Moreover, the association of these genes with immune cell infiltration improves our understanding of the occurrence and development of HCC. |
format | Online Article Text |
id | pubmed-7193721 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-71937212020-05-08 Identification of Crucial Genes Associated With Immune Cell Infiltration in Hepatocellular Carcinoma by Weighted Gene Co-expression Network Analysis Wang, Dengchuan Liu, Jun Liu, Shengshuo Li, Wenli Front Genet Genetics The dreadful prognosis of hepatocellular carcinoma (HCC) is primarily due to the low early diagnosis rate, rapid progression, and high recurrence rate. Valuable prognostic biomarkers are urgently needed for HCC. In this study, microarray data were downloaded from GSE14520, GSE22058, International Cancer Genome Consortium (ICGC), and The Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) were identified among GSE14520, GSE22058, and ICGC databases. Weighted gene co-expression network analysis (WGCNA) was used to establish gene co-expression modules of DEGs, and genes of key modules were examined to identify hub genes using univariate Cox regression in the ICGC cohort. Expression levels and time-dependent receiver operating characteristic (ROC) and area under the curve (AUC) were determined to estimate the prognostic competence of the hub genes. These hub genes were also validated in the Gene Expression Profiling Interactive Analysis (GEPIA) and TCGA databases. TIMER algorithm and GSCALite database were applied to analyze the association of the hub genes with immunocytotic infiltration and their pathway enrichment. Altogether, 276 DEGs were identified and WGCNA described a unique and significantly DEGs-associated co-expression module containing 148 genes, with 10 hub genes selected by univariate Cox regression in the ICGC cohort (BIRC5, FOXM1, CENPA, KIF4A, DTYMK, PRC1, IGF2BP3, KIF2C, TRIP13, and TPX2). Most of the genes were validated in the GEPIA databases, except IGF2BP3. The results of multivariate Cox regression analysis indicated that the abovementioned hub genes are all independent predictors of HCC. The 10 genes were also confirmed to be associated with immune cell infiltration using the TIMER algorithm. Moreover, four-gene signature was developed, including BIRC5, CENPA, FOXM1, DTYMK. These hub genes and the model demonstrated a strong prognostic capability and are likely to be a therapeutic target for HCC. Moreover, the association of these genes with immune cell infiltration improves our understanding of the occurrence and development of HCC. Frontiers Media S.A. 2020-04-24 /pmc/articles/PMC7193721/ /pubmed/32391055 http://dx.doi.org/10.3389/fgene.2020.00342 Text en Copyright © 2020 Wang, Liu, Liu and Li. http://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 Wang, Dengchuan Liu, Jun Liu, Shengshuo Li, Wenli Identification of Crucial Genes Associated With Immune Cell Infiltration in Hepatocellular Carcinoma by Weighted Gene Co-expression Network Analysis |
title | Identification of Crucial Genes Associated With Immune Cell Infiltration in Hepatocellular Carcinoma by Weighted Gene Co-expression Network Analysis |
title_full | Identification of Crucial Genes Associated With Immune Cell Infiltration in Hepatocellular Carcinoma by Weighted Gene Co-expression Network Analysis |
title_fullStr | Identification of Crucial Genes Associated With Immune Cell Infiltration in Hepatocellular Carcinoma by Weighted Gene Co-expression Network Analysis |
title_full_unstemmed | Identification of Crucial Genes Associated With Immune Cell Infiltration in Hepatocellular Carcinoma by Weighted Gene Co-expression Network Analysis |
title_short | Identification of Crucial Genes Associated With Immune Cell Infiltration in Hepatocellular Carcinoma by Weighted Gene Co-expression Network Analysis |
title_sort | identification of crucial genes associated with immune cell infiltration in hepatocellular carcinoma by weighted gene co-expression network analysis |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7193721/ https://www.ncbi.nlm.nih.gov/pubmed/32391055 http://dx.doi.org/10.3389/fgene.2020.00342 |
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