Tumor infiltrating lymphocytes associated competitive endogenous RNA networks as predictors of outcome in hepatic carcinoma based on WGCNA analysis.

<h4>Background</h4>The important regulatory role of competitive endogenous RNAs (ceRNAs) in hepatocellular carcinoma (HCC) has been confirmed. Tumor infiltrating lymphocytes (TILs) are of great significance to tumor outcome and prognosis. This study will systematically analyze the key fa...

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Autores principales:	Ying He, Rui Xu, Li Peng, Xiaoyu Hu
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/332c0070442b41a8af18de59ff4c6cc3
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spelling	oai:doaj.org-article:332c0070442b41a8af18de59ff4c6cc32021-12-02T20:08:57ZTumor infiltrating lymphocytes associated competitive endogenous RNA networks as predictors of outcome in hepatic carcinoma based on WGCNA analysis.1932-620310.1371/journal.pone.0254829https://doaj.org/article/332c0070442b41a8af18de59ff4c6cc32021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0254829https://doaj.org/toc/1932-6203<h4>Background</h4>The important regulatory role of competitive endogenous RNAs (ceRNAs) in hepatocellular carcinoma (HCC) has been confirmed. Tumor infiltrating lymphocytes (TILs) are of great significance to tumor outcome and prognosis. This study will systematically analyze the key factors affecting the prognosis of HCC from the perspective of ceRNA and TILs.<h4>Methods</h4>The Cancer Genome Atlas (TCGA) database was used for transcriptome data acquisition of HCC. Through the analysis of the Weighted Gene Co-expression Network Analysis (WCGNA), the two modules for co-expression of the disease were determined, and a ceRNA network was constructed. We used Cox regression and LASSO regression analysis to screen prognostic factors and constructed a risk score model. The Gene Expression Omnibus (GEO) was used to validate the model. The Kyoto Encyclopedia of Genes and Genomes (KEGG) was used for mRNAs functional analysis. The cell composition of TILs was analyzed by the CIBERSORT algorithm, and Pearson correlation analysis was utilized to explore the correlation between TILs and prognostic factors.<h4>Results</h4>We constructed a ceRNA regulatory network composed of 67 nodes through WGCNA, including 44 DElncRNAs, 19 DEGs, and 4 DEmiRNAs. And based on the expression of 4 DEGs in this network (RRM2, LDLR, TXNIP, and KIF23), a prognostic model of HCC with good specificity and sensitivity was developed. CIBERSORT analyzed the composition of TILs in HCC tumor tissues. Correlation analysis showed that RRM2 is significantly correlated with T cells CD4 memory activated, T cells CD4 memory resting, T cells CD8, and T cells follicular helper, and TXNIP is negatively correlated with B cells memory.<h4>Conclusions</h4>In this study, a ceRNA with prognostic value in HCC was created, and a prognostic risk model for HCC was constructed based on it. This risk score model is closely related to TILs and is expected to become a potential therapeutic target and a new predictive indicator.Ying HeRui XuLi PengXiaoyu HuPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 7, p e0254829 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ying He Rui Xu Li Peng Xiaoyu Hu Tumor infiltrating lymphocytes associated competitive endogenous RNA networks as predictors of outcome in hepatic carcinoma based on WGCNA analysis.
description	<h4>Background</h4>The important regulatory role of competitive endogenous RNAs (ceRNAs) in hepatocellular carcinoma (HCC) has been confirmed. Tumor infiltrating lymphocytes (TILs) are of great significance to tumor outcome and prognosis. This study will systematically analyze the key factors affecting the prognosis of HCC from the perspective of ceRNA and TILs.<h4>Methods</h4>The Cancer Genome Atlas (TCGA) database was used for transcriptome data acquisition of HCC. Through the analysis of the Weighted Gene Co-expression Network Analysis (WCGNA), the two modules for co-expression of the disease were determined, and a ceRNA network was constructed. We used Cox regression and LASSO regression analysis to screen prognostic factors and constructed a risk score model. The Gene Expression Omnibus (GEO) was used to validate the model. The Kyoto Encyclopedia of Genes and Genomes (KEGG) was used for mRNAs functional analysis. The cell composition of TILs was analyzed by the CIBERSORT algorithm, and Pearson correlation analysis was utilized to explore the correlation between TILs and prognostic factors.<h4>Results</h4>We constructed a ceRNA regulatory network composed of 67 nodes through WGCNA, including 44 DElncRNAs, 19 DEGs, and 4 DEmiRNAs. And based on the expression of 4 DEGs in this network (RRM2, LDLR, TXNIP, and KIF23), a prognostic model of HCC with good specificity and sensitivity was developed. CIBERSORT analyzed the composition of TILs in HCC tumor tissues. Correlation analysis showed that RRM2 is significantly correlated with T cells CD4 memory activated, T cells CD4 memory resting, T cells CD8, and T cells follicular helper, and TXNIP is negatively correlated with B cells memory.<h4>Conclusions</h4>In this study, a ceRNA with prognostic value in HCC was created, and a prognostic risk model for HCC was constructed based on it. This risk score model is closely related to TILs and is expected to become a potential therapeutic target and a new predictive indicator.
format	article
author	Ying He Rui Xu Li Peng Xiaoyu Hu
author_facet	Ying He Rui Xu Li Peng Xiaoyu Hu
author_sort	Ying He
title	Tumor infiltrating lymphocytes associated competitive endogenous RNA networks as predictors of outcome in hepatic carcinoma based on WGCNA analysis.
title_short	Tumor infiltrating lymphocytes associated competitive endogenous RNA networks as predictors of outcome in hepatic carcinoma based on WGCNA analysis.
title_full	Tumor infiltrating lymphocytes associated competitive endogenous RNA networks as predictors of outcome in hepatic carcinoma based on WGCNA analysis.
title_fullStr	Tumor infiltrating lymphocytes associated competitive endogenous RNA networks as predictors of outcome in hepatic carcinoma based on WGCNA analysis.
title_full_unstemmed	Tumor infiltrating lymphocytes associated competitive endogenous RNA networks as predictors of outcome in hepatic carcinoma based on WGCNA analysis.
title_sort	tumor infiltrating lymphocytes associated competitive endogenous rna networks as predictors of outcome in hepatic carcinoma based on wgcna analysis.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/332c0070442b41a8af18de59ff4c6cc3
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Tumor infiltrating lymphocytes associated competitive endogenous RNA networks as predictors of outcome in hepatic carcinoma based on WGCNA analysis.

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