Identification of 8 feature genes related to clear cell renal cell carcinoma progression based on co-expression analysis

Backgrounds: To screen biomarkers related to clear cell renal cell carcinoma (ccRCC) progression and prognosis. Methods: 1,026 ccRCC-related genes were dug from 494 ccRCC samples in TCGA based on weighted gene co-expression network analysis, and 7 modules were identified. Afterwards, GO and KEGG enr...

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Autores principales:	Xiaoxia Yu, Hua Wu, Hongmei Wang, He Dong, Bihu Gao
Formato:	article
Lenguaje:	EN
Publicado:	Karger Publishers 2021
Materias:	Dermatology RL1-803 Diseases of the circulatory (Cardiovascular) system RC666-701 Diseases of the genitourinary system. Urology RC870-923
Acceso en línea:	https://doaj.org/article/bf925408642f4dc89481cbc09f2399fd
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spelling	oai:doaj.org-article:bf925408642f4dc89481cbc09f2399fd2021-12-02T12:40:22ZIdentification of 8 feature genes related to clear cell renal cell carcinoma progression based on co-expression analysis1420-40961423-014310.1159/000520832https://doaj.org/article/bf925408642f4dc89481cbc09f2399fd2021-11-01T00:00:00Zhttps://www.karger.com/Article/FullText/520832https://doaj.org/toc/1420-4096https://doaj.org/toc/1423-0143Backgrounds: To screen biomarkers related to clear cell renal cell carcinoma (ccRCC) progression and prognosis. Methods: 1,026 ccRCC-related genes were dug from 494 ccRCC samples in TCGA based on weighted gene co-expression network analysis, and 7 modules were identified. Afterwards, GO and KEGG enrichment analyses were conducted on modules of interest. Genes in these modules were taken as the input to construct a protein-protein interaction network. Thereafter, 30 genes with the highest connectivity were taken as core genes. Univariate Cox regression, LASSO Cox regression and multivariate Cox regression analyses were performed on core genes. Univariate and multivariate Cox regression analyses were performed on patient’s clinical characteristics and risk scores. Results: Stage displayed significantly strong correlations with green module and red module (p<0.001). Genes in modules participated in biological functions including T cell proliferation and regulation of lymphocyte activation. GSEA showed that high- and low-risk groups exhibited significant enrichment differences in pathways related to immunity, cell migration and invasion. Immune infiltration analysis also presented strong correlation between expression of these 8 genes and immune cell infiltration in ccRCC samples. It was displayed that risk score could be an independent factor to assess patient’s prognosis. Conclusion: We determined biomarkers relevant to ccRCC progression, offering candidate targets for ccRCC treatment.Xiaoxia YuHua WuHongmei WangHe DongBihu GaoKarger PublishersarticleDermatologyRL1-803Diseases of the circulatory (Cardiovascular) systemRC666-701Diseases of the genitourinary system. UrologyRC870-923ENKidney & Blood Pressure Research (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Dermatology RL1-803 Diseases of the circulatory (Cardiovascular) system RC666-701 Diseases of the genitourinary system. Urology RC870-923
spellingShingle	Dermatology RL1-803 Diseases of the circulatory (Cardiovascular) system RC666-701 Diseases of the genitourinary system. Urology RC870-923 Xiaoxia Yu Hua Wu Hongmei Wang He Dong Bihu Gao Identification of 8 feature genes related to clear cell renal cell carcinoma progression based on co-expression analysis
description	Backgrounds: To screen biomarkers related to clear cell renal cell carcinoma (ccRCC) progression and prognosis. Methods: 1,026 ccRCC-related genes were dug from 494 ccRCC samples in TCGA based on weighted gene co-expression network analysis, and 7 modules were identified. Afterwards, GO and KEGG enrichment analyses were conducted on modules of interest. Genes in these modules were taken as the input to construct a protein-protein interaction network. Thereafter, 30 genes with the highest connectivity were taken as core genes. Univariate Cox regression, LASSO Cox regression and multivariate Cox regression analyses were performed on core genes. Univariate and multivariate Cox regression analyses were performed on patient’s clinical characteristics and risk scores. Results: Stage displayed significantly strong correlations with green module and red module (p<0.001). Genes in modules participated in biological functions including T cell proliferation and regulation of lymphocyte activation. GSEA showed that high- and low-risk groups exhibited significant enrichment differences in pathways related to immunity, cell migration and invasion. Immune infiltration analysis also presented strong correlation between expression of these 8 genes and immune cell infiltration in ccRCC samples. It was displayed that risk score could be an independent factor to assess patient’s prognosis. Conclusion: We determined biomarkers relevant to ccRCC progression, offering candidate targets for ccRCC treatment.
format	article
author	Xiaoxia Yu Hua Wu Hongmei Wang He Dong Bihu Gao
author_facet	Xiaoxia Yu Hua Wu Hongmei Wang He Dong Bihu Gao
author_sort	Xiaoxia Yu
title	Identification of 8 feature genes related to clear cell renal cell carcinoma progression based on co-expression analysis
title_short	Identification of 8 feature genes related to clear cell renal cell carcinoma progression based on co-expression analysis
title_full	Identification of 8 feature genes related to clear cell renal cell carcinoma progression based on co-expression analysis
title_fullStr	Identification of 8 feature genes related to clear cell renal cell carcinoma progression based on co-expression analysis
title_full_unstemmed	Identification of 8 feature genes related to clear cell renal cell carcinoma progression based on co-expression analysis
title_sort	identification of 8 feature genes related to clear cell renal cell carcinoma progression based on co-expression analysis
publisher	Karger Publishers
publishDate	2021
url	https://doaj.org/article/bf925408642f4dc89481cbc09f2399fd
work_keys_str_mv	AT xiaoxiayu identificationof8featuregenesrelatedtoclearcellrenalcellcarcinomaprogressionbasedoncoexpressionanalysis AT huawu identificationof8featuregenesrelatedtoclearcellrenalcellcarcinomaprogressionbasedoncoexpressionanalysis AT hongmeiwang identificationof8featuregenesrelatedtoclearcellrenalcellcarcinomaprogressionbasedoncoexpressionanalysis AT hedong identificationof8featuregenesrelatedtoclearcellrenalcellcarcinomaprogressionbasedoncoexpressionanalysis AT bihugao identificationof8featuregenesrelatedtoclearcellrenalcellcarcinomaprogressionbasedoncoexpressionanalysis
_version_	1718393764703109120

Identification of 8 feature genes related to clear cell renal cell carcinoma progression based on co-expression analysis

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