Cargando…

Identification of key genes and pathways in IgA nephropathy using bioinformatics analysis

BACKGROUND: IgA nephropathy (IgAN) is the most frequent type of primary glomerulonephritis globally and the leading cause of end-stage renal disease in young adults. Its pathogenesis is not fully known, but is largely attributed to genetic factors. This study was aimed to explore the prognostic valu...

Descripción completa

Detalles Bibliográficos
Autores principales: Hu, Shou-Liang, Wang, Dan, Yuan, Fan-Li, Lei, Qing-Feng, Zhang, Yong, Cheng, Jun-Zhang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Wolters Kluwer Health 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7386957/
https://www.ncbi.nlm.nih.gov/pubmed/32791747
http://dx.doi.org/10.1097/MD.0000000000021372
_version_ 1783564042865999872
author Hu, Shou-Liang
Wang, Dan
Yuan, Fan-Li
Lei, Qing-Feng
Zhang, Yong
Cheng, Jun-Zhang
author_facet Hu, Shou-Liang
Wang, Dan
Yuan, Fan-Li
Lei, Qing-Feng
Zhang, Yong
Cheng, Jun-Zhang
author_sort Hu, Shou-Liang
collection PubMed
description BACKGROUND: IgA nephropathy (IgAN) is the most frequent type of primary glomerulonephritis globally and the leading cause of end-stage renal disease in young adults. Its pathogenesis is not fully known, but is largely attributed to genetic factors. This study was aimed to explore the prognostic values of key genes in IgAN. METHODS: The gene expression profile GSE93798 of 20 IgAN samples and 22 normal samples using glomeruli from kidney biopsy was adopted. Totally 447 upregulated and 719 downregulated differentially expressed genes were found in IgAN patients on the R software. The Gene Ontology enrichment and the Kyoto Encyclopedia of Gene and Genomes pathway were investigated on DAVID, and the protein-protein interaction network and the top 13 hub genes of the differentially expressed genes were built via the plug-in molecular complex detection and cytoHubba of Cytoscape. RESULTS: From the protein-protein interaction network, of the top 13 hub genes, FOS, EGFR, SIRT1, ALB, TFRC, JUN, IGF1, HIF1A, and SOCS3 were upregulated, while CTTN, ACTR2, CREB1, and CTNNB1 were downregulated. The upregulated genes took part in the HIF-1 signaling pathway, Choline metabolism in cancer, Pathways in cancer, Amphetamine addiction, Estrogen, TNF, and FoxO signaling pathways, and Osteoclast differentiation, while the downregulated genes were involved in Pathogenic Escherichia coli infection, Bacterial invasion of epithelial cells, prostate cancer, and melanogenesis. CONCLUSION: This study based on the Gene Expression Omnibus database updates the knowledge about the mechanism of IgAN and may offer new treatment targets.
format Online
Article
Text
id pubmed-7386957
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Wolters Kluwer Health
record_format MEDLINE/PubMed
spelling pubmed-73869572020-08-05 Identification of key genes and pathways in IgA nephropathy using bioinformatics analysis Hu, Shou-Liang Wang, Dan Yuan, Fan-Li Lei, Qing-Feng Zhang, Yong Cheng, Jun-Zhang Medicine (Baltimore) 3500 BACKGROUND: IgA nephropathy (IgAN) is the most frequent type of primary glomerulonephritis globally and the leading cause of end-stage renal disease in young adults. Its pathogenesis is not fully known, but is largely attributed to genetic factors. This study was aimed to explore the prognostic values of key genes in IgAN. METHODS: The gene expression profile GSE93798 of 20 IgAN samples and 22 normal samples using glomeruli from kidney biopsy was adopted. Totally 447 upregulated and 719 downregulated differentially expressed genes were found in IgAN patients on the R software. The Gene Ontology enrichment and the Kyoto Encyclopedia of Gene and Genomes pathway were investigated on DAVID, and the protein-protein interaction network and the top 13 hub genes of the differentially expressed genes were built via the plug-in molecular complex detection and cytoHubba of Cytoscape. RESULTS: From the protein-protein interaction network, of the top 13 hub genes, FOS, EGFR, SIRT1, ALB, TFRC, JUN, IGF1, HIF1A, and SOCS3 were upregulated, while CTTN, ACTR2, CREB1, and CTNNB1 were downregulated. The upregulated genes took part in the HIF-1 signaling pathway, Choline metabolism in cancer, Pathways in cancer, Amphetamine addiction, Estrogen, TNF, and FoxO signaling pathways, and Osteoclast differentiation, while the downregulated genes were involved in Pathogenic Escherichia coli infection, Bacterial invasion of epithelial cells, prostate cancer, and melanogenesis. CONCLUSION: This study based on the Gene Expression Omnibus database updates the knowledge about the mechanism of IgAN and may offer new treatment targets. Wolters Kluwer Health 2020-07-24 /pmc/articles/PMC7386957/ /pubmed/32791747 http://dx.doi.org/10.1097/MD.0000000000021372 Text en Copyright © 2020 the Author(s). Published by Wolters Kluwer Health, Inc. http://creativecommons.org/licenses/by-nc/4.0 This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc/4.0
spellingShingle 3500
Hu, Shou-Liang
Wang, Dan
Yuan, Fan-Li
Lei, Qing-Feng
Zhang, Yong
Cheng, Jun-Zhang
Identification of key genes and pathways in IgA nephropathy using bioinformatics analysis
title Identification of key genes and pathways in IgA nephropathy using bioinformatics analysis
title_full Identification of key genes and pathways in IgA nephropathy using bioinformatics analysis
title_fullStr Identification of key genes and pathways in IgA nephropathy using bioinformatics analysis
title_full_unstemmed Identification of key genes and pathways in IgA nephropathy using bioinformatics analysis
title_short Identification of key genes and pathways in IgA nephropathy using bioinformatics analysis
title_sort identification of key genes and pathways in iga nephropathy using bioinformatics analysis
topic 3500
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7386957/
https://www.ncbi.nlm.nih.gov/pubmed/32791747
http://dx.doi.org/10.1097/MD.0000000000021372
work_keys_str_mv AT hushouliang identificationofkeygenesandpathwaysiniganephropathyusingbioinformaticsanalysis
AT wangdan identificationofkeygenesandpathwaysiniganephropathyusingbioinformaticsanalysis
AT yuanfanli identificationofkeygenesandpathwaysiniganephropathyusingbioinformaticsanalysis
AT leiqingfeng identificationofkeygenesandpathwaysiniganephropathyusingbioinformaticsanalysis
AT zhangyong identificationofkeygenesandpathwaysiniganephropathyusingbioinformaticsanalysis
AT chengjunzhang identificationofkeygenesandpathwaysiniganephropathyusingbioinformaticsanalysis