Cargando…

Screening and identification of biomarkers for systemic sclerosis via microarray technology

Systemic sclerosis (SSc) is a complex autoimmune disease. The pathogenesis of SSc is currently unclear, although like other rheumatic diseases its pathogenesis is complicated. However, the ongoing development of bioinformatics technology has enabled new approaches to research this disease using micr...

Descripción completa

Detalles Bibliográficos
Autores principales: Xu, Chen, Meng, Ling-Bing, Duan, Yu-Chen, Cheng, Yong-Jing, Zhang, Chun-Mei, Zhou, Xing, Huang, Ci-Bo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: D.A. Spandidos 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6777682/
https://www.ncbi.nlm.nih.gov/pubmed/31545397
http://dx.doi.org/10.3892/ijmm.2019.4332
_version_ 1783456656069230592
author Xu, Chen
Meng, Ling-Bing
Duan, Yu-Chen
Cheng, Yong-Jing
Zhang, Chun-Mei
Zhou, Xing
Huang, Ci-Bo
author_facet Xu, Chen
Meng, Ling-Bing
Duan, Yu-Chen
Cheng, Yong-Jing
Zhang, Chun-Mei
Zhou, Xing
Huang, Ci-Bo
author_sort Xu, Chen
collection PubMed
description Systemic sclerosis (SSc) is a complex autoimmune disease. The pathogenesis of SSc is currently unclear, although like other rheumatic diseases its pathogenesis is complicated. However, the ongoing development of bioinformatics technology has enabled new approaches to research this disease using microarray technology to screen and identify differentially expressed genes (DEGs) in the skin of patients with SSc compared with individuals with healthy skin. Publicly available data were downloaded from the Gene Expression Omnibus (GEO) database and intra-group data repeatability tests were conducted using Pearson's correlation test and principal component analysis. DEGs were identified using an online tool, GEO2R. Functional annotation of DEGs was performed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Finally, the construction and analysis of the protein-protein interaction (PPI) network and identification and analysis of hub genes was carried out. A total of 106 DEGs were detected by the screening of SSc and healthy skin samples. A total of 10 genes [interleukin-6, bone morphogenetic protein 4, calumenin (CALU), clusterin, cysteine rich angiogenic inducer 61, serine protease 23, secretogranin II, suppressor of cytokine signaling 3, Toll-like receptor 4 (TLR4), tenascin C] were identified as hub genes with degrees ≥10, and which could sensitively and specifically predict SSc based on receiver operator characteristic curve analysis. GO and KEGG analysis showed that variations in hub genes were mainly enriched in positive regulation of nitric oxide biosynthetic processes, negative regulation of apoptotic processes, extracellular regions, extracellular spaces, cytokine activity, chemo-attractant activity, and the phosphoinositide 3 kinase-protein kinase B signaling pathway. In summary, bioinformatics techniques proved useful for the screening and identification of biomarkers of disease. A total of 106 DEGs and 10 hub genes were linked to SSc, in particular the TLR4 and CALU genes.
format Online
Article
Text
id pubmed-6777682
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher D.A. Spandidos
record_format MEDLINE/PubMed
spelling pubmed-67776822019-10-09 Screening and identification of biomarkers for systemic sclerosis via microarray technology Xu, Chen Meng, Ling-Bing Duan, Yu-Chen Cheng, Yong-Jing Zhang, Chun-Mei Zhou, Xing Huang, Ci-Bo Int J Mol Med Articles Systemic sclerosis (SSc) is a complex autoimmune disease. The pathogenesis of SSc is currently unclear, although like other rheumatic diseases its pathogenesis is complicated. However, the ongoing development of bioinformatics technology has enabled new approaches to research this disease using microarray technology to screen and identify differentially expressed genes (DEGs) in the skin of patients with SSc compared with individuals with healthy skin. Publicly available data were downloaded from the Gene Expression Omnibus (GEO) database and intra-group data repeatability tests were conducted using Pearson's correlation test and principal component analysis. DEGs were identified using an online tool, GEO2R. Functional annotation of DEGs was performed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Finally, the construction and analysis of the protein-protein interaction (PPI) network and identification and analysis of hub genes was carried out. A total of 106 DEGs were detected by the screening of SSc and healthy skin samples. A total of 10 genes [interleukin-6, bone morphogenetic protein 4, calumenin (CALU), clusterin, cysteine rich angiogenic inducer 61, serine protease 23, secretogranin II, suppressor of cytokine signaling 3, Toll-like receptor 4 (TLR4), tenascin C] were identified as hub genes with degrees ≥10, and which could sensitively and specifically predict SSc based on receiver operator characteristic curve analysis. GO and KEGG analysis showed that variations in hub genes were mainly enriched in positive regulation of nitric oxide biosynthetic processes, negative regulation of apoptotic processes, extracellular regions, extracellular spaces, cytokine activity, chemo-attractant activity, and the phosphoinositide 3 kinase-protein kinase B signaling pathway. In summary, bioinformatics techniques proved useful for the screening and identification of biomarkers of disease. A total of 106 DEGs and 10 hub genes were linked to SSc, in particular the TLR4 and CALU genes. D.A. Spandidos 2019-11 2019-09-05 /pmc/articles/PMC6777682/ /pubmed/31545397 http://dx.doi.org/10.3892/ijmm.2019.4332 Text en Copyright: © Xu et al. This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
spellingShingle Articles
Xu, Chen
Meng, Ling-Bing
Duan, Yu-Chen
Cheng, Yong-Jing
Zhang, Chun-Mei
Zhou, Xing
Huang, Ci-Bo
Screening and identification of biomarkers for systemic sclerosis via microarray technology
title Screening and identification of biomarkers for systemic sclerosis via microarray technology
title_full Screening and identification of biomarkers for systemic sclerosis via microarray technology
title_fullStr Screening and identification of biomarkers for systemic sclerosis via microarray technology
title_full_unstemmed Screening and identification of biomarkers for systemic sclerosis via microarray technology
title_short Screening and identification of biomarkers for systemic sclerosis via microarray technology
title_sort screening and identification of biomarkers for systemic sclerosis via microarray technology
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6777682/
https://www.ncbi.nlm.nih.gov/pubmed/31545397
http://dx.doi.org/10.3892/ijmm.2019.4332
work_keys_str_mv AT xuchen screeningandidentificationofbiomarkersforsystemicsclerosisviamicroarraytechnology
AT menglingbing screeningandidentificationofbiomarkersforsystemicsclerosisviamicroarraytechnology
AT duanyuchen screeningandidentificationofbiomarkersforsystemicsclerosisviamicroarraytechnology
AT chengyongjing screeningandidentificationofbiomarkersforsystemicsclerosisviamicroarraytechnology
AT zhangchunmei screeningandidentificationofbiomarkersforsystemicsclerosisviamicroarraytechnology
AT zhouxing screeningandidentificationofbiomarkersforsystemicsclerosisviamicroarraytechnology
AT huangcibo screeningandidentificationofbiomarkersforsystemicsclerosisviamicroarraytechnology