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Identification of candidate biomarkers and pathways associated with psoriasis using bioinformatics analysis
BACKGROUND: The aim of this study was to identify the candidate biomarkers and pathways associated with psoriasis. GSE13355 and GSE14905 were extracted from the Gene Expression Omnibus (GEO) database. Then the differentially expressed genes (DEGs) with |logFC| > 2 and adjusted P < 0.05 were ch...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7364515/ https://www.ncbi.nlm.nih.gov/pubmed/32669126 http://dx.doi.org/10.1186/s41065-020-00141-1 |
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author | Luo, Yongqi Luo, Yangyang Chang, Jing Xiao, Zhenghui Zhou, Bin |
author_facet | Luo, Yongqi Luo, Yangyang Chang, Jing Xiao, Zhenghui Zhou, Bin |
author_sort | Luo, Yongqi |
collection | PubMed |
description | BACKGROUND: The aim of this study was to identify the candidate biomarkers and pathways associated with psoriasis. GSE13355 and GSE14905 were extracted from the Gene Expression Omnibus (GEO) database. Then the differentially expressed genes (DEGs) with |logFC| > 2 and adjusted P < 0.05 were chosen. In addition, the Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses for DEGs were performed. Then, the GO terms with P < 0.05 and overlap coefficient greater than 0.5 were integrated by EnrichmentMap. Additionally, risk subpathways analysis for DEGs was also conducted by using the iSubpathwayMiner package to obtain more psoriasis-related DEGs and pathways. Finally, protein-protein interaction (PPI) network analysis was performed to identify the hub genes, and the DGIdb database was utilized to search for the candidate drugs for psoriasis. RESULTS: A total of 127 DEGs which were mostly associated with keratinization, keratinocyte differentiation, and epidermal cell differentiation biological processes were identified. Based on these GO terms, 3 modules (human skin, epidermis and cuticle differentiation, and enzyme activity) were constructed. Moreover, 9 risk subpathways such as steroid hormone biosynthesis, folate biosynthesis, and pyrimidine metabolism were screened. Finally, PPI network analysis demonstrated that CXCL10 was the hub gene with the highest degree, and CXCR2, CXCL10, IVL, OASL, and ISG15 were the potential gene targets of the drugs for treating psoriasis. CONCLUSION: Psoriasis may be mostly caused by keratinization, keratinocyte differentiation, and epidermal cell differentiation; the pathogeneses were more related with pathways such as steroid hormone biosynthesis, folate biosynthesis, and pyrimidine metabolism. Besides, some psoriasis-related genes such as SPRR genes, HSD11B1, GGH, CXCR2, IVL, OASL, ISG15, and CXCL10 may be important targets in psoriatic therapy. |
format | Online Article Text |
id | pubmed-7364515 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-73645152020-07-20 Identification of candidate biomarkers and pathways associated with psoriasis using bioinformatics analysis Luo, Yongqi Luo, Yangyang Chang, Jing Xiao, Zhenghui Zhou, Bin Hereditas Research BACKGROUND: The aim of this study was to identify the candidate biomarkers and pathways associated with psoriasis. GSE13355 and GSE14905 were extracted from the Gene Expression Omnibus (GEO) database. Then the differentially expressed genes (DEGs) with |logFC| > 2 and adjusted P < 0.05 were chosen. In addition, the Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses for DEGs were performed. Then, the GO terms with P < 0.05 and overlap coefficient greater than 0.5 were integrated by EnrichmentMap. Additionally, risk subpathways analysis for DEGs was also conducted by using the iSubpathwayMiner package to obtain more psoriasis-related DEGs and pathways. Finally, protein-protein interaction (PPI) network analysis was performed to identify the hub genes, and the DGIdb database was utilized to search for the candidate drugs for psoriasis. RESULTS: A total of 127 DEGs which were mostly associated with keratinization, keratinocyte differentiation, and epidermal cell differentiation biological processes were identified. Based on these GO terms, 3 modules (human skin, epidermis and cuticle differentiation, and enzyme activity) were constructed. Moreover, 9 risk subpathways such as steroid hormone biosynthesis, folate biosynthesis, and pyrimidine metabolism were screened. Finally, PPI network analysis demonstrated that CXCL10 was the hub gene with the highest degree, and CXCR2, CXCL10, IVL, OASL, and ISG15 were the potential gene targets of the drugs for treating psoriasis. CONCLUSION: Psoriasis may be mostly caused by keratinization, keratinocyte differentiation, and epidermal cell differentiation; the pathogeneses were more related with pathways such as steroid hormone biosynthesis, folate biosynthesis, and pyrimidine metabolism. Besides, some psoriasis-related genes such as SPRR genes, HSD11B1, GGH, CXCR2, IVL, OASL, ISG15, and CXCL10 may be important targets in psoriatic therapy. BioMed Central 2020-07-15 /pmc/articles/PMC7364515/ /pubmed/32669126 http://dx.doi.org/10.1186/s41065-020-00141-1 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Luo, Yongqi Luo, Yangyang Chang, Jing Xiao, Zhenghui Zhou, Bin Identification of candidate biomarkers and pathways associated with psoriasis using bioinformatics analysis |
title | Identification of candidate biomarkers and pathways associated with psoriasis using bioinformatics analysis |
title_full | Identification of candidate biomarkers and pathways associated with psoriasis using bioinformatics analysis |
title_fullStr | Identification of candidate biomarkers and pathways associated with psoriasis using bioinformatics analysis |
title_full_unstemmed | Identification of candidate biomarkers and pathways associated with psoriasis using bioinformatics analysis |
title_short | Identification of candidate biomarkers and pathways associated with psoriasis using bioinformatics analysis |
title_sort | identification of candidate biomarkers and pathways associated with psoriasis using bioinformatics analysis |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7364515/ https://www.ncbi.nlm.nih.gov/pubmed/32669126 http://dx.doi.org/10.1186/s41065-020-00141-1 |
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