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Identification of diagnostic genes and vital microRNAs involved in rheumatoid arthritis: based on data mining and experimental verification
BACKGROUND: The pathogenesis of rheumatoid arthritis (RA) is complex. This study aimed to identify diagnostic biomarkers and transcriptional regulators that underlie RA based on bioinformatics analysis and experimental verification. MATERIAL AND METHODS: We applied weighted gene co-expression networ...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8127958/ https://www.ncbi.nlm.nih.gov/pubmed/34040897 http://dx.doi.org/10.7717/peerj.11427 |
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author | Ren, Conglin Li, Mingshuang Zheng, Yang Wu, Fengqing Du, Weibin Quan, Renfu |
author_facet | Ren, Conglin Li, Mingshuang Zheng, Yang Wu, Fengqing Du, Weibin Quan, Renfu |
author_sort | Ren, Conglin |
collection | PubMed |
description | BACKGROUND: The pathogenesis of rheumatoid arthritis (RA) is complex. This study aimed to identify diagnostic biomarkers and transcriptional regulators that underlie RA based on bioinformatics analysis and experimental verification. MATERIAL AND METHODS: We applied weighted gene co-expression network analysis (WGCNA) to analyze dataset GSE55457 and obtained the key module most relevant to the RA phenotype. We then conducted gene function annotation, gene set enrichment analysis (GSEA) and immunocytes quantitative analysis (CIBERSORT). Moreover, the intersection of differentially expressed genes (DEGs) and genes within the key module were entered into the STRING database to construct an interaction network and to mine hub genes. We predicted microRNA (miRNA) using a web-based tool (miRDB). Finally, hub genes and vital miRNAs were validated with independent GEO datasets, RT-qPCR and Western blot. RESULTS: A total of 367 DEGs were characterized by differential expression analysis. The WGCNA method divided genes into 14 modules, and we focused on the turquoise module containing 845 genes. Gene function annotation and GSEA suggested that immune response and inflammatory signaling pathways are the molecular mechanisms behind RA. Nine hub genes were screened from the network and seven vital regulators were obtained using miRNA prediction. CIBERSORT analysis identified five cell types enriched in RA samples, which were closely related to the expression of hub genes. Through ROC curve and RT-qPCR validation, we confirmed five genes that were specific for RA, including CCL25, CXCL9, CXCL10, CXCL11, and CXCL13. Moreover, we selected a representative gene (CXCL10) for Western blot validation. Vital miRNAs verification showed that only the differences in has-miR-573 and has-miR-34a were statistically significant. CONCLUSION: Our study reveals diagnostic genes and vital microRNAs highly related to RA, which could help improve our understanding of the molecular mechanisms underlying the disorder and provide theoretical support for the future exploration of innovative therapeutic approaches. |
format | Online Article Text |
id | pubmed-8127958 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-81279582021-05-25 Identification of diagnostic genes and vital microRNAs involved in rheumatoid arthritis: based on data mining and experimental verification Ren, Conglin Li, Mingshuang Zheng, Yang Wu, Fengqing Du, Weibin Quan, Renfu PeerJ Bioinformatics BACKGROUND: The pathogenesis of rheumatoid arthritis (RA) is complex. This study aimed to identify diagnostic biomarkers and transcriptional regulators that underlie RA based on bioinformatics analysis and experimental verification. MATERIAL AND METHODS: We applied weighted gene co-expression network analysis (WGCNA) to analyze dataset GSE55457 and obtained the key module most relevant to the RA phenotype. We then conducted gene function annotation, gene set enrichment analysis (GSEA) and immunocytes quantitative analysis (CIBERSORT). Moreover, the intersection of differentially expressed genes (DEGs) and genes within the key module were entered into the STRING database to construct an interaction network and to mine hub genes. We predicted microRNA (miRNA) using a web-based tool (miRDB). Finally, hub genes and vital miRNAs were validated with independent GEO datasets, RT-qPCR and Western blot. RESULTS: A total of 367 DEGs were characterized by differential expression analysis. The WGCNA method divided genes into 14 modules, and we focused on the turquoise module containing 845 genes. Gene function annotation and GSEA suggested that immune response and inflammatory signaling pathways are the molecular mechanisms behind RA. Nine hub genes were screened from the network and seven vital regulators were obtained using miRNA prediction. CIBERSORT analysis identified five cell types enriched in RA samples, which were closely related to the expression of hub genes. Through ROC curve and RT-qPCR validation, we confirmed five genes that were specific for RA, including CCL25, CXCL9, CXCL10, CXCL11, and CXCL13. Moreover, we selected a representative gene (CXCL10) for Western blot validation. Vital miRNAs verification showed that only the differences in has-miR-573 and has-miR-34a were statistically significant. CONCLUSION: Our study reveals diagnostic genes and vital microRNAs highly related to RA, which could help improve our understanding of the molecular mechanisms underlying the disorder and provide theoretical support for the future exploration of innovative therapeutic approaches. PeerJ Inc. 2021-05-14 /pmc/articles/PMC8127958/ /pubmed/34040897 http://dx.doi.org/10.7717/peerj.11427 Text en ©2021 Ren et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Bioinformatics Ren, Conglin Li, Mingshuang Zheng, Yang Wu, Fengqing Du, Weibin Quan, Renfu Identification of diagnostic genes and vital microRNAs involved in rheumatoid arthritis: based on data mining and experimental verification |
title | Identification of diagnostic genes and vital microRNAs involved in rheumatoid arthritis: based on data mining and experimental verification |
title_full | Identification of diagnostic genes and vital microRNAs involved in rheumatoid arthritis: based on data mining and experimental verification |
title_fullStr | Identification of diagnostic genes and vital microRNAs involved in rheumatoid arthritis: based on data mining and experimental verification |
title_full_unstemmed | Identification of diagnostic genes and vital microRNAs involved in rheumatoid arthritis: based on data mining and experimental verification |
title_short | Identification of diagnostic genes and vital microRNAs involved in rheumatoid arthritis: based on data mining and experimental verification |
title_sort | identification of diagnostic genes and vital micrornas involved in rheumatoid arthritis: based on data mining and experimental verification |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8127958/ https://www.ncbi.nlm.nih.gov/pubmed/34040897 http://dx.doi.org/10.7717/peerj.11427 |
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