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Identification of transcription factors and construction of a novel miRNA regulatory network in primary osteoarthritis by integrated analysis
BACKGROUNDS: As osteoarthritis (OA) disease-modifying therapies are not available, novel therapeutic targets need to be discovered and prioritized. Here, we aim to identify miRNA signatures in patients to fully elucidate regulatory mechanism of OA pathogenesis and advance in basic understanding of t...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8641180/ https://www.ncbi.nlm.nih.gov/pubmed/34856957 http://dx.doi.org/10.1186/s12891-021-04894-2 |
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author | Jiang, Ying Shen, Yi Ding, Liyan Xia, Shengli Jiang, Liying |
author_facet | Jiang, Ying Shen, Yi Ding, Liyan Xia, Shengli Jiang, Liying |
author_sort | Jiang, Ying |
collection | PubMed |
description | BACKGROUNDS: As osteoarthritis (OA) disease-modifying therapies are not available, novel therapeutic targets need to be discovered and prioritized. Here, we aim to identify miRNA signatures in patients to fully elucidate regulatory mechanism of OA pathogenesis and advance in basic understanding of the genetic etiology of OA. METHODS: Six participants (3 OA and 3 controls) were recruited and serum samples were assayed through RNA sequencing (RNA-seq). And, RNA-seq dataset was analysed to identify genes, pathways and regulatory networks dysregulated in OA. The overlapped differentially expressed microRNAs (DEMs) were further screened in combination with the microarray dataset GSE143514. The expression levels of candidate miRNAs were further validated by quantitative real-time PCR (qRT-PCR) based on the GEO dataset (GSE114007). RESULTS: Serum samples were sequenced interrogating 382 miRNAs. After screening of independent samples and GEO database, the two comparison datasets shared 19 overlapped candidate micRNAs. Of these, 9 up-regulated DEMs and 10 down-regulated DEMs were detected, respectively. There were 236 target genes for up-regulated DEMs and 400 target genes for those down-regulated DEMs. For up-regulated DEMs, the top 10 hub genes were KRAS, NRAS, CDC42, GDNF, SOS1, PIK3R3, GSK3B, IRS2, GNG12, and PRKCA; for down-regulated DEMs, the top 10 hub genes were NR3C1, PPARGC1A, SUMO1, MEF2C, FOXO3, PPP1CB, MAP2K1, RARA, RHOC, CDC23, and CREB3L2. Mir-584-5p-KRAS, mir-183-5p-NRAS, mir-4435-PIK3R3, and mir-4435-SOS1 were identified as four potential regulatory pathways by integrated analysis. CONCLUSIONS: We have integrated differential expression data to reveal putative genes and detected four potential miRNA-target gene pathways through bioinformatics analysis that represent new mediators of abnormal gene expression and promising therapeutic targets in OA. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12891-021-04894-2. |
format | Online Article Text |
id | pubmed-8641180 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-86411802021-12-06 Identification of transcription factors and construction of a novel miRNA regulatory network in primary osteoarthritis by integrated analysis Jiang, Ying Shen, Yi Ding, Liyan Xia, Shengli Jiang, Liying BMC Musculoskelet Disord Research BACKGROUNDS: As osteoarthritis (OA) disease-modifying therapies are not available, novel therapeutic targets need to be discovered and prioritized. Here, we aim to identify miRNA signatures in patients to fully elucidate regulatory mechanism of OA pathogenesis and advance in basic understanding of the genetic etiology of OA. METHODS: Six participants (3 OA and 3 controls) were recruited and serum samples were assayed through RNA sequencing (RNA-seq). And, RNA-seq dataset was analysed to identify genes, pathways and regulatory networks dysregulated in OA. The overlapped differentially expressed microRNAs (DEMs) were further screened in combination with the microarray dataset GSE143514. The expression levels of candidate miRNAs were further validated by quantitative real-time PCR (qRT-PCR) based on the GEO dataset (GSE114007). RESULTS: Serum samples were sequenced interrogating 382 miRNAs. After screening of independent samples and GEO database, the two comparison datasets shared 19 overlapped candidate micRNAs. Of these, 9 up-regulated DEMs and 10 down-regulated DEMs were detected, respectively. There were 236 target genes for up-regulated DEMs and 400 target genes for those down-regulated DEMs. For up-regulated DEMs, the top 10 hub genes were KRAS, NRAS, CDC42, GDNF, SOS1, PIK3R3, GSK3B, IRS2, GNG12, and PRKCA; for down-regulated DEMs, the top 10 hub genes were NR3C1, PPARGC1A, SUMO1, MEF2C, FOXO3, PPP1CB, MAP2K1, RARA, RHOC, CDC23, and CREB3L2. Mir-584-5p-KRAS, mir-183-5p-NRAS, mir-4435-PIK3R3, and mir-4435-SOS1 were identified as four potential regulatory pathways by integrated analysis. CONCLUSIONS: We have integrated differential expression data to reveal putative genes and detected four potential miRNA-target gene pathways through bioinformatics analysis that represent new mediators of abnormal gene expression and promising therapeutic targets in OA. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12891-021-04894-2. BioMed Central 2021-12-02 /pmc/articles/PMC8641180/ /pubmed/34856957 http://dx.doi.org/10.1186/s12891-021-04894-2 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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 Jiang, Ying Shen, Yi Ding, Liyan Xia, Shengli Jiang, Liying Identification of transcription factors and construction of a novel miRNA regulatory network in primary osteoarthritis by integrated analysis |
title | Identification of transcription factors and construction of a novel miRNA regulatory network in primary osteoarthritis by integrated analysis |
title_full | Identification of transcription factors and construction of a novel miRNA regulatory network in primary osteoarthritis by integrated analysis |
title_fullStr | Identification of transcription factors and construction of a novel miRNA regulatory network in primary osteoarthritis by integrated analysis |
title_full_unstemmed | Identification of transcription factors and construction of a novel miRNA regulatory network in primary osteoarthritis by integrated analysis |
title_short | Identification of transcription factors and construction of a novel miRNA regulatory network in primary osteoarthritis by integrated analysis |
title_sort | identification of transcription factors and construction of a novel mirna regulatory network in primary osteoarthritis by integrated analysis |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8641180/ https://www.ncbi.nlm.nih.gov/pubmed/34856957 http://dx.doi.org/10.1186/s12891-021-04894-2 |
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