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The identification of hub-methylated differentially expressed genes in osteoarthritis patients is based on epigenomic and transcriptomic data
INTRODUCTION: Osteoarthritis (OA) refers to a commonly seen degenerative joint disorder and a major global public health burden. According to the existing literature, osteoarthritis is related to epigenetic changes, which are important for diagnosing and treating the disease early. Through early tar...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10351907/ https://www.ncbi.nlm.nih.gov/pubmed/37465641 http://dx.doi.org/10.3389/fmed.2023.1219830 |
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author | Chu, Zhen-Chen Cong, Ting Zhao, Jian-Yu Zhang, Jian Lou, Zhi-Yuan Gao, Yang Tang, Xin |
author_facet | Chu, Zhen-Chen Cong, Ting Zhao, Jian-Yu Zhang, Jian Lou, Zhi-Yuan Gao, Yang Tang, Xin |
author_sort | Chu, Zhen-Chen |
collection | PubMed |
description | INTRODUCTION: Osteoarthritis (OA) refers to a commonly seen degenerative joint disorder and a major global public health burden. According to the existing literature, osteoarthritis is related to epigenetic changes, which are important for diagnosing and treating the disease early. Through early targeted treatment, costly treatments and poor prognosis caused by advanced osteoarthritis can be avoided. METHODS: This study combined gene differential expression analysis and weighted gene co-expression network analysis (WGCNA) of the transcriptome with epigenome microarray data to discover the hub gene of OA. We obtained 2 microarray datasets (GSE114007, GSE73626) in Gene Expression Omnibus (GEO). The R software was utilized for identifying differentially expressed genes (DEGs) and differentially methylated genes (DMGs). By using WGCNA to analyze the relationships between modules and phenotypes, it was discovered that the blue module (MEBlue) has the strongest phenotypic connection with OA (cor = 0.92, p = 4e-16). The hub genes for OA, also known as the hub methylated differentially expressed genes, were identified by matching the MEblue module to differentially methylated differentially expressed genes. Furthermore, this study used Gene set variation analysis (GSVA) to identify specific signal pathways associated with hub genes. qRT-PCR and western blotting assays were used to confirm the expression levels of the hub genes in OA patients and healthy controls. RESULTS: Three hub genes were discovered: HTRA1, P2RY6, and RCAN1. GSVA analysis showed that high HTRA1 expression was mainly enriched in epithelial-mesenchymal transition and apical junction; high expression of P2RY6 was mainly enriched in the peroxisome, coagulation, and epithelial-mesenchymal transition; and high expression of RCAN1 was mainly enriched in epithelial-mesenchymal-transition, TGF-β-signaling, and glycolysis. The results of the RT-qPCR and WB assay were consistent with the findings. DISCUSSION: The three genes tested may cause articular cartilage degeneration by inducing chondrocyte hypertrophy, regulating extracellular matrix accumulation, and improving macrophage pro-inflammatory response, resulting in the onset and progression of osteoarthritis. They can provide new ideas for targeted treatment of osteoarthritis. |
format | Online Article Text |
id | pubmed-10351907 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-103519072023-07-18 The identification of hub-methylated differentially expressed genes in osteoarthritis patients is based on epigenomic and transcriptomic data Chu, Zhen-Chen Cong, Ting Zhao, Jian-Yu Zhang, Jian Lou, Zhi-Yuan Gao, Yang Tang, Xin Front Med (Lausanne) Medicine INTRODUCTION: Osteoarthritis (OA) refers to a commonly seen degenerative joint disorder and a major global public health burden. According to the existing literature, osteoarthritis is related to epigenetic changes, which are important for diagnosing and treating the disease early. Through early targeted treatment, costly treatments and poor prognosis caused by advanced osteoarthritis can be avoided. METHODS: This study combined gene differential expression analysis and weighted gene co-expression network analysis (WGCNA) of the transcriptome with epigenome microarray data to discover the hub gene of OA. We obtained 2 microarray datasets (GSE114007, GSE73626) in Gene Expression Omnibus (GEO). The R software was utilized for identifying differentially expressed genes (DEGs) and differentially methylated genes (DMGs). By using WGCNA to analyze the relationships between modules and phenotypes, it was discovered that the blue module (MEBlue) has the strongest phenotypic connection with OA (cor = 0.92, p = 4e-16). The hub genes for OA, also known as the hub methylated differentially expressed genes, were identified by matching the MEblue module to differentially methylated differentially expressed genes. Furthermore, this study used Gene set variation analysis (GSVA) to identify specific signal pathways associated with hub genes. qRT-PCR and western blotting assays were used to confirm the expression levels of the hub genes in OA patients and healthy controls. RESULTS: Three hub genes were discovered: HTRA1, P2RY6, and RCAN1. GSVA analysis showed that high HTRA1 expression was mainly enriched in epithelial-mesenchymal transition and apical junction; high expression of P2RY6 was mainly enriched in the peroxisome, coagulation, and epithelial-mesenchymal transition; and high expression of RCAN1 was mainly enriched in epithelial-mesenchymal-transition, TGF-β-signaling, and glycolysis. The results of the RT-qPCR and WB assay were consistent with the findings. DISCUSSION: The three genes tested may cause articular cartilage degeneration by inducing chondrocyte hypertrophy, regulating extracellular matrix accumulation, and improving macrophage pro-inflammatory response, resulting in the onset and progression of osteoarthritis. They can provide new ideas for targeted treatment of osteoarthritis. Frontiers Media S.A. 2023-07-03 /pmc/articles/PMC10351907/ /pubmed/37465641 http://dx.doi.org/10.3389/fmed.2023.1219830 Text en Copyright © 2023 Chu, Cong, Zhao, Zhang, Lou, Gao and Tang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Medicine Chu, Zhen-Chen Cong, Ting Zhao, Jian-Yu Zhang, Jian Lou, Zhi-Yuan Gao, Yang Tang, Xin The identification of hub-methylated differentially expressed genes in osteoarthritis patients is based on epigenomic and transcriptomic data |
title | The identification of hub-methylated differentially expressed genes in osteoarthritis patients is based on epigenomic and transcriptomic data |
title_full | The identification of hub-methylated differentially expressed genes in osteoarthritis patients is based on epigenomic and transcriptomic data |
title_fullStr | The identification of hub-methylated differentially expressed genes in osteoarthritis patients is based on epigenomic and transcriptomic data |
title_full_unstemmed | The identification of hub-methylated differentially expressed genes in osteoarthritis patients is based on epigenomic and transcriptomic data |
title_short | The identification of hub-methylated differentially expressed genes in osteoarthritis patients is based on epigenomic and transcriptomic data |
title_sort | identification of hub-methylated differentially expressed genes in osteoarthritis patients is based on epigenomic and transcriptomic data |
topic | Medicine |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10351907/ https://www.ncbi.nlm.nih.gov/pubmed/37465641 http://dx.doi.org/10.3389/fmed.2023.1219830 |
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