Linli Zheng1, Weishen Chen1, Guoyan Xian1, Baiqi Pan1, Yongyu Ye1, Minghui Gu1, Yinyue Ma2, Ziji Zhang3, Puyi Sheng4. 1. Department of Joint Surgery, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China. 2. Department of Biological Science, College of Life Science, South China Agricultural University, Guangzhou, 510642, Guangdong, China. 3. Department of Joint Surgery, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China. zhangziji@mail.sysu.edu.cn. 4. Department of Joint Surgery, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, Guangdong, China. shengpy@mail.sysu.edu.cn.
Abstract
OBJECTIVES: To investigate abnormally methylated-differentially expressed genes (DEGs) and their related pathways in osteoarthritis (OA) by comprehensive bioinformatic analysis. METHODS: Gene expression profiles of GSE51588 and GSE114007, and a gene methylation microarray data GSE63695 were downloaded from the Gene Expression Omnibus (GEO) repository. Abnormally methylated DEGs were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of these genes were subsequently performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID). The protein-protein interaction (PPI) network was built from STRING. Module analysis and hub gene identification were performed by using Cytoscape. Co-expression analysis was also constructed using the CEMiTool package. RESULTS: In total, 133 abnormally methylated DEGs were identified, including 85 hypomethylation high-expression genes and 48 hypermethylation low-expression genes. Among biological processes and KEGG pathways of abnormally methylated DEGs, collagen fibril organization was enriched most frequently, and pathways of oxidative stress and aging were enriched, including HIF-1 signaling pathway, AMPK signaling pathway, and FoxO signaling pathway. In PPI networks, the hub genes of hypomethylation high-expression genes were COL1A1, COL3A1, COL1A2, COL5A2, LUM, MMP2, SPARC, COL2A1, COL6A2, and COL7A1, and the hub genes of hypermethylation low-expression genes were VEGFA, SLC2A1, LDHA, PDK1, and BNIP3. Combined with co-expression analysis, COL3A1, LUM, and MMP2 were the critical hypomethylation high-expression hub genes in medial tibia subchondral bone. CONCLUSIONS: Our study implied abnormally methylated DEGs and dysregulated pathways in OA. Common methylation biomarkers included COL3A1, LUM, and MMP2, and we also found that THBS2 may serve as a novel biomarker in end-stage OA. Key Points • Abnormally methylated differentially expressed genes regulate osteoarthritis. • Hypomethylation high-expression genes were related to the extracellular matrix. • Hypermethylation low-expression genes were related to oxidative stress and aging. • COL3A1, LUM, and MMP2 were potential methylation biomarkers for osteoarthritis.
OBJECTIVES: To investigate abnormally methylated-differentially expressed genes (DEGs) and their related pathways in osteoarthritis (OA) by comprehensive bioinformatic analysis. METHODS: Gene expression profiles of GSE51588 and GSE114007, and a gene methylation microarray data GSE63695 were downloaded from the Gene Expression Omnibus (GEO) repository. Abnormally methylated DEGs were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of these genes were subsequently performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID). The protein-protein interaction (PPI) network was built from STRING. Module analysis and hub gene identification were performed by using Cytoscape. Co-expression analysis was also constructed using the CEMiTool package. RESULTS: In total, 133 abnormally methylated DEGs were identified, including 85 hypomethylation high-expression genes and 48 hypermethylation low-expression genes. Among biological processes and KEGG pathways of abnormally methylated DEGs, collagen fibril organization was enriched most frequently, and pathways of oxidative stress and aging were enriched, including HIF-1 signaling pathway, AMPK signaling pathway, and FoxO signaling pathway. In PPI networks, the hub genes of hypomethylation high-expression genes were COL1A1, COL3A1, COL1A2, COL5A2, LUM, MMP2, SPARC, COL2A1, COL6A2, and COL7A1, and the hub genes of hypermethylation low-expression genes were VEGFA, SLC2A1, LDHA, PDK1, and BNIP3. Combined with co-expression analysis, COL3A1, LUM, and MMP2 were the critical hypomethylation high-expression hub genes in medial tibia subchondral bone. CONCLUSIONS: Our study implied abnormally methylated DEGs and dysregulated pathways in OA. Common methylation biomarkers included COL3A1, LUM, and MMP2, and we also found that THBS2 may serve as a novel biomarker in end-stage OA. Key Points • Abnormally methylated differentially expressed genes regulate osteoarthritis. • Hypomethylation high-expression genes were related to the extracellular matrix. • Hypermethylation low-expression genes were related to oxidative stress and aging. • COL3A1, LUM, and MMP2 were potential methylation biomarkers for osteoarthritis.
Authors: Ali Mobasheri; Margaret P Rayman; Oreste Gualillo; Jérémie Sellam; Peter van der Kraan; Ursula Fearon Journal: Nat Rev Rheumatol Date: 2017-04-06 Impact factor: 20.543
Authors: Matthew E Ritchie; Belinda Phipson; Di Wu; Yifang Hu; Charity W Law; Wei Shi; Gordon K Smyth Journal: Nucleic Acids Res Date: 2015-01-20 Impact factor: 16.971
Authors: Ok Hee Jeon; Chaekyu Kim; Remi-Martin Laberge; Marco Demaria; Sona Rathod; Alain P Vasserot; Jae Wook Chung; Do Hun Kim; Yan Poon; Nathaniel David; Darren J Baker; Jan M van Deursen; Judith Campisi; Jennifer H Elisseeff Journal: Nat Med Date: 2017-04-24 Impact factor: 53.440
Authors: Damian Szklarczyk; Annika L Gable; David Lyon; Alexander Junge; Stefan Wyder; Jaime Huerta-Cepas; Milan Simonovic; Nadezhda T Doncheva; John H Morris; Peer Bork; Lars J Jensen; Christian von Mering Journal: Nucleic Acids Res Date: 2019-01-08 Impact factor: 16.971
Authors: Ruth Pidsley; Chloe C Y Wong; Manuela Volta; Katie Lunnon; Jonathan Mill; Leonard C Schalkwyk Journal: BMC Genomics Date: 2013-05-01 Impact factor: 3.969
Authors: Pedro S T Russo; Gustavo R Ferreira; Lucas E Cardozo; Matheus C Bürger; Raul Arias-Carrasco; Sandra R Maruyama; Thiago D C Hirata; Diógenes S Lima; Fernando M Passos; Kiyoshi F Fukutani; Melissa Lever; João S Silva; Vinicius Maracaja-Coutinho; Helder I Nakaya Journal: BMC Bioinformatics Date: 2018-02-20 Impact factor: 3.169
Authors: Selene Pérez-García; Valentina Calamia; Tamara Hermida-Gómez; Irene Gutiérrez-Cañas; Mar Carrión; Raúl Villanueva-Romero; David Castro; Carmen Martínez; Yasmina Juarranz; Francisco J Blanco; Rosa P Gomariz Journal: Int J Mol Sci Date: 2021-06-16 Impact factor: 6.208