Elsevier

Acta Histochemica

Volume 117, Issue 1, January 2015, Pages 40-46
Acta Histochemica

Bioinformatics analysis of gene expression profiles of osteoarthritis

https://doi.org/10.1016/j.acthis.2014.10.010Get rights and content

Abstract

This study aimed to explore the underlying molecular mechanisms of osteoarthritis (OA) by bioinformatics analysis. Synovial tissue samples from five OA and five normal donors (ND) were used to identify the differentially expressed genes (DEGs) by paired t-test. Pathway enrichment analysis of DEGs was performed, followed by construction of a protein–protein interaction (PPI) network. A functional enrichment analysis of the modules identified from the PPI network was performed, and the module with the highest enrichment scores was selected for pathway enrichment analysis. A total of 184 DEGs, including 95 up-regulated and 89 down-regulated DEGs, were identified. Up-regulated DEGs were enriched in 6 pathways, such as MAPK signaling and Wnt signaling pathway, while down-regulated DEGs were mainly enriched in glycolysis/gluconeogenesis. In the PPI network, PTTG1 with the highest connectivity degree of 18 was significantly related to nuclear division, mitosis and the cell cycle. Genes in Module A with the highest functional enrichment scores of 9.27 were mainly enriched in the pathways of oocyte meiosis, cell cycle, ubiquitin mediated proteolysis and progesterone-mediated oocyte maturation. The MAPK signaling and Wnt signaling pathways were closely associated with OA. The DEGs, such as PTTG1, MAP2K6, PPP3CC and CSNK1E, may be the potential targets for OA diagnosis and treatment.

Introduction

Osteoarthritis (OA), as a degenerative disease of articular cartilage, is characterized by an increased tendency for the formation of novel vascular channels (Binks et al., 2013). Characteristic pathological changes of OA include articular cartilage degeneration, angiogenesis, synovial inflammation and osteophyte formation, all of which are related with decreased muscle strength and capsule laxitude (Bonnet and Walsh, 2005, Brandt et al., 2006). Older adults with symptomatic OA undergo significant effects on multiple aspects of health-related quality of life (Salaffi et al., 2005) and OA is an important cause of disability (Peat et al., 2001).

The development and progression of OA have been considered to involve the development of inflammation throughout the disease (Felson, 2006). Magnetic resonance imaging and arthroscopy have demonstrated that progressive OA involves almost all of the articular tissue including proliferation of synovial membranes (Abramson and Attur, 2009, Conaghan et al., 2006). Immunohistochemical studies on patients with early OA have revealed that synovial tissue is characterized by production of pro-inflammatory cytokines, mononuclear cell infiltration and mediators of articular damage (Benito et al., 2005, Smith et al., 1997). The hypothesis that synovial tissue inflammation may be an essential etiological factor for OA is supported by increasing the levels of serum C reactive protein, which has a close association with the progression of OA (Sharif et al., 1997, Sowers et al., 2002). Genetic factors are also critical in the progression of OA. Remst et al. (2013) demonstrated that elevation of transforming growth factor β (TGFβ) in OA could mediate the onset and persistence of synovial fibrosis. In addition, Ghosh et al. (2002) showed that calcium pentosan polysulfate (CaPPS) could inhibit the activity of enzymes, which are responsible for degradation of proteoglycans and collagen, and increase the translation of tissue inhibitor of metalloproteinase-3 (TIMP-3) by synovial fibrosis in OA. However, the specific underlying molecular mechanisms during osteoarthritis associated with synovial tissue development and progression are still poorly understood.

In this study, we downloaded GSE1919 and identified the differentially expressed genes (DEGs) between the OA and normal donors (ND) synovial tissue samples to understand the molecular mechanisms of OA. We also constructed pathway enrichment analysis, protein–protein interaction (PPI) networks and module analysis to study and identify the target genes for diagnosis and treatment of OA. Findings of this study might contribute to a better understanding and lead to an improved diagnosis of OA.

Section snippets

Affymetrix microarray data

The gene expression profile dataset GSE1919 (Ungethuem et al., 2010) was downloaded from the Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) database. The dataset contains 5 OA, 5 rheumatoid arthritis (RA) and 5 normal donors (ND) synovial tissue samples derived from a human study using the Affymetrix Human Genome U95 Array platform. In this paper, the OA and ND samples were analyzed by bioinformatics.

Data preprocessing and analysis of DEGs

The original probe-level data in CEL files were converted into expression

Identification of DEGs between OA and ND samples

A total of 184 DEGs with the cut-off criteria of the adjusted p-value < 0.05 and |log FC| > 2 or |log FC| < 0.5 were selected, including 95 (52%) up-regulated and 89 (48%) down-regulated DEGs.

KEGG pathway enrichment analysis

The significantly enriched pathways for the up-regulated and down-regulated DEGs are shown in Fig. 1. The up-regulated DEGs were enriched in 6 pathways such as MAPK (JUND, PPP3CC, GADD45A and MAP2K6) and Wnt signaling pathway (PPP3CC and CSNK1E). The down-regulated DEGs were enriched in 5 pathways such as

Discussion

The analysis of gene expression profiling revealed the abnormally expressed genes associated with OA and enabled the identification of targets for therapeutic strategy. In this study, a total of 184 DEGs were identified between OA and ND samples through gene expression profile of GSE1919. The up-regulated DEGs were enriched in 6 pathways, such as MAPK signaling and Wnt signaling pathway, while the down-regulated DEGs were enriched in 5 pathways, including glycolysis/gluconeogenesis pathway and

Acknowledgement

This study was Funded by Jiangsu Provincial Special Program of Medical Science (BL 2012004).

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    1

    Qiang Wang and Yufei Li contributed equally to this work.

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