Identification of potential key genes in anaplastic thyroid cancer using bioinformatics analysis

Anaplastic thyroid cancer (ATC) has a high degree of malignancy and poor prognosis. The purpose of this study was to determine differentially expressed genes (DEGs) in ATC through biometric analysis technology, clarify potential interactions between them, and screen genes related to the prognosis of ATC. Using obtained DEGs, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Protein-protein interaction (PPI), and survival analysis were performed. After R integration analysis of the four datasets, 764 DEGs were obtained, i.e., 314 upregulated genes and 450 downregulated genes. Among the hub DEGs obtained from the PPI network, the expression levels of TYMS, FN1, CHRDL1, SDC2, ITGA2, COL1A1, COL9A3, and COL23A1 were associated with ATC prognosis. These results showed that the recurrence-free survival (RFS) of ATC was associated with TYMS, FN1, ITGA2, COL23A1, SDC2, and CHRDL1 statistically significantly in the KM plotter (P<0.05). Thus, the study suggests that TYMS, FN1, ITGA2, COL23A1, SDC2, and CHRDL1 may be used as potential biomarkers of ATC. These findings provide new insights for the detection of novel diagnostic and therapeutic biomarkers for ATC.     

Identification of gene expression profiles and key genes in subchondral bone of osteoarthritis using weighted gene coexpression network analysis

Osteoarthritis (OA) significantly influences the quality life of people around the world. It is urgent to find an effective way to understand the genetic etiology of OA. We used weighted gene coexpression network analysis (WGCNA) to explore the key genes involved in the subchondral bone pathological process of OA. Fifty gene expression profiles of GSE51588 were downloaded from the Gene Expression Omnibus database. The OA‐associated genes and gene ontologies were acquired from JuniorDoc. Weighted gene coexpression network analysis was used to find disease‐related networks based on 21756 gene expression correlation coefficients, hub‐genes with the highest connectivity in each module were selected, and the correlation between module eigengene and clinical traits was calculated. The genes in the traits‐related gene coexpression modules were subject to functional annotation and pathway enrichment analysis using ClusterProfiler. A total of 73 gene modules were identified, of which, 12 modules were found with high connectivity with clinical traits. Five modules were found with enriched OA‐associated genes. Moreover, 310 OA‐associated genes were found, and 34 of them were among hub‐genes in each module. Consequently, enrichment results indicated some key metabolic pathways, such as extracellular matrix (ECM)‐receptor interaction (hsa04512), focal adhesion (hsa04510), the phosphatidylinositol 3'‐kinase (PI3K)‐Akt signaling pathway (PI3K‐AKT) (hsa04151), transforming growth factor beta pathway, and Wnt pathway. We intended to identify some core genes, collagen (COL)6A3, COL6A1, ITGA11, BAMBI, and HCK, which could influence downstream signaling pathways once they were activated. In this study, we identified important genes within key coexpression modules, which associate with a pathological process of subchondral bone in OA. Functional analysis results could provide important information to understand the mechanism of OA.     

Prediction of human disease-related gene clusters by clustering analysis

Since genes associated with similar diseases/disorders show an increased tendency for their protein products to interact with each other through protein-protein interactions (PPI), clustering analysis obviously as an efficient technique can be easily used to predict human disease-related gene clusters/subnetworks. Firstly, we used clustering algorithms, Markov cluster algorithm (MCL), Molecular complex detection (MCODE) and Clique percolation method (CPM) to decompose human PPI network into dense clusters as the candidates of disease-related clusters, and then a log likelihood model that integrates multiple biological evidences was proposed to score these dense clusters. Finally, we identified disease-related clusters using these dense clusters if they had higher scores. The efficiency was evaluated by a leave-one-out cross validation procedure. Our method achieved a success rate with 98.59% and recovered the hidden disease-related clusters in 34.04% cases when removed one known disease gene and all its gene-disease associations. We found that the clusters decomposed by CPM outperformed MCL and MCODE as the candidates of disease-related clusters with well-supported biological significance in biological process, molecular function and cellular component of Gene Ontology (GO) and expression of human tissues. We also found that most of the disease-related clusters consisted of tissue-specific genes that were highly expressed only in one or several tissues, and a few of those were composed of housekeeping genes (maintenance genes) that were ubiquitously expressed in most of all the tissues.     

Identification of susceptibility modules for coronary artery disease using a genome wide integrated network analysis

Although recent genome-wide association studies (GWAS) have identified a handful of variants with best significance for coronary artery disease (CAD), it remains a challenge to summarize the underlying biological information from the abundant genotyping data. Here, we propose an integrated network analysis that effectively combines GWAS genotyping dataset, protein–protein interaction (PPI) database, literature and pathway annotation information. This three-step approach was illustrated for a comprehensive network analysis of CAD as the following. First, a network was constructed from PPI database and CAD seed genes mined from the available literatures. Then, susceptibility network modules were captured from the results of gene-based association tests. Finally, susceptibility modules were annotated with potential mechanisms for CAD via the KEGG pathway database. Our network analysis identified four susceptibility modules for CAD including a complex module that consisted of 15 functional inter-connected sub-modules, AGPAT3–AGPAT4–PPAP2B module, ITGA11–ITGB1 module and EMCN–SELL module. MAPK10 and COL4A2 among the top-scored focal adhesion pathway related module were the most significant genes (MAPK10: OR = 32.5, P = 3.5 × 10^− 11; COL4A2: OR = 2.7, P = 2.8 × 10^− 10). The significance of the two genes were further validated by other two gene-based association tests (MAPK10: P = 0.009 and 0.007; COL4A2: P = 0.001 and 0.023) and another independent GWAS dataset (MAPK10: P = 0.001; COL4A2: P = 0.0004). Furthermore, 34 out of 44 previously reported CAD susceptibility genes were captured by our CAD PPI network and 17 of them were also significant genes. The susceptibility modules identified in our study might provide novel clues for the clarification of CAD pathogenesis in the future.     

Identification of core genes and outcomes in hepatocellular carcinoma by bioinformatics analysis

Hepatocellular carcinoma (HCC) is the most common malignant liver disease in the world. However, the mechanistic relationships among various genes and signaling pathways are still largely unclear. In this study, we aimed to elucidate potential core candidate genes and pathways in HCC. The expression profiles GSE14520, GSE25097, GSE29721, and GSE62232, which cover 606 tumor and 550 nontumour samples, were downloaded from the Gene Expression Omnibus (GEO) database. Furthermore, HCC RNA-seq datasets were also downloaded from the Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) were filtered using R software, and we performed gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis using the online databases DAVID 6.8 and KOBAS 3.0. Furthermore, the protein-protein interaction (PPI) network complex of these DEGs was constructed by Cytoscape software, the molecular complex detection (MCODE) plug-in and the online database STRING. First, a total of 173 DEGs (41 upregulated and 132 downregulated) were identified that were aberrantly expressed in both the GEO and TCGA datasets. Second, GO analysis revealed that most of the DEGs were significantly enriched in extracellular exosomes, cytosol, extracellular region, and extracellular space. Signaling pathway analysis indicated that the DEGs had common pathways in metabolism-related pathways, cell cycle, and biological oxidations. Third, 146 nodes were identified from the DEG PPI network complex, and two important modules with a high degree were detected using the MCODE plug-in. In addition, 10 core genes were identified, TOP2A, NDC80, FOXM1, HMMR, KNTC1, PTTG1, FEN1, RFC4, SMC4, and PRC1. Finally, Kaplan-Meier analysis of overall survival and correlation analysis were applied to these genes. The abovementioned findings indicate that the identified core genes and pathways in this bioinformatics analysis could significantly enrich our understanding of the development and recurrence of HCC; furthermore, these candidate genes and pathways could be therapeutic targets for HCC treatment.     

Collagen receptors integrin alpha2beta1 and discoidin domain receptor 1 regulate maturation of the glomerular basement membrane and loss of integrin alpha2beta1 delays kidney fibrosis in COL4A3 knockout mice

Maturation of the glomerular basement membrane (GBM) is essential for maintaining the integrity of the renal filtration barrier. Impaired maturation causes proteinuria and renal fibrosis in the type IV collagen disease Alport syndrome. This study evaluates the role of collagen receptors in maturation of the GBM, matrix accumulation and renal fibrosis by using mice deficient for discoidin domain receptor 1 (DDR1), integrin subunit α2 (ITGA2), and type IV collagen α3 (COL4A3). Loss of both collagen receptors DDR1 and integrin α2β1 delays maturation of the GBM: due to a porous GBM filtration barrier high molecular weight proteinuria that more than doubles between day 60 and day 100. Thereafter, maturation of the GBM causes proteinuria to drop down to one tenth until day 200. Proteinuria and the porous GBM cause accumulation of glomerular and tubulointerstitial matrix, which both decrease significantly after GBM-maturation until day 250. In parallel, in a disease with impaired GBM-maturation such as Alport syndrome, loss of integrin α2β1 positively delays renal fibrosis: COL4A3^−/−/ITGA2^−/− double knockouts exhibited reduced proteinuria and urea nitrogen compared to COL4A3^−/−/ITGA2^+/− and COL4A3^−/−/ITGA2^+/+ mice. The double knockouts lived 20% longer and showed less glomerular and tubulointerstitial extracellular matrix deposition than the COL4A3^−/− Alport mice with normal integrin α2β1 expression. Electron microscopy illustrated improvements in the glomerular basement membrane structure. MMP2, MMP9, MMP12 and TIMP1 were expressed at significantly higher levels (compared to wild-type mice) in COL4A3^−/−/ITGA2^+/+ Alport mice, but not in COL4A3^+/+/ITGA2^−/− mice. In conclusion, the collagen receptors DDR1 and integrin α2β1 contribute to regulate GBM-maturation and to control matrix accumulation. As demonstrated in the type IV collagen disease Alport syndrome, glomerular cell–matrix interactions via collagen receptors play an important role in the progression of renal fibrosis.     

Priming integrin α5 promotes human dental pulp stem cells odontogenic differentiation due to extracellular matrix deposition and amplified extracellular matrix-receptor activity

Our previous study showed that knocking down integrin α5 (ITGA5) expression by using a lentiviral vector in human dental pulp stem cells (DPSCs) led to weakening proliferation and migration capacity while enhanced odontogenic differentiation. To seek for possible clinical application, we investigated the effect of the ITGA5 priming synthetic cyclic peptide (SCP; GA-CRRETAWAC-GA) on proliferation, migration, and the odontogenic differentiation of DPSCs. Remarkably, the involved mechanism was explored by isobaric tag for relative and absolute quantitation proteomic technique, and the in vivo effect of ITGA5 was investigated by nude mice subcutaneous transplantation of cell and hydroxyapatite/β-tricalcium phosphate complex. Results showed that SCP weakened the proliferation and migration capacity while enhanced odontogenic differentiation of DPSCs as lentivirus. The phosphorylation of FAK, PI3K/AKT, and MEK1/2/ERK1/2, along with IGF2/IGFBP2 and Wnt/β-catenin signaling pathway play an important role in this process. Proteomic Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis revealed the key role of extracellular matrix (ECM) and ECM-receptor activity pathway were involved. ECM constituents, secreted protein acidic and cysteine-rich (SPARC), lumican, vitronectin, prolargin, decorin, collagen type VI α1 chain (COL6A1), COL6A2, COL14A1, and COL5A1 were upregulated in the ITGA5-silenced group. Inhibited expression of ITGA5 in DPSCs increased osteoid tissue formation and stronger related genes expression in vivo. In conclusion, the ITGA5 priming peptide could promote DPSCs odontogenic differentiation as lentivirus. Proteomics and bioinformatic analysis revealed that this may be due to the deposition of ECM and amplified ECM-receptor activity, which could fuel the application process of utilizing priming ITGA5 on dental clinical practice.     

Identification of potential mechanism and hub genes for neuropathic pain by expression-based genome-wide association study

Neuropathic pain (NP) is a common pathological pain state with limited effective treatments. This study was designed to identify potential mechanisms and candidate genes using gene expression–based genome-wide association study (eGWAS). All NP-related microarray experiments were obtained from Gene Expression Omnibus and ArrayExpress. Significantly dysregulated genes were identified between experimental and untreated groups, and the number of microarray experiments in which each gene was dysregulated was calculated. Significantly dysregulated genes were ranked according to P values of the chi-square test. Using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes database, we performed functional and pathway enrichment analysis. Protein-protein interaction (PPI) network and module analysis was performed using Cytoscape software. A total of 115 candidate genes were identified from 19 independent microarray experiments by eGWAS based on the Bonferroni threshold ( P < 2.97 × 10 ⁻⁶). Immune and inflammatory responses, and complement and coagulation cascades, were respectively the most enriched biological process and pathways for candidate genes. The hub genes with highest connectivity in PPI network and two modules Ccl2 and Jun, and Ctss application of the eGWAS methodology can identify mechanisms and candidate genes associated with NP. Our results support the validity and prevalence of inflammatory and immune mechanisms across different NP models, and Ccl2, Jun, and Ctss may be the hub genes for NP.     

Identification of potentially critical genes in the development of heart failure after ST-segment elevation myocardial infarction (STEMI)

Heart failure (HF) remains a common complication after acute ST-segment elevation myocardial infarction (STEMI). Here, we aim to identify critical genes related to the developed HF in patients with STEMI using bioinformatics analysis. The microarray data of GSE59867, including peripheral blood samples from nine patients with post-infarct HF and eight patients without post-infarct HF, were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between HF and non-HF groups were screened by LIMMA package. Functional enrichment analyses of DEGs were conducted, followed by construction of a protein-protein interaction (PPI) network. The dynamic messenger RNA (mRNA) level of the hub genes during the follow-up was analyzed to further elucidate their role in HF development. A total of 58 upregulated and 75 downregulated DEGs were screen out. They were mainly enriched in biological processes about inflammatory response, extracellular matrix organization, response to cAMP, immune response, and positive regulation of cytosolic calcium ion concentration. Pathway analysis revealed that the DEGs were also involved in hematopoietic cell lineage, pathways in cancer, and extracellular matrix-receptor interaction. In the PPI network consisting of 58 nodes and 72 interactions, CXCL8 (degree = 15), THBS1 (degree = 8), FOS (degree = 7), and ITGA2B (degree = 6) were identified as the hub genes. In the comparison of patients with and without post-infarct HF, the mRNA level of these hub genes were all higher within 30 days but reached similar at 6 months after STEMI. In conclusion, CXCL8, THBS1, FOS, and ITGA2B may play important roles in the development of HF after acute STEMI.     

Restoration of miR-29b exerts anti-cancer effects on glioblastoma

Background

Methylation plays an important role in the etiology and pathogenesis of colorectal cancer (CRC). This study aimed to identify aberrantly methylated-differentially expressed genes (DEGs) and pathways in CRC by comprehensive bioinformatics analysis.

Methods

Data of gene expression microarrays (GSE68468, GSE44076) and gene methylation microarrays (GSE29490, GSE17648) were downloaded from GEO database. Aberrantly methylated-DEGs were obtained by GEO2R. Functional and enrichment analyses of selected genes were performed using DAVID database. Protein–protein interaction (PPI) network was constructed by STRING and visualized in Cytoscape. MCODE was used for module analysis of the PPI network.

Results

Totally 411 hypomethylation-high expression genes were identified, which were enriched in biological processes of response to wounding or inflammation, cell proliferation and adhesion. Pathway enrichment showed cytokine–cytokine receptor interaction, p53 signaling and cell cycle. The top 5 hub genes of PPI network were CAD, CCND1, ATM, RB1 and MET. Additionally, 239 hypermethylation-low expression genes were identified, which demonstrated enrichment in biological processes including cell–cell signaling, nerve impulse transmission, etc. Pathway analysis indicated enrichment in calcium signaling, maturity onset diabetes of the young, cell adhesion molecules, etc. The top 5 hub genes of PPI network were EGFR, ACTA1, SST, ESR1 and DNM2. After validation in TCGA database, most hub genes still remained significant.

Conclusion

In summary, our study indicated possible aberrantly methylated-differentially expressed genes and pathways in CRC by bioinformatics analysis, which may provide novel insights for unraveling pathogenesis of CRC. Hub genes including CAD, CCND1, ATM, RB1, MET, EGFR, ACTA1, SST, ESR1 and DNM2 might serve as aberrantly methylation-based biomarkers for precise diagnosis and treatment of CRC in the future.

     

Effects of deer bone extract on the expression of pro-inflammatory cytokine and cartilage-related genes in monosodium iodoacetate-induced osteoarthritic rats

Deer bone extract has the potential to relieve the discomfort or the articular cartilaginous damage associated with osteoarthritic (OA) and may be useful as a natural supplement for OA treatment without serious side effects. We analyzed the expression of pro-inflammatory cytokine and cartilage-related genes in monosodium iodoacetate-induced OA rats. Increases in the levels of serum pro-inflammatory cytokines, such as interleukin-1β, interleukin-6, and tumor necrosis factor-α were significantly inhibited by the administration of deer bone extract (p?<?0.05). Decreases in the expression of collagen type II (COL2) and tissue inhibitors of metalloproteinases (TIMPs) mRNAs in the cartilage were significantly inhibited by deer bone extract treatment (p?<?0.05). The deer bone extract significantly suppressed the expression of matrix metalloproteinases (MMPs) mRNAs in the cartilage. The deer bone extract induced the up-regulation of COL2 and TIMP mRNAs and the down-regulation of MMP mRNAs by suppressing the expression of pro-inflammatory cytokine mRNAs.     

Gene coexpression analysis offers important modules and pathway of human lung adenocarcinomas

Lung adenocarcinomas injured greatly on the people worldwide. Although clinic experiments and gene profiling analyses had been well performed, to our knowledge, systemic coexpression analysis of human genes for this cancer is still limited to date. Here, using the published data GSE75037, we built the coexpression modules of genes by Weighted Gene Co-Expression Network Analysis (WGCNA), and investigated function and protein–protein interaction network of coexpression genes by Database for Annotation, visualization, and Integrated Discovery (DAVID) and String database, respectively. First, 11 coexpression modules were conducted for 5,000 genes in the 83 samples recently. Number of genes for each module ranged from 90 to 1,260, with the mean of 454. Second, interaction relationships of hub-genes between pairwise modules showed great differences, suggesting relatively high scale independence of the modules. Third, functional enrichment of the coexpression modules showed great differences. We found that genes in modules 8 significantly enriched in the biological process and/or pathways of cell adhesion, extracellular matrix (ECM)–receptor interaction, focal adhesion, and PI3K-Akt signaling pathway, and so forth. It was inferred as the key module underlying lung adenocarcinomas. Furthermore, PPI analysis revealed that the genes COL1A1, COL1A2, COL3A1, CTGF, and BGN owned the largest number of adjacency genes, unveiling that they may functioned importantly during the occurrence of lung adenocarcinomas. To summary, genes involved in cell adhesion, ECM–receptor interaction, focal adhesion, and PI3K-Akt signaling pathway play crucial roles in human lung adenocarcinomas.     

Extracellular matrix of adipogenically differentiated mesenchymal stem cells reveals a network of collagen filaments,mostly interwoven by hexagonal structural units

Extracellular matrix (ECM) is the non-cellular component of tissues, which not only provides biological shelter but also takes part in the cellular decisions for diverse functions. Every tissue has an ECM with unique composition and topology that governs the process of determination, differentiation, proliferation, migration and regeneration of cells. Little is known about the structural organization of matrix especially of MSC-derived adipogenic ECM. Here, we particularly focus on the composition and architecture of the fat ECM to understand the cellular behavior on functional bases. Thus, mesenchymal stem cells (MSC) were adipogenically differentiated, then, were transferred to adipogenic propagation medium, whereas they started the release of lipid droplets leaving bare network of ECM. Microarray analysis was performed, to indentify the molecular machinery of matrix. Adipogenesis was verified by Oil Red O staining of lipid droplets and by qPCR of adipogenic marker genes PPARG and FABP4. Antibody staining demonstrated the presence of collagen type I, II and IV filaments, while alkaline phosphatase activity verified the ossified nature of these filaments. In the adipogenic matrix, the hexagonal structures were abundant followed by octagonal structures, whereas they interwoven in a crisscross manner. Regarding molecular machinery of adipogenic ECM, the bioinformatics analysis revealed the upregulated expression of COL4A1, ITGA7, ITGA7, SDC2, ICAM3, ADAMTS9, TIMP4, GPC1, GPC4 and downregulated expression of COL14A1, ADAMTS5, TIMP2, TIMP3, BGN, LAMA3, ITGA2, ITGA4, ITGB1, ITGB8, CLDN11. Moreover, genes associated with integrins, glycoproteins, laminins, fibronectins, cadherins, selectins and linked signaling pathways were found. Knowledge of the interactive-language between cells and matrix could be beneficial for the artificial designing of biomaterials and bioscaffolds.     

Identification of MYH6 as the potential gene for human ischaemic cardiomyopathy

The present study aimed to explore the potential hub genes and pathways of ischaemic cardiomyopathy (ICM) and to investigate the possible associated mechanisms. Two microarray data sets ( GSE5406 and GSE57338 ) were downloaded from the Gene Expression Omnibus (GEO) database. The limma package was used to analyse the differentially expressed genes (DEGs). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, Disease Ontology (DO) and Gene Ontology (GO) annotation analyses were performed. A protein-protein interaction (PPI) network was set up using Cytoscape software. Significant modules and hub genes were identified by the Molecular Complex Detection (MCODE) app. Then, further functional validation of hub genes in other microarrays and survival analysis were performed to judge the prognosis. A total of 1065 genes were matched, with an adjusted p < 0.05, and 17 were upregulated and 25 were downregulated with|log₂ (fold change)|≥1.2. After removing the lengthy entries, GO identified 12 items, and 8 pathways were enriched at adjusted p < 0.05 (false discovery rate, FDR set at <0.05). Three modules with a score >8 after MCODE analysis and MYH6 were ultimately identified. When validated in GSE23561 , MYH6 expression was lower in patients with CAD than in healthy controls (p < 0.05). GSE60993 data suggested that MYH6 expression was also lower in AMI patients (p < 0.05). In the GSE59867 data set, MYH6 expression was lower in CAD patients than in AMI patients and lower in heart failure (HF) patients than in non-HF patients. However, there was no difference at different periods within half a year, and HF was increased when MYH6 expression was low (p < 0.05–0.01). We performed an integrated analysis and validation and found that MYH6 expression was closely related to ICM and HF. However, whether this marker can be used as a predictor in blood samples needs further experimental verification.     

Microarray gene expression profiling of osteoarthritic bone suggests altered bone remodelling, WNT and transforming growth factor-β/bone morphogenic protein signalling

Osteoarthritis (OA) is characterized by alterations to subchondral bone as well as articular cartilage. Changes to bone in OA have also been identified at sites distal to the affected joint, which include increased bone volume fraction and reduced bone mineralization. Altered bone remodelling has been proposed to underlie these bone changes in OA. To investigate the molecular basis for these changes, we performed microarray gene expression profiling of bone obtained at autopsy from individuals with no evidence of joint disease (control) and from individuals undergoing joint replacement surgery for either degenerative hip OA, or fractured neck of femur (osteoporosis [OP]). The OP sample set was included because an inverse association, with respect to bone density, has been observed between OA and the low bone density disease OP. Compugen human 19K-oligo microarray slides were used to compare the gene expression profiles of OA, control and OP bone samples. Four sets of samples were analyzed, comprising 10 OA-control female, 10 OA-control male, 10 OA-OP female and 9 OP-control female sample pairs. Print tip Lowess normalization and Bayesian statistical analyses were carried out using linear models for microarray analysis, which identified 150 differentially expressed genes in OA bone with t scores above 4. Twenty-five of these genes were then confirmed to be differentially expressed (P < 0.01) by real-time PCR analysis. A substantial number of the top-ranking differentially expressed genes identified in OA bone are known to play roles in osteoblasts, osteocytes and osteoclasts. Many of these genes are targets of either the WNT (wingless MMTV integration) signalling pathway (TWIST1, IBSP, S100A4, MMP25, RUNX2 and CD14) or the transforming growth factor (TGF)-β/bone morphogenic protein (BMP) signalling pathway (ADAMTS4, ADM, MEPE, GADD45B, COL4A1 and FST). Other differentially expressed genes included WNT (WNT5B, NHERF1, CTNNB1 and PTEN) and TGF-β/BMP (TGFB1, SMAD3, BMP5 and INHBA) signalling pathway component or modulating genes. In addition a subset of genes involved in osteoclast function (GSN, PTK9, VCAM1, ITGB2, ANXA2, GRN, PDE4A and FOXP1) was identified as being differentially expressed in OA bone between females and males. Altered expression of these sets of genes suggests altered bone remodelling and may in part explain the sex disparity observed in OA.