CN114333979A - Osteoarthritis related gene screening and function analysis method - Google Patents
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Abstract
The invention discloses a method for screening and analyzing functions of osteoarthritis-related genes, which comprises the following steps: searching osteoarthritis related gene expression chip results from the GEO database, and obtaining gene expression results; searching the same differential gene in the two chip data sets by using a Venn diagram; and (4) performing gene enrichment function analysis by using a bioinformatics technology. The invention downloads the expression difference genes by using various online databases, finds the genes which are commonly expressed and differentiated in two chip data sets, and carries out bioinformatics analysis on the commonly expressed difference genes, thereby providing meaningful exploration and basis for osteoarthritis synovial tissue related marker screening, molecular pathogenesis and the like.
Description
Technical Field
The invention belongs to the technical field of biology, and relates to an osteoarthritis related gene screening and function analysis method.
Background
Osteoarthritis (OA) is a chronic inflammatory disease of the joints that is more common clinically and is characterized by progressive cartilage degradation, synovial inflammation and bone remodeling. There is increasing evidence that synovial inflammation and subsequent proinflammatory and destructive mediators play a central role in the pathogenesis and progression of OA. The most typical clinical symptoms of osteoarthritis patients are joint pain, swelling and limited joint mobility; OA has a higher incidence affecting the quality of life in about 28% of the elderly, and the incidence increases with age of the patient, with women having a higher incidence than men. In recent years, with the aggravation of the aging of China, the proportion of the elderly population is gradually increased, and more people are threatened by osteoarthritis; osteoarthritis is reported to be the leading cause of dyskinesia and disability in the elderly, and seriously affects the quality of life of the elderly, so that osteoarthritis research is one of the hot topics in clinical medicine.
At present, the pathogenesis of OA is not clear, and its occurrence is associated with various factors such as age, obesity, inflammation, trauma and genetics. The initial stage of OA is insidious and early diagnosis of OA is crucial; the existing methods for examining articular cartilage mainly comprise X-ray, CT, arthroscopy, MR conventional imaging sequences (SE, FSE, GRE, 3I)) and the like, and are all based on cartilage morphological changes: although the X-ray and CT can show the condition of bone destruction and erosion, the X-ray and CT cannot directly show pathological changes of OA, such as synovial membrane exudation, synovial membrane hyperplasia, pannus formation, articular cartilage destruction, ligament and tendon abnormality and the like, thereby delaying early treatment and losing the value of early diagnosis; for OA patients with mild or lack of active inflammation, MRI is not more sensitive than other imaging techniques, and there is a certain false positive rate in diagnosing OA by MRI due to the influence of factors such as surrounding structure mixing and image artifacts. Therefore, the conventional OA imaging detection method has poor sensitivity and accuracy and has hysteresis for early diagnosis of OA. Therefore, there is a strong need for a convenient, rapid, and quantitative indicator for early diagnosis of OA. Detection of biomarkers as a prospective approach may play an important role in the monitoring of OA. The biomarker can reflect chondropathy before imaging change, can be used for early diagnosis of OA, and provides a candidate drug target for clinical treatment of OA.
There is increasing evidence that synovial inflammation and subsequent proinflammatory and destructive mediators play a central role in the pathogenesis and progression of OA. Synovial tissue (synovial tissue) is tissue located in the joint capsule and functions to produce synovial fluid and maintain smooth and moving joints. During OA, the synovium initially shows signs of inflammation, such as macrophage infiltration. These macrophages subsequently produce pro-inflammatory cytokines and matrix degrading enzymes, destroy articular cartilage, release cartilage breakdown products with pro-inflammatory properties, and form a malignant positive feedback loop. Synovial inflammation is involved in the development and progression of OA. Elucidation of the basic pathological processes of OA synovium will provide new diagnostic and therapeutic opportunities.
Therefore, the search for biomarkers related to osteoarthritic synovial tissue has become a hot problem in the early diagnosis and treatment research of osteoarthritis. The early diagnosis of OA can help doctors to make a reasonable early treatment strategy and improve the treatment effect, can greatly reduce the treatment cost of patients, and has important clinical and scientific significance.
The Gene Expression database (GEO) is the largest and most comprehensive public Gene Expression data resource at present, and comprises the wide classification of high-throughput experimental data, single-channel and double-channel microarray-based mRNA abundance measurement; experimental data for genomic DNA and protein molecules. To date, the GEO database contains data that covers roughly 10000 hybridization experiments and is derived from 30 different organisms. The database is simple to operate, comprehensive in data and free to share, and a good platform is provided for later-stage data mining and information popularization. The GEO database has wide application prospect in the field of molecular biology, and provides an optimal platform for the mining and screening of osteoarthritis-related genes.
Disclosure of Invention
The invention aims to provide a method for screening and function analysis of osteoarthritis-related genes, which is characterized in that data of GSE55235 and GSE82107 chips of osteoarthritis synovial tissue and normal synovial tissue in a GEO database are used for analysis, a Venn diagram is used for making and determining a difference gene shared by the two chips, a DAVID database is used for carrying out GO function enrichment analysis and KEGG pathway enrichment analysis on the screened difference gene, an STRING database is used for carrying out protein interaction analysis, and meaningful exploration and basis are provided for osteoarthritis marker screening, molecular pathogenesis and the like.
In order to achieve the purpose, the specific technical scheme of the invention is as follows:
the invention firstly provides an osteoarthritis related gene screening and function analysis method, which comprises the following steps:
1) screening a research series meeting the conditions by utilizing a G E O database: finding the osteoarthritis related mRNA expression chip result from GEO database http:// www.ncbi.nlm.nih.gov/GEO/with the search conditions defined as: (1) osteoarthritis; (2) a normal control must be present; (3) the chip series is mRNA expression detection; (4) the specimen source is synovial tissue, and after screening, two chip series are included in the research: GSE55235 and GSE82107, wherein GSE55235 is based on GPL96 platform and GSE82107 is based on GPL570 platform, for a total of 20 osteoarthritic synovial tissue samples and 17 normal control samples;
(2) the osteoarthritis synovial tissue gene expression profile dataset was downloaded in the gene expression database GEO (https:// www.ncbi.nlm.nih.gov/GEO /): GSE55235 and GSE82107, and screening the difference gene according to the screening standard of P value <0.05 and | log2FC | > 1;
(3) venn plots were used to find the same mRNA gene expression results in two studies: selecting genes with mRNA expression up-regulated or down-regulated in the two chip series, and finding 972 up-regulated and 553 up-regulated differential genes in GSE 55235; generating a Venn diagram by using an online Venn diagram manufacturing tool, wherein 94 differential genes are up-regulated and 63 differential genes are down-regulated in the two researches;
(4) performing GO function annotation and KEGG access enrichment analysis on the differential genes by using a DAVID database;
(5) constructing a protein interaction network (PPI) of common difference genes by using a STRING database, and performing central protein network analysis by using a Cytoscape plug-in;
(6) core genes associated with osteoarthritis were retrieved by retrieving the CTD database.
In some embodiments, the GO function annotation in step (4) comprises mainly Molecular Function (MF), Biological Pathway (BP), and Cellular Component (CC);
the application of DAVID online software to carry out GO functional enrichment analysis on 157 common differential expression genes shows that the common genes mainly enrich the biological processes of transcription, cell adhesion, immune response, extracellular matrix (ECM) organization and redox process; molecular function: DNA binding, collagen binding, receptor binding, oxidoreductase activity and endopeptidase activity; cell components: exosomes, membranes and ECMs;
KEGG (Kyoto Encyclopedia of Genes and genomes) is a database of systematic gene function and genomic information that helps researchers to study gene and expression information as a whole network. Analysis of the KEGG pathway revealed that these differentially expressed genes were primarily involved in ECM receptor interactions, focal adhesion, AMPK signaling pathways and fatty acid metabolism.
In some embodiments, the core protein selected in step (5), which shows the first 20 higher scoring proteins, comprises proteins encoded by FN1, SPP1, TIMP1, MMP13, IGF1, GOLM1, BGN, TNFSF11, TNC, PRSS23, STC2, CLU, PPARGC1A, FASN, PDK4, ACADL, ACACB, SCD, COL5a1, and SDC1 genes.
In some embodiments, the 20 core genes from the screening are screened by searching literature for genes that have been reported to be associated with osteoarthritis, with the remaining core genes GOLM1, PRSS23, ACACB and ACADL as key candidates.
In some embodiments, the screening of step (6) for expression of genes associated with osteoarthritis comprises GOLM1, PRSS23, ACACB, and ACADL.
Further, the present invention provides a candidate marker for early diagnosis of osteoarthritis, which is selected from one or more combinations of the following FN1, SPP1, TIMP1, MMP13, IGF1, GOLM1, BGN, TNFSF11, TNC, PRSS23, STC2, CLU, PPARGC1A, FASN, PDK4, ACADL, ACACB, SCD, COL5a1, and SDC1 genes.
In some embodiments, the candidate marker for early diagnosis of osteoarthritis consists of GOLM1, PRSS23, ACADL, ACACB genes.
Furthermore, the invention provides the application of the candidate marker in preparing products for early diagnosis of osteoarthritis.
In some embodiments, the product comprises an agent that detects the level of expression of the candidate marker in a tissue sample.
In some embodiments, the tissue sample is synovial tissue.
The invention utilizes GSE55235 and GSE82107 chip data of osteoarthritis synovial tissue and normal synovial tissue in GEO database to analyze, excavate and screen related genes of osteoarthritis synovial tissue, and screens core Differential Expression Genes (DEGs), namely GOLM1, PRSS23, ACACACACACCB and ACADL, in synovium of OA patients through function enrichment analysis, PPI network, module and hub gene identification and gene-disease relationship evaluation. It is hoped that the biological properties of osteoarthritis and the basic molecular mechanism in the process of generating and developing osteoarthritis can be deeply understood, a detection marker and a new treatment point are provided for the diagnosis of osteoarthritis, and a reliable scientific basis is provided for the prevention and treatment of diseases.
Drawings
FIG. 1 is a graph of differentially expressed genes, Wien, in two screening datasets. Overlap of DEG illustrates OA versus normal control in a four-way venn plot. The overlapping regions represent common genes and the numbers represent the number of genes per region. Selecting the circled common DEG for further analysis;
FIG. 2 utilizes DAVID database for functional enrichment, panels A, B, C, D represent: GO-BP, GO-MF, GO-CC and KEGG-Pathway (Top5 terms);
FIG. 3 protein-protein interaction (PPI) network;
FIG. 4 assesses the gene-disease relationship of core genes to OA by comparing the toxicological genomics database (CTD). Calculating the scoring condition of four locked genes of (A) GOLM1, (B) PRSS23, (C) ACACACCB and (D) ACADL in skeletal diseases;
figure 5 demonstrates the relative mRNA expression of the core genes in GSE55235 and GSE 82107. Relative expression of GOLM1(a and B), PRSS23(C and D), ACACB (E and F) and ACADL (G and H) was verified in GSE55235 and GSE82107, respectively. Green dots indicate expression in normal tissue and red dots indicate in OA tissue. (. P <0.05,. P <0.01,. P <0.001,. P < 0.0001).
Detailed Description
The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention. Unless otherwise specified, the technical means used in the examples are conventional means well known to those skilled in the art.
The present invention is directed to the identification of core genes in synovial tissue associated with Osteoarthritis (OA) by bioinformatic analysis. A consensus Differentially Expressed Gene (DEG) in synovial tissue between OA patients and normal controls from microarray data of GSE55235 and GSE82107 was identified, including 94 up-regulated genes and 63 down-regulated genes. GO and KEGG pathway enrichment analysis of DEGs by DAVID database showed that these differential genes enriched multiple GO biological processes closely related to the organization and metabolism of ECM (e.g. ECM tissue, wound healing), cell adhesion, redox processes, cell response to hypoxia etc. Molecular functions including collagen binding, receptor binding, integrin binding and oxidoreductase activity; cellular components including membranes and ECM; and the KEGG pathway includes ECM-receptor interactions, focal adhesions and HIF-1 signaling pathways, which are described above in connection with the development of osteoarthritis. More importantly, KEGG pathway analysis also revealed that down-regulated DEG is rich in AMPK signaling pathway, insulin signaling pathway and fatty acid metabolism, which are involved in dysfunctional energy metabolism contributing to the development of OA. And protein-protein interaction (PPI) networks sharing DEG are respectively constructed by STRING and Cytoscape, and analysis shows that FN1, SPP1, TIMP1, MMP13, IGF1, BGN, TNFSF11, TNC, STC2, CLU, PPARGC1A, FASN, PDK4, SCD, COL5A1 and SDC1 are core genes. In addition, the gene-disease relationship with OA was assessed by comparing the toxicological genomics database (CTD). CTD analysis indicated that expression of GOLM1, PRSS23, ACACB and ACADL was significantly associated with OA. The identification of core genes not only helps to understand the pathogenesis and progression of OA, but also provides new prognostic and therapeutic opportunities.
Example 1
1. Microarray dataset acquisition and processing
Screening a research series meeting the conditions by utilizing a G E O database: finding the osteoarthritis related mRNA expression chip result from GEO database http:// www.ncbi.nlm.nih.gov/GEO/with the search conditions defined as: (1) osteoarthritis; (2) a human source; (3) the research type is chip expression profile detection; (4) the specimen source is synovial tissue, and after screening, two chip series are included in the research: GSE55235 and GSE 82107.
GSE55235 comprises 20 synovial tissue samples based on the GPL96[ HG-U133A ] Affymetrix human genome U133A array platform, 10 of which were from OA patients and 10 from healthy controls; GSE82107 is based on the GPL570[ HG-U133_ Plus _2] Affymetrix human genome U133 Plus 2.0 array, comprising 17 synovial tissue samples, 10 from OA patients and 7 from non-arthritic disease individuals.
2. Differential Expression Gene (DEG) analysis
The genes were grouped and analyzed using online analysis software GEO2R into an osteoarthritis group and a normal group. The P value and fold difference (FC) are set for screening of differential genes. It is believed that when P <0.01 and/log2Differences in FC/> 1.5 are statistically significant. Wenn plots were made online using venny (https:// bioinfogp. cnb. scic. es/tools/venny/index. html) and the intersection of the DEG's of the two datasets was selected for further analysis.
3. Functional enrichment analysis of consensus differential genes
DAVID database (https:// DAVID. ncifcrf. gov /) functional GO (Gene ontology) functional annotation and KEGG (Kyoto Encyclopedia of Genes and genomes) signaling pathway analysis of differential Genes, exploring the involved biological processes, molecular functions, cellular components and pathways.
4. Key target screening and protein action network construction
When the protein interaction network is disrupted, it causes a dysfunction of cellular function; studying these interactions helps to build relevant network models to explain the molecular mechanisms of cellular and even disease development.
The protein interaction network (PPI) of consensus differential genes was obtained using the STRING database (https:// STRING-db.org /). And obtaining the core gene (hub gene) in the PPI network by utilizing cytoHubba plug-in analysis in Cytoscape.
5. Gene-disease relationship assessment
By comparison of the poison genome database (CTD) (CTD)http://ctdbase.org/) The CTD database retrieves osteoarthritis-associated genes. And checking the grading condition of the locked gene in the skeletal diseases by using a CTD (computer-to-device) database, and screening the high-grade gene.
6. Results
(1) Screening of related differential genes in osteoarthritic synovial tissue
Two microarray datasets GSE55235 and GSE82107 were obtained from the GEO database, with 972 and 553 upregulated differential genes found in GSE 55235; 639 up-regulated and 2811 down-regulated differential genes were found in GSE 82107. A total of 94 up-and 63 down-regulated differential genes were determined for the two datasets for further analysis (fig. 1).
(2) Analysis of consensus Difference Gene Functions
And performing GO function annotation and KEGG pathway enrichment analysis on the common differential genes of the two data sets by using a DAVID database, and exploring possible related biological functions and key pathway information of the differential genes.
GO analysis showed that DEGs mainly enrich biological processes such as transcription, cell adhesion, immune response, extracellular matrix (ECM) organization and redox processes (fig. 2A); molecular functions such as DNA binding, collagen binding, receptor binding, oxidoreductase activity and endopeptidase activity (fig. 2B); cellular components, such as exosomes, membranes and ECM (fig. 2C). KEGG pathway analysis showed that DEGs were mainly enriched in ECM receptor interactions, focal adhesions, AMPK signaling pathway and fatty acid metabolism (fig. 2D).
(3) PPI network construction of consensus difference genes
To investigate the interaction between the common differential genes, the protein interaction network constructed by the STRING database contained 140 nodes and 203 interactions (FIG. 3A). The screening of core genes was performed by Cytoscape Cyto-Hubba plug-in, using MCC algorithm, showing the first 20 higher scoring genes, FN1, SPP1, TIMP1, MMP13, IGF1, GOLM1, BGN, TNFSF11, TNC, PRSS23, STC2, CLU, PPARGC1A, FASN, PDK4, ACADL, ACACB, SCD, COL5a1 and SDC1, respectively. Relevant documents in which PubMed (www.ncbi.nlm.nih.gov/PubMed) searched for these core genes, in "title/summary", with "gene symbol (gene name) and osteoarthritis" as query keys, a total of 16 genes were retrieved that were related to OA, including FN1, SPP1, TIMP1, MMP13, IGF1, BGN, TNFSF11, TNC, STC2, CLU, PPARGC1A, FASN, PDK4, SCD, COL5a1, and SDC 1.
For example, fibronectin 1(FN1) encodes one of the most abundant proteins in OA synovium; diglycol (BGN) is an essential component of the ECM and has been shown to have pro-inflammatory properties; recent research also finds that secretory phosphoprotein 1(SPP1) is highly expressed in OA, which is consistent with the research result of the inventor and further proves that miR-186/SPP1/PI3K-AKT signals can inhibit OA chondrocyte apoptosis. According to abundant GO biological processes-ECM tissues, the three molecules are ECM structural components pre-shaped in OA synovium (fig. 2A). In addition, molecules that regulate ECM metabolism have also been found to be upregulated, such as MMP13, TIMP1, and SDC 1. The proinflammatory cytokine TNF superfamily member 11(TNFSF11), also known as RANKL, is a key factor in osteoclast differentiation and activation and is reported to be up-regulated in OA synovium. IGF1, a key bone anabolic growth factor, has also been found to be upregulated in OA.
Finally, 4 core genes not studied to report association with osteoarthritis, including GOLM1, PRSS23, ACACB and ACADL, were screened for the following analysis.
(4) Gene-disease relationship assessment
To explore the relationship of selected core genes to OA disease, analysis by CTD assessment showed that expression of GOLM1 (fig. 4A), PRSS23 (fig. 4B), ACACB (fig. 4C) and ACADL (fig. 4D) was significantly associated with OA.
GP73(GOLM1, golgi membrane protein 1), is highly expressed in several types of cancer, particularly hepatocellular carcinoma (HCC). Jin et al demonstrated that GP73 enhances MMP13 expression through cAMP response element binding protein (CREB) -mediated transcriptional activation, and GP 73-CREB-MMP 13 signaling enhances cancer cell invasiveness. Wei et al demonstrate that GP73 is essential for the activation of Endoplasmic Reticulum (ER) stress in neighboring macrophages in liver cancer. GP73 stimulates ER stress and cytokine release from neighboring macrophages via GRP78, then releases pro-inflammatory cytokines and causes tumors to promote inflammation. In addition, viral infections such as HBV and HCV have been reported to activate GP73 expression in HCC cells.
Since HBV or HCV infection is a major risk factor for chronic inflammation, GOLM1 may be associated with inflammation. Given the shared inflammatory properties of tumors and OA, up-regulation of GP73 may play an important role in OA development.
PRSS23(serine protease 23) is a serine protease that has been reported to be involved in tumor progression in many types of cancer. PRSS23 can degrade type I collagen, one of the major proteins in synovial ECM. Upregulation of PRSS23 may lead to structural and functional alterations in synovial ECM and ultimately to inflammation and OA development.
ACACCB (acetyl-CoA carboxylase beta) and ACARDL (Acyl-CoA dehydrogenase) are involved in fatty acid oxidation. ACACB catalyzes the carboxylation of acetyl-coa to malonyl-coa, which is the rate-limiting step in fatty acid uptake and oxidation, while ACADL catalyzes the initial step in mitochondrial β -oxidation of straight chain fatty acids. Down-regulation of ACACB and ACADL may be associated with elevated levels of circulating free fatty acids, hyperglycemia and oxidative stress, and promote matrix destruction and the development of OA.
(5) Relative expression of core genes
The expression of the GOL1, PRSS23, ACACB and ACADL genes was detected in GSE55235(10 cases of OA, 10 cases of health) and GSE82107(10 cases of OA, 7 cases of non-joint disease). The results show that GOLM1 (fig. 5A and B) and PRSS23 (fig. 5C and D) were significantly up-regulated, while ACACB (fig. 5E and F) and ACADL (fig. 5G and H) were significantly down-regulated in synovial tissue of osteoarthritis patients.
Although the invention has been described in detail hereinabove with respect to a general description and specific embodiments thereof, it will be apparent to those skilled in the art that modifications or improvements may be made thereto based on the invention. Accordingly, such modifications and improvements are intended to be within the scope of the invention as claimed.
Claims (10)
1. A method for screening and analyzing functions of osteoarthritis-related genes is characterized by comprising the following steps:
1) screening a research series meeting the conditions by utilizing a G E O database: finding the osteoarthritis related mRNA expression chip result from GEO database http:// www.ncbi.nlm.nih.gov/GEO/with the search conditions defined as: (1) osteoarthritis; (2) a human source; (3) the research type is chip expression profile detection; (4) the specimen source is synovial tissue, and after screening, two chip series are included in the research: GSE55235 and GSE82107, wherein GSE55235 is based on GPL96 platform and GSE82107 is based on GPL570 platform, for a total of 20 osteoarthritic synovial tissue samples and 17 normal control samples;
(2) the osteoarthritis synovial tissue gene expression profile dataset was downloaded in the gene expression database GEO (https:// www.ncbi.nlm.nih.gov/GEO /): GSE55235 and GSE82107, and screening the difference gene according to the screening standard of P value <0.05 and | log2FC | > 1;
(3) venn plots were used to find identical mRNA gene expression results in both chip datasets: selecting genes with mRNA expression up-regulated or down-regulated in the two chip series, and finding 972 up-regulated and 553 up-regulated differential genes in GSE 55235; generating a Venn diagram by using an online Venn diagram manufacturing tool, wherein the two data sets share 94 differential genes with up-regulated expression and 63 differential genes with down-regulated expression;
(4) performing GO function annotation and KEGG passage enrichment analysis on the differential genes by using a DAVID database;
(5) constructing a protein interaction network (PPI) of common difference genes by using a STRING database, and performing core protein network analysis by using a Cytoscape plug-in;
(6) core genes associated with osteoarthritis were collected by searching CTD databases.
2. The osteoarthritis-associated gene screening and function analysis method as claimed in claim 1, wherein in step (4), the functional annotation of GO mainly comprises molecular functions, biological pathways and cellular components;
the DAVID online software is selected to carry out GO functional enrichment analysis on 157 common differential expression genes, and the analysis shows that the common differential genes mainly enrich the biological process: transcription, cell adhesion, immune responses, extracellular matrix (ECM) organization and redox processes; molecular function: DNA binding, collagen binding, receptor binding, oxidoreductase activity and endopeptidase activity; cell components: exosomes, membranes and ECMs;
KEGG pathway analysis found that these differentially expressed genes were mainly involved in ECM receptor interactions, focal adhesion, AMPK signaling pathway and fatty acid metabolism.
3. The method for screening and functional analysis of osteoarthritis-associated genes as claimed in claim 1, wherein the core protein screened in step (5) shows the first 20 higher scoring proteins, including those encoded by FN1, SPP1, TIMP1, MMP13, IGF1, GOLM1, BGN, TNFSF11, TNC, PRSS23, STC2, CLU, PPARGC1A, FASN, PDK4, ACARDL, ACACACACCB, SCD, COL5A1 and SDC1 genes.
4. The method for screening and functional analysis of osteoarthritis-associated genes as claimed in claim 3, wherein 20 core genes selected in claim 3 are screened out by searching literature, and remaining core genes of GOLM1, PRSS23, ACACB and ACADL are used as key candidates.
5. The method for screening and functional analysis of osteoarthritis-associated genes as claimed in claim 1, wherein said screening of said osteoarthritis-associated genes in step (6) comprises expression of GOLM1, PRSS23, ACACB and ACADL.
6. A candidate marker for early diagnosis of osteoarthritis, wherein the candidate marker for early diagnosis of osteoarthritis is selected from one or more of the following FN1, SPP1, TIMP1, MMP13, IGF1, GOLM1, BGN, TNFSF11, TNC, PRSS23, STC2, CLU, PPARGC1A, FASn, PDK4, ACADL, ACACB, SCD, COL5a1 and SDC1 genes.
7. Candidate marker for early diagnosis of osteoarthritis, wherein said candidate marker for early diagnosis of osteoarthritis consists of GOLM1, PRSS23, ACADL, ACACB genes.
8. Use of a candidate marker according to claim 4 or 5 for the manufacture of a product for the early diagnosis of osteoarthritis.
9. The use of claim 8, wherein the product comprises a reagent that detects the level of expression of the candidate marker of claim 4 or 5 in a tissue sample.
10. The use of claim 9, wherein the tissue sample is synovial tissue.
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