| Background: Osteoarthritis is a common joint disease,the lesions involve all joints throughout the body,age,gender,weight and lifestyle can be the cause of osteoarthritis and aggravation.Pain and the accompanying stiffness are the main symptoms of patients with osteoarthritis,which seriously affects the patient’s daily life and work.However,in the face of the onset and progression of osteoarthritis,there are certain limitations to the treatment we can currently administer.Therefore,it is imperative to find new biomarkers and to find treatment ways to prevent and improve osteoarthritis from a genetic point of view.In recent years,many researchers through the screening of a large number of data,the use of bioinformatics to find suitable non-codingRNA as a biomarker of osteoarthritis has become a hot spot,of which miRNA is widely distributed,can be observed in different tissues and cells,according to the development needs of fine adjustment of different expression amounts,miRNA in the gene sequence of high homology and gene position conservation characteristics make it play an important role in gene expression regulation.Therefore,by screening miRNA gene databases to find miRNA biomarkers and genes that regulate them in specific osteoarthritis,we can develop new ideas and further study the formation mechanism of osteoarthritis and its treatment.Objective: Bioinformatics technology is used to find key miRNAs and key mRNAs that affect the pathogenesis of osteoarthritis,as biomarkers of osteoarthritis and joints,and become potential therapeutic targets for osteoarthritis.Methods: Firstly,the suitable miRNA dataset(GSE48266)and mRNA dataset(GSE8077,GSE74898)were found in the gene expression database(GEO),and the dataset was downloaded through the GEO 2R analysis program.Then via Settings | Log FC|greater than 0.5,adj P Value<0.05 as the screening condition to obtain the differentially expressed genes of the three datasets;the differentially expressed genes of the mRNA dataset GSE8077 and GSE74898 were taken from the intersection of the up-and down-regulated genes,and the differentially expressed miRNAs of the miRNA dataset GSE48266 were used to find relevant target genes on the target gene website;the mRNA differentially expressed genes and the target genes of miRNA were further cross-matched;the crossmatched common genes were enriched and analyzed,including gene ontology(Gene).Ontology(GO)and the Kyoto Encyclopedia of Genes and Genomes(KEGG)to understand gene function and related signaling pathways;Cross-matched miRNA-mRNA pairs were visually analyzed on Cytoscape to construct miRNA-mRNA networks.Key miRNAs and key mRNAs were screened out in the network diagram;According to the innovation,gene function and related signaling pathways involved,rat PCR verification was performed on some key mRNAs and key miRNAs to verify their abnormal expression in osteoarthritis.Results: The miRNA dataset GSE48266 included 3 normal group samples and 3osteoarthritis group samples,with a total of 55 differentially expressed genes,of which 3upregulated DEMs and 52 downregulated DEMs.The mRNA dataset GSE8077 included5 normal group samples and 5 osteoarthritis group samples,with a total of 402 differentially expressed genes,of which 210 upregulated DEGs and 192 down-regulated DEGs,GSE74898 included 3 normal group samples and 3 osteoarthritis group samples,with a total of 2009 differentially expressed genes,of which 968 upregulated DEGs and1041 downregulated DEGs.After the intersection of the two mRNA datasets,there were16 genes with common differential expression,including 10 up-regulated and 6 downregulated.After cross-matching with miRNA target genes,two sets of corresponding networks were obtained: 5 upregulated miRNAs-3 down-regulated mRNAs,40 downregulated miRNAs-9 up-regulated mRNAs,GO and KEGG enrichment analysis were performed on common differential genes to obtain their functional effects and signaling pathways,and the two sets of networks were screened by Cyto Hubba to obtain 3 downregulated mRNAs-3 up-regulated miRNAs(CAMK4,ELOVL6,GMPR,rno-mi R-29a-5p,rno-mi R-223-5p,rno-mi R-223-3p)and 5 upregulated mRNAs-5 downregulated miRNAs(ATP10a,TSPAN2,CAV1,CABLES1,INHBA,rno-mi R-300-5p,rno-mi R-345-3p,rnomi R-322-3p,rno-mi R-181b-1-3p,rno-mi R-300-3p),and in rat PCR experiments,it was confirmed that ATP10 a,TSPAN2,CAMK4,rno-mi R-29a-5p,rno-mi R-300-5p,and rnomi R-322-3p were expressed differently in rat osteoarthritis,among which ATP10 a,TSPAN2,and rno-mi R-29a-5p were upregulated in osteoarthritis diseases.The expression of CAMK4 in osteoarthritis diseases was down-regulated,which was consistent with our bioinformation analysis results,but rno-mi R-300-5p and rno-mi R-322-3p were upregulated in the experiment,which was inconsistent with our bioinformation analysis results.Conclusion: In this experimental study,we used bioinformatics technology to find CAMK4,ELOVL6,GMPR,rno-mi R-300-5p,rno-mi R-345-3p,rno-mi R-322-3p,rnomi R-181b-1-3p,rno-mi R-300-3p expression down-regulated in osteoarthritis,ATP10 a,TSPAN2,CAV1,CABLES1,INHBA,rno-mi R-29a-5p,rno-mi R-223-5p,and rno-mi R-223-3p were expressed up-regulated in bone joints.In addition,the expression of ATP10 a,TSPAN2,CAMK4,rno-mi R-29a-5p,rno-mi R-300-5p,and rno-mi R-322-3p in osteoarticular was verified by animal PCR,and it was found that the expression of ATP10 a,TSPAN2 and rno-mi R-29a-5p was upregulated and the expression of CAMK4 was downregulated in osteoarthritis diseases,which was consistent with the results of our bioinformatics analysis.The upregulation of rno-mi R-300-5p and rno-mi R-322-3p in osteoarthritis disease is inconsistent with our bioinformatics analysis results,which needs further analysis and verification.ATP10 a,TSPAN2,CAMK4,rno-mi R-29a-5p may be used as novel markers for the pathogenesis of osteoarthritis and potential therapeutic targets for osteoarthritis. |
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