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| Screening and bioinformatics analysis of meniscus differential genes in osteoarthritis based on GEO database |
| XU Wenfei1 MING Chunyu2 MEI Qijie1 YUE Xing1 YUAN Changshen1 GUO Jinrong1 DUAN Kan1 LIANG Xiaohui3▲ |
1.Department of Orthopaedics of Extremities, the First Affiliated Hospital of Guangxi University of Chinese Medicine, Guangxi Zhuang Autonomous Region, Nanning 530023, China;
2.Department of Geriatrics, Ruikang Affiliated Hospital of Guangxi Medical University, Guangxi Zhuang Autonomous Region, Nanning 530011, China;
3.Department of Traumatic Orthopedics, the Second Affiliated Hospital of Hunan University of Chinese Medicine, Hunan Province, Changsha 410005, China |
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Abstract Objective To screen osteoarthritis meniscus-related differentially expressed genes (DEGs) based on the GEO database and bioinformatics, and to analyse their biological functions, with a view to providing new ideas and methods for the treatment of osteoarthritis. Methods The dataset (GSE98918) was downloaded from GEO database and divided into 12 cases in normal group and 12 cases in osteoarthritis group. The difference analysis of the data was conducted using R language to obtain DEGs. The GO and KEGG signaling pathways of DEGs were analyzed using DAVID 6.8 database. After that, STRING database and Cytoscape software were used for proteinprotein interaction (PPI) and key target genes were obtained. The expression of joint target genes was further verified by fluorescence quantitative PCR. Results The final score was 220 DEGs, including 114 up-regulated and 106 down-regulated DEGs. GO mainly involves extracellular region, extracellular space, collagen trimer, collagen fibril tissue, protein extracellular matrix, and other processes. KEGG was mainly related to signaling pathways such as complement and coagulation cascade, staphylococcus aureus infection, extracellular matrix receptor interaction, protein digestion and absorption, prion disease, etc. Five key target genes were identified by PPI analysis, including VEGFA, MMP9, COL1A1, SPI1, and ITGB1. The top five predicted miRNAs were hsa-miR-4728-5p, hsa-miR-6750-3p, hsa-miR-6727-3p, hsa-miR-6734-3p, and hsa-miR-6780b-3p. Conclusion Bioinformatics analysis is used to elucidate the potential characteristics of the differential genes between osteoarthritis and healthy joints, effectively analyze the pathogenesis, and provide a new direction for clinical treatment of osteoarthritis.
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