Cross-Tissue Analysis Using Machine Learning to Identify Novel Biomarkers for Knee Osteoarthritis.

Background: Knee osteoarthritis (KOA) is a common degenerative joint disease. In this study, we aimed to identify new biomarkers of KOA to improve the accuracy of diagnosis and treatment. Methods: GSE98918 and GSE51588 were downloaded from the Gene Expression Omnibus database as training sets, with a total of 74 samples. Gene differences were analyzed by Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway, and Disease Ontology enrichment analyses for the differentially expressed genes (DEGs), and GSEA enrichment analysis was carried out for the training gene set. Through least absolute shrinkage and selection operator regression analysis, the support vector machine recursive feature elimination algorithm, and gene expression screening, the range of DEGs was further reduced. Immune infiltration analysis was carried out, and the prediction results of the combined biomarker logistic regression model were verified with GSE55457. Results: In total, 84 DEGs were identified through differential gene expression analysis. The five biomarkers that were screened further showed significant differences in cartilage, subchondral bone, and synovial tissue. The diagnostic accuracy of the model synthesized using five biomarkers through logistic regression was better than that of a single biomarker and significantly better than that of a single clinical trait. Conclusions: CX3CR1, SLC7A5, ARL4C, TLR7, and MTHFD2 might be used as novel biomarkers to improve the accuracy of KOA disease diagnosis, monitor disease progression, and improve the efficacy of clinical treatment.

Chinese herbal medicine Du-Huo-Ji-Sheng-decoction for knee osteoarthritis: A protocol for systematic review and meta-analysis.

BACKGROUND: As a classic prescription for treating knee osteoarthritis, Du-Huo-Ji-Sheng-decoction has been widely recognized for its clinical efficacy. The purpose of this systematic review and meta-analysis is to evaluate the effectiveness and safety of Du-Huo-Ji-Sheng-decoction in the treatment of knee osteoarthritis. METHODS: The following databases will be searched from January 2011 to December 2020: PubMed, Embase, Web of Science, Cochrane Library, Chinese Biomedical Medical Database, China National Knowledge Infrastructure, Chinese Science and Technology Periodical Database, and Wanfang Database. Statistical analysis will be processed by RevMan V.5.3 software. RESULTS: This study will provide an assessment of the current state of DHJSD in the treatment of KOA, aiming to show the efficacy and safety of DHJSD. CONCLUSION: This study will provide evidence to judge whether DHJSD is an effective intervention for KOA.

Identification of potential diagnostic gene biomarkers in patients with osteoarthritis.

The current study was aimed to identify diagnostic gene signature for osteoarthritis (OA). The differentially expressed genes (DEGs) in synovial membrane samples and blood samples were respectively identified from the GEO dataset. The intersection DEGs between synovial membrane and blood were further screened out, followed by the functional annotation of these common DEGs. The optimal intersection gene biomarkers for OA diagnostics were determined. The GSE51588 dataset of articular cartilage was used for expression validation and further diagnostic analysis validation of identified gene biomarkers for OA diagnostics. There were 379 intersection DEGs were obtained between the synovial membrane and blood samples of OA. 22 DEGs had a diagnostic value for OA. After further screening, a total of 9 DEGs including TLR7, RTP4, CRIP1, ZNF688, TOP1, EIF1AY, RAB2A, ZNF281 and UIMC1 were identified for OA diagnostic. The identified DEGs could be considered as potential diagnostic biomarkers for OA.