Zhitao Feng

Objective: To evaluate the methodological, reporting and evidence quality of systematic reviews or meta-analyses of Janus kinases (JAK) inhibitors for the treatment of rheumatoid arthritis (RA).Methods: Our study systematically retrieved reviews from various databases, spanning from inception to June 2024. Two evaluators independently assessed the methodological, reporting, and evidence quality of each review using the AMSTAR-2 and PRIAMA2020 tools. The evidence quality was evaluated according to GRADE criteria. Six aspects were evaluated: publication year, study type, homogeneity, risk of publication bias, AMSTAR-2 methodology, and PRIAMA2020 reporting quality. Excel 2016 facilitated conversion of scores into radar plots.Results: Following stringent selection criteria, a total of 18 relevant studies were identified. The AMSTAR-2 scores ranged from 4 to 13 points, with five studies rated as low quality and the remaining 13 as critically low quality. All studies encompassed populations, interventions, controls, and outcome measures, demonstrating commendable integrity. However, there is room for improvement in study protocol development and registration, comprehensive search strategies, inclusion and exclusion criteria, conflict of interest disclosure, and discussion of heterogeneity. PRIAMA2020 assessments ranged from 14.5 to 21 points, with two studies scoring below 15 points due to increased bias risk from data transformation and sensitivity analysis. Notably, all reviews (100%) adhered to PRIAMA2020 guidelines for certain items but none met all criteria. GRADE evaluation included 446 outcome measures, with 158 of moderate, 156 of low, and 132 of very low quality, indicating JAK inhibitors is effective in improving RA. According to radar chart, the average rank score was 13.13. One study achieved a balanced score across all dimensions, while 11 exceeded the average, five showed significant differences in PRIAMA2020 scores, and four in AMSTAR two scores.Conclusion: Despite summarizing the efficacy and safety of JAK inhibitors in treating RA, the included studies exhibited poor methodological and reporting quality, along with low-quality evidence overall. Therefore, caution is warranted among decision-makers regarding the use of JAK inhibitors in RA treatment. Urgent requirements include high-quality, multicenter studies investigating JAK inhibitors for RA.Systematic Review registration: https://www.crd.york.ac.uk/PROSPERO, identifier 413415.

Background Sinomenine (SIN) were extracted from Caulis Sinomenii and achieved an remarkable therapeutic effect for Rheumatoid Arthritis (RA). However, the mechanism of SIN acting on RA is not clear yet. Objectives To excavate potential targets and mechanisms of SIN for RA through bioinformatics. Methods The microarray data were downloaded from the Gene Expression Omnibus (GEO) database. GEO2R was used to identify differentially expressed genes (DEGs) and the unique value was retained. The potential targets of active compounds from various databases were screened. Based on the overlapping genes, Cytoscape 3.7.2 software was used to construct a protein-protein interactions (PPI) network and to visualize the mechanisms of the treatment by Gene Ontology (GO) enrichment analysis Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis by DAVID database. Finally, we used AutoDockTools 1.5.6 for molecular docking. Results A total of 5053 DEGs and 1070 intersections were obtained, including 486 up-regulated and 584 down-regulated targets. 298 SIN targets were collected from various databases, 84 potential targets were obtained by intersecting with DEGs. There are 80 nodes and 305 edges were obtained in PPI network. Based on the degree, the top 10 target genes were AKT1, RGFR, MTOR, JAK2, NOS3, IL2, IL6, MMP9, MAPK8, HSP9OAA1. The core targets was most relevant to protein phosphorylation, signal transduction though GO analysis. The results of the KEGG enrichment analysis included PI3K-Akt signaling pathway and Neuroactive ligand-receptor interaction. Following analysis found that AKT1, EGFR, MTOR and JAK2 existed in the PI3K-Akt signaling pathway. Molecular docking was used to confirm that the binding energy of AKT1 was -7.68 kJ mol ^-1, EGFR was -5.33kJ mol^-1, and MTOR was -4.77 kJ mol^-1,JAK2 was -3.25 KJ mol^-1. AKT1 and EGFR was further identified as the core targets. Conclusion Present study show that AKT1 and EGFR may be the key targets of SIN acting on the PI3K-Akt signaling pathway, thereby inhibiting the progression of disease and improving RA. Keywords: Sinomenine; Rheumatoid Arthritis; bioinformatics; Disclosure of Interests None declared Funding This project was supported by grants from National Natural Science Foundation of China (No. 81703783 and 81503415); Identification of new biomarkers for Sinomenine treatment in Rheumatoid Arthritis based on bioinformatics analysis XL Shen¹, ^2L,YQ L¹, ^2Y,X Guo¹, ²,LF Wang¹, ²,JK Zhang¹, ²,ZT Feng¹, ^2* 1. Third-Grade Pharmacological Laboratory on Chinese Medicine Approved by State Administration of Traditional Chinese Medicine, Medical College of China Three Gorges University, Yichang, Hubei 443002, China 2. Medical College of China Three Gorges University, Yichang, Hubei 443002, China †These authors contributed equally to this work. *Authors to whom correspondence should be addressed: Correspondence: Zhitao Feng E-mail: zhitao.feng@ctgu.edu.cn (Zhitao Feng) Disclosure of Interests None declared

Objective: To investigate the anti-angiogenesis mechanisms and key targets of total saponins of Panax japonicus (TSPJ) in the treatment of rheumatoid arthritis (RA).Methods: RStudio3.6.1 software was used to obtain differentially expressed genes (DEGs) by analyzing the differences in gene expression in the synovial tissue of RA and to predict the potential targets of active compounds from TSPJ by the PharmMapper and SwissTargetPrediction databases. We evaluated the overlapping genes by intersectional analysis of DEGs and drug targets. Based on the overlapping genes, we used Cytoscape 3.7.2 software to construct a protein–protein interactions (PPI) network and applied Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis to determine the mechanisms of the treatment. Finally, the correlations with angiogenesis-related genes were explored. Collagen-induced arthritis (CIA) model was established and treated with different doses of TSPJ. The manifestations of CIA were determined by evaluation of arthritis index and histology score. Serum levels of vascular endothelial growth factor (VEGF) and the hypoxia-inducible factor 1 (HIF-1) were tested by ELISA. The mRNA levels of IL-1β and IL-17A were detected by real time-quantitative PCR.Results: Altogether, 2670 DEGs were obtained by differential analysis, and 371 drug targets were predicted for four active components (Araloside A, Chikusetsusaponin IVa, Ginsenoside Rg2, and Ginsenoside Ro). A total of 52 overlapping genes were included in the PPI network and the KEGG analysis. However, only 41 genes in the PPI network had protein interactions. The results of the KEGG enrichment analysis were all related to angiogenesis, including VEGF and HIF-1 signaling pathways. Seven genes with negative correlations and 16 genes with positive correlations were obtained by correlational analysis of DEGs in the VEGF and HIF-1 signaling pathways. SRC proto-oncogene, nonreceptor tyrosine kinase (SRC), and the signal transducer and activator of transcription 3 (STAT 3) had a higher value of degree and showed a significant correlation in the pathways; they were regarded as key targets. Compared with the model group, TSPJ significantly relieved the symptoms and decreased the expression of VEGFA, HIF-1α, IL-1β, and IL-17A in serum or spleens of CIA mice.Conclusion: In the current study, we found that antiangiogenesis is one of the effective strategies of TSPJ against RA; SRC and STAT 3 may be the key targets of TSPJ acting on the VEGF and HIF-1 signaling pathways, which will provide new insight into the treatment of RA by inhibiting inflammation and angiogenesis.

Myeloid-derived suppressor cells (MDSCs) are a group of cells that regulate the immune response and exert immunosuppressive effects on various immune cells. Current studies indicate that MDSCs have both anti-inflammatory effects and proinflammatory effects on rheumatoid arthritis (RA) and RA animal models. MDSCs inhibit CD4⁺ T cells, which secrete proinflammatory factors such as IFN-γ, IL-2, IL-6, IL-17, and TNF-α, by inhibiting iNOS, ROS, and IFN-γ and promoting the production of the anti-inflammatory factor IL-10. MDSCs can suppress dendritic cells by reducing MHC-II and CD86 expression, expand Treg cells in vitro through the action of IL-10, inhibit B cells through NO and PGE₂, and promote Th17 cell responses by secreting IL-1β. As a type of osteoclast precursor cell, MDSCs can differentiate into osteoclasts through activation of the NF-κB pathway via IL-1α. Overall, our study reviews the research progress related to MDSCs in RA, focusing on the effects of MDSCs on various types of cells and aiming to provide ideas to help reveal the important role of MDSCs in RA.