Rheumatoid arthritis (RA) is a multi-systemic inflammatory autoimmune disease involving peripheral joints, and the pathogenesis is not clear. Studies showed that DNA methylation and expression might also be involved in the pathogenesis of RA. This study integrated three expression profile datasets (GSE55235, GSE12021, and GSE55457) and one methylation profile dataset GSE111942 to elucidate the potential essential candidate genes and pathways in RA. Differentially expressed genes (DEGs) and differentially methylation genes (DMGs) were identified by R programming software, using Limma package and ChAMP package, respectively. DAVID performed gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs. Functional annotation and construction of a protein–protein interaction (PPI) network and the Molecular Complex Detection Algorithm (MCODE) were analysed by STRING and Cystoscope, respectively. Then the connection analysis of DEGs and DMGs was carried out, and further to analyse the relationship between methylation and gene expression, aiming to screen out the potential genes. In this study, 288 DEGs and 228 DMGs were identified, and the majority of DEGs were up-regulated. Enrichment analysis represented that DEGs mainly involved immune response and participated in the Cytokine–cytokine receptor interaction signal pathway. 282 nodes were identified from DEGs PPI network and MCODE, filtering the most significant 2 modules, 23 core node genes were identified and most of them are involved in the T cell receptor signalling pathway and chemokine-mediated signalling pathway. Cross-analysis revealed 4 genes [KNTC1 (cg 01277763), LRRC8D (cg 07600884), DHRS9 (cg 05961700), and UCP2 (cg 05205664)] that exhibited differential expression and methylation in RA simultaneously. Therefore, the four genes could be used as the target for RA.

Pelvic organ prolapse (POP) has become one of the most common serious diseases affecting parous women. Weakening of pelvic ligaments plays an essential role in the pathophysiology of POP. Currently, synthetic materials are widely applied for pelvic reconstructive surgery. However, synthetic nondegradable meshes for POP therapy cannot meet the clinical requirements due to its poor biocompatibility. Herein, we fabricated electrospun core–shell nanofibers of poly(l-lactic acid)-hyaluronic acid (PLLA/HA). After that, we combined them with mouse bone marrow-derived mesenchymal stem cells (mBMSCs) to assess the cellular response and pelvic ligament tissue engineering in vitro. The cellular responses on the composite nanofibers showed that the core–shell structure nanofibers displayed with excellent biocompatibility and enhanced cellular activity without cytotoxicity. Moreover, compared with PLLA nanofibers seeded with mBMSCs, PLLA/HA nanofibers exhibited more cellular function, as revealed by the quantitative real-time polymerase chain reaction (RT-qPCR) for pelvic ligament-related gene markers including Col1a1, Col1a3 and Tnc. These features suggested that this novel core–shell nanofiber is promising in stem cell-based tissue engineering for pelvic reconstruction.

Objective: Osteoarthritis (OA) is a multifactorial disorder, in which genetic factors are strongly associated with its development. However, the pathogenesis of OA is still unclear, and recently it has been observed that epigenetic modifications are also involved in the pathogenesis of OA. This study aims to study the potential role of m⁶A-related genes in the occurrence and development of OA. Design: We downloaded the OA expression profile data (GSE55235) from the Gene Expression Omnibus database. First, function enrichment analysis of 17 representative m⁶A methylation regulatory factors was performed using the DAVID database and Metascape online tool. Then, we analyzed the expression of 17 m⁶A methylation regulatory factors in OA and the correlation between regulatory factors using Perl software. Finally, receiver operating characteristic (ROC) curve analysis and the area under the ROC curve were used to evaluate the diagnostic effectiveness of m⁶A-related genes for discriminating patients with OA and healthy. Results: We first identified that 12 of the 17 genes were differentially expressed in OA. ALKBH1, EIF3, IGF2BP3, WTAP, and YTHDC1 were associated with early diagnosis and prognosis of OA. Conclusions: m⁶A RNA methylation regulator factors are key players in the progression of OA and have potential role in the stratification of prognosis and the formulation of treatment strategies.

This study aims to explore the relationship between MRI structural morphotypes and intermittent and constant pain in knee osteoarthritis (KOA). This cross-sectional study utilised data from the osteoarthritis initiative 48-month visit, including 6631 knees from 3494 individuals. Pain was assessed using the ICOAP Score and categorized into four types (no pain (NP), constant pain (CP), intermittent (IP), both intermittent and constant pain (IACP)). MRI images were acquired using 3T scanner and assessed by ROAMES. MRNet CNN and deep learning methods were used to calculate the probability of bone, meniscus/cartilage, inflammatory and hypertrophic structural morphotypes. The predictive model was based on the best subset algorithm. Structural morphotypes, T2 relaxation times and cartilage thickness varied significantly among the NP, IP, CP, and IACP groups. Structural phenotypes probabilities generally increased from NP to IP to CP, being highest in IACP group. The inflammatory morphotype was associated with all pain subgroups, the bone morphotype was mainly associated with IP, the meniscus/cartilage morphotype was associated with IP and IACP, and the hypertrophic morphotype was more strongly associated with CP than IP. ROC analyses showed modest discriminatory performance, with AUCs up to 0.70 for bone and 0.73 for hypertrophic morphotype probability. Pain types in OA were associated with MRI structural morphotypes. The probability of the hypertrophic morphotype can be used to distinguish between IP and CP subgroups. A predictive model that incorporates the bone morphotype, inflammatory morphotype, and hypertrophic morphotype can effectively predict the presence or absence of pain.