Exploring the Mystery of Osteoarthritis using Bioinformatics Analysis of Cartilage Tissue.

BACKGROUND: Osteoarthritis (OA) is a kind of chronic disease relating to joints, which seriously affectsthe daily life activities of the elderly and can also lead to disability. However, the pathogenesis of OA is still unclear, which leads to limited treatment and the therapeutic effect far from people's expectations. This study aims to filter out key genes in the pathogenesis of OA and explore their potential role in the occurrence and development of OA. METHODS: The dataset of GSE117999 was obtained and analyzed in order to identify the differentially expressed genes (DEGs), hub genes and key genes. We also identified potential miRNAs which may play a major role in the pathogenesis of OA, and verified their difference in OA by real-time quantitative PCR (RT-qPCR). DGldb was found to serve as an indicator to identify drugs with potential therapeutic effects on key genes and Receiver Operating Characteristic (ROC) analysis was used for identifying underlying biomarkers of OA. RESULTS: We identified ten key genes, including MDM2, RB1, EGFR, ESR1, UBE2E3, WWP1, BCL2, OAS2, TYMS and MSH2. Then, we identified hsa-mir-3613-3p, hsa-mir-548e-5p and hsamir- 5692a to be potentially related to key genes. In addition, RT-qPCR confirmed the differential expression of identified genes in mouse cartilage with or without OA. We then identified Etoposide and Everolimus, which were potentially specific to the most key genes. Finally, we speculated that ESR1 might be a potential biomarker of OA. CONCLUSION: In this study, potential key genes related to OA and their biological functions were identified, and their potential application value in the diagnosis and treatment of OA has been demonstrated, which will help us to improve the therapeutic effect of OA.

Diagnostic and therapeutic values of PMEPA1 and its correlation with tumor immunity in pan-cancer.

AIMS: The prostate transmembrane protein, androgen induced 1 (PMEPA1) is differentially expressed in pan-cancer. However, PMEPA1 specific role in cancers has not been fully clarified. This study aims to explore the potential role of Pmepa1 in pan-cancer and specific cancer, with a view to deepening the research on the pathological mechanism of cancer. MAIN METHODS: The Perl language and R language were used to identify the correlation between PMEPA1 expression level and clinical indicators, prognosis values, tumor microenvironment, immune cells' infiltration, immune checkpoint genes, TMB and MSI. The Therapeutic Target Database was used for identifying potential therapeutic drugs that target the pathways that are significantly affected by PMEPA1 expression. KEY FINDINGS: PMEPA1 differential expression significantly correlated with patients' age, race, tumors' stage and status. PMEPA1 high expression was closely correlated with poor prognosis in many cancer types, excluding prostate adenocarcinoma. PMEPA1 expression was closely related to tumor cells and the immune microenvironment in stromal and immune cells' level, immune cells' infiltration, immune checkpoint genes, tumor mutational burden and microsatellite instability. We also found that the activity of the olfactory transduction pathway was closely related to PMEPA1 expression. In pan-cancer, Trifluoperazine and Halofantrine have the potential to reduce PMEPA1 expression. SIGNIFICANCE: This study integrated existing data to explore PMEPA1 potential function in cancers, provided insights for the future cancer-related studies.

Gene Expression Profiling Analysis to Identify Key Genes and Underlying Mechanisms in Meniscus of Osteoarthritis Patients.

BACKGROUND: Osteoarthritis (OA) is a degenerative joint disease that seriously affects the quality of life of elderly. Regrettably, the pathological mechanism for OA has not yet been fully elucidated. METHODS: This study is committed to distinguishing key genes and the underlying mechanisms for OA. Raw data was acquired from the Gene Expression Omnibus (GEO) database. We identified differentially expressed genes (DEGs), hub genes, and key genes through bioinformatics analysis. Subsequently, we predicted the microRNAs (miRNAs) and circular RNAs (circRNAs) associated with these key genes that may play key roles in OA using web tools. We also constructed a protein- drug network and found potentially effective drugs by analyzing the relationships between the drugs and the key genes. RESULTS: The analysis revealed 360 DEGs, 24 hub genes, and 15 key genes enriched in many categories potentially related to the pathological mechanism of OA. hsa-miR-29a-3p, hsa-miR-29b-3p, and hsa-miR-29c-3p were predicted to be important miRNAs for OA, while hsa_circ_0025119, hsa_circ_0025113, hsa_circ_0009897, and hsa_circ_0002447 were predicted to be the most important circRNAs. Further studies indicated that Ocriplasmin and Collagenase clostridium histolyticum may be effective drugs for the treatment of OA. Finally, CD34 and VWF were inferred to be the most meaningful biomarkers for OA. CONCLUSION: In conclusion, we determined the underlying key genes, miRNAs, and circRNAs for OA, predicted potentially effective drugs, and identified the most meaningful biomarkers for the disease. Our findings may provide insight into the pathological mechanism of OA and guide future research.

Incidence and risk factors of joint stiffness after Anterior Cruciate Ligament reconstruction.

BACKGROUND: Joint stiffness is a common complication after anterior cruciate ligament (ACL) reconstruction, which seriously affects the efficacy of the operation and patient satisfaction. After ACL reconstruction, the identification of joint stiffness' risk factors can help its prevention. This meta-analysis was conducted to evaluate joint stiffness' risk factors and incidence after ACL reconstruction and provide guidance on its prevention. METHODS: PubMed, Embase, and Cochrane Library were searched to obtain relevant studies. The odds ratios (ORs) with 95% confidence intervals (CIs) for all potential risk factors were analyzed using fixed or random-effects meta-analysis in RevMan 5.2. RESULTS: In total, there were 37 studies and 113,740 patients that were included in this study. After ACL reconstruction, joint stiffness' incidence negatively correlated with the studies publication time (R = -0.62, P = 0.0094). After ACL reconstruction, the joint stiffness overall pooled incidence was 3% (95% CI, 3-4%). Gender (OR, 0.51; 95% CI, 0.38-0.68; P < 0.00001) was identified as a risk factor. Potential risk factors, such as trauma to surgery time interval, graft type, and concomitant surgery with meniscus injury, have no significant correlation with joint stiffness after ACL reconstruction. CONCLUSION: This study indicated that joint stiffness' incidence after ACL reconstruction is 3% and that gender is a risk factor for joint stiffness after ACL reconstruction.