Objective: Systemic lupus erythematosus (SLE) is a disease characterised by immune inflammation and damage to multiple organs. Recent investigations have linked competing endogenous RNAs (ceRNAs) to lupus. However, the exact mechanism through which the ceRNAs network affects SLE is still unclear. This study aims to investigate the regulatory functions of the ceRNAs network, which are important pathways that control the pathophysiological processes of SLE.Methods: CircRNA microarray for our tested assays were derived from bone marrow samples from three healthy individuals and three SLE patients in our hospital. The other sequencing data of circRNA, miRNA and mRNA were obtained from Gene Expression Omnibus (GEO) datasets. Using the limma package of R program, the differential expression of mRNA and miRNA in the GEO database was discovered. Then predicted miRNA-mRNA and circRNA-miRNA were established using miRMap, miRanda, miRDB, TargetScan, and miTarBase. CircRNA-miRNA-mRNA ceRNA network was constructed using Cytoscape, and hub genes were screened using a protein-protein interaction network. Immune infiltration analysis of the hub gene was also performed by CIBERSORT and GSEA.Results: 230 overlapped circRNAs, 86 DEmiRNAs and 2083 DEmRNAs were identified in SLE patients as compared to healthy controls. We constructed a circRNA–miRNA–mRNA ceRNAs network contained 11 overlapped circRNAs, 9 miRNAs and 51 mRNAs. ESR1 and SIRT1 were the most frequently associated protein-protein interactions in the PPI network. KEGG analysis showed that DEGs was enriched in FoxO signaling pathway as well as lipids and atherosclerosis. We constructed a novel circRNA-miRNA-mRNA ceRNA network (HSA circ 0000345- HSA miR-22-3-P-ESR1/SIRT1) that may have a major impact on SLE.Conclusion: Through this bioinformatics and integrated analysis, we suggest a regulatory role for ceRNA network in the pathogenesis and treatment of SLE.

Objective: Early prediction of long-term outcomes in patients with systemic lupus erythematosus (SLE) remains a great challenge in clinical practice. Our study aims to develop and validate predictive models for the mortality risk.Methods: This observational study identified patients with SLE requiring hospital admission from the Medical Information Mart for Intensive Care (MIMIC-IV) database. We downloaded data from Fujian Provincial Hospital as an external validation set. Variable selection was performed using the Least Absolute Shrinkage and Selection Operator (LASSO) regression. Then, we constructed two predictive models: a traditional nomogram based on logistic regression and a machine learning model employing a stacking ensemble approach. The predictive ability of the models was evaluated by the areas under the receiver operating characteristic curve (AUC) and the calibration curve.Results: A total of 395 patients and 100 patients were enrolled respectively from MIMIC-IV database and the validation cohort. The LASSO regression identified 18 significant variables. Both models demonstrated good discrimination, with AUCs above 0.8. The machine learning model outperformed the nomogram in terms of precision and specificity, highlighting its potential superiority in risk prediction. The SHapley additive explanations analysis further elucidated the contribution of each variable to the model’s predictions, emphasising the importance of factors such as urine output, age, weight and alanine aminotransferase.Conclusions: The machine learning model provides a superior tool for predicting mortality risk in patients with SLE, offering a basis for clinical decision-making and potential improvements in patient outcomes.

Objective: We conducted a retrospective case–control study to investigate the risk factors for osteonecrosis of the femoral head (ONFH) in rheumatoid arthritis (RA) patients.Methods: The clinical data of patients diagnosed with RA at Fujian Provincial Hospital from January 2013 to December 2020 were retrospectively collected and evaluated. Twenty‐two patients with ONFH were identified. Eighty‐eight age‐, sex‐, and disease duration‐matched RA patients without symptomatic ONFH were randomly selected as controls in a ratio of 1:4. Logistic regression analysis was used to analyze the risk factors.Results: The anticardiolipin (ACL)‐immunoglobulin G (IgG) concentration, clinical disease activity index, simplified disease activity index, incidence of hyperlipidemia in the case group were higher than those in the control group. Multivariate logistic regression analysis did not find the independent risk factor in ONFH patients with RA.Conclusion: The pathogenesis of ONFH in RA is related to many factors such as ACL IgG, disease activity index, and hyperlipidemia. While, we went to great lengths to explore the relationship between antiphospholipid antibodies and ONFH, but it plays a very small role.

Systemic lupus erythematosus (SLE) is a complex autoimmune disease with heterogeneous clinical manifestations. Understanding the molecular mechanisms of SLE is crucial for developing effective therapeutic strategies. This study downloaded microarray datasets from the Gene Expression Omnibus (GEO) database. Single-cell RNA sequencing (scRNA-seq) data was processed to identify 19 clusters and annotated five major cell types. Then we calculated mitochondrial-related genes (MRGs) and ferroptosis-related genes (FRGs) scores. FRGs scored the highest in Megakaryocytes, while MRGs scored the highest in B cells. By employing pseudotime analysis, cell-cell communication analysis, and Single-Cell Regulatory Network Inference and Clustering (SCENIC) analysis, we explored the heterogeneity of cells in SLE. Hub genes were identified using high-dimensional weighted correlation network analysis (hdWGNCA) and machine learning algorithms, leading to the development of a predictive diagnostic model with high predictive accuracy. Immune infiltration analysis revealed significant correlations between diagnostic biomarkers and various immune cells. Lastly, molecular docking studies suggested Doxorubicin may exert therapeutic effects by affecting these diagnostic biomarkers. This study offers new insights into the pathogenesis of SLE and provide valuable directions for future therapeutic research.

Background: Lupus nephritis (LN) is a severe complication of systemic lupus erythematosus (SLE) with poor treatment outcomes. The role and underlying mechanisms of ferroptosis in LN remain largely unknown. We aimed to explore ferroptosis-related molecular subtypes and assess their prognostic value in LN patients.Methods: Molecular subtypes were classified on the basis of differentially expressed ferroptosis-related genes (FRGs) via the Consensus ClusterPlus package. The enriched functions and pathways, immune infiltrating levels, immune scores, and immune checkpoints were compared between the subgroups. A scoring algorithm based on the subtype-specific feature genes identified by artificial neural network machine learning, referred to as the NeuraLN, was established, and its immunological features, clinical value, and predictive value were evaluated in patients with LN. Finally, immunohistochemical analysis was performed to validate the expression and role of feature genes in glomerular tissues from LN patients and controls.Results: A total of 10 differentially expressed FRGs were identified, most of which showed significant correlation. Based on the 10 FRGs, LN patients were classified into two ferroptosis subtypes, which exhibited significant differences in immune cell abundances, immune scores, and immune checkpoint expression. A NeuraLN-related protective model was established based on nine subtype-specific genes, and it exhibited a robustly predictive value in LN. The nomogram and calibration curves demonstrated the clinical benefits of the protective model. The high-NeuraLN group was closely associated with immune activation. Clinical specimens demonstrated the alterations of ALB, BHMT, GAMT, GSTA1, and HAO2 were in accordance with bioinformatics analysis results, GSTA1 and BHMT were negatively correlated with the severity of LN.Conclusion: The classification of ferroptosis subtypes and the establishment of a protective model may form a foundation for the personalized treatment of LN patients.

Full Papers; Authors: Q. Wei, J. He, C. Wu, P. Makota, F. Gao, H. Lin, L. Cai, Z. Chen