Role of miR-155 in inflammatory autoimmune diseases: a comprehensive review.

PURPOSE: MiR-155 is a member of the microRNAs (miRNAs) family and regulates gene expression post-transcriptionally by binding to the 3'UTR of target mRNA. MiR-155 has a critical role in both innate and adaptive immunity. MiR-155 is aberrantly expressed in inflammatory autoimmune diseases such as rheumatoid arthritis, systemic lupus erythematosus, multiple sclerosis, type 1 diabetes, Sjögren's syndrome, systemic sclerosis, and inflammatory bowel disease. Functional studies suggest that miR-155 is involved in development of these diseases. In vitro and in vivo experiments have shown that inhibition of miR-155 can alter disease progression or ameliorate disease symptoms. MATERIALS AND METHODS: A systematic review of relevant literatures published between January 1, 2005, and March 1, 2022 about miR-155 and its role in immune cells, autoimmune diseases was searched on PubMed, EMBASE, Google Scholar. CONCLUSION: In this review, we comprehensively discussed the effects of miR-155, including role of miR-155 in different downstream signaling, which then differently regulate immune cells expression and functions. Furthermore, miR-155-mediated dysfunction of immune cells contributed to development of inflammatory autoimmune diseases. Therefore, miR-155 is expected to be a therapeutic target for inflammatory autoimmune diseases.

Prediction model for developing neuropsychiatric systemic lupus erythematosus in lupus patients.

OBJECTIVE: This study aimed to construct a predictive model for assessing the risk of development of neuropsychiatric systemic lupus erythematosus (NPSLE) among patients with SLE based on clinical, laboratory, and meteorological data. METHODS: A total of 2232 SLE patients were included and were randomly assigned into training and validation sets. Variables such as clinical and laboratory data and local meteorological data were screened by univariate and least absolute shrinkage and selection operator (LASSO) logistic regression modelling. After 10-fold cross-validation, the predictive model was built by multivariate logistic regression, and a nomogram was constructed to visualize the risk of NPSLE. The efficacy and accuracy of the model were assessed by receiver operating characteristic (ROC) curve and calibration curve analysis. Net clinical benefit was assessed by decision curve analysis. RESULTS: Variables that were included in the predictive model were anti-dsDNA, anti-SSA, lymphocyte count, hematocrit, erythrocyte sedimentation rate, pre-albumin, retinol binding protein, creatine kinase isoenzyme MB, Nterminal brain natriuretic peptide precursor, creatinine, indirect bilirubin, fibrinogen, hypersensitive C-reactive protein, CO, and mild contamination. The nomogram showed a broad prediction spectrum; the area under the curve (AUC) was 0.895 (0.858-0.931) for the training set and 0.849 (0.783-0.916) for the validation set. CONCLUSION: The model exhibits good predictive performance and will confer clinical benefit in NPSLE risk calculation. Key Points • Clinical, laboratory, and meteorological data were incorporated into a predictive model for neuropsychiatric systemic lupus erythematosus (NPSLE) in SLE patients. • Anti-dsDNA, anti-SSA, LYM, HCT, ESR, hsCRP, IBIL, PA, RBP, CO, Fib, NT-proBNP, Crea, CO, and mild contamination are predictors of the development of NPSLE and may have potential for research. • The nomogram has good predictive performance and clinical value and can be used to guide clinical diagnosis and treatment.