Dual Anti-Glomerular Basement Membrane and Anti-Neutrophil Cytoplasmic Antibodies-Positive Rapidly Progressive Glomerulonephritis with Rheumatoid Arthritis and Sjogren's Syndrome: A Case Report and Literature Review.

Rapidly progressive glomerulonephritis (RPGN) is a life-threatening disease characterized by rapid progressive deterioration of renal function and extensive formation of crescents. Some antibodies tend to be positive, such as a perinuclear anti-neutrophil cytoplasmic antibody (p-ANCA) and anti-glomerular basement membrane (anti-GBM) antibodies, in most patients with the disease. However, cases of double positivity for the above antibodies are considered to be rare. In addition, both rheumatoid arthritis (RA) and Sjogren's syndrome (SS) are deemed to be independent immune disorders that can cause renal impairment. Nevertheless, the association between RPGN and these two diseases has not been elucidated in previous studies. Here, we provide a case of RPGN with the concurrence of RA and SS characterized by double positivity in anti-GBM antibodies and p-ANCA. After aggressive treatment with cyclophosphamide, glucocorticoids, and plasma exchange, the patient improved significantly. Despite the malignant event of arteriovenous fistula rupture and bleeding during treatment, the patient survived with renal function recovery for the rest of the follow-up period.

Identification of Unique Genetic Biomarkers of Various Subtypes of Glomerulonephritis Using Machine Learning and Deep Learning.

(1) Objective: Identification of potential genetic biomarkers for various glomerulonephritis (GN) subtypes and discovering the molecular mechanisms of GN. (2) Methods: four microarray datasets of GN were downloaded from Gene Expression Omnibus (GEO) database and merged to obtain the gene expression profiles of eight GN subtypes. Then, differentially expressed immune-related genes (DIRGs) were identified to explore the molecular mechanisms of GN, and single-sample gene set enrichment analysis (ssGSEA) was performed to discover the abnormal inflammation in GN. In addition, a nomogram model was generated using the R package "glmnet", and the calibration curve was plotted to evaluate the predictive power of the nomogram model. Finally, deep learning (DL) based on a multilayer perceptron (MLP) network was performed to explore the characteristic genes for GN. (3) Results: we screened out 274 common up-regulated or down-regulated DIRGs in the glomeruli and tubulointerstitium. These DIRGs are mainly involved in T-cell differentiation, the RAS signaling pathway, and the MAPK signaling pathway. ssGSEA indicates that there is a significant increase in DC (dendritic cells) and macrophages, and a significant decrease in neutrophils and NKT cells in glomeruli, while monocytes and NK cells are increased in tubulointerstitium. A nomogram model was constructed to predict GN based on 7 DIRGs, and 20 DIRGs of each subtype of GN in glomeruli and tubulointerstitium were selected as characteristic genes. (4) Conclusions: this study reveals that the DIRGs are closely related to the pathogenesis of GN and could serve as genetic biomarkers in GN. DL further identified the characteristic genes that are essential to define the pathogenesis of GN and develop targeted therapies for eight GN subtypes.