Identification of Hub Biomarkers and Immune-Related Pathways Participating in the Progression of Antineutrophil Cytoplasmic Antibody-Associated Glomerulonephritis.

Background: Antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is a systemic autoimmune disease that generally induces the progression of rapidly progressive glomerulonephritis (GN). The purpose of this study was to identify key biomarkers and immune-related pathways involved in the progression of ANCA-associated GN (ANCA-GN) and their relationship with immune cell infiltration. Methods: Gene microarray data were downloaded from the Gene Expression Omnibus (GEO). Hub markers for ANCA-GN were mined based on differential expression analysis, weighted gene co-expression network analysis (WGCNA) and lasso regression, followed by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA) of the differential genes. The infiltration levels of 28 immune cells in the expression profile and their relationship to hub gene markers were analysed using single-sample GSEA (ssGSEA). In addition, the accuracy of the hub markers in diagnosing ANCA-GN was subsequently evaluated using the receiver operating characteristic curve (ROC). Results: A total of 651 differential genes were screened. Twelve co-expression modules were obtained via WGCNA; of which, one hub module (black module) had the highest correlation with ANCA-GN. A total of 66 intersecting genes were acquired by combining differential genes. Five hub genes were subsequently obtained by lasso analysis as potential biomarkers for ANCA-GN. The immune infiltration results revealed the most significant relationship among monocytes, CD4+ T cells and CD8+ T cells. ROC curve analysis demonstrated a prime diagnostic value of the five hub genes. According to the functional enrichment analysis of the differential genes, hub genes were mainly enhanced in immune- and inflammation-related pathways. Conclusion: B cells and monocytes were closely associated with the pathogenesis of ANCA-GN. Hub genes (CYP3A5, SLC12A3, BGN, TAPBP and TMEM184B) may be involved in the progression of ANCA-GN through immune-related signal pathways.

Meta-Analytical Accuracy of ANCA Renal Risk Score for Prediction of Renal Outcome in Patients With ANCA-Associated Glomerulonephritis.

Background: To evaluate the diagnostic accuracy of antineutrophil cytoplasmic antibody (ANCA) renal risk score (ARRS) for prediction of renal outcome in patients with ANCA-associated glomerulonephritis (ANCA-GN). Methods: We searched PubMed, EMBASE, Ovid, Web of Science, the Cochrane Library, and ClinicalTrials.gov for studies, which used ARRS to predict end-stage renal disease (ESRD) in patients with ANCA-GN. Two reviewers independently screened articles for inclusion, assessed the quality of studies with both an adapted Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. We calculated the combined patients with ESRD in the ARRS categories and presented the summary and individual estimates based on the ARRS categories. Then, the sensitivity, specificity, diagnostic odds ratio (DOR), positive/negative likelihood ratio, and the area under the receiver operating characteristic (AUROC) curves of the pooled data for ARRS were used to assess the accuracy of the "above the low-risk threshold" (ARRS ≥ 2) and "high-risk grade" (ARRS ≥ 8) for renal outcome of patients with ANCA-GN. The hierarchical summary ROC (HSROC) was used to verify the accuracy value. The clinical utility of ARRS was evaluated by the Fagan plot. Heterogeneity was explored using meta-regression and subgroup analysis. Results: A total of 12 distinct cohorts from 11 articles involving 1,568 patients with ANCA-GN were analyzed. The cumulative patients with ESRD at the maximum follow-up of 60 months was 5% (95% CI: 0.02-0.07; p < 0.001) for ANCA-GN with low ARRS (0-1 points) and significantly increased to 22% (95% CI: 0.15-0.29; p < 0.001) medium ARRS (2-7 points). The combined cumulative patients with ESRD was 59% (95% CI: 0.49-0.69; p < 0.001) high ARRS (8-11 points). The pooled sensitivity of ARRS ≥ 2 in predicting ESRD was 98% with a specificity of 30% and a DOR of 15.08 and the mean AUROC value was 0.82. The pooled sensitivity of ARRS ≥ 8 in predicting ESRD was 58% with a specificity of 86% and a DOR of 7.59. The meta-regression and subgroup analysis indicated that variation in the geographic regions, study design, index risk, follow-up time, age of patient, publication year, and number of patient could be the potential sources of heterogeneity in the diagnosis of ARRS ≥ 8. Conclusion: This meta-analysis emphasized the good performance of the ARRS score in predicting the renal outcome in patients with ANCA-GN. However, these findings should be verified by future large-scale prospective studies.