---
title: "Transcriptomic links and candidate biomarkers between ANCA‑GN and IgG4‑RD: ITGAM and CXCL1 identif"
id: "plos-one-22-transcriptomic-associations-and-biomarkers-in-anca-associated"
canonical_url: "https://medichelpline.com/clinical-feed/plos-one-22-transcriptomic-associations-and-biomarkers-in-anca-associated"
content_type: "clinical_feed_article"
specialty: "Nephrology"
source_name: "PLOS ONE (Medicine)"
source_url: "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358953"
published_at: "2026-09-24T14:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Transcriptomic links and candidate biomarkers between ANCA‑GN and IgG4‑RD: ITGAM and CXCL1 identif
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/plos-one-22-transcriptomic-associations-and-biomarkers-in-anca-associated
- **Specialty:** [Nephrology](https://medichelpline.com/clinical-feed/nephrology.md)
- **Primary Source:** PLOS ONE (Medicine)
- **Source URL:** [Original Journal Publication](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0358953)
- **Published At:** 2026-09-24T14:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- This bioinformatics and machine learning study compared glomerular transcriptomes from ANCA-associated glomerulonephritis (**ANCA‑GN**, GSE104948) with labial salivary gland samples from IgG4-related disease (**IgG4‑RD**, GSE40568) to identify cross‑tissue transcriptional associations and candidate biomarkers. - After batch correction and differential expression analysis, 305 DEGs were identified in ANCA‑GN (209 upregulated, 96 downregulated). The IgG4‑RD cohort yielded many DEGs but was used primarily for trend validation because of small sample size. - Enrichment of ANCA‑GN DEGs highlighted immune and inflammatory pathways, including **PI3K–Akt** and **MAPK** signaling, and multiple immune-related biological processes. - Protein–protein interaction (PPI) network analysis and machine learning (LASSO + Boruta) were applied to prioritize candidate genes from ANCA‑GN DEGs. - Cross‑trend comparison of log2 fold‑change directions with IgG4‑RD identified **ITGAM** and **CXCL1** as candidate cross‑tissue markers; validation emphasized trend concordance rather than statistical significance in the small IgG4 cohort. - Diagnostic performance was evaluated by ROC analysis with internal bootstrap validation in GSE104948 and external validation in independent ANCA‑GN dataset GSE108109. **ITGAM** showed robust predictive performance (AUC 0.921 internal, AUC 0.844 external). **CXCL1** had high internal AUC (0.937) but poor external AUC (0.622), indicating limited generalizability. - Immune infiltration analyses (CIBERSORT, ssGSEA, MCPcounter) revealed increased B cells, T cells, and monocytes in both diseases; neutrophils predominated in **ANCA‑GN**, whereas plasma cells and M2 macrophages predominated in **IgG4‑RD**. - Regulatory network analysis implicated transcription factors: **SPI1** as a potential regulator of ITGAM, and **CEBPD**, **NFKB1**, **RELA** as candidates driving CXCL1 under inflammatory conditions. - The study underscores shared transcriptomic trends and immune pathway enrichment between the two diseases and proposes ITGAM (more robust) and CXCL1 (requiring further validation) as candidate cross‑tissue biomarkers. - All findings are derived from in silico analyses of public GEO datasets (GSE104948, GSE40568, GSE108109) and require experimental and clinical validation; the limited IgG4‑RD sample size restricts statistical power and generalizability.
## Clinical Analysis & Structured Key Points
Transcriptomic associations and biomarkers in ANCA‑associated Glomerulonephritis and IgG4‑related Disease: A bioinformatics and machine learning study | PLOS One Browse Subject Areas ? Click through the PLOS taxonomy to find articles in your field. For more information about PLOS Subject Areas, click here . Article Authors Metrics Comments Media Coverage Reader Comments Figures Figures Abstract Introduction Anti-neutrophil cytoplasmic antibody-associated glomerulonephritis (ANCA-GN) and immunoglobulin G4-related disease (IgG4-RD) are rare autoimmune conditions. Although case reports suggest potential clinical and pathophysiological overlaps, the cross-tissue transcriptional associations between these diseases remain largely unexplored. Methods We retrieved the ANCA-GN glomerular dataset (GSE104948) and the IgG4-RD labial salivary gland dataset (GSE40568) from the Gene Expression Omnibus. Differentially expressed genes (DEGs) in ANCA-GN were screened via protein–protein interaction networks, least absolute shrinkage and selection operator regression, and the Boruta algorithm. Given tissue heterogeneity and small IgG4-RD sample size, we used expression trend consistency (log-fold change direction) rather than differential validation to explore shared transcriptional features. Receiver operating characteristic curves assessed discriminative potential. Pathway associations were explored using gene set enrichment analysis, and immune infiltration was evaluated with CIBERSORT, single-sample gene set enrichment analysis, and MCPcounter. Transcription factor–mRNA–microRNA networks were also constructed. Results We identified 305 DEGs (209 upregulated, 96 downregulated) in ANCA-GN, enriched in immune/inflammatory pathways including phosphatidylinositol 3-kinase–Akt and mitogen-activated protein kinase signaling. After machine learning and cross‑trend comparison with IgG4‑RD, ITGAM and CXCL1 were identified as candidate cross‑tissue markers, with ITGAM demonstrating robust predictive performance in both internal and external validation (AUC = 0.921, AUC = 0.844, respectively). CXCL1 showed good internal discrimination (AUC = 0.937) but substantially lower external validity (AUC = 0.622), suggesting limited generalizability. ITGAM therefore emerges as a more reliable cross‑disease biomarker candidate, whereas CXCL1 requires further validation. Both diseases showed increased B‑cell, T‑cell, and monocyte infiltration, but effector profiles differed: neutrophils dominated in ANCA‑GN, while plasma cells/M2 macrophages dominated in IgG4‑RD. Regulatory analysis suggested SPI1 may regulate ITGAM, and CEBPD, NFKB1, and RELA may drive CXCL1 expression under inflammation. Conclusion This study systematically revealed shared differential expression trends and immune‑related pathway enrichment features between ANCA‑GN and IgG4‑RD at the transcriptomic level, and identified ITGAM and CXCL1 as potential cross‑tissue candidate biomarkers, offering novel insights into the transcriptomic‑level cross‑tissue associations between the two diseases. However, these findings are entirely derived from bioinformatics predictions and require further experimental validation. The low AUC value of CXCL1 in the external validation underscores this need. Citation: Li X, Qiao Y (2026) Transcriptomic associations and biomarkers in ANCA‑associated Glomerulonephritis and IgG4‑related Disease: A bioinformatics and machine learning study. PLoS One 21(9): e0358953. https://doi.org/10.1371/journal.pone.0358953 Editor: Tomasz W. Kaminski, Versiti Blood Research Institute, UNITED STATES OF AMERICA Received: April 5, 2026; Accepted: September 8, 2026; Published: September 24, 2026 Copyright: © 2026 Li, Qiao. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: The dataset provided in this study can be downloaded in the online website. GEO: https://www.ncbi.nlm.nih.gov/geo . (accession number(s) GSE104948,GSE40568,GSE108109).The code used in this study is available at the following link: https://github.com/XvLi2001/Code/tree/main . Funding: The author(s) received no specific funding for this work. Competing interests: The authors have declared that no competing interests exist. 1. Introduction ANCA-associated vasculitis (AAV) is a group of rare, heterogeneous autoimmune diseases characterized by necrotizing vasculitis of small and medium-sized vessels and the production of anti-neutrophil cytoplasmic antibodies (ANCA). The clinical presentation of AAV typically involves multiple organ systems. As the kidney is an organ highly sensitive to perfusion, the most common and severe manifestation of AAV is rapidly progressive glomerulonephritis (RPGN). This presents with acute decline in renal function, proteinuria, microscopic hematuria, and hypertension, and is termed ANCA-associated glomerulonephritis (ANCA-GN) [ 1 ]. IgG4-related disease (IgG4-RD) is an immune-mediated, multisystem, fibrotic, inflammatory disorder characterized by tumor-like lesions, infiltration of IgG4 + plasma cells, layered fibrosis, and occlusive venulitis. Its imaging manifestations reveal multiorgan fibrosis, predominantly involving the head and neck salivary glands (especially the submandibular gland), liver, kidneys, and lungs [ 2 ]. IgG4-related kidney disease (IgG4-RKD) is an umbrella term encompassing all forms of IgG4-related kidney disease. The most common manifestations are IgG4-related tubulointerstitial nephritis (IgG4-TIN) and IgG4-related membranous glomerulonephritis (IgG4-MGN). Clinically, IgG4-RKD may present as acute or chronic kidney injury, nephrotic syndrome, space-occupying lesions, and obstruction [ 3 ]. In 2017, François-Xavier Danlos et al. reported findings on ANCA-associated vasculitis (AAV)/IgG4-related disease (IgG4-RD) overlapped syndrome, suggesting potential overlapping mechanisms in the pathogenic pathways of AAV and IgG4-RD [ 4 ]. Increasing case reports have documented the overlap between ANCA-GN and IgG4-RKD. However, differing perspectives persist regarding whether these represent an overlapping syndrome or distinct mechanisms within a single disease entity [ 5 – 10 ]. In current clinical practice, rituximab serves as first-line therapy for this overlap syndrome, providing symptom relief for both conditions [ 11 ]. However, the efficacy of rituximab—a component of treatment regimens for various autoimmune diseases—does not necessarily indicate a shared pathogenic mechanism between the two conditions. Therefore, identifying pathways or biomarkers that are common to both diseases could aid in optimizing therapeutic strategies for overlap syndromes and ultimately improving patient outcomes. The advancement of bioinformatics provides a novel approach and perspective for investigating the shared genetic mechanisms underlying ANCA-GN and IgG4-RKD. Since no IgG4-RKD dataset exists in the GEO database, we selected labial salivary gland (LSG) tissue from patients with IgG4-related sialadenitis for renal tissue to elucidate the shared cross‑tissue transcriptomic signals between ANCA-GN and IgG4-RD. We screened for differentially expressed genes (DEGs) in ANCA‑GN, constructed protein‑protein interaction (PPI) networks, and identified key candidate genes using machine learning algorithms. These findings were further validated in IgG4‑RD to explore biomarkers shared between the two diseases. Additionally, we examined the correlations between these genes and various immune cell types, and constructed a TF–mRNA–miRNA regulatory network. Therefore, we propose that ANCA-GN and IgG4-RD share common cross-tissue transcriptomic features and candidate biomarkers. 2. Materials and methods 2.1 Data collection and processing We searched the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo ) of the National Center for Biotechnology Information (NCBI) for relevant datasets using the keywords “ANCA-associated vasculitis-related glomerulonephritis(ANCA-GN)” and “IgG4-related disease (IgG4-RD)”. Our screening criteria required datasets with ≥8 samples and inclusion of both disease and control groups. For ANCA-GN, we selected GSE104948 based on the GPL22945 platform, comprising 22 ANCA-GN patients and 18 healthy controls. Tissue samples originated from glomeruli of the patients and living donors. For IgG4-RD, we selected GSE40568 based on the GPL570 platform, including 5 IgG4-RD patients and 3 healthy controls. Tissue sources were labial salivary gland (LSG) samples from IgG4-RD patients and healthy individuals. In addition, for external validation, we selected the independent ANCA‑GN dataset GSE108109, which was generated using the GPL19983 platform and comprises 15 ANCA-GN patients and 6 healthy controls. The tissue samples in this dataset were derived from glomeruli. The overall workflow is summarized in Fig 1 . Download: PNG larger image TIFF original image Fig 1. Overall workflow of this study. https://doi.org/10.1371/journal.pone.0358953.g001 Schematic workflow of this study: (1) Data retrieval from GEO (GSE104948, ANCA‑GN; GSE40568, IgG4‑RD) and batch correction; (2) DEG identification (limma, |log 2 FC| > 1, adj.P 1. Finally, gene correlation heatmaps and volcano plots were generated using R (version 4.5.1) to visualize DEGs. 2.3 Functional enrichment analysis Given the limited sample size of the IgG4‑RD dataset (GSE40568), the enrichment analysis results may be statistically underpowered; consequently, we restricted our functional annotation analysis to the differentially expressed genes (DEGs) derived from the ANCA‑GN dataset (GSE104948).The “clusterProfiler” package was employed to perform Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on the genes of GSE104948, providing further insights into their biological functions and physiological significance. Data with adjusted p 0 was used as the cutoff to define genes that were also upregulated in IgG4‑RD, which were considered to exhibit a consistent upregulation trend across both diseases, whereas a log 2 FC 1. https://doi.org/10.1371/journal.pone.0358953.g002 3.2 Enrichment analysis GO enrichment analysis ( Fig 3B ) revealed that the upregulated DEGs in the ANCA‑GN dataset were predominantly involved in biological processes including gland development, positive regulation of cell development, positive regulation of protein localization, positive regulation of cell adhesion, response to peptide hormone, regulation of body fluid levels, signal release, response to metal ion, response to xenobiotic stimulus, and response to steroid hormone. In terms of molecular function, these genes were enriched in DNA‑binding transcription factor binding, RNA polymerase II‑specific DNA‑binding transcription factor binding, DNA‑binding transcription activator activity, DNA‑binding transcription activator activity, RNA p
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