{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T18:10:17Z","timestamp":1790273417223,"version":"4.1.0"},"reference-count":45,"publisher":"Public Library of Science (PLoS)","issue":"9","license":[{"start":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T00:00:00Z","timestamp":1790208000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:sec id=\"sec001\">\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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\u2013protein 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\u2013mRNA\u2013microRNA networks were also constructed.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We identified 305 DEGs (209 upregulated, 96 downregulated) in ANCA-GN, enriched in immune\/inflammatory pathways including phosphatidylinositol 3-kinase\u2013Akt and mitogen-activated protein kinase signaling. After machine learning and cross\u2011trend comparison with IgG4\u2011RD, ITGAM and CXCL1 were identified as candidate cross\u2011tissue markers, with ITGAM demonstrating robust predictive performance in both internal and external validation (AUC\u2009=\u20090.921, AUC\u2009=\u20090.844, respectively). CXCL1 showed good internal discrimination (AUC\u2009=\u20090.937) but substantially lower external validity (AUC\u2009=\u20090.622), suggesting limited generalizability. ITGAM therefore emerges as a more reliable cross\u2011disease biomarker candidate, whereas CXCL1 requires further validation. Both diseases showed increased B\u2011cell, T\u2011cell, and monocyte infiltration, but effector profiles differed: neutrophils dominated in ANCA\u2011GN, while plasma cells\/M2 macrophages dominated in IgG4\u2011RD. Regulatory analysis suggested SPI1 may regulate ITGAM, and CEBPD, NFKB1, and RELA may drive CXCL1 expression under inflammation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec004\">\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study systematically revealed shared differential expression trends and immune\u2011related pathway enrichment features between ANCA\u2011GN and IgG4\u2011RD at the transcriptomic level, and identified ITGAM and CXCL1 as potential cross\u2011tissue candidate biomarkers, offering novel insights into the transcriptomic\u2011level cross\u2011tissue 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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pone.0358953","type":"journal-article","created":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T17:35:44Z","timestamp":1790271344000},"page":"e0358953","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Transcriptomic associations and biomarkers in ANCA\u2011associated Glomerulonephritis and IgG4\u2011related Disease: A bioinformatics and machine learning study"],"prefix":"10.1371","volume":"21","author":[{"given":"Xu","family":"Li","sequence":"first","affiliation":[{"name":"Shanxi Medical University","place":["Taiyuan, China"],"department":["The Nephrology Department of Shanxi Provincial People\u2019s 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