PloS one · 2025
Integrative machine learning and multi-omics framework identifies shared biomarkers for rheumatoid arthritis and ulcerative colitis.
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Liu Meili, Ge Jun, Guo Lei, Wu Zhengzheng, Cheng Zimo, Liu Zenggen, Liu Yi
Аннотация
BACKGROUND: Rheumatoid arthritis (RA) and ulcerative colitis (UC) are chronic inflammatory diseases with shared immune pathologies but limited common diagnostic biomarkers, which hinders the development of targeted therapies.
METHODS: Public gene expression datasets were analyzed to identify differentially expressed genes (DEGs) common to both RA and UC. Functional enrichment and immune infiltration analyses revealed dysregulated pathways. A comprehensive machine learning framework that incorporated 12 algorithms and cross-validation was applied to screen for robust diagnostic biomarkers. Further, RA- and UC-related molecular subtypes were delineated, and the relationship between these shared biomarkers and immune infiltration characteristics was explored. Key findings were validated using single-cell RNA sequencing (scRNA-seq) of UC tissue to localize gene expression and qRT-PCR in cell models mimicking RA and UC.
RESULTS: Analysis identified 19 shared DEGs, with functional enrichment analysis highlighting IL-17 signaling. Machine learning prioritized four key biomarkers (DUOX2, IDO1, NPY1R, SELL) with high diagnostic performance. scRNA-seq localized these genes predominantly to a pro-inflammatory "Macrophage-High" subpopulation and revealed VEGF-mediated crosstalk with endothelial cells. qRT-PCR confirmed significant expression changes of IDO1 and NPY1R in both RA-like and UC-like inflammation models.
CONCLUSION: This integrative approach identifies DUOX2, IDO1, NPY1R, and SELL as shared RA-UC biomarkers potentially linked to macrophage-driven inflammation and VEGF signaling. These findings provide insights into the common pathogenesis and potential targets for dual-disease diagnostics and therapeutics.
METHODS: Public gene expression datasets were analyzed to identify differentially expressed genes (DEGs) common to both RA and UC. Functional enrichment and immune infiltration analyses revealed dysregulated pathways. A comprehensive machine learning framework that incorporated 12 algorithms and cross-validation was applied to screen for robust diagnostic biomarkers. Further, RA- and UC-related molecular subtypes were delineated, and the relationship between these shared biomarkers and immune infiltration characteristics was explored. Key findings were validated using single-cell RNA sequencing (scRNA-seq) of UC tissue to localize gene expression and qRT-PCR in cell models mimicking RA and UC.
RESULTS: Analysis identified 19 shared DEGs, with functional enrichment analysis highlighting IL-17 signaling. Machine learning prioritized four key biomarkers (DUOX2, IDO1, NPY1R, SELL) with high diagnostic performance. scRNA-seq localized these genes predominantly to a pro-inflammatory "Macrophage-High" subpopulation and revealed VEGF-mediated crosstalk with endothelial cells. qRT-PCR confirmed significant expression changes of IDO1 and NPY1R in both RA-like and UC-like inflammation models.
CONCLUSION: This integrative approach identifies DUOX2, IDO1, NPY1R, and SELL as shared RA-UC biomarkers potentially linked to macrophage-driven inflammation and VEGF signaling. These findings provide insights into the common pathogenesis and potential targets for dual-disease diagnostics and therapeutics.