Explainable machine learning-based identification of transcriptomic biomarkers in CD1c+ dendritic cells for non-infectious uveitis: an integrative analysis of bulk RNA-seq data.
A 20-gene blood immune-cell signature accurately identifies non-infectious uveitis
Explainable machine learning-based identification of transcriptomic biomarkers in CD1c+ dendritic cells for non-infectious uveitis: an integrative analysis of bulk RNA-seq data.
Non-infectious uveitis (non-infectious uveitis) is a leading cause of intraocular inflammation, and the underlying immunological mechanisms remain incompletely understood.
This study aims to classify non-infectious uveitis at the molecular level and interpret its biological mechanisms by applying machine learning and explainable artificial intelligence (XAI) methods to transcriptomic data from CD1c+ conventional dendritic cells type 2 (cDC2) isolated from the peripheral blood of non-infectious uveitis patients.
Two independent cohorts (GSE194060 and GSE195501; n = 78) from the Gene Expression Omnibus (Gene Expression Omnibus) database were integrated using a strict, leakage-free pipeline.
Leave-One-Dataset-Out (Leave-One-Dataset-Out) validation supported cross-platform generalizability (mean AUC: 0.82).
Permutation testing confirmed the model's statistical significance (p = 0.005).
SHAP analysis identified CD180 and TLR7 as the most important biomarkers.
Pathway enrichment analysis revealed significant enrichment in chemokine receptor activity, MYD88-mediated signaling, Toll-like receptor cascades, and G protein-coupled receptor (g protein-coupled receptor) signaling pathways.
These findings demonstrate the potential of XAI to elucidate disease mechanisms from transcriptomic data and present an interpretable 20-gene signature as a candidate diagnostic biomarker panel for non-infectious uveitis that warrants further clinical validation.