Network-based machine learning to identify biomarkers for systemic lupus erythematosus.

Abstract

Background: Systemic lupus erythematosus (SLE) is a complex autoimmune disease, making accurate diagnosis and effective treatment challenging. Despite the critical need for reliable biomarkers, conventional differential gene expression (DGE) analyses often yield high false-positive rates and have limited ability to identify clinically actionable targets, leaving a significant gap in SLE precision medicine.

Results: To address this, we developed NetSLE, a network-based machine learning framework that integrates diverse SLE-related prior knowledge with comprehensive biological networks. By effectively filtering out false positives from differentially expressed genes (DEGs), NetSLE identified a robust panel of 150 key biomarkers. Clinically, the NetSLE-derived biomarkers outperformed conventional markers and full transcriptomes in predicting disease activity across independent cohorts. Furthermore, they successfully identified experimentally validated drug repurposing candidates (e.g., lipid-modifying and antithrombotic agents) and enabled precise stratification of patients into distinct immunological subtypes (AS1 and AS2).

Conclusions: NetSLE offers a translatable approach to overcome the limitations of traditional biomarker discovery. The clinically sized 150-gene panel provides a practical tool for enhancing diagnostic precision, guiding targeted treatments, and advancing personalized medicine in SLE.