Differential diagnosis of systemic lupus erythematosus and Sjögren's syndrome using machine learning and multi-omics data

• Published in: Computers in biology and medicine 2023-01, Vol.152, p.106373, Article 106373
• Main Authors: Martorell-Marugán, Jordi, Chierici, Marco, Jurman, Giuseppe, Alarcón-Riquelme, Marta E., Carmona-Sáez, Pedro
• Format: Article
• Language: English
• Subjects: Autoimmune diseases; Bioinformatics; Biomarkers; Cell cycle; Chronic conditions; Classification and association rules; Clustering; Data processing; Diagnosis; Diagnosis, Differential; Differential diagnosis; Disease; DNA methylation; Gene expression; Health; Heterogeneity; Humans; Impact analysis; Inflammation; Information technology; Learning algorithms; Lupus; Lupus Erythematosus, Systemic - diagnosis; Lupus Erythematosus, Systemic - genetics; Machine Learning; Medical diagnosis; Medicin och hälsovetenskap; Modeling and prediction; Multiomics; Performance prediction; Prediction models; Sjogren's syndrome; Sjogren's Syndrome - diagnosis; Sjogren's Syndrome - genetics; Systemic lupus erythematosus
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2022.106373

Systemic lupus erythematosus and primary Sjogren's syndrome are complex systemic autoimmune diseases that are often misdiagnosed. In this article, we demonstrate the potential of machine learning to perform differential diagnosis of these similar pathologies using gene expression and methylation data from 651 individuals. Furthermore, we analyzed the impact of the heterogeneity of these diseases on the performance of the predictive models, discovering that patients assigned to a specific molecular cluster are misclassified more often and affect to the overall performance of the predictive models. In addition, we found that the samples characterized by a high interferon activity are the ones predicted with more accuracy, followed by the samples with high inflammatory activity. Finally, we identified a group of biomarkers that improve the predictions compared to using the whole data and we validated them with external studies from other tissues and technological platforms. •Multi-omics data were used for differential diagnosis of SLE and pSjS.•Molecular heterogeneity drives the classifiers performance.•Biomarkers for gene expression and DNA methylation were proposed and validated.