Machine learning-driven immunophenotypic stratification of mixed connective tissue disease, corroborating the clinical heterogeneity.

ABSTRACT:

Objective

The objective of this study was to stratify patients with MCTD, based on their immunophenotype.

Methods

We analysed the immunophenotype and transcriptome of 24 immune cell subsets [from patients with MCTD, SLE, idiopathic inflammatory myopathy (IIM) and SSc] from our functional genome database, ImmuNexUT (https://www.immunexut.org/). MCTD patients were stratified by employing machine-learning models, including Random Forest, trained by immunophenotyping data from SLE, IIM and SSc patients. The transcriptomes were analysed with gene set variation analysis (GSVA), and the clinical features of the MCTD subgroups were compared.

Results

This study included 215 patients, including 22 patients with MCTD. Machine-learning models, constructed to classify SLE, IIM and

SUBMITTER: Izuka S 

PROVIDER: S-EPMC11879315 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature