Splicing-based biomarkers define a robust multigene classifier for relapsing-remitting multiple sclerosis.

Area of Science:

• Neuroimmunology
• Molecular Biology
• Biomarker Discovery

Background:

• Alternative splicing (AS) plays a critical role in the pathogenesis of multiple sclerosis (MS).
• Developing accurate diagnostic tools for relapsing-remitting MS (RRMS) is crucial for timely intervention.

Purpose of the Study:

• To develop and validate a multivariate classifier, the MS-Splicing Score (MS-SS), for differentiating RRMS patients from healthy controls.
• To explore the potential of AS events as biomarkers in neuroimmunology.

Main Methods:

• Selected three key AS events (IFNAR2, NFAT5, PRKCA) based on functional and literature evidence.
• Quantified AS isoforms using fluorescent-competitive RT-PCR in peripheral blood RNA from two independent cohorts (Italy and USA).
• Developed the MS-SS using logistic regression and validated its performance with ROC analysis and PCA-based refinement.

Main Results:

• The MS-SS significantly distinguished RRMS patients from controls in both independent cohorts (AUCs ranging from 0.71 to 0.77).
• In the pooled dataset, the MS-SS remained a significant differentiator (AUC=0.72), with PCA refinement improving accuracy to an AUC of 0.87.
• The classifier achieved 80% sensitivity and 86% specificity at the optimal cutoff, with interpretable thresholds identified by a logic-learning machine algorithm.

Conclusions:

• Introduced a novel and robust AS-based classifier (MS-SS) for RRMS diagnosis.
• Proposed a strategy for developing transcriptome-based biomarkers in neuroimmunology.
• Acknowledged the need for larger validation studies to confirm the clinical utility of the MS-SS due to sample size limitations.