Comprehensive meta-analysis of differentially expressed proteins in cerebrospinal fluid associated with multiple sclerosis

To advance our understanding of multiple sclerosis (MS), accurate identification of protein expression profiles as biomarkers for MS in cerebrospinal fluid (CSF) is critical. However, proteomic studies investigating MS have yielded inconsistent findings due to variability in sample sizes, diagnostic criteria, and data processing methods. We aimed to tackle these challenges by performing a thorough meta-analysis of proteomics datasets sourced from multiple independent studies. We conducted a thorough database search to gather all relevant studies using appropriate keywords. We screened articles using defined inclusion and exclusion criteria, and finally, six studies were included. We retrieved and combined data from five CSF datasets for discovery and two additional datasets for validation in 368 MS patients and controls. After data preprocessing, we calculated Z-scores for all datasets and for the integrated dataset. We used logistic regression models using training and validation datasets. We identified 11 differentially expressed proteins in the integrated dataset, revealing significant alterations in key pathways involved in immune response, neuroinflammation, and synaptic function. Notably, IGKC exhibited strong diagnostic potential, with an AUROC of 0.81. These findings highlight the value of re-analysing publicly available proteomics data to develop robust biomarker panels for MS diagnosis.