The processed data used in this study are available at figshare.com

Differentially expressed genes were identified using DESeq2, and weighted gene co-expression network analysis (WGCNA) was used to define the disease-related gene modules. The functional annotations and pathway analyses were then applied to identify MS-significant pathways using Enrichr and GSEA. To predict upstream miRNAs regulating target genes, different databases were used, such as MIRMAP, mirDB, TargetScan, mirDIP, DIANA Tools, and miRWalk.

The StarBase database was used to find possible upstream long-noncoding RNA (lncRNA) by searching for the selected miRNA. Expression of the three biomarkers was determined by using Real-Time PCR in MS patients and healthy controls. Results: 4,156 genes were found to be significantly dysregulated in MS.

The darkviolet module was identified as the key module related to disease status. Experimental validation of SLCO5A1 expression in MS disease compared to healthy state, significant upregulation of expression (log2FC= 2.27, p value<0.0001), STAG3L5P-PVRIG2P-PILRB, from the disease state was observed, along with significant upregulation of expression in the disease state compared to healthy state (log2FC=7.48, p value< 0.0001) and has-miR-218-5p showed a significant down regulation in the disease state against healthy state (log2FC=0.041, p value<0.0001).

Conclusion: These findings suggest a novel ceRNA regulatory network that may contribute to MS pathogenesis, prognosis, and therapeutic targeting.</p><p dir="ltr"><b>Keywords:</b> Multiple sclerosis; systems biology; miRNA; lncRNA; ceRNA</p>