Understanding the role of potential biomarkers in attenuating multiple sclerosis progression via multiomics and network-based approach.

Background: Multiple sclerosis (MS) is a complex neurological disorder marked by neuroinflammation and demyelination. Understanding its molecular basis is vital for developing effective treatments. This study aims to elucidate the molecular progression of MS using multiomics and network-based approach.

Methods: We procured differentially expressed genes in MS patients and healthy controls by accessing mRNA dataset from a publicly accessible database. The DEGs were subjected to a non-trait weighted gene co-expression network (WGCN) for hub DEGs identification. These hub DEGs were utilized for enrichment, protein-protein interaction network (PPIN), and feed-forward loop (FFL) analyses.

Results: We identified 880 MS-associated DEGs. WGCN revealed a total of 122 hub DEGs of which most significant pathway, gene ontology (GO)-biological process (BP), GO-molecular function (MF) and GO-cellular compartment (CC) terms were assembly and cell surface presentation of N-methyl-D-aspartate (NMDA) receptors, regulation of catabolic process, NAD(P)H oxidase H2O2 forming activity, postsynaptic recycling endosome. The intersection of top 10 significant pathways, GO-BP, GO-MF, GO-CC terms, and PPIN top cluster genests identified STAT3 and CREB1 as key biomarkers. Based on essential centrality measures, CREB1 was retained as the final biomarker. Highest-order subnetwork FFL motif comprised one TF (KLF7), one miRNA (miR-328-3p), and one mRNA (CREB1) based on essential centrality measures.

Conclusions: This study provides insights into the roles of potential biomarkers in MS progression and offers a system-level view of its molecular landscape. Further experimental validation is needed to confirm these biomarkers' significance, which will lead to early diagnostic and therapeutic advancements.