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

Abstract

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.

Fig 1

(A) Volcano plot showing the distribution of MS-associated DEGs (red and green dots indicate upregulation and downregulation) and nonsignificant genes (grey dots). (B) Annotation heatmap showing the expression distribution of top MS-associated DEGs (up and downregulated) across MS and healthy patient samples. Left and top sides of the plot signify cluster dendrograms demonstrating Euclidean distance-based hierarchical clustering for columns and rows. Sample-type annotation bars are displayed at the top of heatmap.

Fig 2

(A) Hierarchical clustering dendrogram of 658 MS-associated DEGs clustered on the basis of dissTOM and four color-coded communities (i.e., blue, turquoise, grey, brown). (B) MDS plot where each colored point signify a gene belonging to a respective color-coded community. (C) Heatmap plot signifying TOM among blue, turquoise, brown community genes. Plot’s left and top side panels signify community assignments and hierarchically clustered gene dendrograms. Dark-colored blocks along the diagonal represent communities. (D) Expression heatmap of blue community genes, wherein the columns and rows relate to samples and genes. The red- and green-colored bands in the heatmaps signify higher and lower expression levels, respectively. Also, the corresponding ME expression levels (y-axis) across the samples (x-axis) are represented at the base panel of each community heatmap as bar plots.

Fig 3

Chord plots demonstrating the association of hub DEGs with top 10 significant (A) GO-BP, (B) GO-MF, (C) GO-CC, (D) pathway terms via undirected and unweighted colored edges.

Fig 4

(A) Unweighted and undirected PPIN comprising 71 nodes and 72 edges corresponding to an interaction score >0.4. (B) Highest-scoring PPIN hub module (MCODE score = 4.5) comprising 5 nodes and 9 edges. Red and green colored nodes signify up and downregulated expression status of DEGs.

Fig 5

(A) Venn plot showing two overlapping hub DEG(s) between GO-BP, GO-MF, GO-CC, pathway, and MCODE top cluster genesets. Red, green, yellow, magenta, blue-colored areas signify MCODE, GO-BP, GO-MF, GO-CC, pathway genesets. Box-and-whisker plots showing the expression intensity distribution of (B) STAT3 and (C) CREB1 across MS and healthy patient samples. Red-and green-colored areas signify healthy normal and MS patient samples. The top and bottom of the boxes signify 75th and 25th percentile of distribution. Horizontal lines within the boxes represent the median values while the axes endpoints are labeled by minimum and maximum values.

Fig 6

(A) Unweighted and undirected miRNA-mRNA-TF regulatory network comprising nodes and edges. (B) Highest-order subnetwork motif comprising one TF (KLF7), one miRNA (miR-328-3p), and one mRNA (CREB1). Magenta-colored rectangular nodes, green-colored circular nodes and red-colored diamond nodes signify miRNAs, mRNAs, and TFs, respectively.

Fig 7. Topological/centrality distributions showing betweenness, closeness, node degree, topological coefficient, ASPL, and clustering coefficient of 3-node miRNA FFL.