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bioRxiv · 10.1101/2023.12.05.570305

Identification of candidate biomarkers and pathways associated with multiple sclerosis using bioinformatics and next generation sequencing data analysis

Vastrad, B. M.·Vastrad, C. M.
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Abstract

Multiple sclerosis (MS) is an autoinflammatory disease that might lead to severe disability. The diagnosis of MS is defined due to the urgency for biomarkers with both reliability and efficiency. Demyelination of axons are deeply involved in the pathogenesis of MS. Our study aims to identify the underlying molecular mechanism and screening for related biomarkers and signaling pathways. We obtained next generation sequencing (NGS) dataset (GSE138614) from the GEO database. Differentially expressed genes (DEGs) were screened by the DESeq2 package in R bioconductor with considering specific criteria. Gene Ontology (GO) enrichment analysis, REACTOME pathway enrichment analysis were performed; a protein-protein interaction (PPI) network was constructed; significant modules were analyzed and hub genes were identified by Human Integrated Protein-Protein Interaction rEference (HiPPIE). Subsequently, miRNA-hub gene regulatory network, TF-hub gene regulatory network and drug-hub gene interaction network were built by Cytoscape to predict the underlying microRNAs (miRNAs), transcription factors (TFs) and drugs associated with hub genes. Receiver operating characteristic (ROC) curves analysis was performed to calculate diagnostic value of hub genes. Finally, we performed molecular docking study for prediction of drug molecules against protein targets. A total of 959 DEGs (479 up-regulated and 480 down-regulated genes) were identified in the MS samples and compared with normal control samples. The DEGs were predominantly enriched in an ensemble of genes encoding the immune system process, developmental process, immune system and regulation of cholesterol biosynthesis by SREBP (SREBF). A PPI network was obtained through HiPPIE analysis, and the results were imported into Cytoscape software. The DEGs were sequenced by the Network Analyzer plug-in by various calculation methods, and 10 hub genes (LCK, PYHIN1, SLAMF1, DOK2, TAB2, CFTR, RHOB, LMNA, EGLN3 and ERBB3) were finally selected. Based on the miRNA-hub gene regulatory network and TF-hub gene regulatory network construction, miRNAs including hsa-mir-6794-3p, hsa-mir-3689a-3p, hsa-mir-4651, hsa-mir-548q, BRCA1, HNF4A, TFAP2C and NR2F1 were determined to be potential key biomarkers. Drug-hub gene interaction network constructed from DrugBank, which identified targeted therapeutic drugs (Palivizumab, Cu-Bicyclam, Lumacaftor and Zonisamide) for the hub genes. From molecular docking study we showed good drug - protein bind affinity and amino acid interactions. This study identified novel biomarkers for MS and established a reliable diagnostic model as well as predicted novel drug molecules. The transcriptional changes identified may help to reveal the pathogenesis and molecular mechanisms of MS.

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Vastrad, B. M., Vastrad, C. M.. 2023-12-07. Identification of candidate biomarkers and pathways associated with multiple sclerosis using bioinformatics and next generation sequencing data analysis. https://doi.org/10.1101/2023.12.05.570305

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