Identification of key genes and microRNAs for multiple sclerosis using bioinformatics analysis

Identification of shared pathogenic signatures of multiple sclerosis and chronic obstructive pulmonary disease: an integrated transcriptomic analysis of blood specimens

Patients with multiple sclerosis (MS) face a heightened risk of developing chronic obstructive pulmonary disease (COPD). Despite this widely reported association, the pathogenic contributors and processes that may favor the development of COPD in MS patients have yet to be identified. Recent studies have suggested peripheral blood leukocytes as a potential link between COPD and autoimmune disorders. Therefore, this study aimed to unveil shared molecular signatures between MS and COPD using blood transcriptomes. To this end, gene expression datasets obtained from MS and COPD blood specimens were retrieved from the Gene Expression Omnibus (GEO) database. By integrating datasets belonging to each disorder, differentially expressed genes (DEGs) were determined for each disease. Then, the protein-protein interaction (PPI) network was constructed for shared DEGs between MS and COPD. Subsequently, the network was analyzed to identify hub genes and key regulatory miRNAs. The integrated data for MS encompassed 51 samples (28 from MS patients and 23 from controls), and the integrated data for COPD included 450 samples (275 from COPD patients and 175 from controls). A total of 246 genes were found to exhibit identical directions of expression in both MS and COPD. By applying a high confidence threshold (0.7), a PPI network with 74 nodes was constructed. TP53, H4C6, SNRPE, and RPS11 were identified as hub genes according to the degree measure. In addition, 8 miRNAs were identified as key regulators, each interacting with 6 mRNAs. Among these miRNAs, miR-218-5p and miR-142-5p have been previously reported to contribute to the pathogenesis of these diseases, and here they were identified as key regulators of the shared PPI network, suggesting a potential epigenetic link between MS and COPD. In conclusion, the results highlighted the potential role of peripheral blood leucocytes as a bridge between MS and COPD. These findings broaden our understanding of pathogenic contributors linking MS and COPD. While this transcriptomics study identified multiple key players, such as TP53, miR-218-5p, and miR-142-5p, the assessment of their therapeutic efficacy demands further experimental studies.

Multiple Sclerosis: Roles of miRNA, lcnRNA, and circRNA and Their Implications in Cellular Pathways

Multiple sclerosis (MS) is a degenerative condition characterized by axonal damage and demyelination induced by autoreactive immune cells that occur in the Central Nervous System (CNS). The interaction between epigenetic changes and genetic factors can be widely involved in the onset, development, and progression of the disease. Although numerous efforts were made to discover new therapies able to prevent and improve the course of MS, definitive curative treatments have not been found yet. However, in recent years, it has been reported that non-coding RNAs (ncRNAs), including microRNAs (miRNAs), long ncRNAs (lncRNAs), and circular RNAs (circRNAs), acting as gene expression regulators, could be used as potential therapeutic targets or biomarkers to diagnose and fight MS. In this review, we discussed the role of miRNAs, lncRNAs, and circRNAs, as well as their expression level changes and signaling pathways that are related to preclinical and human MS studies. Hence, the investigation of ncRNAs could be important to provide additional information regarding MS pathogenesis as well as promote the discovery of new therapeutic strategies or biomarkers.

Investigation of differentially expressed genes and dysregulated pathways involved in multiple sclerosis

Multiple Sclerosis (MS) is a neurodegenerative autoimmune and organ-specific demyelinating disorder, known to affect the central nervous system (CNS). While genetic studies have revealed several critical genes and diagnostic biomarkers associated with MS, the etiology of the disease remains poorly understood. This study is aimed at screening and identifying the key genes and canonical pathways associated with MS. Gene expression profiling of the microarray dataset GSE38010 was used to analyze two control brain samples (control 1; GSM931812, control 2; GSM931813), active inflammation stage samples (CAP1; GSM931815, CAP2; GSM931816) and late subsided stage samples (CP1; GSM931817, CP2; GSM931818) collected from patients ranging between 23 and 54years and both genders. This analysis yielded a list of 58,866 DEGs (29,433 for active-inflammation stage and 29,433 for late-subsided Stage). The interactions between the DEGs were then studied using STRING, Cytoscape software, and MCODE was employed to find the genes that form clusters. Functional enrichment and integrative analysis were performed using ClueGO/CluePedia and MetaCore™. Our data revealed dysregulated key canonical pathways in MS patients. In addition, we identified three hub genes (SCN2A, HTR2A, and HCN1) that may serve as potential biomarkers for the prognosis of MS. Furthermore, the expression patterns of HPCA and PLCB1 provide insights into the progressive stages of MS, indicating that these genes could be used in predicting MS progression. We were able to map potential biomarkers that could be used for the prognosis and diagnosis of MS.