Table 2.
The preclinical and human studies that demonstrated alterations in miRNA expression levels could be useful as potential biomarkers in MS.
| MS Type | Sample | Experimental Models | MicroRNAs | Role | Ref. |
|---|---|---|---|---|---|
| PPMS, RRMS, and SPMS | Blood from patients (serum) | 50 MS patients | miR-143.3p ↑↓; miR-92a.3p ↑; miR-486.5p ↑; and miR-142.5p ↓ | Potentially useful as biomarkers related to MS severity | [101] |
| MS | CSF | 151 MS patients and EAE mice model | miR-142-3p ↑ | IL-1beta/miR-142-3p/GLAST pathway and also as a negative MS biomarker in CSF | [102] |
| RRMS, SPMS, and PPMS | Blood from patients (serum) | 57 MS patients, 18 clinically isolated syndrome patients, and 32 healthy controls over the four-year follow-up | miR-128-3p ↑, miR-191-5p ↑, miR-24-3p nd, and miR-223-3p nd | Potentially useful as biomarkers related to disability accumulation | [103] |
| RRMS | CSF, serum, and PBMC | 10 patients with relapsing MS and 10 HC | ↑ miR-15a-3p/124-5p/149-3p/29c-3p/33a-3p/34c-5p/297 | Distinctive markers of intrathecal inflammation | [104] |
| RRMS | Serum | cohort study | ↑ miR-320b and miR-25-3p | Used as biomarkers for MS severity | [105] |
| MS | Serum | 30 MS patients and 30 HC | ↑ miRNA146a and miRNA155 | Higher levels associated with MS disability grade | [106] |
| MS | Bioinformatics analysis | gene miRNA dataset GSE17846 and mRNA dataset GSE21942 | ↑ hsa-miR-142-3p, hsa-miR-107, hsa-miR-140-5p, and hsa-mi0R-613 | Different target hub genes | [107] |
| RRMS | Blood samples | 32 MS patients and 32 HC | ↓ miR-18a-5p | P53 signaling, MAPK signaling pathway, and apoptosis; prognostic biomarkers of high risk | [108] |
| RRMS | Blood samples | 19 pregnant patients | ↓ miR-1, miR-20a, miR-28, miR-95, miR-146a, miR-335, and miR-625 | Possible makers of the beneficial effects of pregnancy over MS | [109] |
| SPMS and RRMS | Blood samples | 84 Egyptian patients | ↓ miR-300 and miR-450b-5p | Changes in the expression levels of ROCK2 and miRNAs 300 and 450b-5p could be useful as biomarkers, as well as related to the degree of disability and MS progression | [110] |
| MS | Bioinformatics analysis | / | ↓ hsa-miR-106a-5p | Possible future biomarkers of MS diagnosis | [111] |
| MS | Various analysis | EAE mice | ↓ miRNA-7188-5p and miR-7235 | Downstream inflammatory targets | [112] |
| RRMS | PBMCs | 75 patients and 75 HC | ↓ miRNA-145 and miRNA-155 | Regulation of inflammatory mechanisms | [113] |
| RRMS | CSF, serum exosomes | 30 untreated RRMS patients and 30 HC | ↑ Let-7 g-5p, miR-18a-5p, miR-145-5p, miR-374a-5p, miR-150-5p, and miR-342-3p ↓ miR-132-5p, miR-320a-5p, and miR-17-5p ↑ miR-15a-5p, miR-19b-3p, and miR-432-5p |
Possible future use as MS biomarkers | [114] |
| PPMS | CSF, serum | multicentric study | ↓ let-7b-5p; miR-143-3p in CSF; miR-20a-5p and miR-320b, dysregulated in serum; ↑ miR-142-5p in RRMS compared with other neurological conditions |
Possible markers in MS to be further studied | [115] |
| RRMS | Blood samples | 20 patients + 20 HC | ↑ miR-20, ↓ miR-21, miR-26, miR-155 Let-7 |
Alterations in the miRNA expression levels in MS patients compared to HC | [116] |
| RRMS, SPMS, and PPMS | Blood samples | 261 patients with MS and 250 HC | hsa-miR-146a and hsa-miR-223 | Genotype variants considered as risk factors for MS | [117] |
| RRMS | Blood samples | 194 patients and 188 HC | DROSHA and DICER1 XPO5 RAN AGO1 g variations) | Genotype variants of miRNA processing genes considered as risk factors for MS | [118] |
| MS | Bioinformatics analysis | deep-learning analysis | hsa-miR-605-5p, hsa-miR-15b-5p, and hsa-miR-16-5p | Could be involved in a possible role in MS pathogenesis | [119] |
| MS | Bioinformatics analysis | peripheral blood samples from NCBI GEO datasets using GEO2R | mir-142–3p, mir-98–5p, mir-629–5p, and mir-212–3p | PI3K-Akt, MAPK, and JAK-STAT | [120] |
| MS | Bioinformatics analysis | rs540457553 and rs76149940 SNPs | miR-21 and miR146a/b | Interaction with RNA-binding proteins, such as AGO36 and DGCR8 | [121] |
| MS | GEO database | expression data | hsa-mir-16-5p | Possible new target in MS pathogenesis | [122] |
| MS | Integrated bioinformatics approaches | integrated bioinformatics approaches | hsa-mir-155-5p, hsa-mir-182-5p, hsa-mir-320a, hsa-mir-148b-3p, and has-mir-301a5p | Interaction with BDNF, IFN-β, NEFL, NTF3, IL-10, NGF, and CH3L1 | [123] |
MS: multiple sclerosis; PPMS: primary progressive MS; RRMS: relapsing–remitting MS; SPMS: secondary progressive MS; CSF: cerebrospinal fluid; EAE: experimental autoimmune encephalomyelitis; GLAST: glial glutamate-aspartate transporter; HC: healthy controls; PBMCs: peripheral blood mononuclear cells; ROCK2: rho-associated coiled-coil-containing kinase isoform 2; PI3K/AKT: phosphatidylinositol 3-kinase/protein kinase B; JAK-STAT: Janus kinase-signal transducer of activation; BDNF: brain-derived neurotrophic factor; INF-β: interferon-β; NEFL: neurofilament L; NTF3: neurotrophin 3; IL-10: interleukin-10; NGF: nerve growth factor; CH3L1: chitinase-3-like protein 1; ↑: upregulation; ↓: downregulation.