Serum miR-199a-3p and miR-103a-3p are possible biomarkers for the onset of multiple sclerosis

Abstract

Multiple Sclerosis (MS) is a multifactorial and complex disease; currently only few and relatively invasive biomarkers have shown a moderate prognostic value. Finding new, more reliable and non-invasive biomarkers could allow earlier MS diagnosis and improve the conduction of therapeutic and rehabilitative protocols. We investigated whether miR-199a-3p and miR-103a-3p can be useful for this purpose. Fifty-seven healthy controls (HC) and 185 people with a diagnosis of either progressive (P-MS; N=63), relapsing (R-MS; N=63), or within 2 years since the diagnosis (short disease duration, S-MS; n=59) MS were enrolled, serum concentration of miR-199a-3p and miR-103a-3p was measured in all individuals by droplet digital PCR (ddPCR). Whereas miR-199a-3p was significantly up-regulated in the overall group of MS patients compared to HC, miR-103a-3p was significantly up-regulated in S-MS and R-MS, but not in P-MS. Interestingly, both miRNAs were up-regulated in S-MS, and their combined measurement had a good power to discriminate between S-MS and HC. These results suggest that the measurement of miR-199a-3p and miR-103a-3p serum concentration might be a useful biomarker for MS, particularly in the very initial stages of disease.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-026-42973-3.

Introduction

Multiple Sclerosis (MS) is a chronic inflammatory and immune-mediated disorder characterized by central nervous system inflammation, demyelination and neurodegeneration, and by a wide range of symptoms, including vision alterations, paresthesia, muscle weakness, fatigue and speech problems¹. The main clinical subtypes of MS are defined as Relapsing-MS (R-MS) and Progressive MS (P-MS). R-MS is characterized by episodes of acute neurological worsening followed by partial or complete recovery and periods of clinical stability; P-MS is instead characterized by a progressive disability worsening that starts from disease onset (Primary-Progressive PP), or after a period of relapse course (Secondary-Progressive SP), with or without superimposed clinical or magnetic resonance imaging (MRI) activity¹.

MicroRNAs (miRNAs) are short RNAs that do not code for proteins, but regulate gene expression². To do this, miRNAs mostly interact with the 3’UTR (untranslated region) of a gene mRNAs, blocking, or at least reducing mRNAs translation into proteins³. miRNAs play a fundamental and crucial role in biological pathways and their alteration is often associated with ongoing diseases. Measurement of miRNAs in biological samples, including blood, serum, urine and saliva has therefore become the focus of numerous analyses, in the hope to find novel and easy-to-detect disease-associated biomarkers^4,5.

miR-199a is a miRNA localized in chromosome 19 (19p13.2), known to be involved in several cancer types, acting either as an oncogene or as a tumor suppressor⁶. In particular, among many other functions, miR-199a-3p suppresses glioma cell proliferation by regulating the AKT/mTOR signaling pathway⁷. Notably, this pathway plays an important role in the pathogenesis of experimental autoimmune encephalomyelitis (EAE)⁸, the best characterized animal model for MS⁹.

miR-103a is localized on chromosome 20 (20p13). In particular miR-103a-3p, like miR-199a-3p, is deregulated in several tumors^10–12. We have recently shown that this miRNA could be involved in MS pathogenesis, as it was found up-regulated in serum of those R-MS patients who within 10 years converted in SP-MS compared to those that remained in relapse course¹³.

Based on these findings, we verified whether the expression of these two miRNAs is deregulated in MS, and whether their expression could represent a biomarker of the disease course.

Results

Demographic and clinical characterization of the subjects

Clinical characterization of the individuals enrolled in the study is presented in Table 1. Gender distribution was comparable in all the groups, whereas, as expected age of P-MS was significantly higher compared to the other groups (p<0.0001 for all comparisons). Differences, again, as per inclusion criteria, were also seen when Expanded Disability Status Scale (EDSS) score (p<0.0001 for all comparisons) and disease duration (S-MS vs. P-MS and S-MS vs. R-MS: p<0.0001; P-MS vs. R-MS: p=0.01) were compared among the three groups of PwMS.

Table 1.

Demographic and clinical characteristics of the individuals enrolled in the study.

	S-MS	P-MS	R-MS	PwMS	HC
N	59	63	63	185	57
Gender (Male %)	39	46	30	38	40
Age (years)	38.02±10.72	53.02±11.87*	40.11±9.30	43.84±12.54**	36.00±12.62
Disease duration (years)	0.87±0.76#	17.94±11.23##	13.42±0.63	10.94±10.87	---
EDSS	1.63±1.25#	5.81±0.29###	4.00±0.48	3.86±2.41	---

Data are expressed as mean ± standard deviation. PwMS: people with Multiple Sclerosis; S-MS: people with Multiple Sclerosis with short disease duration; P-MS: people with Multiple Sclerosis with a clinical diagnosis of progressive disease; R-MS: people with Multiple Sclerosis with a clinical diagnosis of relapsing disease; HC: healthy controls; EDSS: Expanded Disability Status Scale. *p<0.0001 vs. S-MS, vs. R-MS and vs. HC; **p=0.0001 vs. HC; ^#p<0.0001 vs. P-MS and vs. R-MS; ^##p=0.01 vs. R-MS; ^###p<0.0001 vs. R-MS.

Serum concentration of miR-199a-3p and miR-103a-3p

Analysis of miR-199a-3p and miR-103a-3p serum concentration by ddPCR in the enrolled subjects showed that both miRNAs are significantly more expressed in people with Multiple Sclerosis (PwMS) compared to HC. (miR-199a-3p: p=0.001; miR-103a-3p: p=0.05) (Figure 1). When different MS subgroups were compared to HC, serum concentration of miR-199a-3p was significantly increased in each group (p≤0.05 for all the comparison); comparing the MS subgroups with each other, miR-199a-3p serum concentration was observed to be significantly increased in S-MS compared to both P-MS (p=0.01) and R-MS (p=0.02) (Figure 2). No statistically significant differences were observed when P-MS and R-MS were compared.

Fig. 1.

miR-199a-3p (panel a) andmiR-103a-3p (panel b) serum concentration in enrolled subjects. PwMS: people with Multiple Sclerosis; HC: healthy controls.

Fig. 2.

miR-199a-3p serum concentration in enrolled subjects. S-MS: people with Multiple Sclerosis with short disease duration; P-MS: people with Multiple Sclerosis with a clinical diagnosis of progressive disease; R-MS: people with Multiple Sclerosis with a clinical diagnosis of relapsing disease; HC: healthy controls.

Regarding miR-103a-3p, its serum concentration was significantly increased in S-MS (p=0.007) and in R-MS (p=0.009), but not in P-MS compared to HC. Comparing all the MS subgroups, miR-103a-3p serum concentration was observed to be significantly decreased in P-MS compared to both S-MS (p=0.0009) and R-MS (p=0.003) (Figure 3). No statistically significant differences were observed when S-MS and R-MS were compared.

Fig. 3.

miR-103a-3p serum concentration in enrolled subjects. S-MS: people with Multiple Sclerosis with short disease duration; P-MS: people with Multiple Sclerosis with a clinical diagnosis of progressive disease; R-MS: people with Multiple Sclerosis with a clinical diagnosis of relapsing disease; HC: healthy controls.

Twenty-nine out of sixty-three P-MS patients were treated with disease-modifying therapies (DMTs). We thus compared serum expression of both miRNAs between those patients who did or did not receive such therapies; no statistical differences were observed between these two groups, suggesting that DMTs did not affect the expression of the examined miRNAs.

Because age, disease duration and EDSS are different among the three groups of PwMS, a correlation analysis between these three parameters and miRNAs concentration was performed and did not reveal any significant relationship (see Supplementary Fig. S1).

As indicated above, serum concentration of miR-199a-3p and miR-103a-3p were significantly increased in S-MS. Receiver Operating Characteristic (ROC) curve analysis indicated a discriminatory capacity of serum miR-199a-3p concentrations between S-MS and HC (p=0.0004; area under curve – AUC: 0.684; 95 % confidence interval: 0.591-0.767), with a sensitivity of 75.4, and a specificity of 67.8. The same was true for miR-103a-3p serum concentration (p=0.005; area under curve – AUC: 0.643; 95 % confidence interval: 0.549-0.730), with a sensitivity of 68.4, and a specificity of 57.6. Moreover, when serum concentration of two miRNAs was combined in a multivariate logistic regression model, the potential of these miRNAs to discriminate between S-MS and HC increased compared to values observed when the single serum concentration miRNA was considered (AUC: 0.737; sensitivity: 75.4; specificity: 72.9) (Figure 4).

Fig. 4.

Receiver Operating Characteristic (ROC) curve analysis, showing discriminative power between S-MS and HC of miR-199a-3p (orange dotted line), miR-103a-3p (light blue dotted line) and the combination of two miRNAs (black line). S-MS: people with Multiple Sclerosis with short disease duration; HC: Healthy Controls.

Finally, the results of bioinformatic analysis using miR-Path 4.0 showed that miR-103a-3p and miR-199a-3p are involved in several different pathways, as shown in Figure 5. To note, among these, there are specific pathways dealing with fatty acids, as metabolism, biosynthesis, elongation and degradation. The list of genes commonly targeted by both miRNAs is reported in Supplementary Table S1.

Fig. 5.

Hierarchical clustering and miRNA/KEGG pathway heatmap of miR-199a-3p and miR-103a-3p generated using Diana miRPath v.4 (DIANA Tools). KEGG pathway annotations were obtained from the KEGG database (Kyoto Encyclopedia of Genes and Genomes)³⁴.

Discussion

The finding of easy-to-detect biomarkers that can identify the presence of a disease at its earlier stages constitutes a critical challenge for medical field, particularly in the case of conditions with potentially severe and debilitating effects on patients. In the present study we showed that the serum expression of miR-199a-3p and miR-103a-3p is significantly higher in PwMS compared to healthy controls. Moreover, when PwMS are sub-grouped for disease duration, the increasing of miR-199a-3p seems to be more evident in the early stages of the disease, whereas no differences are observed between progressive and relapsing PwMS longer disease duration form. PwMS at the onset of the disease are characterized also by the increase of miR-103a-3p, but in this case its expression is even higher in R-MS, whereas it is decreased in P-MS, coming back to the values of HC.

miR-199a-3p is known to be deregulated in different tumor types^14–16. The main and more studied function of miR-199a-3p is its inhibitory activity against AKT/mTOR signaling pathway⁷, a pathway involved on cellular proliferation in tumor cells¹⁷. A direct involvement of AKT/mTOR in MS is not yet demonstrated, but several studies showed that this pathway resulted deregulated in experimental autoimmune encephalomyelitis (EAE), the animal model of MS^8,18. In particular, in EAE this pathway induces a chronic inflammatory status, and its key proteins resulted phosphorylated in the spinal cords of EAE mice⁸.

It was already reported that the expression of this miRNA is deregulated in the cerebrospinal fluid¹⁹, and inside the lymphocytes of PwMS²⁰, particularly in the remitting phase of the disease²¹, as well as in active brain white matter lesions²², but, at our knowledge, no study analyzed its expression in serum.

Regarding miR-103a-3p, we recently showed that its serum expression in PwMS can predict the conversion in secondary progressive form¹³, but its expression was similar to HC after the conversion, similarly with the results of the present study, where only R-MS and S-MS, but not P-MS, had high concentration of miR-103a-3p.

When these two miRNAs were analyzed by bioinformatics, both miRNAs are reported to be involved in several pathways dealing with fatty acids, as biosynthesis, metabolism, degradation and elongation, as similarly showed in our recent study¹³, focused on miR-103a-3p and other two miRNAs (miR34a-5p and miR-376a-3p) but not miR-199a-3p. Because fatty acids facilitate the blood-brain barrier integrity preservation²³ and their synthesis by oligodendrocytes plays a role on CSF myelination and demyelination²⁴, the present results reinforce the hypothesis that the increasing of miRNAs modulating fatty acids metabolism can interfere with myelination and remyelination, both in the phase of conversion to progressive form¹³ as well in the early disease phases following its clinical onset.

Another possible hypothesis regarding the involvement of these two miRNAs in MS pathogenesis is their involvement in neuro-inflammation. In fact, both miRNAs have anti-inflammatory activity. As mentioned above, miR-199a-3p, inhibiting AKT/mTOR pathway, turns off its signaling, counteracts the AKT-mTOR-driven inflammation that was found in the MS mouse model. Moreover, AKT/mTOR pathway positively regulates Th17 differentiation²⁵, and several studies demonstrated that Th17-driven inflammation is a typical characteristic of MS^26,27.

Also miR-103a-3p seems to have an anti-inflammatory activity, as it was found downregulated in activated B-cells²⁸ and its transfection in activated microglia causes the inhibition of inflammatory factors as TNF-α and IL-1β²⁹.

For these reasons, it is possible to hypothesize that in PwMS at the earlier stages or with a non-disabling course, the inflammatory components of the disease are efficiently counterbalanced by an up-regulation of anti-inflammatory miRNAs like miR-103a-3p and miR-199a-3p, whereas this “defensive” mechanism switched off in the progressive phase of MS.

Because we found that miR-103a-3p and miR-199a-3p are high in the very first stages of the disease, future studies on the measurements of these two miRNAs in the serum of people with clinically isolated (CIS) or radiologically isolated syndrome (RIC) suggestive of MS will be important to verify they could be helpful to an earlier diagnosis of MS and to choose/monitor treatment strategies with the best risk/benefit trade-off.

One limitation of this study is that follow-up samples are not available, so that measuring longitudinally the same miRNAs is not possible: a validation of our results with an independent longitudinal study appears necessary to give a higher causality and predictive value of our findings. Another limitation is the lacking of analysis on miRNAs target genes: future studies analyzing their expression are planned and will be very informative in shedding further light on the metabolic pathways possibly involved in the different stages of the disease. Furthermore, while we hypothesize a link between fatty acid metabolism and anti-inflammatory pathways, direct functional validation is lacking, future in vitro studies will be essential to confirm suck links. Finally, since no patients had relapses or received corticosteroids in the 90 days before sampling, S-MS and R-MS patients were treatment-free for at least 12 weeks before sample collection. In addition, no differences in serum miR-199a-3p and miR-103a-3p levels were found in P-MS patients between those treated and untreated with DMTs. However, the possible effects of relapses, corticosteroid use, and different DMTs cannot be completely ruled out, especially in terms of long-term effects. Therefore, further studies are needed to better explore these factors.

Taken together, the present results suggest that the measurement of serum miR-199a-3p and miR-103a-3p, in particular when analyzed together and combined, can be biomarkers to discriminate between HC and PwMS, in particular at the onset of the disease, and have the potential to serve as paraclinical tools to monitor treatment intervention.

Methods

Patients and controls

Two-hundred-forty-two individuals were enrolled in the study: 185 people with MS (PwMS), according to McDonald criteria³⁰, and 57 gender-matched healthy controls (HC) volunteers. All PwMS were recruited by the Multiple Sclerosis Unit of IRCCS Fondazione Don Gnocchi at the beginning of a rehabilitation program. Sixty-three PwMS were classified as having a progressive disease (P-MS) and 63 a relapsing disease (R-MS); the remaining 59 PwMS was enrolled within 2 years since the appearance of the first symptoms (short MS disease duration, S-MS), all with relapse course. No PwMS underwent disease relapses or needed corticosteroid treatment in the 90 days before sampling. All enrolled S-MS and R-MS patients were treatment-free for at least 12 weeks prior to sampling. Among P-MS, 34/63 were treatment-free for at least 12 weeks prior to sampling, whereas the other 29 were treated with disease-modifying therapies (DMT). In particular, 12/29 were treated with Natalizumab, 8/29 with platform therapies, 7/29 with first-line oral DMTs and 2/29 with azathioprine.

Subjects with neoplastic and severe cardiological diseases as well as other autoimmune diseases were not included in the enrollment. The study complies with the ethical principles of the Declaration of Helsinki; written informed consent was provided by all the individuals involved in the study and was designed according to a protocol approved by the local ethics committee of IRCCS Fondazione Don Carlo Gnocchi (protocol number: #07_11/05/2017).

Serum miRNAs extraction and cDNA reverse transcription

For each enrolled subject, whole blood was collected in vacutainer SST II tubes (Becton Dickinson & Co., Rutheford, NJ, US). Serum was obtained from blood by centrifugation (2000 g x 10’ at room temperature); absence of hemolysis was evaluated by visual inspection and by spectrophotometric measurement of hemoglobin absorbance at 414 nm³¹; after the collection, sera were immediately stored at -80°C. Total miRNAs were semi-automatically extracted from 200 µl of serum using a column-based kit (miRNeasy serum/plasma kit, Qiagen GmBH, Hilden, Germany) by robot workstation (Qiacube, Qiagen), according to manufacturer’s instruction. miRNAs were quantified by Qubit microRNA assay kit (Thermo Fisher, Foster City, CA, US) using a Qubit 3.0 Fluorometer (Thermo Fisher), according to manufacturer’s recommendation. For all samples an equal concentration of extracted miRNAs was retro-transcribed in cDNA (miRCURY LNA RT kit, Qiagen) with the following protocol: 60’ at 42°C, heat-inactivation of reverse transcriptase enzyme for 5’ at 95°C, and a hold at 4°C. All the variables involved in the procedure were kept consistent throughout the study to avoid variations due to sample differences and handling.

miR-199a-3p and miR-103a-3p quantification by droplet digital PCR (ddPCR)

Absolute quantification of miR-199a-3p and miR-103a-3p was performed by droplet digital PCR (ddPCR, QX200, Bio-Rad, Hercules. CA, US). Briefly, 3 µl of cDNA (1:10 for miR-199a-3p; 1:25 for miR-103a-3p), mixed with the specific LNA^TM primers (miR-199a3-p: YP00204536; miR-103a-3p: YP00204063; Qiagen) and ddPCR EvaGreen Supermix (Bio-Rad), were emulsified with droplet generator oil (Bio-Rad) using the Automated Droplet Generator (Bio-Rad). Droplets were transferred to a 96-well reaction plate and heat-sealed with a pierceable sealing foil sheet by a PCR plate sealer (PX1, Bio-Rad). PCR amplification was performed in sealed 96- well plate using a T00 thermal cycler (Bio-Rad) as follows: 10’ at 95 °C, 40 cycles at 94 °C for 30’’ and 58°C for 60’’, followed by 10’ at 98 °C and a hold at 4 °C. The plate was then transferred to a QX200 droplet reader (Bio-Rad) for the quantification. Each well was queried for fluorescence to determine the quantity of positive events (droplets), and the results were displayed as dot plots. The miRNA concentration was expressed as copies/ng extracted RNA.

Data analysis and statistics

For ddPCR analysis, the QuantaSoft software, version 1.7.4.0917 (Bio-Rad) was used. Thresholds were defined based on the fluorescence amplitude of negative control, which included a no template control in each run. Samples with no droplets exceeding the threshold were classified as undetectable³². For quantitative and statistical analyses, undetectable samples were assigned an arbitrary value of 0.001 copies/ng of DNA. This value, which is below the assay’s limit of detection, was used to enable logarithmic transformation and statistical comparison across samples, while avoiding overestimation of target abundance. Normally distributed data were reported as mean ± standard deviation, whereas not-normally distributed data were reported as median and interquartile range (IQR). The distribution of categorical data was compared using Chi square test. Normally distributed data were analyzed by parametric analysis of variance (ANOVA) and Student’s t-test, whereas not-normally distributed data were analyzed by non parametric Kruskal-Wallis test and Mann-Whitney U test. Receiver operating characteristic (ROC) analysis and area under the ROC curve (AUC) were used to evaluate potential biomarkers to detect MS in comparison with HC. Biomarkers miR-199a-3p and miR-103a-3p were considered alone and in combination, after a multivariate logistic regression model and extracting the model linear predictor. p values corresponding to ≤0.05 are described as statistically significant in the text. The statistical analyses were performed using commercial software (MedCalc Statistical Software, version 14.10.2, Ostend, Belgium). Bioinformatics analysis was conducted by applying the web-based tool miRPath (version 4.0)³³ to identify the shared pathways targeted by miR-103a-3p and miR-199a-3p.

Author contributions

S.A., R.M., R.N., M.B.P. and M.C. participated in study design, data collection, statistical analysis/ interpretation, manuscript drafting, and manuscript revisions. D.C., and M.R. collected clinical data. L.A. participated in study design and analyzed data. All authors have read and approved the manuscript.

Funding

The work of RM was supported by #NEXTGENERATIONEU (NGEU) and funded by the Ministry of University and Research (MUR), National Recovery and Resilience Plan (NRRP), Project MNESYS (PE00000006) - A multiscale integrated approach to the study of the nervous system in health and disease (DN. 1 5 53 11.10.2022). The work was also supported and funded by the Italian Ministry of Health – Ricerca Corrente 2025-2027, and partially supported by grants from Fondazione Romeo ed Enrica Invernizzi.

Data availability

The datasets generated during and/or analysed during the current study are available in the Zenodo repository, 10.5281/zenodo.18374563.

Declarations

Competing interests

The authors declare no competing interests.