Skip to main content
Egyptian Journal of Medical Human Genetics
Download PDF

Expression and diagnostic values of MIAT, H19, and NRON long non-coding RNAs in multiple sclerosis patients

Egyptian Journal of Medical Human Genetics volume 23, Article number: 46 (2022) Cite this article

Abstract

Background

Multiple sclerosis (MS) is a chronic inflammatory disease. Various long non-coding RNAs (lncRNAs) appear to have an important role in the pathophysiology of MS. This study aimed at evaluating the expression levels of lncRNAs, MIAT, H19, and NRON in peripheral blood of MS cases to a healthy control group. We collected blood samples of 95 MS cases (76 relapsing–remitting (RR) and 19 secondary progressive (SP) MS) and 95 controls. We used quantitative real-time PCR for the evaluation of gene expression. The correlation between expression with clinical parameters was analyzed by a multiple linear regression model. Receiver operating characteristic (ROC) curve analysis was carried out to detect the diagnostic potential of lncRNAs levels according to the area under the curve (AUC).

Results

MIAT, H19, and NRON were significantly increased in the RRMS and SPMS subgroups compared to the controls. We found that the H19 and MIAT expression significantly were higher in SPMS compared with RRMS. Patients with RRMS had a greater level of the average NRON expression is compared with SPMS patients. The expression level of H19 significantly was higher in females relative to male patients. Based on the area under curve (AUC) values, NRON had the best performance in the differentiation of MS patients from controls (AUC = 0.95, P < 0.0001). A combination of MIAT, H19, and NRON expression levels could be useful in differentiating MS patients with 93.6% sensitivity, 98.9% specificity, and diagnostic power of 0.96 (P < 0.0001).

Conclusions

The levels of MIAT, H19, and NRON in peripheral blood could be important biomarkers for MS diagnosis.

Background

Multiple sclerosis (MS) is a chronic complex autoimmune disease that causes the demyelination of neurons in the central nervous system (CNS) [1]. MS is especially common in young female adults [2]. A broad range of symptoms may appear in MS patients, such as pain, visual sensory disturbance, motor impairments, cognitive deficits, and fatigue. The different factors such as autoimmunity, genetic predisposition, and environmental influenced the pathophysiology of MS diseases. Evidence indicates that the reaction between abnormal T cells and CNS autoantigens can cause inflammation, demyelination, and neurodegeneration [3]. In addition, B cells act as an important source of plasma cells that generate antibodies while also regulating T cell production and autoimmune processes. B cells may affect the pro-inflammatory process of other immune cells [4].

Many genomic studies have demonstrated the presence of a large portion of DNA that doesn't code for proteins, but is transcribed to the different types of RNAs. The lncRNAs are a type of non-coding RNAs known to possess the ability to regulate cell proliferation through different mechanisms [5]. The dysregulation of lncRNAs is considered in the cause of many neurological disorders [6,7,8]. Research works have indicated that the expression of lncRNAs is significantly dysregulated in MS patients [9,10,11,12,13].

Immune regulation was affected by the function of different lncRNAs. A previous study revealed an important connection between the lncRNA MIAT (Myocardial infarction-associated transcript) and inflammatory response [14]. MIAT regulates the differentiation of neural stem cells into oligodendrocytes in the CNS [15]. The NRON (Non-Coding Repressor Of NFAT) lncRNA is an endogenous ribonucleic acid that is detected in human T cells and can regulate IL-2 expression and NFAT Ca2+-activated transcription factor in activated T cells [16].

The lncRNA H19 is a lncRNA with a critical role in immune response regulation. It is also an imprinted gene that is expressed only from the maternal allele [17, 18].

Although the role of MIAT, H19, and NRON in cellular pathways and different cancers has been assessed, only one study has been conducted to evaluate the expression level of NRON lncRNA in 10 MS cases. Because there is evidence showing that lncRNAs are stable in human serum, circulating serum RNA levels could serve as a biomarker in non-invasive diagnostic applications [19, 20].

Research on the role of lncRNAs in MS pathogenesis is just beginning. The significance of the present study becomes more apparent due to high incidence and prevalence of MS in Iran [21]. Therefore, we aimed to assess the effectiveness of using known lncRNAs as potential biomarkers for the diagnosis of MS.

Methods

Study subjects

The present case and control study was carried out to assess the expression levels of three lncRNAs (MIAT, H19, and NRON) in 95 MS cases, who were compared to 95 controls from August 2018 to August 2020 at Namazi Hospital in Shiraz. Among all patients, 76 individuals were relapsing–remitting (RR) and 19 were secondary progressive (SP). Controls comprised a representative sample of Shiraz’s population; sex- and age-matched controls were randomly selected from neighbor controls at the nearest case’s place matched.

All participants in the present study were clinically diagnosed by an expert neurologist according to McDonald’s criteria [22]. All cases included in this study showed EDSS progression with no evidence of a relapse in the last 24 months. In the present study, we considered the following characteristics as exclusion criteria: recent infection, smoking history, malignancy, alcohol abuse, and the presence of any other inflammatory or autoimmune diseases. The examined subjects were aged 20–56 years.

Ethics approval for this research was obtained by the local ethics committee of the Kazeroon Branch, Islamic Azad University, Iran (IR.IAU.KAU.REC.1398.034). The subjects completed a written informed consent and voluntarily agreed to participate in the present study as a part of a large prospective project.

Collection of blood samples

After obtaining an informed consent form, the peripheral venous blood samples were collected from MS cases and control individuals in ethylene diamine tetra acetic acid-coated (EDTA) tubes as an anticoagulant.

RNA extraction and cDNA synthesis

A total RNA extraction kit (Favorgen, Taiwan) was used to isolate Total RNA from peripheral blood according to the manufacturer’s instructions. cDNA synthesis Kit (Yektatajhiz, Iran) was employed for cDNA synthesis from RNA samples. For cDNA synthesis, RNA samples with theA260/A230 and A260/A280 ratios greater than 1.7 were selected.

Quantitative RT-PCR measurement of the lncRNA levels

Primer Express and Gene Runner software v.3.0 were applied to design and analyze the oligonucleotide primers. By searching the BLAST website, the primers were confirmed to avoid non-specific PCR product formation. Specific primers’ evaluations of MIAT, H19, NRON, and the TBP gene (used as a reference gene) are shown in Table 1. Gene expression was normalized relative to the housekeeping genes GAPDH and TBP. TBP was selected as the preferential reference gene given its low variance in Ct between the different samples (data not shown). The relative abundance of the lncRNAs’ levels was calculated by real-time RT-PCR using Real Q Plus 2 × Master Mix Green Low ROX™ (Ampliqon, Denmark). We used the thermal-cycling settings of 10 min at 95 °C (one repeat) accompanied by 40 cycles for 15 s at 95 °C and 1 min at 60 °C. Every complete amplification phase was accompanied by a melting phase (15 s at 95 °C, 30 s at 60 °C, and 15 s at 95 °C). Cycle threshold (Ct) values were used to express the differences in relative levels. ΔCt indicates the difference between the Ct values of TBP and lncRNAs. The relative lncRNAs expression levels for each subject were determined using 2−ΔCt [23].

Table 1 The nucleotide sequence of primers
Full size table

Statistical analysis

The IncRNAs relative expression levels were reported as mean ± SE. Student’s t test was employed to compare the mean expression of each lncRNA gene between case and control groups. The Beta coefficient of lncRNAs expression levels was estimated in univariate and multiple linear regression analyses. Using a multiple linear regression model, we adjusted for the most important clinical factors. Receiver operating characteristic (ROC) curve analysis was carried out to detect lncRNAs levels as a diagnostic biomarker for MS according to the area under the curve (AUC). We used the multiple logistic regressions to combine the age and sex variables in ROC analysis. The analyses were performed using the GraphPad Prism 5.0 and the SPSS software (version 19.0). The P value of < 0.05 was regarded as statistically significant.

Results

Clinical and demographic characteristics

Clinical and demographic characteristics of MS cases and healthy controls are summarized in Table 2. No significant difference was demonstrated in sex and age ratio (P > 0.05).

Table 2 Demographic and clinical characteristic of healthy controls and patients
Full size table

LncRNAs (MIAT, H19 and NRON) expression levels

Figure 1 demonstrates that MIAT, H19, and NRON levels were significantly higher in MS cases than in healthy controls (P < 0.0001), the median log2 fold changes were as follows, respectively 1.10, 2.79, and 1.30. The significantly high expression of MIAT, H19, and NRON can distinguish MS cases from healthy cases (Fig. 1A). Then, lncRNAs expression levels were compared in RRMS and SPMS subtypes with healthy controls separately. MIAT, H19, and NRON expression levels were significantly increased in the RRMS and SPMS subgroups when compared to the controls separately (P < 0.001). One-way ANOVA analysis showed that the MIAT and H19 expression in SPMS significantly was higher than RRMS, while NRON level in RRMS was higher relative to SPMS (Fig. 1B).

Fig. 1
figure 1

The LncRNAs of MIAT, H19 and NRON versus the TBP gene levels in the MS cases and control, (I) the levels of MIAT (A), H19 (B), and NRON (C) in controls were significantly lower compared to MS patients, (II) the relative expression of MIAT (A), H19 (B), and NRON (C) (with log2 fold change) in MS subgroups in comparison with normal healthy controls. Data were shown as mean ± SE. The data were analyzed using Student’s t test or one-way ANOVA. 2−ΔCt values for each individual were used to create each figure. ***P < 0.001 significant differences vs. control subjects. Abbreviations: RR, relapsing–remitting; SP, secondary progressive

Full size image

Association between clinical variables and LncRNAs expression

Multiple linear regression analysis was done to detect the association between RRMS and SPMS patients and lncRNAs expression levels after adjusting for several clinical confounders (Table 3). The lncRNAs expression levels were significantly associated with RRMS and SPMS.

Table 3 Association between MIAT, H19, and NRON levels with clinical parameters in MS patients: using univariate and multiple linear regression models
Full size table

Patients with SPMS had a greater level of the average MIAT expression compared with RRMS patients [Beta = 1.48, 95% CI (0.62–2.35) P = 0.001]. Compared with RRMS patients, patients with SPMS have a higher levels of H19 expression [Beta = 1.92, 95% CI (0.54–3.29) P = 0.007]. Also, H19 expression in female patients was significantly higher than male patients [Beta = 2.76 95% CI (1.25–4.28) P = 0.001]. Patients with RRMS had a greater level of the average NRON expression compared with SPMS patients [Beta = 1.49, 95% CI (0.48–2.51) P = 0.004].

Predictive power of MIAT, H19, and NRON in MS diagnosis

ROC curve analyses were done to assess the diagnostic values of MIAT, H19, NRON, and whole lncRNAs expression levels and their usefulness in predicting MS. The results of these analyses are presented in Fig. 2 and Table 4. Three lncRNAs were shown to be effective in differentiating MS cases from controls. MIAT had an AUC of 0.861 (95% CI: 0.804–0.918; sensitivity = 89.47%, specificity = 80.00%), H19 had an AUC of 0.941 (95% CI: 0.903–0.979; sensitivity = 91.58%, specificity = 91.58%), NRON had an AUC of 0.951 (95% CI: 0.922–0.981; sensitivity = 93.68%, specificity = 91.58%), and whole lncRNAs expression had an AUC of 0.966 (95% CI: 0.935–0.997; sensitivity = 93.68%, specificity = 98.95%). These findings demonstrated that MIAT, H19, NRON, and whole lncRNAs expression can be considered important biomarkers for MS diagnosis.

Fig. 2
figure 2

ROC curves. A ROC curve analysis of MIAT for diagnosis MS patients from the controls, B ROC curve analysis of H19 for diagnosis MS patients from the controls, C ROC curve analysis of NRON for diagnosis MS patients from the controls, D ROC curve analysis of whole expression for diagnosis MS patients from the controls

Full size image
Table 4 Prediction of diagnostic value by selected biomarkers (log2 fold change)
Full size table

Discussion

The present case–control study was performed on southwest Iranian individuals. We reported an upregulation of MIAT, H19, and NRON lncRNAs in the peripheral blood of MS patients in comparison with healthy subjects, the median log2 fold changes were as follows, respectively, 1.10, 2.79, and 1.30. The previous studies reported the dysregulation of various lncRNAs in MS cases [9,10,11]. For example, Shaker et al. found a significant increase in the expression levels of lnc-DC and MALAT1 in MS patients [10]. Dastmalchi et al. showed that NEAT1, PANDA, and TUG1 lncRNAs were over-expressed in MS cases when compared with controls [11]. One previous study [13] showed that three lncRNAs (NEAT1, TUG1, and RN7SKRNA) were up-regulated in RR patients. The downregulation and upregulation of six lncRNAs in MS cases were investigated in another study [24]. We found that the expression levels of MIAT and H19 in SP patients were significantly higher than in RR patients. However, NRON expression levels in RR patients showed significantly higher expression relative to SP patients.

NRON is a lncRNA repressor that acts as a specific regulator of NFAT nuclear trafficking and interacts with the importin-beta superfamily [25]. NFATs regulate the transcriptional induction of genes that encode for immune activators/modulators such as GM-CSF, IL- 2, IL-3, IL-4, IL-5, IL-13, L-8, IFNγ,CD5, CD25, CD28, and CD40 [26]. In T cells, NFAT proteins govern gene expression, thereby regulating their activation, differentiation, and development in addition to the maintenance and induction of T cell tolerance [27]. The expression level of the NRON lncRNA was significantly higher in MS patients than in controls. This outcome is contrary to that of Fenoglio et al., who found that NRON was downregulated in peripheral blood of 10 MS cases in comparison with controls. They showed that NRON correlates with MS duration [12]. In Fenoglio study, the downregulation of NRON in 10 cases (5 RRMS and 5 PPMS) was evaluated, while in our study, lncRNAs expression was evaluated in 95 MS patients (76 RRMS and 19 SPMS). We also reported the significant upregulation of NRON in two subtypes of MS relative to control. Additionally, our results showed no significant correlation between the expression levels of NRON with disease duration, EDSS score, and age among MS patients. The mean disease duration in our study was 7.82 ± 6.00 years it seems that the difference in duration of the MS disease in the two studies has led to this controversy. NRON expression is likely to be high in the early years of the MS disease and then gradually decreases.

The H19 lncRNA has an important role in the regulation of immune response [17]. H19 is an imprinted gene that is expressed from its maternal allele [18]. The deletion of H19 genes facilitates cell cycle progression in hematopoietic stem cells through the induction of maternal IGF2 [28]. H19 might contribute to the pathogenesis of MS based on its role in the IGF system and the importance of hematopoietic stem cells in the regulation of immune response [29, 30]. One previous investigation demonstrated the participation of H19 in rheumatoid arthritis (RA) pathology according to its overexpression in RA cases and its hypersensitivity to cytokine regulation/starvation in these cases [31]. The upregulation of H19 has been reported in ischemic stroke patients with a potential for the early diagnosis of IS [32]. Zeis et al. demonstrated  that over-expression of the IGF1 and IGF2 genes was found in inactive demyelinated lesions [33]. We demonstrated that the expression level of H19 was significantly up-regulated in the blood of MS cases in comparison with controls. We also observed that H19 expression levels correlated with sex so that the higher expression in females was detected compared with male patients. The female-specific elevated expression of H19 was reported in a previous study [34]. Adriaenssens et al. reported that 17-beta-estradiol stimulated the endogenous H19 gene in MCF-7 cells [35]. On the other hand, the estrogen–ERα–H19 signaling axis may also be important in the development of breast cancer [36]. This gene acts as an estrogen receptor modulator [37]. As many articles have suggested the importance of sex hormones in determining the onset and outcome of multiple sclerosisin in females [38, 39]. These findings have raised the challenging question of whether estrogen may be present in the pathophysiology of MS disease by overexpression in H19 lncRNA? Further studies with a larger sample size are needed to confirm the strong correlation between sex and H19 expression level in MS patients. Our results also showed that the H19 expression in SPMS patients was 5.56 times higher than that in RRMS patients. SPMS patients are usually older than RRMS with higher disability and hospitalization rates relative to RR [40]. But in our study, patients with SPMS (n = 19) had not have higher EDSS scores and age compared with RRMS (76). Thus, we can suppose that a larger sample size for SPMS patients is needed to clarify the significant difference between age and EDSS with RRMS. This result indirectly suggests that H19 expression in MS disease may have a positive correlation with the disability score of patients but larger sample size is needed to reach this correlation.

MIAT is a well-characterized lncRNA that affects cellular functions such as apoptosis, invasion, and proliferation in many human diseases. The regulatory mechanism of MIAT is very complex [41]. In patients with coronary artery disease (CAD) compared with controls, the serum level of MIAT was significantly increased and positively correlated with serum IL-6 and TNF-α [42]. Previous research works have shown that the MIAT level can be used as a biomarker for the diagnosis and prognosis of different diseases such as CAD [42, 43], various cancers [44, 45], and ischemic stroke [46]. Zhu et al. demonstrated that the expression levels of MIAT were significantly up-regulated in the leukocytes of ischemic stroke (IS) patients in comparison with controls [46]. We demonstrated that the expression level of MIAT was significantly higher in MS cases than in controls. Also, in the present study, we showed that MIAT expression was 5.93 times higher in SP cases than in RR cases. We could not detect the positive correlation between MIAT expression and EDSS score but significantly high expression of MIAT in SPMS relative to RRMS indirectly may show the positive association between MIAT and MS severity.

A valuable method for the early screening of MS is developing a high-sensitive non-invasive blood biomarker. Evidence shows that circulating lncRNAs could be considered MS biomarkers [10, 11, 47]. Elevated MIAT, H19, and NRON expression levels have been reported as characteristics of various disorders (48,49,50,51,52,53). Therefore, they could also be used as a non-specific biomarker for MS. Among the three lncRNAs investigated in the present study, NRON showed the best efficiency as a biomarker-based on its sensitivity, specificity, and AUC values.

In the present study, all patients were chosen consecutively from hospitals during the same period. The participants represented a variety of ethnic groups, though selection bias could not be avoided. More accurate findings could be observed with larger sample sizes to verify our results. Also, in the current investigation, we evaluated the expression levels of lncRNAs without performing a full transcriptome analysis. Furthermore, more investigations are needed to detect the exact molecular mechanisms by which MIAT, H19, and NRON participate in MS pathology. Although the current study indicates that circulating MIAT, H19, and NRON can be used as diagnostic biomarkers for MS, further research is required to find reliable blood biomarkers for clinical use.

Conclusions

This research showed the upregulation of three specific lncRNAs (MIAT, H19, and NRON) in the peripheral blood of 95 MS cases. Additional research with larger sample size is needed to confirm the present findings and clarify the molecular mechanisms of these lncRNAs in the pathogenesis of MS. Their dysregulation profiles indicate that they could be considered potential biomarkers for predicting the course of MS or patients’ responses to treatment.

Availability of data and materials

Data and materials will be available if needed.

Abbreviations

MS:

Multiple sclerosis

lncRNAs:

Long non-coding RNAs

RR:

Relapsing–remitting

SP:

Secondary progressive

AUC:

Area under the curve

CNS:

Central nervous system

EDSS:

Expanded disability status scale

RA:

Rheumatoid arthritis

PR:

Progressive-relapsing

PP:

Primary-progressive

Ct:

Cycle threshold

OR:

Odds ratio

ROC:

Receiver operating characteristic

IS:

Ischemic stroke

References

  1. Abolhasani Foroughi A, Salahi R, Nikseresht A, Heidari H, Nazeri M, Khorsand A (2017) Comparison of diffusion-weighted imaging and enhanced T1-weighted sequencing in patients with multiple sclerosis. Neuroradiol J 30(4):347–351

    PubMed  PubMed Central  Article  Google Scholar 

  2. Foroughi AA, Saeedi-Moghadam M, Zeinali-Rafsanjani B, Nazeri M (2019) Comparison between T2, STIR and PSIR sequences, for detection of cervical cord MS plaques. Iran J Radiol 16(3):e82134

    Google Scholar 

  3. Münz C, Lünemann JD, Getts MT, Miller SD (2009) Antiviral immune responses: triggers of or triggered by autoimmunity? Nat Rev Immunol 9(4):246–258

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  4. Arneth BM (2019) Impact of B cells to the pathophysiology of multiple sclerosis. J Neuroinflammation 16(1):1–9

    Article  Google Scholar 

  5. Fang Y, Fullwood MJ (2016) Roles, functions, and mechanisms of long non-coding RNAs in cancer. Genom Proteom Bioinform 14(1):42–54

    Article  Google Scholar 

  6. Qureshi IA, Mattick JS, Mehler MF (2010) Long non-coding RNAs in nervous system function and disease. Brain Res 1338:20–35

    CAS  PubMed  Article  Google Scholar 

  7. Pastori C, Wahlestedt C (2012) Involvement of long noncoding RNAs in diseases affecting the central nervous system. RNA Biol 9(6):860–870

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  8. Ghafouri-Fard S, Taheri M (2020) A comprehensive review of non-coding RNAs functions in multiple sclerosis. Eur J Pharmacol 879:173127

    CAS  PubMed  Article  Google Scholar 

  9. Eftekharian MM, Ghafouri-Fard S, Soudyab M, Omrani MD, Rahimi M, Sayad A et al (2017) Expression analysis of long non-coding RNAs in the blood of multiple sclerosis patients. J Mol Neurosci 63(3–4):333–341

    CAS  PubMed  Article  Google Scholar 

  10. Shaker OG, Mahmoud RH, Abdelaleem OO, Ibrahem EG, Mohamed AA, Zaki OM et al (2019) LncRNAs, MALAT1 and lnc-DC as potential biomarkers for multiple sclerosis diagnosis. Biosci Rep 39(1):1–11

    Article  Google Scholar 

  11. Dastmalchi R, Ghafouri-Fard S, Omrani MD, Mazdeh M, Sayad A, Taheri M (2018) Dysregulation of long non-coding RNA profile in peripheral blood of multiple sclerosis patients. Mult Scler Relat Disord 25:219–226

    PubMed  Article  Google Scholar 

  12. Fenoglio C, Oldoni E, Serpente M, Milena A, Arcaro M, D’Anca M et al (2018) LncRNAs expression profile in peripheral blood mononuclear cells from multiple sclerosis patients. J Neuroimmunol 324:129–135

    CAS  PubMed  Article  Google Scholar 

  13. Santoro M, Nociti V, Lucchini M, De Fino C, Losavio FA, Mirabella M (2016) Expression profile of long non-coding RNAs in serum of patients with multiple sclerosis. J Mol Neurosci 59(1):18–23

    CAS  PubMed  Article  Google Scholar 

  14. Li C, Pan S, Song Y, Li Y, Qu J (2019) Silence of lncRNA MIAT protects ATDC5 cells against lipopolysaccharides challenge via up-regulating miR-132. Artif Cells Nanomed Biotechnol 47(1):2521–2527

    CAS  PubMed  Article  Google Scholar 

  15. Mercer TR, Dinger ME, Sunkin SM, Mehler MF, Mattick JS (2008) Specific expression of long noncoding RNAs in the mouse brain. Proc Natl Acad Sci 105(2):716–721

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  16. Chen J, Ao L, Yang J (2019) Long non-coding RNAs in diseases related to inflammation and immunity. Ann Transl Med 7(18):494

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  17. Safari MR, Rezaei FM, Dehghan A, Noroozi R, Taheri M, Ghafouri-Fard S (2019) Genomic variants within the long non-coding RNA H19 confer risk of breast cancer in Iranian population. Gene 701:121–124

    CAS  PubMed  Article  Google Scholar 

  18. Ariel I, de Groot N, Hochberg A (2000) Imprinted H19 gene expression in embryogenesis and human cancer: the oncofetal connection. Am J Med Genet 91(1):46–50

    CAS  PubMed  Article  Google Scholar 

  19. Shaker OG, Senousy MA, Elbaz EM (2017) Association of rs6983267 at 8q24, HULC rs7763881 polymorphisms and serum lncRNAs CCAT2 and HULC with colorectal cancer in Egyptian patients. Sci Rep 7(1):1–11

    CAS  Article  Google Scholar 

  20. Duan W, Du L, Jiang X, Wang R, Yan S, Xie Y et al (2016) Identification of a serum circulating lncRNA panel for the diagnosis and recurrence prediction of bladder cancer. Oncotarget 7(48):78850

    PubMed  PubMed Central  Article  Google Scholar 

  21. Azami M, YektaKooshali MH, Shohani M, Khorshidi A, Mahmudi L (2019) Epidemiology of multiple sclerosis in Iran: a systematic review and meta-analysis. PLoS ONE 14(4):e0214738

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  22. Thompson AJ, Banwell BL, Barkhof F, Carroll WM, Coetzee T, Comi G et al (2018) Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol 17(2):162–173

    Article  PubMed  Google Scholar 

  23. Schmittgen TD, Livak KJ (2008) Analyzing real-time PCR data by the comparative C T method. Nat Protoc 3(6):1101

    CAS  PubMed  Article  Google Scholar 

  24. Zhang F, Liu G, Bu Y, Ma X, Hao J (2016) Expression profile of long noncoding RNAs and mRNAs in peripheral blood mononuclear cells from myasthenia gravis patients. J Neuroimmunol 299:124–129

    CAS  PubMed  Article  Google Scholar 

  25. Turner M, Galloway A, Vigorito E (2014) Noncoding RNA and its associated proteins as regulatory elements of the immune system. Nat Immunol 15(6):484

    CAS  PubMed  Article  Google Scholar 

  26. Kipanyula MJ, Kimaro WH, Etet PF (2016) The emerging roles of the calcineurin-nuclear factor of activated T-lymphocytes pathway in nervous system functions and diseases. J Aging Res 2016:5081021

    PubMed  PubMed Central  Article  Google Scholar 

  27. Dietz L, Frommer F, Vogel AL, Vaeth M, Serfling E, Waisman A et al (2015) NFAT1 deficit and NFAT2 deficit attenuate EAE via different mechanisms. Eur J Immunol 45(5):1377–1389

    CAS  PubMed  Article  Google Scholar 

  28. Venkatraman A, He XC, Thorvaldsen JL, Sugimura R, Perry JM, Tao F et al (2013) Maternal imprinting at the H19–Igf2 locus maintains adult haematopoietic stem cell quiescence. Nature 500(7462):345–349

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  29. Chesik D, Wilczak N, De Keyser J (2007) The insulin-like growth factor system in multiple sclerosis. Int Rev Neurobiol 79:203–226

    CAS  PubMed  Article  Google Scholar 

  30. Granick JL, Simon SI, Borjesson DL (2012) Hematopoietic stem and progenitor cells as effectors in innate immunity. Bone Marrow Res 2012:165107

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  31. Stuhlmüller B, Kunisch E, Franz J, Martinez-Gamboa L, Hernandez MM, Pruss A et al (2003) Detection of oncofetal h19 RNA in rheumatoid arthritis synovial tissue. Am J Pathol 163(3):901–911

    PubMed  PubMed Central  Article  Google Scholar 

  32. Rezaei M, Mokhtari MJ, Bayat M, Safari A, Dianatpuor M, Tabrizi R et al (2021) Long non-coding RNA H19 expression and functional polymorphism rs217727 are linked to increased ischemic stroke risk. BMC Neurol 21(1):54

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  33. Zeis T, Howell O, Reynolds R, Schaeren-Wiemers N (2018) Molecular pathology of multiple sclerosis lesions reveals a heterogeneous expression pattern of genes involved in oligodendrogliogenesis. Exp Neurol 305:76–88

    CAS  PubMed  Article  Google Scholar 

  34. Reinius B, Kanduri C (2013) Elevated expression of H19 and Igf2 in the female mouse eye. PLoS ONE 8(2):e56611

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  35. Adriaenssens E, Lottin S, Dugimont T, Fauquette W, Coll J, Dupouy JP et al (1999) Steroid hormones modulate H19 gene expression in both mammary gland and uterus. Oncogene 18(31):4460–4473

    CAS  PubMed  Article  Google Scholar 

  36. Basak P, Chatterjee S, Weger S, Bruce MC, Murphy LC, Raouf A (2015) Estrogen regulates luminal progenitor cell differentiation through H19 gene expression. Endocr Relat Cancer 22(4):505–517

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. Basak P, Chatterjee S, Bhat V, Su A, Jin H, Lee-Wing V et al (2018) Long non-coding RNA H19 acts as an estrogen receptor modulator that is required for endocrine therapy resistance in ER+ breast cancer cells. Cell Physiol Biochem 51(4):1518–1532

    CAS  PubMed  Article  Google Scholar 

  38. Avila M, Bansal A, Culberson J, Peiris AN (2018) The role of sex hormones in multiple sclerosis. Eur Neurol 80(1–2):93–99

    CAS  PubMed  Article  Google Scholar 

  39. Bove R, Chitnis T (2014) The role of gender and sex hormones in determining the onset and outcome of multiple sclerosis. Mult Scler 20(5):520–526

    CAS  PubMed  Article  Google Scholar 

  40. Gross HJ, Watson C (2017) Characteristics, burden of illness, and physical functioning of patients with relapsing-remitting and secondary progressive multiple sclerosis: a cross-sectional US survey. Neuropsychiatr Dis Treat 13:1349–1357

    PubMed  PubMed Central  Article  Google Scholar 

  41. Sun C, Huang L, Li Z, Leng K, Xu Y, Jiang X et al (2018) Long non-coding RNA MIAT in development and disease: a new player in an old game. J Biomed Sci 25(1):1–7

    Article  CAS  Google Scholar 

  42. Tan J, Liu S, Jiang Q, Yu T, Huang K (2019) LncRNA-MIAT increased in patients with coronary atherosclerotic heart disease. Cardiol Res Pract 2019:6280194

    PubMed  PubMed Central  Article  Google Scholar 

  43. Azat M, Huojiahemaiti X, Gao R, Peng P (2019) Long noncoding RNA MIAT: a potential role in the diagnosis and mediation of acute myocardial infarction. Mol Med Rep 20(6):5216–5222

    CAS  PubMed  Google Scholar 

  44. Zhou S, Xu A, Song T, Gao F, Sun H, Kong X (2020) lncRNA MIAT regulates cell growth, migration, and invasion through sponging miR-150-5p in ovarian cancer. Cancer Biother Radiopharm 35(9):650–660

    CAS  PubMed  Article  Google Scholar 

  45. Ye T, Feng J, Cui M, Yang J, Wan X, Xie D et al (2021) LncRNA MIAT services as a noninvasive biomarker for diagnosis and correlated with immune infiltrates in breast cancer. Int J Womens Health 13:991–1004

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  46. Zhu M, Li N, Luo P, Jing W, Wen X, Liang C et al (2018) Peripheral blood leukocyte expression of lncRNA MIAT and its diagnostic and prognostic value in ischemic stroke. J Stroke Cerebrovasc Dis 27(2):326–337

    PubMed  Article  Google Scholar 

  47. Pahlevan Kakhki M, Nikravesh A, Shirvani Farsani Z, Sahraian MA, Behmanesh M (2018) HOTAIR but not ANRIL long non-coding RNA contributes to the pathogenesis of multiple sclerosis. Immunology 153(4):479–487

    CAS  PubMed  Article  Google Scholar 

  48. Sun W, Yang Y, Xu C, Xie Y, Guo J (2016) Roles of long noncoding RNAs in gastric cancer and their clinical applications. J Cancer Res Clin Oncol 142(11):2231–2237

    CAS  PubMed  Article  Google Scholar 

  49. Abdollahzadeh S, Ghorbian S (2019) Association of the study between LncRNA-H19 gene polymorphisms with the risk of breast cancer. J Clin Lab Anal 33(3):e22826

    PubMed  Article  CAS  Google Scholar 

  50. Blackshaw S, Harpavat S, Trimarchi J, Cai L, Huang H, Kuo WP et al (2004) Genomic analysis of mouse retinal development. PLoS Biol 2(9):E247

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  51. Sone M, Hayashi T, Tarui H, Agata K, Takeichi M, Nakagawa S (2007) The mRNA-like noncoding RNA Gomafu constitutes a novel nuclear domain in a subset of neurons. J Cell Sci 120(15):2498–2506

    CAS  PubMed  Article  Google Scholar 

  52. Ishii N, Ozaki K, Sato H, Mizuno H, Saito S, Takahashi A et al (2006) Identification of a novel non-coding RNA, MIAT, that confers risk of myocardial infarction. J Hum Genet 51(12):1087–1099

    CAS  PubMed  Article  Google Scholar 

  53. Xiong T, Huang C, Li J, Yu S, Chen F, Zhang Z et al (2020) LncRNA NRON promotes the proliferation, metastasis and EMT process in bladder cancer. J Cancer 11(7):1751

    CAS  PubMed  PubMed Central  Article  Google Scholar 

Download references

Acknowledgements

The present study was derived from a thesis submitted by Mehrnoosh Amiri in partial fulfillment of the requirement for the degree of Master of Science in Genetics. This work was supported by Islamic Azad University, Zarghan Branch, Iran.

Funding

Not applicable.

Author information

Authors and Affiliations

  1. Department of Biology, Zarghan Branch, Islamic Azad University, Zarghan, Iran

    Mehrnoosh Amiri & Mohammad Javad Mokhtari

  2. Clinical Neurology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran

    Mahnaz Bayat & Afshin Borhani-Haghighi

  3. Stem Cells Technology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran

    Anahid Safari & Mehdi Dianatpuor

  4. Non-Communicable Diseases Research Center, Fasa University of Medical Sciences, Fasa, Iran

    Reza Tabrizi

Authors
  1. Mehrnoosh Amiri
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad Javad Mokhtari
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mahnaz Bayat
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Anahid Safari
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mehdi Dianatpuor
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Reza Tabrizi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Afshin Borhani-Haghighi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

MA contributed to study concept and design, acquisition of data. MJ contributed to study concept and design, acquisition of data, analysis and interpretation of data, study supervision, drafting/revising the manuscript for content. MB contributed to study concept and design, acquisition of data drafting/revising the manuscript for content. AS contributed to acquisition of data drafting. MD contributed to acquisition of data drafting. RT contributed to analysis and interpretation of data. AB contributed to study concept and design, acquisition of data, study supervision, drafting/ revising the manuscript for content. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Mohammad Javad Mokhtari or Mahnaz Bayat.

Ethics declarations

Ethics approval and consent to participate

Ethics approval for this research was obtained by the local ethics committee of the Kazeroon Branch, Islamic Azad University, Iran (IR.IAU.KAU.REC.1398.034). The subjects completed a written informed consent and voluntarily agreed to participate in the present study as a part of a large prospective project.

Consent for publication

Not applicable.

Competing interests

All authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Amiri, M., Mokhtari, M.J., Bayat, M. et al. Expression and diagnostic values of MIAT, H19, and NRON long non-coding RNAs in multiple sclerosis patients. Egypt J Med Hum Genet 23, 46 (2022). https://doi.org/10.1186/s43042-022-00260-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s43042-022-00260-6

Keywords

  • LncRNA
  • Multiple sclerosis
  • Biomarkers
  • Real-time PCR
  • Gene expression