eISSN: 1509-572x
ISSN: 1641-4640
Folia Neuropathologica
Current issue Archive Manuscripts accepted About the journal Editorial board Reviewers Abstracting and indexing Subscription Contact Instructions for authors Ethical standards and procedures
SCImago Journal & Country Rank
 
2/2022
vol. 60
 
Share:
Share:
more
 
 
Original paper

Diagnostic performance of miR-21, miR-124, miR-132, and miR-200b serums in post-stroke cognitive impairment patients

Mei Yuan
1
,
Yi-Sha Guo
2
,
Xin-Xin Zhang
3
,
Zhen-Kun Gao
4
,
Xin-Ya Shen
4
,
Yu Han
2
,
Xia Bi
1

1.
Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital, China
2.
Shanghai University of Sport, China
3.
Yandu Community Health Service Center, Jiangsu Province, China
4.
Shanghai University of Traditionary Chinese Medicine, China
Folia Neuropathol 2022; 60 (2): 228-236
Online publish date: 2022/06/30
Article file
- Diagnostic.pdf  [0.14 MB]
Get citation
ENW
EndNote
BIB
JabRef, Mendeley
RIS
Papers, Reference Manager, RefWorks, Zotero
AMA
APA
Chicago
Harvard
MLA
Vancouver
 
 

Introduction

Stroke is a common cerebrovascular disease, with high incidence and disability rate, and also is a leading cause of death worldwide. With aging population and an incidence of stroke in younger age [6,8], stroke patients have a longer life expectancy to live with disability, leading to a reduced quality of life. The burden caused by a stroke is expected to be higher in the future. Post-stroke cognitive impairment (PSCI) is one of the common complications of a stroke. There are 51.9% stroke survivors accompanied by cognitive impairment at 6 months after stroke [24], which is strongly related to poor prognosis of motor and speech deficits, thus prolonging the cost and duration of hospitalization [32].
Currently, magnetic resonance image (MRI) [1] and cognitive assessment scales [18] usually served as diagnostic indicators of PSCI. They are effective, but also have various limitations. Specifically, it is challenging to detect functional changes of patients without brain structural changes in MRI [12]. Cognitive assessment scales have the following limitations: a systematic review identify that there are 25 different types of cognitive assessment scales in clinical practice [17]. There is no consensus which cognitive assessment scale is the best screening tool for PSCI. Preferred assessment would depend on purpose of testing [22]. Moreover, there are different versions even for the same scale [21]. Optimal cut-off value of the same assessment tool at different disease stages may be different [30]. Furthermore, cognitive examination is subjective, and clinical applications have found that different people’s assessments are likely to produce different results. They are also easily affected by culture, language, education, and other factors. Together, it is necessary to find a more sensitive and objective diagnostic biomarker.
MicroRNAs (miRNAs, miRs) are 20-23 nucleotides non-coding RNAs that negatively regulate gene expression at post-transcriptional level via mRNA degradation or translational inhibition [23]. MiRNA is considered a novel and valuable biomarker of disease diagnosis [19]. A total of four miRNAs (miR-21, miR-124, miR-132, and miR-200b) were selected in this study since these miRNAs have been reported to be closely related to stroke and cognitive impairment in previous literature. MiR-21 [38] and miR-200b [16] play a neuro-protective role in stroke. MiR-124 is the most abundant miRNA in the brain [26]. MiR-132 is one of the most studied miRNAs related to cognitive function, possibly by regulating synaptic plasticity [11]. It is not yet known which miRNA can be a diagnostic biomarker in PSCI patients. Here, we characterized differential expression of serum miRNAs between PSCI and post-stroke cognitive normality (PSCN) patients, especially the expressed level of miRNAs. In addition, we detected the link between miRNA expression levels and neuro-imaging diffusion tensor imaging (DTI) results and calculated their diagnostic performance for PSCI patients.

Material and methods

Participants and serum samples

This study was approved by the Ethics Committee of Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital in China (Project ID: 2019-WJW-BX). Written informed consents for participation in this study were obtained from all individuals. Patients with a first-ever stroke were admitted to Shanghai University of Medicine and Health Sciences Affiliated Zhoupu Hospital from January 2019 to December 2019. Finally, a total of 77 patients, including 45 PSCI patients and 32 PSCN patients, completed all mini-mental state examination (MMSE) assessments. Inclusion criteria of PSCI group consisted of: 1) diagnosed with a first-ever stroke based on MRI and clinical evaluation; 2) MMSE scores ≤ 24; 3) age older than 18 years old. Exclusion criteria consisted of: 1) a history of stroke; 2) additional comorbidities of nervous system, pregnancy, infectious disease, and cancer. PSCN group had the same inclusion and exclusion criteria, except for MMSE scores (> 24).
At 14th day after stroke onset, peripheral blood (4 ml) was collected from all participants after fasting for 12 hours. Blood samples were centrifuged at 800 g for 10 min at 4°C. About 1 ml of supernatant was then transferred to a clean 1.5 ml centrifuge tube, and was centrifuged again at 1,600 g for 10 min at 4°C to obtain serum. Samples were stored at –80°C until further analysis.

Cognitive function assessment

Cognitive function was evaluated with mini-mental state examination (MMSE) scores [3] at 14th day after stroke. MMSE scores ranged from 0 to 30, consisting of orientation, attention, and calculation, recall, language, and praxis. A higher score indicated better cognitive function. A score greater than 24 showed normal cognitive function, while a score less than or equal to 24 indicated impaired cognitive function. Higher score suggested better cognitive function. All assessments were performed by the same rehabilitation therapist.

Diffusion tensor imaging analyses

To evaluate the alteration of white matter fiber bundles, diffusion tensor imaging (DTI) data were acquired by a 1.5T MRI system (TOSHIBA EXCELART Vantage) equipped with an 8-element receive head and neck coils array. The procedure was performed by a skilled radiologist. DTI images were obtained by applying a single excitation spin-echo planer sequence using the following parameters: repetition time = 12,000 ms, echo time = 100 ms, average number of signals = 40, diffuse b-value = 1,000 s/mm2, field of view = 22, matrix size = 128 × 128, and direction number of diffusion sensitive gradient = 6. Factional anisotropy (FA) value of region of interest (ROI) selected from the infarct brain areas was calculated by a software.

Quantification of miRNAs via qRT-PCR

According to the manufacturer’s instructions, total RNA was isolated from serum sample using mirVana Paris kit (Ambion, Austin, Texas) and quantified by Nano Drop ultrafine spectrophotometer. Four miRNAs (miR-124, miR-132, miR-200b, and miR-21) were selected for our study by searching and reading the relevant literature. RNA reverse transcription reaction was performed using Bio-Rad reverse transcription kit to synthesize miRNA-specific cDNA.
The expression of specific miRNA was detected by quantitative real-time PCR (qRT-PCR) using SYBR GREEN fluorescence system. Briefly, to normalize between samples, miR-39 was added. Cycling conditions for qRT-PCR included pre-denaturation at 95°C for 3 minutes, followed by a total of 40 cycles according to a cycle of 15 s at 95°C, 30 s at 60°C, and 30 s at 72°C. Then, a melt-curve analysis was obtained to evaluate PCR specificity. Relative expression of specific miRNAs was calculated by using 2−DDCt method. All reactions were repeated three times. PCR primers are presented in Table I.

Statistical analysis

All statistical analyses were performed using SPSS 18.0 statistical software, and graphs were generated by GraphPad Prism 8 software. Data were described as mean ± standard deviation (SD). Descriptive statistics, Shapiro-Wilk normality test were used to investigate characteristics of the baseline data. Normal distribution of the data was evaluated by an independent-samples’ t-test, and non-normal distribution data was evaluated by a non-parametric Mann-Whitney test. Similarly, a non-parametric Mann-Whitney test was performed to analyze differentially expressed serum miR-124, miR-132, miR-200b, and miR-21 between PSCI and PSCN patients. To estimate the correlation between miRNAs levels and cognitive status, Spearman’s correlation analysis was applied. Furthermore, using a receiver operating characteristic curve (ROC) analysis, the area under the curve (AUC) was calculated to evaluate diagnostic performance of miRNAs. P-value < 0.05 was considered statistically significant.

Results

Subject characteristics

A total of 77 stroke patients were involved in this study, including 45 PSCI patients and 32 PSCN patients. There was no significant differences in age, height, modified ranking scale (mRS) scores, and blood pressure among the groups, while PSCI patients had a smaller bodyweight than PSCN patients. Information of these stroke patients are shown in Table II.

Differential expression of miRNAs between PSCI and PSCN patients

To investigate whether these four miRNAs can serve as diagnostic biomarkers of PSCI, qRT-PCR was used to explore the differentially expressed miRNAs. The relative expression of miRNAs is demonstrated in Figure 1. The level of miR-21 showed a higher expression (4.57 ±2.67 vs. 1.29 ±1.55, Mann-Whitney test, p < 0.001) in PSCN patients than in PSCI patients. The level of miR-132 (7.39 ±8.24 vs. 2.88 ±4.24) and miR-200b (4.98 ±4.88 vs. 2.41 ±4.00) was also higher in PSCN patients, respectively (Mann-Whitney test, p < 0.01). However, there was no significant difference in the level of miR-124 among PSCI and PSCN patients.

Correlation between miRNAs level and cognitive status

The present study investigated the correlation between the miRNAs level and MMSE scores via Spearman’s correlation analysis. Results found that a significant positive correlation (r = 0.752, p < 0.001) existed between the level of miR-21 and MMSE scores (Fig. 2A). The poor positive correlation was observed in miR-132 (r = 0.319, Fig. 2B) and miR-200b (r = 0.379, Fig. 2C).

Correlation between miRNAs level and FA value

To investigate the relationship of miRNA level and brain health in PSCI patients, we conducted a Spearman’s correlation analysis to find the relationship between the miRNAs level and FA value of DTI analysis, which reflected the alteration of white matter fiber bundles. The results found that only miR-21 was closely related with FA value (r = 0.636, Fig. 3B), miR-21 was a valuable biomarker to reflect the alteration of white matter.

Diagnostic performance of differentially expressed miRNAs

ROC analysis was performed to detect the diagnostic performance of differentially expressed miRNAs in discriminating PSCI from PSCN. miR-21 performed best and showed the most significant AUC of 0.912. Moreover, miR-132 and miR-200b showed a significant performance with AUC of 0.673 and 0.692, respectively (Fig. 4A). We further explored the diagnostic performance of the combined miRNAs. The results found that miR-200b and miR-132 failed to enhance the diagnostic value of miR-21 (Fig. 4B). The combination of the three miRNAs had the most excellent value, but it was not significant compared with miR-21 alone. Overall, the diagnosis of miR-21 alone was sensitive and efficient in PSCI. The FA value can reflect the change of white matter in the brain, and was also a good diagnostic indicator for the diagnosis of PSCI. Statistical analysis showed that the combined diagnosis of miR-21 and FA value was better (Fig. 4C). These data are shown in Table III. Taken together, results suggested that miR-21 alone, or the combination of miR-21 and FA value could be diagnostic biomarkers for distinguishing PSCI and PSCN.

Discussion

Post-stroke cognitive impairment contributes to patient’s pronged hospitalization and reduced quality of life [32]. Early diagnosis is necessary. Currently, the clinical diagnosis of PSCI mainly depends on neuro-psychological evaluation and imaging assessment. However, neuro-psychological evaluation is subjective and not yet standardized [4]. Imaging assessment is expensive, and it is difficult to distinguish early cognitive impairment [12]. An early and sensitive diagnosis of PSCI is challenging.
Circulating miRNA are highly stable in the blood. Its’ detection is simple, convenient, and efficient. Recent studies have confirmed that circulating miRNAs are potential biomarkers for diagnosing diseases [27]. miR-29a and miR-146a are potential diagnostic biomarkers in distinguishing Alzheimer’s disease (AD) and vascular dementia (VaD) from healthy control subjects [7,28]. However, less research has focused on detecting potential miRNAs that discriminate PSCI from PSCN. Sessa and colleagues [29] found that higher levels of miR-200b and miR-21 indicate age-related cognitive impairment. Besides, the higher levels of miR200b, miR21, and miR124 suggest brain stroke. In this study, we confirmed that miR-21, miR-132, and miR-200b were valuable diagnostic biomarkers of PSCI. Furthermore, the level of miR-21 was substantially correlated with MMSE scores, with the best diagnostic performance. As far as we know, this is the first time that miR-21 and miR-200b were reported to serve as a diagnostic biomarkers in PSCI. Our study provides data for similar research in the future.
This study found that miR-21 could be PSCI diagnostic biomarker and corelated with cognitive status. MiR-21 has also been repeatedly proved to be closely related to cognitive function before. The expression of miR-21 was consistently positively correlated with the volume of cerebral hematoma in less than 7 days of cerebral hemorrhage, suggesting that miR-21 can reflect the development process of stroke pathology [25]. Two clinical studies have explored the performance of miR-21 in diagnosing vascular cognitive impairment. Marchegiani et al. [20] analyzed the expression differences of miR-21 in the cerebrospinal fluid between vascular cognitive impairment, AD, and cognitive functioning normal groups in a study among 17 VaD patients, showing no significant differences between the three groups. Sorensen et al. [31] obtained similar results; they compared the miR-21 expression in AD group and other dementia groups, including vascular cognitive impairment. No significant difference was found in cerebrospinal fluid or blood samples. It is worth noting that this clinical study enrolled only 4 vascular cognitive impairment patients. The results may not be sufficient to prove that miR-21 failed in distinguishing AD and VaD. Overall, current studies have shown limited use of miR-21 as a biomarker for VCI diagnosis. The diagnostic of miR-21 in PSCI has not been studied before. Moreover, miR-21 has been repeatedly proved to be a powerful anti-apoptotic factor, and plays a neuro-protective role in cerebral ischemic and refusion injury [2,37,38]. Zhou and colleagues [38] found that miR-21 upregulates the level of anti-apoptotic Bcl2 protein. Similar results were observed by Yang and colleagues [37], who found that miR-21 inhibited apoptosis by increasing the ratio of Bcl2/Bax via PTEN/Akt-dependent mechanism after cerebral ischemic and refusion injury. Overexpression of miR-21 also significantly alleviated lipid accumulation and inflammatory responses through TLR4-NFkB pathway [9]. Accordingly, miR-21 is considered to be an attractive therapeutic prospect for the treatment of stroke.
Although miR-124 failed to be PSCI diagnostic biomarker, miR-124 was confirmed to be an anti-apoptotic factor. Wang et al. [34] found that the overexpression of miR-124 upregulated Bcl2 protein in PC12 cells to alleviate cell death after ischemic stroke, possibly by activating the PI3K/Akt signaling pathway [34]. Additionally, miR-124 plays an important neuro-protective role by repressed NF-kB signaling activation and reactive oxygen species production [35].
MiR-132 and miR-200b are proved to be possible diagnostic biomarkers of PSCI in this study. MiR-132 is a well-known regulator of cognitive capacity, and is necessary for memory formation and retention [10,11]. MiR-132 has been proven to be a risk marker for PSCI [14]. Similar to our study results, the most recent research found that, compared with PSCN patients, the level of miR-132 in cerebrospinal fluid was down-regulated in PSCI patients [36]. However, miR-132 also has been found to up-regulate the serum of PSCI patients relative to PSCN patients [14]. Given that the level of miRNA depends on the time and space, and that the sample size of our study was small, results still need further investigation to confirm our findings. The possible mechanism of miR-132 protecting memory impairment was identified by down-regulating the expression of Nav1.1 and Nav1.2 [13]. Furthermore, miR-132 has shown to have protective effect on ischemia-induced hippocampal CA1 neuronal death and blood-brain barrier disruption in ischemic stroke [15,39]. Therefore, miR-132 was considered a novel therapeutic target for amelioration of cognitive deficits. MiR-200b was associated with age-related cognitive impairment and stroke consequences in humans [29], and was found to play neuro-protective roles by downregulating prolyl hydroxylase 2 levels in mice [16].
Cerebral white matter fiber bundles play an important role in the transmission of information in each brain region. Vascular lesions may harm neural network structure and function by damaging white matter. Studies have also reported that high white matter signals are predictors of PSCI [5]. These suggest that brain white matter fiber bundles are closely related to cognitive impairment. DTI, one of the best markers associated with cognitive decline, is a magnetic resonance technique that can identify the integrity of brain white matter fiber bundles, and FA value is the main parameter that reflects the integrity of white fiber bundles. The higher score of FA value indicates the better integrity of white fiber bundles [33]. This is the first time the relationship between FA value and miRNA expression was investigated.
The present study has the following limitations. Firstly, the current clinical diagnosis of PSCI is mainly done through cognitive assessment scales. Although the cognitive assessment scales are insufficient, MMSE was used as the main diagnostic scale. Therefore, the miRNAs detected in this study can only be used as assisting diagnostic markers. Secondly, MMSE is a robust test, and other more sensitive neuro-psychological tests are lacking to further detect cognitive impairment. Thirdly, the sample size of this study was relatively small, and no power calculation was included. Also, hemolysis is an important questionable subject in a miRNA study, but we did not record the hemolysis data in detail. Finally, performing array-based technique helps screen for probable and possible miRNAs. However, we mainly obtained candidates miRNAs according to the literature.
In summary, miR-21, miR-132, and miR-200b are valuable diagnostic biomarkers of PSCI. Especially the miR-21 alone, or the combination of miR-21 and FA value could be diagnostic biomarkers for distinguishing PSCI and PSCN.

Funding

This work was supported by the Scientific Research Projects of Shanghai Municipal Health Bureau in China, No. 201940031 (to XB), and the Leading Personnel Training Project of Shanghai Pudong New District Municipal Health Bureau in China, No. PWR12018-04 (to XB).

Disclosure

The authors report no conflict of interest.

References

1. Betrouni N, Yasmina M, Bombois S, Petrault M, Dondaine T, Lachaud C, Laloux C, Mendyk AM, Henon H, Bordet R. Texture features of magnetic resonance images: an early marker of post-stroke cognitive impairment. Transl Stroke Res 2020; 11: 643-652.
2. Buller B, Liu X, Wang X, Zhang RL, Zhang L, Hozeska-Solgot A, Chopp M, Zhang ZG. MicroRNA-21 protects neurons from ischemic death. FEBS J 2010; 277: 4299-4307.
3. Cumming TB, Churilov L, Linden T, Bernhardt J. Montreal Cognitive Assessment and Mini-Mental State Examination are both valid cognitive tools in stroke. Acta Neurol Scand 2013; 128: 122-129.
4. Dautzenberg G, Lijmer J, Beekman A. Diagnostic accuracy of the Montreal Cognitive Assessment (MoCA) for cognitive screening in old age psychiatry: determining cut-off scores in clinical practice. Avoiding spectrum-bias caused by healthy controls. Int J Geriatr Psychiatry 2020; 35: 261-269.
5. Dichgans M, Leys D. Vascular cognitive impairment. Circul Res 2017; 120: 573-591.
6. Dichgans M, Pulit SL, Rosand J. Stroke genetics: discovery, biology, and clinical applications. Lancet Neurol 2019; 18: 587-599.
7. Dong H, Li J, Huang L, Chen X, Li D, Wang T, Hu C, Xu J, Zhang C, Zen K, Xiao S, Yan Q, Wang C, Zhang CY. Serum microRNA profiles serve as novel biomarkers for the diagnosis of Alzheimer’s disease. Dis Markers 2015; 2015: 625659.
8. Ekker MS, Boot EM, Singhal AB, Tan KS, Debette S, Tuladhar AM, de Leeuw FE. Epidemiology, aetiology, and management of ischaemic stroke in young adults. Lancet Neurol 2018; 17: 790-801.
9. Feng J, Li A, Deng J, Yang Y, Dang L, Ye Y, Li Y, Zhang W. miR-21 attenuates lipopolysaccharide-induced lipid accumulation and inflammatory response: potential role in cerebrovascular disease. Lipids Health Dis 2014; 13: 27.
10. Hansen KF, Karelina K, Sakamoto K, Wayman GA, Impey S, Obrietan K. miRNA-132: a dynamic regulator of cognitive capacity. Brain Struct Funct 2013; 218: 817-831.
11. Hernandez-Rapp J, Smith PY, Filali M, Goupil C, Planel E, Magill ST, Goodman RH, Hebert SS. Memory formation and retention are affected in adult miR-132/212 knockout mice. Behav Brain Res 2015; 287: 15-26.
12. Hilal S, Xu X, Ikram MK, Vrooman H, Venketasubramanian N, Chen C. Intracranial stenosis in cognitive impairment and dementia. J Cereb Blood Flow Metab 2017; 37: 2262-2269.
13. Hu XL, Wang XX, Zhu YM, Xuan LN, Peng LW, Liu YQ, Yang H, Yang C, Jiao L, Hang PZ, Sun LH. MicroRNA-132 regulates total protein of Nav1.1 and Nav1.2 in the hippocampus and cortex of rat with chronic cerebral hypoperfusion. Behav Brain Res 2019; 366: 118-125.
14. Huang S, Zhao J, Huang D, Zhuo L, Liao S, Jiang Z. Serum miR-132 is a risk marker of post-stroke cognitive impairment. Neurosci Lett 2016; 615: 102-106.
15. Hwang JY, Kaneko N, Noh KM, Pontarelli F, Zukin RS. The gene silencing transcription factor REST represses miR-132 expression in hippocampal neurons destined to die. J Mol Biol 2014; 426: 3454-3466.
16. Lee ST, Chu K, Jung KH, Yoon HJ, Jeon D, Kang KM, Park KH, Bae EK, Kim M, Lee SK, Roh JK. MicroRNAs induced during ischemic preconditioning. Stroke 2010; 41: 1646-1651.
17. Lees R, Selvarajah J, Fenton C, Pendlebury ST, Langhorne P, Stott DJ, Quinn TJ. Test accuracy of cognitive screening tests for diagnosis of dementia and multidomain cognitive impairment in stroke. Stroke 2014; 45: 3008-3018.
18. Lees RA, Hendry Ba K, Broomfield N, Stott D, Larner AJ, Quinn TJ. Cognitive assessment in stroke: feasibility and test properties using differing approaches to scoring of incomplete items. Int J Geriatr Psychiatry 2017; 32: 1072-1078.
19. Lu D, Thum T. RNA-based diagnostic and therapeutic strategies for cardiovascular disease. Nat Rev Cardiol 2019; 16: 661-674.
20. Marchegiani F, Matacchione G, Ramini D, Marcheselli F, Recchioni R, Casoli T, Mercuri E, Lazzarini M, Giorgetti B, Cameriere V, Paolini S, Paciaroni L, Rossi T, Galeazzi R, Lisa R, Bonfigli AR, Procopio AD, De Luca M, Pelliccioni G, Olivieri F. Diagnostic performance of new and classic CSF biomarkers in age-related dementias. Aging (Albany NY) 2019; 11: 2420-2429.
21. McDicken JA, Elliott E, Blayney G, Makin S, Ali M, Larner AJ, Quinn TJ. Accuracy of the short-form Montreal Cognitive Assessment: systematic review and validation. Int J Geriatr Psychiatry 2019; 34: 1515-1525.
22. Mijajlovic MD, Pavlovic A, Brainin M, Heiss WD, Quinn TJ, Ihle-Hansen HB, Hermann DM, Assayag EB, Richard E, Thiel A, Kliper E, Shin YI, Kim YH, Choi S, Jung S, Lee YB, Sinanovic O, Levine DA, Schlesinger I, Mead G, Milosevic V, Leys D, Hagberg G, Ursin MH, Teuschl Y, Prokopenko S, Mozheyko E, Bezdenezhnykh A, Matz K, Aleksic V, Muresanu D, Korczyn AD, Bornstein NM. Post-stroke dementia – a comprehensive review. BMC Med 2017; 15: 11.
23. Mirzaei H, Momeni F, Saadatpour L, Sahebkar A, Goodarzi M, Masoudifar A, Kouhpayeh S, Salehi H, Mirzaei HR, Jaafari MR. MicroRNA: relevance to stroke diagnosis, prognosis, and therapy. J Cell Physiol 2018; 233: 856-865.
24. Nijsse B, Visser-Meily JM, van Mierlo ML, Post MW, de Kort PL, van Heugten CM. Temporal evolution of poststroke cognitive impairment using the Montreal Cognitive Assessment. Stroke 2017; 48: 98-104.
25. Ouyang Y, Li D, Wang H, Wan Z, Luo Q, Zhong Y, Yin M, Qing Z, Li Z, Bao B, Chen Z, Yin X, Zhu LQ. MiR-21-5p/dual-specificity phosphatase 8 signalling mediates the anti-inflammatory effect of haem oxygenase-1 in aged intracerebral haemorrhage rats. Aging Cell 2019; 18: e13022.
26. Ponomarev ED, Veremeyko T, Barteneva N, Krichevsky AM, Weiner HL. MicroRNA-124 promotes microglia quiescence and suppresses EAE by deactivating macrophages via the C/EBP-alpha-PU.1 pathway. Nat Med 2011; 17: 64-70.
27. Prabhakar P, Chandra SR, Christopher R. Circulating microRNAs as potential biomarkers for the identification of vascular dementia due to cerebral small vessel disease. Age Ageing 2017; 46: 861-864.
28. Ragusa M, Bosco P, Tamburello L, Barbagallo C, Condorelli AG, Tornitore M, Spada RS, Barbagallo D, Scalia M, Elia M, Di Pietro C, Purrello M. miRNAs Plasma Profiles in Vascular Dementia: Biomolecular Data and Biomedical Implications. Front Cell Neurosci 2016; 10: 51.
29. Sessa F, Maglietta F, Bertozzi G, Salerno M, Di Mizio G, Messina G, Montana A, Ricci P, Pomara C. Human brain injury and miRNAs: an experimental study. Int J Mol Sci 2019; 20: 1546.
30. Shi D, Chen X, Li Z. Diagnostic test accuracy of the Montreal Cognitive Assessment in the detection of post-stroke cognitive impairment under different stages and cutoffs: a systematic review and meta-analysis. Neurol Sci 2018; 39: 705-716.
31. Sorensen SS, Nygaard AB, Christensen T. miRNA expression profiles in cerebrospinal fluid and blood of patients with Alzheimer’s disease and other types of dementia – an exploratory study. Transl Neurodegener 2016; 5: 6.
32. Sun JH, Tan L, Yu JT. Post-stroke cognitive impairment: epidemiology, mechanisms and management. Ann Transl Med 2014; 2: 80.
33. Tang Y, Chen A, Zhu S, Yang L, Zhou J, Pan S, Shao M, Zhao L. Repetitive transcranial magnetic stimulation for depression after basal ganglia ischaemic stroke: protocol for a multicentre randomised double-blind placebo-controlled trial. BMJ Open 2018; 8: e018011.
34. Wang C, Wei Z, Jiang G, Liu H. Neuroprotective mechanisms of miR-124 activating PI3K/Akt signaling pathway in ischemic stroke. Exp Ther Med 2017; 13: 3315-3318.
35. Wu Y, Yao J, Feng K. miR-124-5p/NOX2 axis modulates the ROS production and the inflammatory microenvironment to protect against the cerebral I/R injury. Neurochem Res 2020; 45: 404-417.
36. Yang FW, Wang H, Wang C, Chi GN. Upregulation of acetylcholinesterase caused by downregulation of microRNA-132 is responsible for the development of dementia after ischemic stroke. J Cell Biochem 2020; 121: 135-141.
37. Yang Q, Yang K, Li A. microRNA-21 protects against ischemia-reperfusion and hypoxia-reperfusion-induced cardiocyte apoptosis via the phosphatase and tensin homolog/Akt-dependent mechanism. Mol Med Rep 2014; 9: 2213-2220.
38. Zhou J, Zhang J. Identification of miRNA-21 and miRNA-24 in plasma as potential early stage markers of acute cerebral infarction. Mol Med Rep 2014; 10: 971-976.
39. Zuo X, Lu J, Manaenko A, Qi X, Tang J, Mei Q, Xia Y, Hu Q. MicroRNA-132 attenuates cerebral injury by protecting blood-brain-barrier in MCAO mice. Exp Neurol 2019; 316: 12-19.
Copyright: © 2022 Mossakowski Medical Research Centre Polish Academy of Sciences and the Polish Association of Neuropathologists. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License (http://creativecommons.org/licenses/by-nc-sa/4.0/), allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material, provided the original work is properly cited and states its license.
Quick links
© 2022 Termedia Sp. z o.o. All rights reserved.
Developed by Bentus.