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. 2018 Jan 9;18(1):4.
doi: 10.1186/s12883-017-1008-x.

Plasma long non-coding RNA BACE1 as a novel biomarker for diagnosis of Alzheimer disease

Affiliations

Plasma long non-coding RNA BACE1 as a novel biomarker for diagnosis of Alzheimer disease

Liang Feng et al. BMC Neurol. .

Abstract

Backgrounds: Long non-coding RNA (LncRNA) have been reported to be involved in the pathogenesis of neurodegenerative diseases, but whether it can serve as a biomarker for Alzheimer disease (AD) is not yet known.

Methods: The present study selected four specific LncRNA (17A, 51A, BACE1 and BC200) as possible AD biomarker. RT-qPCR was performed to validate the LncRNA. Receiver operating characteristic curve (ROC) and area under the ROC curve (AUC) were applied to study the potential of LncRNA as a biomarker in a population of 88 AD patients and 72 control individuals.

Results: We found that the plasma LncRNA BACE1 level of AD patients was significantly higher than that of healthy controls (p = 0.006). Plasma level of LncRNA 17A, 51A and BC200 did not show a significant difference between two groups (p = 0.098, p = 0.204 and p = 0.232, respectively). ROC curve analysis showed that LncRNA BACE1 was the best candidate of these LncRNA (95% CI: 0.553-0.781, p = 0.003). In addition, no correlation was found for expression of these LncRNA in both control and AD groups with age or MMSE scale (p > 0.05).

Conclusions: Our present study compared the plasma level of four LncRNA between AD and non-AD patients, and found that the level of the BACE1 is increased in the plasma of AD patients and have a high specificity (88%) for AD, indicating BACE1 may be a potential candidate biomarker to predict AD.

Keywords: Alzheimer disease; Biomarker; Diagnosis; Long non-coding RNA.

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Conflict of interest statement

Ethics approval and consent to participate

The study was approved by the Ethics Committee of the First Affiliated Hospital of Wenzhou Medical University. In addition, written informed consent was obtained from all patients or their families in accordance with the Declaration of Helsinki.

Consent for publication

Written informed consent for participation and publication was obtained by every participant.

Competing interests

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

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

Figures

Fig. 1
Fig. 1
Levels of 17A (a), 51A (b), BACE1 (c) and BC200 (d) in plasma of Alzheimer disease (AD) patients and controls. Expression of lncRNAs was expressed relative to their respective level of cel-miR-39. The bar represents median with interquartile range
Fig. 2
Fig. 2
Correlation analyses between 17A, 51A, BACE1 and BC200 in plasma of Alzheimer disease (AD) patients and control (Ctrl) groups. Spearman’s rank correlation coefficient (r) and (r2) along with 95% confidence intervals (CI) and P values are listed above each chart. 17A vs 51A (a); 17A vs BACE1 (b); 17A vs BC200 (c); 51A vs BACE1 (d); 51A vs BC200 (e); and BACE1 vs BC200 (f)
Fig. 3
Fig. 3
ROC curve analyses of LncRNA 17A (a), 51A (b), BACE1 (c) and BC200 (d) for diagnosis of AD in pilot samples. Statistical differences between different ROC curves (e). Original set included 88 AD patients and 72 control subjects. AUC = area under the ROC curve, ROC = receiver operating characteristic

References

    1. Ferri CP, Prince M, Brayne C, Brodaty H, Fratiglioni L, Ganguli M, Hall K, Hasegawa K, Hendrie H, Huang Y, Jorm A, Mathers C, Menezes PR, Rimmer E, Scazufca M. Alzheimer's disease international. Global prevalence of dementia: a Delphi consensus study. Lancet. 2005;366:2112–2117. doi: 10.1016/S0140-6736(05)67889-0. - DOI - PMC - PubMed
    1. Prince M, Bryce R, Albanese E, Wimo A, Ribeiro W, Ferri CP. The global prevalence of dementia: a systematic review and metaanalysis. Alzheimers Dement. 2013;9:63–75. doi: 10.1016/j.jalz.2012.11.007. - DOI - PubMed
    1. Anand R, Gill KD, Mahdi AA. Therapeutics of Alzheimer's disease: past, present and future. Neuropharmacology. 2014;76:27–50. doi: 10.1016/j.neuropharm.2013.07.004. - DOI - PubMed
    1. Janssen JC, Beck JA, Campbell TA, Dickinson A, Fox NC, Harvey RJ, Houlden H, Rossor MN, Collinge J. Early onset familial Alzheimer's disease: mutation frequency in 31 families. Neurology. 2003;60:235–239. doi: 10.1212/01.WNL.0000042088.22694.E3. - DOI - PubMed
    1. Gatz M, Reynolds CA, Fratiglioni L, Johansson B, Mortimer JA, Berg S, Fiske A, Pedersen NL. Role of genes and environments for explaining Alzheimer disease. Arch Gen Psychiatry. 2006;63:168–174. doi: 10.1001/archpsyc.63.2.168. - DOI - PubMed

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