Jüvenil miyoklonik epilepside potansiyel biyobelirteç olarak miR-1179'un değerlendirilmesi

Yıl 2022, Cilt: 12 Sayı: 1, 1 - 5, 18.03.2022

Seda Süsgün Ceyhun Toruntay Alişan Bayrakoğlu Ferda Uslu Emrah Yücesan

https://doi.org/10.26650/experimed.2021.1033564

Öz

Amaç: Juvenil miyoklonik epilepsi (JME), çocukluk çağı epilepsilerinin en yaygın tiplerinden biridir ve tüm epilepsilerin %5-10'unu oluşturur. Birçok hastalıkta olduğu gibi epilepside de mikroRNA'ların (miRNA'lar) değişen ekspresyon seviyeleri bildirilmiştir. Bilindiği gibi, miRNA'lar gen ekspresyonunu post-transkripsiyonel olarak düzenlerler ve klinik örneklerdeki stabiliteleri sayesinde tanısal biyobelirteç potansiyeline sahiptirler. Burada, JME hastalarında miR-1179 ifade düzeyini belirlemeyi ve miR-1179'un tanısal biyobelirteç potansiyelini değerlendirmeyi amaçladık.
Gereç ve Yöntem: Bu çalışmaya, 20 hasta ve 20 sağlıklı kontrol dahil edilmiş ve katılımcıların periferik kan örneklerinden total RNA ekstrakte edilmiştir. miR-1179'un rölatif ekspresyon seviyesini hesaplamak için qRT-PZR gerçekleştirilmiştir. Ek olarak, JME'de miR-1179'un tanısal değerini değerlendirmek için ROC eğrileri oluşturulmuştur.
Bulgular: JME tanılı hastalarda sağlıklı kontrollere kıyasla miR-1179’un ifade seviyesi istatistiksel olarak anlamlı bir şekilde artmıştır (p<0,0001). ROC analizi miR-1179'un 0,89 AUC değeri ile iyi bir tanısal biyobelirteç olduğunu ortaya koymuştur.
Sonuç: miR-1179, JME tanısında dikkate değer bir biyobelirteç olarak değerlendirilebilir. miR-1179 ve CALM1 arasındaki etkileşim fonksiyonel çalışmalarla güçlendirilmelidir. Daha büyük kohortlarla yapılacak ileri araştırmalar JME'nin etiyopatogenezini aydınlatmaya yardımcı olacaktır.

Anahtar Kelimeler

Jüvenil miyoklonik epilepsi, miR-1179, ROC eğrisi, biyobelirteç

Assessment of miR-1179 As a Potential Biomarker in Juvenile Myoclonic Epilepsy

Öz

Objective: Juvenile myoclonic epilepsy (JME) is one of the most common childhood types of epilepsy and comprises 5-10% of all epilepsies. Altered expression levels of microRNAs (miRNAs) have been reported in epilepsy as in many diseases. As is known, miRNAs regulate gene expression post-transcriptionally and have potential as diagnostic biomarkers due to their stability in clinical samples. Herein, this study aimed to evaluate miR-1179 levels of JME patients and assess the potential of miR-1179 as a diagnostic biomarker.

Material and Method: Twenty patients and 20 healthy controls were recruited in this study and total RNA was extracted from peripheral blood samples of participants. Quantitative real-time polymerase chain reaction (qRT-PCR) was performed to calculate the relative expression level of miR-1179. Additionally, receiver operating characteristic (ROC) curve was conducted to evaluate the diagnostic value of miR-1179 in JME.

Results: Expression levels of miR-1179 were statistically significantly increased in patients with JME compared to healthy controls (p<0.0001). ROC analysis revealed that miR-1179 is a well diagnostic biomarker with an area under the curve (AUC) of 0.89.

Conclusion: miR-1179 may be considered a remarkable biomarker in the diagnosis of JME. The interaction between miR-1179 and its target Calmodulin 1 (CALM1) should be reinforced through functional studies. Further research in larger cohorts will help to enlighten the etiopathogenesis of JME.

Anahtar Kelimeler

Juvenile myoclonic epilepsy, miR-1179, ROC curve, biomarker

Kaynakça

• 1. Moshe SL, Perucca E, Ryvlin P, Tomson T. Epilepsy: new advances. Lancet 2015; 385(9971): 884-98. [CrossRef] google scholar
• 2. Pitkanen A, Loscher W, Vezzani A, Becker AJ, Simonato M, Lukasiuk K, et al. Advances in the development of biomarkers for epilepsy. Lancet Neurol 2016; 15(8): 843-56. [CrossRef] google scholar
• 3. Pietrafusa N, La Neve A, de Palma L, Boero G, Luisi C, Vigevano F, et al. Juvenile myoclonic epilepsy: Long-term prognosis and risk factors. Brain Dev 2021; 43(6): 688-97. [CrossRef] google scholar
• 4. Amrutkar C, Riel-Romero RM. Juvenile Myoclonic Epilepsy. 2021 Aug 11. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2022. google scholar
• 5. Vishnoi A, Rani S. MiRNA Biogenesis and Regulation of Diseases: An Overview. Methods Mol Biol 2017; 1509: 1-10. [CrossRef] google scholar
• 6. Hydbring P, Badalian-Very G. Clinical applications of microRNAs. F1000Res 2013; 2: 136. [CrossRef] google scholar
• 7. Süsgün S, Karacan İ, Yücesan E. Optimization of One Step Reverse Transcription Quantitative PCR Method for miRNA Expression Analyses. Experimed 2021; 11(2): 113-9. [CrossRef] google scholar
• 8. Condrat CE, Thompson DC, Barbu MG, Bugnar OL, Boboc A, Cre-toiu D, et al. miRNAs as Biomarkers in Disease: Latest Findings Regarding Their Role in Diagnosis and Prognosis. Cells 2020; 9(2). [CrossRef] google scholar
• 9. Huang HY, Lin YC, Li J, Huang KY, Shrestha S, Hong HC, et al. miR-TarBase 2020: updates to the experimentally validated microR-NA-target interaction database. Nucleic Acids Res 2020; 48(D1): D148-D54. [CrossRef] google scholar
• 10. Szklarczyk D, Gable AL, Nastou KC, Lyon D, Kirsch R, Pyysalo S, et al. The STRING database in 2021: customizable protein-protein net-works, and functional characterization of user-uploaded gene/mea-surement sets. Nucleic Acids Res 2021; 49(D1): D605-D12. [CrossRef] google scholar
• 11. Dziadkowiak E, Chojdak-Lukasiewicz J, Olejniczak P, Paradowski B. Regulation of microRNA Expression in Sleep Disorders in Patients with Epilepsy. Int J Mol Sci 2021; 22(14). [CrossRef] google scholar
• 12. Riedel G, Rudrich U, Fekete-Drimusz N, Manns MP, Vondran FW, Bock M. An extended DeltaCT-method facilitating normalisation with multiple reference genes suited for quantitative RT-PCR anal-yses of human hepatocyte-like cells. PLoS One 2014; 9(3): e93031. [CrossRef] google scholar
• 13. Martins-Ferreira R, Chaves J, Carvalho C, Bettencourt A, Chorao R, Freitas J, et al. Circulating microRNAs as potential biomarkers for genetic generalized epilepsies: a three microRNA panel. Eur J Neurol 2020; 27(4): 660-6. [CrossRef] google scholar
• 14. Krauskopf J, Verheijen M, Kleinjans JC, de Kok TM, Caiment F. De-velopment and regulatory application of microRNA biomarkers. Biomark Med 2015; 9(11): 1137-51. [CrossRef] google scholar
• 15. Ma Y. The Challenge of microRNA as a Biomarker of Epilepsy. Curr Neuropharmacol 2018; 16(1): 37-42. [CrossRef] google scholar
• 16. An N, Zhao W, Liu Y, Yang X, Chen P. Elevated serum miR-106b and miR-146a in patients with focal and generalized epilepsy. Epilepsy Res 2016; 127: 311-6. [CrossRef] google scholar
• 17. Hsu MJ, Chang YC, Hsueh HM. Biomarker selection for medical diagnosis using the partial area under the ROC curve. BMC Res Notes 2014; 7:25. [CrossRef] google scholar
• 18. Jensen HH, Brohus M, Nyegaard M, Overgaard MT. Human Calm-odulin Mutations. Front Mol Neurosci 2018; 11: 396. [CrossRef] google scholar
• 19. Mori MX, Vander Kooi CW, Leahy DJ, Yue DT. Crystal structure of the CaV2 IQ domain in complex with Ca2+/calmodulin: high-res-olution mechanistic implications for channel regulation by Ca2+. Structure 2008; 16(4): 607-20. [CrossRef] google scholar
• 20. Chioza B, Wilkie H, Nashef L, Blower J, McCormick D, Sham P, et al. Association between the alpha(1a) calcium channel gene CAC-NA1A and idiopathic generalized epilepsy. Neurology 2001; 56(9): 1245-6. [CrossRef] google scholar