In Silico Analysis of miRNA-mediated ceRNAs as Potential MolecularBiomarkers in Glioblastoma
Yıl: 2021 Cilt: 48 Sayı: 3 Sayfa Aralığı: 451 - 467 Metin Dili: İngilizce DOI: 10.5798/dicletip.987908 İndeks Tarihi: 14-10-2021
In Silico Analysis of miRNA-mediated ceRNAs as Potential MolecularBiomarkers in Glioblastoma
Öz: Objectives: Glioblastoma multiforme (GBM) is defined as the most frequent and lethal form of the primary brain tumors in the central nervous system (CNS) in adults. Recent studies have focused on the identification of the new targets for the diagnosis and treatment of GBM and resulted in great interest for miRNAs due to their regulatory effects in cancer pathogenesis. Thus, we aimed to characterize novel molecular biomarkers for GBM by computational analysis. Methods: 118 miRNAs that are clinically related with glioblastoma and proven by experimentally were exported through miRTarBase database. 1016 genes projected by these 118 miRNAs were determined via ComiR database. Subsequently, the genes with transcribed ultraconserved regions (T-UCRs) in their exonic regions were designated and the genes which have potential competing endogenous RNA (ceRNA) activities were extracted. Genes with remarkable expression profile differences between glioblastoma and normal brain tissues among ceRNAs that are associated with glioblastoma involving T-UCR were identified. Results: The statistical analysis of the correlation between PBX3 and NRXN3 genes and glioblastoma was carried out by Spearman correlation test. PBX3 and NRXN3 expression was significantly higher and lower in glioblastoma than in normal brain tissues, respectively. On the other hand, the other genes did not have any remarkable differential expression pattern. Conclusion: Based on the findings of the current study, it is determined that NRXN3 acts as a tumor suppressor gene and NRXN3 gene is downregulated in GBM. PBX3 gene functions as an oncogene and is upregulated in GBM.
Anahtar Kelime: Glioblastomada Potansiyel Moleküler Biyobelirteçler Olarak miRNA Aracılı ceRNA’ların İn Siliko Analizi
Öz: Amaç: Glioblastoma multiforme (GBM), yetişkinlerde santral sinir sistemi (SSS)’ndeki primer beyin tümörlerinin en sıkgörülen ve en öldürücü tipi olarak tanımlanmaktadır. Son yıllardaki çalışmalar, GBM’nin teşhisi ve tedavisi için yenihedeflerin tanımlanmasına odaklanmış ve kanser patogenezindeki düzenleyici etkileri nedeniyle miRNA’lara büyük ilgiuyandırmıştır. Bu nedenle, bu çalışmada GBM için yeni moleküler biyobelirteçlerin hesaplamalı analizlerle tanımlanmasıamaçlanmıştır. Yöntemler: Glioblastoma ile klinik olarak ilişkili olan ve deneysel olarak kanıtlanmış 118 miRNA, miRTarBase veritabanından elde edildi. Elde edilen 118 miRNA tarafından hedeflenen 1016 gen ComiR veri tabanı aracılığıyla belirlendi.Akabinde, ekzonik bölgelerinde transkribe edilmiş ultra-korunmuş bölgelere (T-UCR) sahip genler belirlendi vepotansiyel olarak endojen rekabetçi RNA (ceRNA) aktivitelerine sahip olan genler ekstrakte edildi. T-UCR içerenglioblastoma ile ilişkili ceRNA’lar arasından glioblastoma ve normal beyin dokuları arasında önemli ekspresyon profilifarklılıklarına sahip genler tanımlandı. Bulgular: PBX3 ve NRXN3 genleri ile glioblastoma arasındaki korelasyonun istatistiksel analizi Spearman koralasyontesti ile gerçekleştirildi. Normal beyin dokularına göre glioblastomada PBX3 gen ekspresyonu daha yüksek iken NRXN3gen ekspresyonu daha düşüktü. Diğer taraftan, diğer genler anlamlı farklılık gösteren ekspresyon paternine sahip değildi. Sonuç: Mevcut çalışmanın bulgularına göre, NRXN3 geninin tümör baskılayıcı olarak işlev gördüğü ve GBM’dedownregüle edildiği ve PBX3 geninin onkogen olarak görev aldığı ve GBM’de upregüle edildiği belirlendi.
Anahtar Kelime: Belge Türü: Makale Makale Türü: Diğer Erişim Türü: Erişime Açık
- 1. Yool AJ, Ramesh S. Molecular targets for combined therapeutic strategies to limit glioblastoma cell migration and invasion. Front Pharmacol. 2020; 11: 358.
- 2. Haar CP, Hebbar P, Wallace GC, et al. Drug resistance in glioblastoma: A mini review. Neurochem Res. 2012; 37: 1192-200.
- 3. Yılmaz A, Altug F, Duz E, et al. Treatment options and effects of survival in glioblastoma multiforme. J Kartal TR. 2012; 23: 25-9.
- 4. Laffont B, Rayner KJ. MicroRNAs in the pathobiology of atherosclerosis. CJC. 2017; 33: 313- 24.
- 5. Witwer KW, Halushka MK. Toward the promise of microRNAs- Enhancing reproducibility and rigor in microRNA research. RNA Biol. 2016; 13: 1103-16.
- 6. Sherin K, Nair AS. Review of computational prediction of competing endogenous RNA. J Proteom Bioinform. 2019; 12.
- 7. Qi M, Yu B, Yu H, et al. Integrated analysis of a ceRNA network reveals potential prognostic lncRNAs in gastric cancer. Cancer Med. 2020; 9: 1798-817.
- 8. Fassan M, Dall’Olmo L, Galasso M, et al. Transcribed ultraconserved noncoding RNAs (TUCRs) are involved in Barret’s esophagus carcinogenesis. Oncotarget. 2014; 5: 7162-71.
- 9. Huang HY, Lin YCD, Li J, et al. miRTarBase 2020: updates to the experimentally validated microRNAtarget interaction database. Nucleic Acids Res. 2020; 48: D148-D154.
- 10. Ergun S. In silico analysis of biomarker potentials of miRNA-mediated ceRNAs in prostate cancer. Dicle Med J. 2018; 45: 415-29.
- 11. Coronnello C, Benos PV. ComiR: combinatorial microRNA target prediction tool. Nucleic Acids Res. 2013; 41: W159-W164.
- 12. Us Altay D, Ergun S. In silico analysis of biomarker potentials of miRNA mediated ceRNAs in gastric neoplasms. MBSJHS. 2019; 5: 106-19.
- 13. Avsar O. Analysis of miRNA-mediated ceRNAs in the pathogenesis of renal cell carcinoma: An in silico approach. HJSE. 2020; 7: 223-38.
- 14. Bejerano G, Pheasant M, Makunin I, et al. Ultraconserved elements in the human genome. Science. 2004; 304:1321-5.
- 15. Tang Z, Li C, Kang B, et al. GEPIA: a web server for cancer and normal gene expression profiling and interactive analysis. Nucleic Acids Res. 2017; 45: W98-W102.
- 16. Zhao Y, Huang W, Kim TM, et al. MicroRNA-29a activates a multi-component growth and invasion program in glioblastoma. J Exp Clin Cancer Res. 2019; 38.
- 17. Touat M, Idbaih A, Sanson M, et al. Glioblastoma targeted therapy: updated approaches from recent biological insights. Ann Oncol. 2017; 28: 1457-72.
- 18. Shea A, Harish V, Afzal Z, et al. MicroRNAs in glioblastoma multiforme pathogenesis and therapeutics. Cancer Med. 2016; 5: 1917-46.
- 19. Morgan R, Pandha HS. PBX3 in cancer. Cancers. 2020; 12.
- 20. Li YS, Zou Y, Dai DQ. MicroRNA-320a suppresses tumor progression by targeting PBX3 in gastric cancer and is downregulated by DNA methylation. World J Gastrointest Oncol. 2019; 11: 842-56.
- 21. Pan C, Gao H, Zheng N, et al. Mir-320 inhibits the growth of glioma cells through downregulating PBX3. Biol Res. 2017; 50.
- 22. Xu X, Bao Z, Liu Y, et al. PBX3/MEK/ERK1/2/LIN28/let-7b positive feedback loop enhances mesenchymal phenotype to promote glioblastoma migration and invasion. J Exp Clin Cancer Res. 2018; 37: 158.
- 23. Harkin LF, Lindsay SJ, Xu Y, et al. Neurexins 1-3 each have a distinct pattern of expression in the early developing human cerebral cortex. Cereb Cortex. 2017; 27: 216-32.
- 24. Sun HT, Cheng SX, Tu Y, et al. FoxQ1 promotes glioma cells proliferation and migration by regulating NRXN3 expression. PLoS One. 2013; 8: e55693.
- 25. Xiang XJ, Deng J, Liu YW, et al. Mir-1271 inhibits cell proliferation, invasion and EMT in gastric cancer by targeting FOXQ1. Cell Physi ol Biochem. 2015; 36: 1382-94.
- 26. Kusinska R, Gorniak P, Pastorczak A, et al. Influence of genomic variation in FTO at 16q12.2, MC4R at 18q22 and NRXN3 at 14q31 genes on breast cancer risk. Mol Biol Rep. 2012; 39: 2915-19.
- 27. Yang Q, Wang R, Wei B, et al. Gene and microRNA signatures are associated with the development and survival of glioblastoma patients. DNA Cell Biol. 2019; 38: 688-99
| APA | Avsar O (2021). In Silico Analysis of miRNA-mediated ceRNAs as Potential MolecularBiomarkers in Glioblastoma. , 451 - 467. 10.5798/dicletip.987908 |
| Chicago | Avsar Orcun In Silico Analysis of miRNA-mediated ceRNAs as Potential MolecularBiomarkers in Glioblastoma. (2021): 451 - 467. 10.5798/dicletip.987908 |
| MLA | Avsar Orcun In Silico Analysis of miRNA-mediated ceRNAs as Potential MolecularBiomarkers in Glioblastoma. , 2021, ss.451 - 467. 10.5798/dicletip.987908 |
| AMA | Avsar O In Silico Analysis of miRNA-mediated ceRNAs as Potential MolecularBiomarkers in Glioblastoma. . 2021; 451 - 467. 10.5798/dicletip.987908 |
| Vancouver | Avsar O In Silico Analysis of miRNA-mediated ceRNAs as Potential MolecularBiomarkers in Glioblastoma. . 2021; 451 - 467. 10.5798/dicletip.987908 |
| IEEE | Avsar O "In Silico Analysis of miRNA-mediated ceRNAs as Potential MolecularBiomarkers in Glioblastoma." , ss.451 - 467, 2021. 10.5798/dicletip.987908 |
| ISNAD | Avsar, Orcun. "In Silico Analysis of miRNA-mediated ceRNAs as Potential MolecularBiomarkers in Glioblastoma". (2021), 451-467. https://doi.org/10.5798/dicletip.987908 |
| APA | Avsar O (2021). In Silico Analysis of miRNA-mediated ceRNAs as Potential MolecularBiomarkers in Glioblastoma. Dicle Tıp Dergisi, 48(3), 451 - 467. 10.5798/dicletip.987908 |
| Chicago | Avsar Orcun In Silico Analysis of miRNA-mediated ceRNAs as Potential MolecularBiomarkers in Glioblastoma. Dicle Tıp Dergisi 48, no.3 (2021): 451 - 467. 10.5798/dicletip.987908 |
| MLA | Avsar Orcun In Silico Analysis of miRNA-mediated ceRNAs as Potential MolecularBiomarkers in Glioblastoma. Dicle Tıp Dergisi, vol.48, no.3, 2021, ss.451 - 467. 10.5798/dicletip.987908 |
| AMA | Avsar O In Silico Analysis of miRNA-mediated ceRNAs as Potential MolecularBiomarkers in Glioblastoma. Dicle Tıp Dergisi. 2021; 48(3): 451 - 467. 10.5798/dicletip.987908 |
| Vancouver | Avsar O In Silico Analysis of miRNA-mediated ceRNAs as Potential MolecularBiomarkers in Glioblastoma. Dicle Tıp Dergisi. 2021; 48(3): 451 - 467. 10.5798/dicletip.987908 |
| IEEE | Avsar O "In Silico Analysis of miRNA-mediated ceRNAs as Potential MolecularBiomarkers in Glioblastoma." Dicle Tıp Dergisi, 48, ss.451 - 467, 2021. 10.5798/dicletip.987908 |
| ISNAD | Avsar, Orcun. "In Silico Analysis of miRNA-mediated ceRNAs as Potential MolecularBiomarkers in Glioblastoma". Dicle Tıp Dergisi 48/3 (2021), 451-467. https://doi.org/10.5798/dicletip.987908 |
