In:
Neuro-Oncology, Oxford University Press (OUP), Vol. 21, No. Supplement_6 ( 2019-11-11), p. vi98-vi98
Abstract:
Cerebrospinal fluid (CSF) baths extracellular environment of the central nervous system, and thus, it is ideal source of tumor diagnostic biomarkers like microRNAs (miRNAs), short non-coding RNAs involved in the pathogenesis of many cancers. As dysregulated levels of brain tumor specific miRNAs have been already observed in CSF, analysis of CSF miRNAs in brain tumor patients might help to develop new diagnostic platform. Next-Generation sequencing (NGS) was performed for analysis of small RNAs in 89 CSF samples taken from 32 glioblastomas (GBM), 14 low-grade gliomas (LGG), 11 meningiomas, 13 brain metastases and 19 non-tumor donors. Subsequently, according to NGS results levels of 10 miRNAs were measured in independent set of CSF samples (41 GBM, 44 meningiomas, 12 brain metastases and 20 non-tumor donors) using TaqMan Advanced miRNA Assays. NGS analysis revealed 22, 12 and 35 CSF miRNAs with significantly different levels in GBM, meningiomas, and brain metastases (adj.p 〈 0.0005, adj.p 〈 0.01, and adj.p 〈 0.005) respectively, in comparison with non-tumor CSF samples. Subsequent validation of selected CSF miRNAs has confirmed different levels of 7 miRNAs in GBM, 2 in meningiomas, and 2 in brain metastases compared to non-tumors. Panel of miR-30e-5p and miR-140-5p was able to distinguish brain metastases with 65% sensitivity and 100% specificity compared to non-tumor samples (AUC = 0.8167); panel of miR-21-3p and miR-196-5p classified metastatic patients with 78% sensitivity and 92 % specificity in comparison to GBM (AUC = 0.90854) and with 75% sensitivity and 83% specificity compared to meningiomas (AUC = 0.84848). We have observed that CSFs from patients with various primary brain tumors and metastases are characterized by specific miRNA signatures. This work was supported by the Ministry of Health, Czech Republic grant nr. NV18-03-00398 and the Ministry of Education, Youth and Sports, Czech Republic under the project CEITEC 2020 (LQ1601).
Type of Medium:
Online Resource
ISSN:
1522-8517
,
1523-5866
DOI:
10.1093/neuonc/noz175.405
Language:
English
Publisher:
Oxford University Press (OUP)
Publication Date:
2019
detail.hit.zdb_id:
2094060-9
