Plasmatic circRNA Predicting the Occurrence of Human Glioblastoma
Authors Chen A, Zhong L, Ju K, Lu T, Lv J, Cao H
Received 6 February 2020
Accepted for publication 9 April 2020
Published 29 April 2020 Volume 2020:12 Pages 2917—2923
Checked for plagiarism Yes
Review by Single-blind
Peer reviewer comments 2
Editor who approved publication: Dr Antonella D'Anneo
Ainian Chen,1 Lingling Zhong,1 Keju Ju,1 Ting Lu,1 Jia Lv,2 Hua Cao1
1Department of Neurology, The Affiliated Huai’an No.1 People’s Hospital of Nanjing Medical University, Huai’an, Jiangsu 223300, People’s Republic of China; 2Department of Neurosurgery, The Affiliated Huai’an No.1 People’s Hospital of Nanjing Medical University, Huai’an, Jiangsu 223300, People’s Republic of China
Correspondence: Hua Cao
Department of Neurology, The Affiliated Huai’an No. 1 People’s Hospital of Nanjing Medical University, 01 Huanghe Road West, Huai’an 223300, Jiangsu, People’s Republic of China
Tel/Fax +86 13861565810
Background: Glioblastoma (GBM) is the most common primary malignant tumor in adult central nervous system and results in disappointing survival outcomes. Although the diagnosis and therapy approach have been developed recently, the prognosis of GBM remains poor. A novel, minimally invasive biomarker for GBM is necessary for early diagnosis or prognosis prediction.
Methods: All circRNAs were detected by qRT-PCR in GBM samples including training and validation sets. We used the risk score analysis to assume the diagnosis ability for GBM. The receiver operating characteristic curve was also employed.
Results: Among the 14 candidates, circRNA, circNT5E, circFOXO3, circ_0001946, circ_0029426, circ-SHPRH, and circMMP9 were detected with increased levels in the training set. Further investigation in the validation set indicated that circFOXO3, circ_0029426, and circ-SHPRH might be the fingerprints for GBM compared with controls. The risk score analysis revealed that the combination of three circRNAs could distinguish the GBM from healthy control with the area under curve value of 0.980 and 0.906, respectively.
Conclusion: The three circRNAs might be novel fingerprints for predicting the occurrence of GBM.
Keywords: glioma, circRNA, plasma, biomarker, serum
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