MicroRNA-21 and microRNA-375 from oral cytology as biomarkers for oral tongue cancer detection

• Type of Material: Article
• Authors: Qianting He, University of Illinois at Chicago; Zujian Chen, University of Illinois at Chicago; Robert J. Cabay, University of Illinois at Chicago; Leitao Zhang, University of Illinois at Chicago; Xianghong Luan, University of Illinois at Chicago; Dan Chen, University of Illinois at Chicago; Tianwei Yu, Emory University; Anxun Wang, Sun Yat-Sen University; Xiaofeng Zhou, University of Illinois at Chicago
• Language: English
• Date: 2016-03-31
• Publisher: Elsevier
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2016 Elsevier Ltd. All rights reserved.
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1016/j.oraloncology.2016.03.017
• Title of Journal or Parent Work: Oral Oncology
• ISSN: 1368-8375
• Volume: 57
• Start Page: 15
• End Page: 20
• Grant/Funding Information: This work was supported in part by NIH PHS grants (CA139596 and CA171436), the Lilly USA Research Award in Cancer Prevention and Early Detection, and a seed grant from CMBOD Oral Cancer Research Program (UIC College of Dentistry) to X.Z., and grants from National Nature Science Foundation of China (NSFC81472523, NSFC81272953) to A.W.
• Keywords: miR-21; Squamous cell carcinoma; Biomarker; Small non-coding RNA; Oral cytology; Head and neck/oral cancer; miR-375
• Research Categories: Biology, Biostatistics; Biology, Bioinformatics; Health Sciences, Medicine and Surgery

Abstract

Objective: We previously performed a meta-analysis of microRNA profiling studies on head and neck/oral cancer (HNOC), and identified 11 consistently dysregulated microRNAs in HNOC. Here, we evaluate the diagnostic values of these microRNAs in oral tongue squamous cell carcinoma (OTSCC) using oral cytology samples. Materials and methods: The levels of 11 microRNAs were assessed in 39 oral cytology samples (19 OTSCC and 20 normal subjects), and 10 paired OTSCC and adjacent normal tissues. The predictive power of these microRNAs was analyzed by receiver operating characteristic curve (ROC) and random forest (RF) model. A classification and regression trees (CART) model was generated using miR-21 and miR-375, and further validated using both independent oral cytology validation sample set (14 OTSCC and 11 normal subjects) and tissue validation sample set (12 paired OTSCC and adjacent normal tissues). Results: Differential expression of miR-21, miR-100, miR-125b and miR-375 was validated in oral cytology training sample set. Based on the RF model, the combination of miR-21 and miR-375 was selected which provide best prediction of OTSCC. A CART model was constructed using miR-21 and miR-375, and was tested in both oral cytology and tissue validation sample sets. A sensitivity of 100% and specificity of 64% was achieved in distinguishing OTSCC from normal in the oral cytology validation set, and a sensitivity of 83% and specificity of 83% was achieved in the tissue validation set. Conclusion: The utility of microRNA from oral cytology samples as biomarkers for OTSCC detection is successfully demonstrated in this study.