Qianting He1, Zujian Chen2, Robert J Cabay3, Leitao Zhang4, Xianghong Luan5, Dan Chen1, Tianwei Yu6, Anxun Wang7, Xiaofeng Zhou8. 1. Center for Molecular Biology of Oral Diseases, Department of Periodontics, College of Dentistry, University of Illinois at Chicago, Chicago, IL, USA; Department of Oral and Maxillofacial Surgery, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China. 2. Center for Molecular Biology of Oral Diseases, Department of Periodontics, College of Dentistry, University of Illinois at Chicago, Chicago, IL, USA. 3. Department of Pathology, College of Medicine, University of Illinois at Chicago, Chicago, IL, USA. 4. Center for Molecular Biology of Oral Diseases, Department of Periodontics, College of Dentistry, University of Illinois at Chicago, Chicago, IL, USA; Department of Oral and Maxillofacial Surgery, Nan Fang Hospital, Southern Medical University, Guangzhou, China. 5. Department of Oral Biology, College of Dentistry, University of Illinois at Chicago, Chicago, IL, USA. 6. Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA. 7. Department of Oral and Maxillofacial Surgery, The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China. Electronic address: anxunwang@yahoo.com. 8. Center for Molecular Biology of Oral Diseases, Department of Periodontics, College of Dentistry, University of Illinois at Chicago, Chicago, IL, USA; UIC Cancer Center, Graduate College, University of Illinois at Chicago, Chicago, IL, USA. Electronic address: xfzhou@uic.edu.
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.
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.
Authors: K Thomas Robbins; Garry Clayman; Paul A Levine; Jesus Medina; Roy Sessions; Ashok Shaha; Peter Som; Gregory T Wolf Journal: Arch Otolaryngol Head Neck Surg Date: 2002-07
Authors: Antonia Kolokythas; Joel L Schwartz; Kristen B Pytynia; Suchismita Panda; Mike Yao; Brian Homann; Herve Y Sroussi; Joel B Epstein; Sara C Gordon; Guy R Adami Journal: Oral Oncol Date: 2011-05-05 Impact factor: 5.337
Authors: Nham Tran; Tessa McLean; Xiaoying Zhang; Chuan Jia Zhao; John Michael Thomson; Christopher O'Brien; Barbara Rose Journal: Biochem Biophys Res Commun Date: 2007-04-09 Impact factor: 3.575
Authors: Lidia Maria Rebolho Batista Arantes; Ana Carolina Laus; Matias Eliseo Melendez; Ana Carolina de Carvalho; Bruna Pereira Sorroche; Pedro Rafael Martins De Marchi; Adriane Feijó Evangelista; Cristovam Scapulatempo-Neto; Luciano de Souza Viana; André Lopes Carvalho Journal: Oncotarget Date: 2017-02-07
Authors: Camile B Lopes; Leandro L Magalhães; Carolina R Teófilo; Ana Paula N N Alves; Raquel C Montenegro; Massimo Negrini; Ândrea Ribeiro-Dos-Santos Journal: BMC Cancer Date: 2018-07-06 Impact factor: 4.430