A novel saliva-based miRNA profile to diagnose and predict oral cancer.

Autor: Balakittnen J; Saliva & Liquid Biopsy Translational Laboratory, Griffith Institute for Drug Discovery, Griffith University, Nathan, QLD, Australia.; Department of Medical Laboratory Sciences, Faculty of Allied Health Sciences, University of Jaffna, Jaffna, Sri Lanka., Ekanayake Weeramange C; Saliva & Liquid Biopsy Translational Laboratory, Griffith Institute for Drug Discovery, Griffith University, Nathan, QLD, Australia.; Menzies Health Institute, Griffith University, Gold Coast, QLD, Australia., Wallace DF; School of Biomedical Sciences, Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia., Duijf PHG; Centre for Cancer Biology, Clinical and Health Sciences, University of South Australia & SA Pathology, Adelaide, SA, Australia.; Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway.; Department of Medical Genetics, Oslo University Hospital, Oslo, Norway., Cristino AS; Griffith Institute for Drug Discovery, Griffith University, Nathan, QLD, Australia., Hartel G; QIMR Berghofer Medical Research Institute, Statistics Unit, Brisbane, QLD, Australia.; School of Public Health, The University of Queensland, Brisbane, QLD, Australia.; School of Nursing, Queensland University of Technology, Brisbane, QLD, Australia., Barrero RA; eResearch, Research Infrastructure, Academic Division, Queensland University of Technology, Brisbane, QLD, Australia., Taheri T; Department of Anatomical Pathology, Royal Brisbane and Women's Hospital, Herston, QLD, Australia.; Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia., Kenny L; Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia.; Royal Brisbane and Women's Hospital, Cancer Care Services, Herston, QLD, Australia., Vasani S; Saliva & Liquid Biopsy Translational Laboratory, Griffith Institute for Drug Discovery, Griffith University, Nathan, QLD, Australia.; Royal Brisbane and Women's Hospital, Cancer Care Services, Herston, QLD, Australia.; Department of Otolaryngology, Royal Brisbane and Women's Hospital, Herston, QLD, Australia., Batstone M; Department of Oral and Maxillofacial Surgery, Royal Brisbane and Women's Hospital, Herston, QLD, Australia., Breik O; Royal Brisbane and Women's Hospital, Cancer Care Services, Herston, QLD, Australia.; Department of Oral and Maxillofacial Surgery, Royal Brisbane and Women's Hospital, Herston, QLD, Australia., Punyadeera C; Saliva & Liquid Biopsy Translational Laboratory, Griffith Institute for Drug Discovery, Griffith University, Nathan, QLD, Australia. c.punyadeera@griffith.edu.au.; Menzies Health Institute, Griffith University, Gold Coast, QLD, Australia. c.punyadeera@griffith.edu.au.
Jazyk: angličtina
Zdroj: International journal of oral science [Int J Oral Sci] 2024 Feb 18; Vol. 16 (1), pp. 14. Date of Electronic Publication: 2024 Feb 18.
DOI: 10.1038/s41368-023-00273-w
Abstrakt: Oral cancer (OC) is the most common form of head and neck cancer. Despite the high incidence and unfavourable patient outcomes, currently, there are no biomarkers for the early detection of OC. This study aims to discover, develop, and validate a novel saliva-based microRNA signature for early diagnosis and prediction of OC risk in oral potentially malignant disorders (OPMD). The Cancer Genome Atlas (TCGA) miRNA sequencing data and small RNA sequencing data of saliva samples were used to discover differentially expressed miRNAs. Identified miRNAs were validated in saliva samples of OC (n = 50), OPMD (n = 52), and controls (n = 60) using quantitative real-time PCR. Eight differentially expressed miRNAs (miR-7-5p, miR-10b-5p, miR-182-5p, miR-215-5p, miR-431-5p, miR-486-3p, miR-3614-5p, and miR-4707-3p) were identified in the discovery phase and were validated. The efficiency of our eight-miRNA signature to discriminate OC and controls was: area under curve (AUC): 0.954, sensitivity: 86%, specificity: 90%, positive predictive value (PPV): 87.8% and negative predictive value (NPV): 88.5% whereas between OC and OPMD was: AUC: 0.911, sensitivity: 90%, specificity: 82.7%, PPV: 74.2% and NPV: 89.6%. We have developed a risk probability score to predict the presence or risk of OC in OPMD patients. We established a salivary miRNA signature that can aid in diagnosing and predicting OC, revolutionising the management of patients with OPMD. Together, our results shed new light on the management of OC by salivary miRNAs to the clinical utility of using miRNAs derived from saliva samples.
(© 2024. The Author(s).)
Databáze: MEDLINE
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