Oral Presentation NSW State Cancer Conference 2023

Novel Salivary miRNA Signatures for Early Diagnosis and Prediction of Oral Cancer (#19)

Jaikrishna Balakittnen 1 2 , Chameera Ekanayake Weeramange 1 , Daniel F Wallace 3 , Pascal H.G. Duijf 3 4 5 6 , Alexandre S. Cristino 7 , Gunter Hartel 8 , Touraj Taheri 9 10 , Roberto A. Barrero 11 , Lizbeth Kenny 12 13 , Sarju Vasani 12 14 , Martin Batstone 15 , Omar Breik 12 15 , Chamindie Punyadeera 1 3 16
  1. Saliva & Liquid Biopsy Translational Laboratory, Griffith Institute for Drug Discovery, Griffith University, Nathan, Queensland, Australia
  2. Department of Medical Laboratory Sciences, Faculty of Allied Health Sciences, University of Jaffna,, Jaffna, Sri Lanka
  3. School of Biomedical Sciences, Faculty of Health , Queensland University of Technology, Brisbane, Queensland, Australia
  4. Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway
  5. Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
  6. Centre for Cancer Biology, Clinical and Health Sciences, University of South Australia, Adelaide, South Australia, Australia
  7. Griffith Institute for Drug Discovery, Griffith University, Nathan, Queensland, Australia
  8. Statistics Unit, QIMR Berghofer Medical Research Institute, Herston, Queensland, Australia
  9. Department of Anatomical Pathology, Royal Brisbane and Women's Hospital, Herston, Queensland, Australia
  10. School of Medicine, The University of Queensland,, Saint Lucia, Queensland, Australia
  11. eResearch, Research Infrastructure, Academic Division, Queensland University of Technology, Brisbane, Queensland, Australia
  12. Cancer Care Services, Royal Brisbane and Women’s Hospital, Herston, Queensland, Australia
  13. Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia
  14. Department of Otolaryngology, Royal Brisbane and Women’s Hospital, Herston, Queensland, Australia
  15. Department of Oral and Maxillofacial Surgery, Royal Brisbane and Women's Hospital, Herston, Queensland, Australia
  16. Menzies Health Institute, Griffith University, Gold Coast, Queensland, Australia

Background: Oral cancer (OC) is the sixteenth most prevalent cancer worldwide . More importantly, 8% of OC arises from oral potentially malignant disorders (OPMD). Despite the high incidence, there are no biomarkers for the early detection of OC, and patients are often diagnosed at their advanced stages (5-year overall patient survival is approximately 50%). When OC is detected at an early stage (Stage I or II), the overall five-year survival rate exceeds 80%, whereas late-stage diagnosis (Stage III or IV) results in a survival rate of only 20% . miRNAs are small non-coding RNA molecules that can regulate multiple genes and are involved in various biological processes, including cell differentiation, development, and disease. Therefore, this study aimed to discover, develop and validate novel saliva-based microRNA signatures to early diagnose OC and predict the risk of developing OC in patients with oral potentially malignant disorders (OPMD). 

Methods:  Eight candidate miRNAs (miR-A, B, C, D, E, F, G, H) were discovered when considering The Cancer Genome Atlas (TCGA) miRNA sequencing data and in-house small RNA sequencing of saliva samples (OC: n=8, and controls: n=8). Expression levels of candidate miRNAs were validated in saliva samples of OC (n=50), OPMD (n=52), and controls (n=60) using quantitative real-time PCR. Further, validation was carried out in tumour tissue and normal oral cavity tissue samples of OC (n=6) and controls (n=5), respectively. 

Results: We have developed miRNA signatures for the early detection of OC and discrimination of OC from OPMD patients. The discriminative efficiency of our eight-miRNA signature between OC and controls was based on Area Under Curve (AUC): 0.954, sensitivity: 86%, specificity: 90%, positive predictive value (PPV) of 87.8%, and negative predictive value (NPV) of 88.5% whereas between OC and OPMD was based on AUC: 0.911, sensitivity: 90%, specificity: 82.7%, PPV of 74.2% and NPV of 89.6%. More importantly, we have developed a risk probability score to stratify OPMD patients at risk of developing OC.

Conclusions: Our results highlight that salivary miRNAs can be used as biomarkers to diagnose OC and predict OC risk in OPMD patients early. Especially the risk probability score provides promising results in stratifying the OPMD patients at high risk of developing OC. However, validating the above signatures in larger cohorts and high-risk populations may benefit their clinical translation.

  1. American Society of Clinical Oncology, 2022
  2. World Cancer Research Fund International, 2022
  3. Gurizzan et al., 2021
  4. Balakittnen et al., 2022