MicroRNA-based classifiers for diagnosis of oral cavity squamous cell carcinoma in tissue and plasma. (August 2018)

• Record Type: Journal Article
• Authors: Pedersen, Nicklas Juel; Jensen, David Hebbelstrup; Lelkaitis, Giedrius; Kiss, Katalin; Charabi, Birgitte Wittenborg; Ullum, Henrik; Specht, Lena; Schmidt, Ane Yde; Nielsen, Finn Cilius; von Buchwald, Christian
• Is Part Of: Oral oncology. Volume 83(2018)
• Page Start: 46
• Page End: 52
• Publication Date: 2018-08
• Subjects: Oral squamous cell carcinoma -- Biomarker -- Plasma -- miRNA -- REMARK guidelines -- miRNome; OSCC oral squamous cell carcinoma -- miRNA MicroRNA -- REMARK Reporting Recommendation for Tumor Marker -- SNB sentinel lymph node biopsy -- H&E hematoxylin and eosin -- FFPE formalin-fixed paraffin-embedded -- EDTA ethylene diamine tetra acetic acid -- qPCR quantitative polymerase chain reaction -- NOM normal oral mucosa -- CPM counts per million -- ROC receiver operating curves -- AUC area under the curve -- LOH loss of heterozygosity; Mouth -- Cancer -- Periodicals; Mouth -- Tumors -- Periodicals; Mouth Diseases -- Periodicals; Mouth Neoplasms -- Periodicals; Bouche -- Cancer -- Périodiques; Bouche -- Tumeurs -- Périodiques; Tumeurs -- Périodiques; Electronic journals; 616.9943105
• DOI: 10.1016/j.oraloncology.2018.05.020
• Languages: English
• ISSNs: 1368-8375

Abstract

Highlights: In-depth miRNA-sequencing of the complete OSCC miRNome. A novel combination of miRNAs can distinguish OSCC from normal tissue. Plasma miR-30a-5p and miR-769-5p are specific for oral squamous cell carcinomas. Plasma microRNA could not detect occult lymph node metastases. Abstract: Background: MicroRNAs (miRNAs) hold promise as diagnostic cancer biomarkers. Here we aimed to define the miRNome in oral squamous cell carcinoma (OSCC) and normal oral mucosa (NOM), and to identify and validate new diagnostic miRNAs and miRNA combinations in formalin-fixed paraffin-embedded (FFPE) tissue samples and plasma samples. Methods: We performed next-generation miRNA sequencing in FFPE tissue samples of OSCC ( n = 80) and NOM ( n = 8). Our findings were validated by quantitative polymerase chain reaction (qPCR) analysis of OSCC ( n = 195) and NOM ( n = 103) FFPE tissue samples, and plasma samples from OSCC patients ( n = 55) and healthy persons ( n = 18). Results: The OSCC miRNome included 567 miRNAs, 66 of which were differentially expressed between OSCC and NOM. Using qPCR data, we constructed receiver operating curves to classify patients as NOM or OSCC based on miRNA combinations. The area under the curve was of 0.92 from FFPE tissue (miR-204-5p, miR-370, miR-1307, miR-193b-3p, and miR-144-5p), and 1.0 from plasma samples (miR-30a-5p and miR-769-5p). Model calibration and discrimination were evaluated using 10-fold cross-validation. Conclusions: Analysis of the miRNome from many OSCC cases improves our knowledge of the importance of individual miRNAs and their predictive potential in OSCC. We successfully identified miRNA classifiers in FFPE OSCC tissue and plasma with a high discriminatory ability between OSCC and NOM. The proposed combination of miR-30a-5p and miR-769-5p in plasma from OSCC patients could serve as a minimal invasive biomarker for diagnosis and control of T-site recurrences.