Nicklas Juel Pedersen1, David Hebbelstrup Jensen1, Giedrius Lelkaitis2, Katalin Kiss2, Birgitte Wittenborg Charabi1, Henrik Ullum3, Lena Specht4, Ane Yde Schmidt5, Finn Cilius Nielsen5, Christian von Buchwald6. 1. Department of Otorhinolaryngology, Head and Neck Surgery and Audiology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark. 2. Department of Pathology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark. 3. Department of Clinical Immunology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark. 4. Department of Oncology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark. 5. Centre for Genomic Medicine, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark. 6. Department of Otorhinolaryngology, Head and Neck Surgery and Audiology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark. Electronic address: christian.von.buchwald@regionh.dk.
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.
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.
Authors: Maria Menini; Emanuele De Giovanni; Francesco Bagnasco; Francesca Delucchi; Francesco Pera; Domenico Baldi; Paolo Pesce Journal: J Pers Med Date: 2021-02-04
Authors: Óscar Rapado-González; Rafael López-López; José Luis López-Cedrún; Gabriel Triana-Martínez; Laura Muinelo-Romay; María Mercedes Suárez-Cunqueiro Journal: Cells Date: 2019-12-17 Impact factor: 6.600