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Published on: March 14, 2019
Combined identification of three miRNAs in serum as effective diagnostic biomarkers for HNSCC
Chao Liu1, Zhaoyan Yu2, Shengyun Huang3
1Department of Stomatology, Shandong Provincial Hospital Affiliated to Shandong University, 324 Jingwuweiqi Road, Jinan, Shandong 250021, China; Department of Oromaxillofacial Head and Neck Oncology, Shanghai Ninth People's Hospital, College of Stomatology, Shanghai Jiao Tong University School of Medicine, Shanghai 200011, China.
Background:
Head and neck squamous cell carcinoma (HNSCC) is a disastrous disease with substantial morbidity and mortality. This study aims to explore the effective diagnostic and prognostic biomarkers for HNSCC.
Methods:
MiRNA expression data and corresponding clinical information of HNSCC from The Cancer Genome Atlas (TCGA) database were analyzed comprehensively to identify the miRNAs with diagnostic and prognostic power. The predictive ability of different classifications was analyzed for the three-miRNA combinations. Diagnostic and prognostic value were then evaluated and verified in clinical patients.
Findings:
128 differentially expressed miRNAs in HNSCC tissues were identified in the TCGA dataset, and 10 miRNAs were finally selected for further study. Classification analysis developed a three-miRNA signature of hsa-mir-383, hsa-mir-615, and hsa-mir-877 with the best diagnosis power, which was verified in validation patients. Survival analysis indicated that different expression levels of hsa-mir-383, rather than that of hsa-mir-615 or hsa-mir-877 led to significantly different survival rates in both cohorts. Furthermore, the multivariate Cox hazards analysis suggested that the microRNA signature yielded statistical significance to predict clinical outcome independently from other clinical variables in validation patients.
Interpretation:
A three-miRNA signature of hsa-mir-383, hsa-mir-615, and hsa-mir-877 may serve as an excellent diagnostic biomarker for HNSCC, and potential prognostic significance for HNSCC patients.
Funding:
This work was supported by the grants of the National Natural Science Foundation of China (81901021), Key Research and Development Program of Shandong (2019GSF108277), China postdoctoral Scinence Foundation Grant (2019M652380), Fundamental Research Funds of Shandong University (2018CJ047).
