A novel seven‑miRNA prognostic model to predict overall survival in head and neck squamous cell carcinoma patients

Lu,Le; Wu,Yufan; Feng,Min; Xue,Xiaofeng; Fan,Yuan

doi:10.3892/mmr.2019.10665

A novel seven‑miRNA prognostic model to predict overall survival in head and neck squamous cell carcinoma patients

Authors:
- Le Lu
- Yufan Wu
- Min Feng
- Xiaofeng Xue
- Yuan Fan
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Affiliations: Jiangsu Key Laboratory of Oral Disease, Nanjing Medical University, Nanjing, Jiangsu 210029, P.R. China , Department of General Surgery, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu 215000, P.R. China, Department of Oral Medicine, The Stomatological Hospital Affiliated to Soochow University, Suzhou, Jiangsu 215000, P.R. China
Published online on: September 10, 2019 https://doi.org/10.3892/mmr.2019.10665
Pages: 4340-4348

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Abstract

Head and neck squamous cell carcinoma (HNSCC) is highly prevalent worldwide, and the outcome of HNSCC is still difficult to predict due to the lack of appropriate prognostic markers. In the present study, a prognostic model based on a miRNA panel was established to better predict the survival of HNSCC patients. miRNA expression data and clinical information regarding HNSCC patients were acquired from The Cancer Genome Atlas (TCGA) database. Accompanying clinical data was obtained from the University of California, Santa Cruz (UCSC) Xena browser. Using this data, 140 differentially expressed miRNAs (DEMs) were identified between HNSCC tissue samples (n=525) and adjacent normal tissue samples (n=44). The present prognostic model included seven miRNAs (i.e. hsa‑miR‑499a, hsa‑miR‑548k, hsa‑miR‑3619, hsa‑miR‑99a, hsa‑miR‑137, hsa‑miR‑3170, and hsa‑miR‑654), which were identified using least absolute shrinkage and selection operator (LASSO) and Cox regression analyses. The independence of the predictive power of this model was validated by further analyses using clinical information. The outstanding performance of the seven‑miRNA prognostic model was confirmed by time‑dependent receiver operating characteristic curve (ROC) analysis. These results indicated that combining the miRNA panel with pathological characteristics may provide a more accurate prognosis for HNSCC. Functional identification of the target genes of the focal miRNAs using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were also performed. The present study demonstrated that the novel miRNA panel reported here may be useful in making different prognoses and may improve the clinical management of patients with HNSCC.

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