Construction of an 11-microRNA-based signature and a prognostic nomogram to predict the overall survival of head and neck squamous cell carcinoma patients

BackgroundHead and neck squamous cell carcinoma (HNSCC) is a fatal malignancy owing to the lack of effective tools to predict overall survival (OS). MicroRNAs (miRNAs) play an important role in HNSCC occurrence, development, invasion and metastasis, significantly affecting the OS of patients. Thus, the construction of miRNA-based risk signatures and nomograms is desirable to predict the OS of patients with HNSCC. Accordingly, in the present study, miRNA sequencing data of 71 HNSCC and 13 normal samples downloaded from The Cancer Genome Atlas (TCGA) were screened to identify differentially expressed miRNAs (DEMs) between HNSCC patients and normal controls. Based on the exclusion criteria, the clinical information and miRNA sequencing data of 67 HNSCC samples were selected and used to establish a miRNA-based signature and a prognostic nomogram. Forty-three HNSCC samples were assigned to an internal validation cohort for verifying the credibility and accuracy of the primary cohort. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to explore the functions of 11 miRNA target genes.ResultsIn total, 11 DEMs were successfully identified. An 11-miRNA risk signature and a prognostic nomogram were constructed based on the expression levels of these 11 DEMs and clinical information. The signature and nomogram were further validated by calculating the C-index, area under the curve (AUC) in receiver-operating characteristic curve analysis, and calibration curves, which revealed their promising performance. The results of the internal validation cohort shown the reliable predictive accuracy both of the miRNA-based signature and the prognostic nomogram. GO and KEGG analyses revealed that a mass of signal pathways participated in HNSCC proliferation and metastasis.ConclusionOverall, we constructed an 11-miRNA-based signature and a prognostic nomogram with excellent accuracy for predicting the OS of patients with HNSCC.