Prediction of radiotherapy response with a 5‐microRNA signature‐based nomogram in head and neck squamous cell carcinoma

Radiotherapy is unlikely to benefit all patients with head and neck squamous cell carcinoma ( HNSCC ). Therefore, novel method is warranted to predict the radiotherapy response. Our study aimed to construct a micro RNA (mi RNA )‐based nomogram to predict clinical outcomes of patients with HNSCC receiving radiotherapy. We screened out 56 differential mi RNA s by analyzing 44 paired tumor and adjacent normal samples mi RNA expression profiles from The Cancer Genome Atlas ( TCGA ). A total of 307 patients with HNSCC receiving adjuvant radiotherapy were randomly divided into a training set ( n  = 154) and a validation set ( n  = 153). In the training set, we combined the differential mi RNA profiles with clinical outcomes, and LASSO regression model was applied to establish a 5‐mi RNA signature. The prediction accuracy of the 5‐mi RNA signature was further validated. In addition, target genes of these mi RNA s were predicted, and Gene Ontology ( GO ) analysis as well as KEGG pathway analysis was executed. A 5‐mi RNA signature including miR‐99a, miR‐31, miR‐410, miR‐424, and miR‐495 was identified. With a cutoff value of 1.2201 from Youden's index, the training set was divided into high‐risk and low‐risk groups, and the 5‐year overall survival was significantly different (30% vs. 73%, HR 3.65, CI 2.46–8.16; P  <   0.0001). Furthermore, our 5‐mi RNA signature revealed that only low‐risk group would benefit from radiotherapy. Then, a nomogram combining 5‐mi RNA signature with clinical variables to predict radiotherapy response was constructed. The analysis of 108 target genes of these mi RNA s revealed some potential mechanisms in HNSCC radiotherapy response for future investigations. In conclusion, the 5‐mi RNA signature‐based nomogram is useful in predicting radiotherapy response in HNSCC and might become a promising tool to optimize radiation strategies.