miRNA-Based Feature Classifier Is Associated with Tumor Mutational Burden in Head and Neck Squamous Cell Carcinoma

Abstract

Tumor mutation burden (TMB) is considered to be an independent genetic biomarker that can predict the tumor patient’s response to immune checkpoint inhibitors (ICIs). Meanwhile, microRNA (miRNA) plays a key role in regulating the anticancer immune response. However, the correlation between miRNA expression patterns and TMB is not elucidated in HNSCC. In the HNSCC cohort of the TCGA dataset, miRNAs that were differentially expressed in high TMB and low TMB samples were screened. The least absolute contraction and selection operator (LASSO) method is used to construct a miRNA-based feature classifier to predict the TMB level in the training set. The test set is used to verify the classifier. The correlation between the miRNA-based classifier index and the expression of three immune checkpoints (PD1, PDL1, and CTLA4) was explored. We further perform functional enrichment analysis on the miRNA contained in the miRNA-based feature classifier. Twenty-five differentially expressed miRNAs are used to build miRNA-based feature classifiers to predict TMB levels. The accuracy of the 25-miRNA-based signature classifier is 0.822 in the training set, 0.702 in the test set, and 0.774 in the total set. The miRNA-based feature classifier index showed a low correlation with PD1 and PDL1, but no correlation with CTLA4. The enrichment analysis of these 25 miRNAs shows that they are involved in many immune-related biological processes and cancer-related pathways. The miRNA expression patterns are related to tumor mutation burden, and miRNA-based feature classifiers can be used as biomarkers to predict TMB levels in HNSCC.