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.
Highlights
Head and neck squamous cell carcinoma (HNSCC) remains the primary cause of cancer-related mortality in the world, which is characterized by advanced diagnosis [1], poor prognosis [2], low overall survival [3], and high recurrence [4]
Based on the optimal cut-off point, the HNSCC patients were divided into two groups: the high Tumor mutation burden (TMB) and low TMB groups
We performed principal component analysis prior to and after the least absolute contraction and selection operator (LASSO) method, and the results indicated that samples with different TMB levels are more distinguished using the 25 miRNAs (Figures 6(b) and 6(c))
Summary
Head and neck squamous cell carcinoma (HNSCC) remains the primary cause of cancer-related mortality in the world, which is characterized by advanced diagnosis [1], poor prognosis [2], low overall survival [3], and high recurrence [4]. There are approximately 400,000 oral and pharyngeal diseases, 160,000 laryngeal cancers, and 300,000 deaths worldwide each year [5,6,7]. HNSCC is a common heterogeneous tumor that exists in the oral, pharyngeal, and larynx lesions [8]. Risk factors for oral cancer may include mutant oncogenes, the presence of extensive P53, and lower levels of tumor hypoxia, smoke, alcohol, age factor (>65 years), and HPV or EBV infection, but the mechanism remains to be explored in detail. Existing treatments are deficient for patients with locally advanced or distantly metastatic HNSCC. Looking for a new treatment method is currently urgent
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