Laryngeal Cancer Diagnosis via miRNA-based Decision Tree Model

Abstract

Purpose Laryngeal cancer (LC) is the most common head and neck cancer, which often goes undiagnosed due to the expensiveness and inaccessible nature of current diagnosis methods. Many recent studies have shown that microRNAs (miRNAs) are crucial biomarkers for a variety of cancers. Methods In this study, we create a decision tree model for the diagnosis of laryngeal cancer using a calculated miRNAs’ attributes, such as sequence-based characteristics, predicted miRNA target genes, and gene pathways. This series of attributes is extracted from both differentially expressed blood-based miRNAs in laryngeal cancer and random, non-associated with cancer miRNAs. Results Several machine-learning (ML) algorithms were tested in the ML model, and the Hoeffding Tree (HT) classifier yields the highest accuracy (86.8%) in miRNAs-based recognition of laryngeal cancer. Furthermore, HT-based model is validated with the independent laryngeal cancer datasets and can accurately diagnose laryngeal cancer with 86% accuracy. We also explored the biological relationships of the attributes used in HT-based model to understand their relationship with cancer proliferation or suppression pathways. Conclusion Our study demonstrates that the proposed model and an inexpensive miRNA testing strategy have the potential to serve as a cost-effective and accessible method for diagnosing laryngeal cancer.