Laryngeal cancer diagnosis via miRNA-based decision tree model.
Eur Arch Otorhinolaryngol
; 281(3): 1391-1399, 2024 Mar.
Article
en En
| MEDLINE
| ID: mdl-38147113
ABSTRACT
PURPOSE:
Laryngeal cancer (LC) is the most common head and neck cancer, which often goes undiagnosed due to the inaccessible nature of current diagnosis methods in some parts of the world. 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 created series of miRNA 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 classifier yields the highest accuracy (86.8%) in miRNAs-based recognition of laryngeal cancer. Furthermore, our 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 our 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 an additional method for diagnosing laryngeal cancer.Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Neoplasias Laríngeas
/
MicroARNs
Límite:
Humans
Idioma:
En
Revista:
Eur Arch Otorhinolaryngol
Asunto de la revista:
OTORRINOLARINGOLOGIA
Año:
2024
Tipo del documento:
Article
País de afiliación:
Estados Unidos