Arora Aarav, Tsigelny Igor F, Kouznetsova Valentina L
REHS Program, San Diego Supercomputer Center, UC San Diego, La Jolla, CA, USA.
San Diego Supercomputer Center, UC San Diego, La Jolla, CA, USA.
Eur Arch Otorhinolaryngol. 2024 Mar;281(3):1391-1399. doi: 10.1007/s00405-023-08383-1. Epub 2023 Dec 26.
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.
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.
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.
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.
喉癌（LC）是最常见的头颈癌，由于世界上某些地区当前诊断方法难以触及，其往往未被诊断出来。最近许多研究表明，微小RNA（miRNA）是多种癌症的关键生物标志物。
在本研究中，我们使用一系列创建的miRNA属性创建了一个用于诊断喉癌的决策树模型，这些属性包括基于序列的特征、预测的miRNA靶基因和基因通路。这一系列属性既从喉癌中差异表达的血液miRNA中提取，也从随机的、与癌症无关的miRNA中提取。
在机器学习（ML）模型中测试了几种ML算法，霍夫丁树分类器在基于miRNA的喉癌识别中产生了最高准确率（86.8%）。此外，我们的模型用独立的喉癌数据集进行了验证，能够以86%的准确率准确诊断喉癌。我们还探索了模型中使用的属性的生物学关系，以了解它们与癌症增殖或抑制通路的关系。
我们的研究表明，所提出的模型和一种低成本的miRNA检测策略有可能作为诊断喉癌的一种额外方法。
