Li Xinghang, Kouznetsova Valentina L, Tsigelny Igor F
IUL Scientific Program, La Jolla, CA 92038, USA.
San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA.
Biomedicines. 2024 Oct 21;12(10):2404. doi: 10.3390/biomedicines12102404.
MicroRNAs (miRNAs) are crucial regulators of gene expression, playing significant roles in various cellular processes, including cancer pathogenesis. Traditional cancer diagnostic methods, such as biopsies and histopathological analyses, while effective, are invasive, costly, and require specialized skills. With the rising global incidence of cancer, there is a pressing need for more accessible and less invasive diagnostic alternatives.
This research investigates the potential of machine-learning (ML) models based on miRNA attributes as non-invasive diagnostic tools for oral cancer. Methods and Tools: We utilized a comprehensive methodological framework involving the generation of miRNA attributes, including sequence characteristics, target gene associations, and cancer-specific signaling pathways.
The miRNAs were classified using various ML algorithms, with the BayesNet classifier demonstrating superior performance, achieving an accuracy of 95% and an area under receiver operating characteristic curve (AUC) of 0.98 during cross-validation. The model's effectiveness was further validated using independent datasets, confirming its potential clinical utility.
Our findings highlight the promise of miRNA-based ML models in enhancing early cancer detection, reducing healthcare burdens, and potentially saving lives.
This study paves the way for future research into miRNA biomarkers, offering a scalable and adaptable diagnostic approach for various cancers.
微小RNA（miRNA）是基因表达的关键调节因子，在包括癌症发病机制在内的各种细胞过程中发挥着重要作用。传统的癌症诊断方法，如活检和组织病理学分析，虽然有效，但具有侵入性、成本高且需要专业技能。随着全球癌症发病率的上升，迫切需要更易获得且侵入性更小的诊断替代方法。
本研究调查基于miRNA属性的机器学习（ML）模型作为口腔癌非侵入性诊断工具的潜力。方法和工具：我们采用了一个全面的方法框架，包括生成miRNA属性，如序列特征、靶基因关联和癌症特异性信号通路。
使用各种ML算法对miRNA进行分类，贝叶斯网络分类器表现出卓越的性能，在交叉验证期间准确率达到95%，受试者工作特征曲线下面积（AUC）为0.98。使用独立数据集进一步验证了该模型的有效性，证实了其潜在的临床实用性。
我们的研究结果突出了基于miRNA的ML模型在加强早期癌症检测、减轻医疗负担以及潜在挽救生命方面的前景。
本研究为未来miRNA生物标志物的研究铺平了道路，为各种癌症提供了一种可扩展且适应性强的诊断方法。
