目的:该研究的重点是通过早期发现来提高乳腺癌(BC)的预后,旨在建立一种非侵入性的,使用特定血清miRNA水平的临床可行BC筛查方法。
方法:涉及不列颠哥伦比亚省的11,349名参与者,其他11种癌症,和对照组,该研究通过特征选择鉴定了血清生物标志物,并使用6种机器学习算法开发了两种BC筛查模型.这些模型经过了测试评估,内部,和外部验证集,评估性能指标,如准确性,灵敏度,特异性,和曲线下面积(AUC)。进行亚组分析以测试模型的稳定性。
结果:基于三种血清miRNA生物标志物(miR-1307-3p,miR-5100和miR-4745-5p),BC筛选模型,SM4BC3miR模型,已开发。该模型在测试中实现了0.986、0.986和0.939的AUC性能,内部,和外部集,分别。此外,SSM4BC模型,利用miR-1307-3p/miR-5100和miR-4745-5p/miR-5100的比率评分显示AUC分别为0.973,0.980和0.953.亚组分析强调了这两个模型的稳健性和稳定性。
结论:这项研究引入了SM4BC3miR和SSM4BC模型,利用三种特异性血清miRNA生物标志物进行乳腺癌筛查。具有很高的准确性和稳定性,这些模型为乳腺癌的早期检测提供了一种有希望的方法.然而,其在临床中的实际应用和有效性仍有待进一步验证.
OBJECTIVE: The study focuses on enhancing breast cancer (BC) prognosis through early detection, aiming to establish a non-invasive, clinically viable BC screening method using specific serum miRNA levels.
METHODS: Involving 11,349 participants across BC, 11 other cancer types, and control groups, the study identified serum biomarkers through feature selection and developed two BC screening models using six machine learning algorithms. These models underwent evaluation across test, internal, and external validation sets, assessing performance metrics like accuracy, sensitivity, specificity, and the area under the curve (AUC). Subgroup analysis was conducted to test model stability.
RESULTS: Based on the three serum miRNA biomarkers (miR-1307-3p, miR-5100, and miR-4745-5p), a BC screening model, SM4BC3miR model, was developed. This model achieved AUC performances of 0.986, 0.986, and 0.939 on the test, internal, and external sets, respectively. Furthermore, the SSM4BC model, utilizing ratio scores of miR-1307-3p/miR-5100 and miR-4745-5p/miR-5100, showed AUCs of 0.973, 0.980, and 0.953, respectively. Subgroup analyses underscored both models\' robustness and stability.
CONCLUSIONS: This research introduced the SM4BC3miR and SSM4BC models, leveraging three specific serum miRNA biomarkers for breast cancer screening. Demonstrating high accuracy and stability, these models present a promising approach for early detection of breast cancer. However, their practical application and effectiveness in clinical settings remain to be further validated.