目的:方法:该研究提出了一种结合机器学习(ML)技术和可解释人工智能(XAI)的模型,以预测乳腺癌(BC)转移并揭示转移患者中重要的基因组生物标志物。
方法:共分析了98个原发性BC样本,包括来自在5年随访期内发生远处转移的患者的34个样本和来自诊断后至少5年无病患者的44个样本。然后对基因组数据进行生物统计分析,其次是弹性网特征选择方法的应用。该技术鉴定了与BC转移相关的有限数量的基因组生物标志物。光梯度增压机(LightGBM),分类提升(CatBoost),极端梯度提升(XGBoost),梯度增强树(GBT),和Ada增强(AdaBoost)算法用于预测。要评估模型的预测能力,准确性,F1得分,精度,召回,ROC曲线下面积(AUC),和Brier得分作为绩效评估指标。为了提高可解释性并克服ML模型的“黑匣子”问题,采用了沙普利添加剂扩张(SHAP)方法。
结果:LightGBM模型优于其他模型,产生96%的显著准确度和99.3%的AUC。除了生物统计学评估,在基于XAI的SHAP结果中,TSPYL5,ATP5E的表达水平增加,CA9,NUP210,SLC37A1,ARIH1,PSMD7,UBQLN1,PRAME,和UBE2T(p≤0.05)被发现与BC转移的发生率增加有关。最后,CACTIN表达水平降低,TGFβ3,SCUBE2,ARL4D,OR1F1,ALDH4A1,PHF1和CROCC(p≤0.05)基因也被确定为增加BC中转移的风险。
结论:这项研究的结果可能通过推荐BC患者的定制治疗方法来预防疾病进展和转移,并可能改善临床结局。
OBJECTIVE: Method: This research presents a model combining machine learning (ML) techniques and eXplainable artificial intelligence (XAI) to predict breast cancer (BC) metastasis and reveal important genomic biomarkers in metastasis patients.
METHODS: A total of 98 primary BC samples was analyzed, comprising 34 samples from patients who developed distant metastases within a 5-year follow-up period and 44 samples from patients who remained disease-free for at least 5 years after diagnosis. Genomic data were then subjected to biostatistical analysis, followed by the application of the elastic net feature selection method. This technique identified a restricted number of genomic biomarkers associated with BC metastasis. A light gradient boosting machine (LightGBM), categorical boosting (CatBoost), Extreme Gradient Boosting (XGBoost), Gradient Boosting Trees (GBT), and Ada boosting (AdaBoost) algorithms were utilized for prediction. To assess the models\' predictive abilities, the accuracy, F1 score, precision, recall, area under the ROC curve (AUC), and Brier score were calculated as performance evaluation metrics. To promote interpretability and overcome the \"black box\" problem of ML models, a SHapley Additive exPlanations (SHAP) method was employed.
RESULTS: The LightGBM model outperformed other models, yielding remarkable accuracy of 96% and an AUC of 99.3%. In addition to biostatistical evaluation, in XAI-based SHAP results, increased expression levels of TSPYL5, ATP5E, CA9, NUP210, SLC37A1, ARIH1, PSMD7, UBQLN1, PRAME, and UBE2T (p ≤ 0.05) were found to be associated with an increased incidence of BC metastasis. Finally, decreased levels of expression of CACTIN, TGFB3, SCUBE2, ARL4D, OR1F1, ALDH4A1, PHF1, and CROCC (p ≤ 0.05) genes were also determined to increase the risk of metastasis in BC.
CONCLUSIONS: The findings of this study may prevent disease progression and metastases and potentially improve clinical outcomes by recommending customized treatment approaches for BC patients.