Cancer Metastasis Prediction and Genomic Biomarker Identification through Machine Learning and eXplainable Artificial Intelligence in Breast Cancer Research

Burak Yagin; Fatma Hilal Yagin; Cemil Colak; Feyza Inceoglu; Seifedine Kadry; Jungeun Kim

doi:10.3390/diagnostics13213314

Cancer Metastasis Prediction and Genomic Biomarker Identification through Machine Learning and eXplainable Artificial Intelligence in Breast Cancer Research

Diagnostics (Basel). 2023 Oct 26;13(21):3314. doi: 10.3390/diagnostics13213314.

Authors

Burak Yagin¹, Fatma Hilal Yagin¹, Cemil Colak¹, Feyza Inceoglu², Seifedine Kadry^{3

4

5}, Jungeun Kim⁶

Affiliations

¹ Department of Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, Malatya 44280, Turkey.
² Department of Biostatistics, Faculty of Medicine, Malatya Turgut Ozal University, Malatya 44090, Turkey.
³ Department of applied Data science, Noroff University College, 4612 Kristiansand, Norway.
⁴ Artificial Intelligence Research Center (AIRC), Ajman University, Ajman 346, United Arab Emirates.
⁵ Department of Electrical and Computer Engineering, Lebanese American University, Byblos 36, Lebanon.
⁶ Department of Software, Kongju National University, Cheonan 31080, Republic of Korea.

Abstract

Aim: 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.

Method: 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.

Conclusion: The findings of this study may prevent disease progression and metastases and potentially improve clinical outcomes by recommending customized treatment approaches for BC patients.

Keywords: SHAP; breast cancer metastasis; eXplainable artificial intelligence; genomic biomarkers; machine learning algorithms.

Grants and funding

This research was partly supported by the Technology Development Program of MSS (No. S3033853) and by the research grant of the Kongju National University in 2023.