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. 2024 Feb 28;10(5):e27054.
doi: 10.1016/j.heliyon.2024.e27054. eCollection 2024 Mar 15.

Joint global and local interpretation method for CIN status classification in breast cancer

Liangliang Liu  1 , Pei Zhang  1 , Zhihong Liu  1 , Tong Sun  1 , Hongbo Qiao  1
Affiliations

Joint global and local interpretation method for CIN status classification in breast cancer

Liangliang Liu et al. Heliyon. .

Abstract

Breast cancer is among the cancer types with the highest numbers of new cases. The study of this disease from a microscopic perspective has been a prominent research topic. Previous studies have shown that microRNAs (miRNAs) are closely linked to chromosomal instability (CIN). Correctly predicting CIN status from miRNAs can help to improve the survival of breast cancer patients. In this study, a joint global and local interpretation method called GL_XGBoost is proposed for predicting CIN status in breast cancer. GL_XGBoost integrates the eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanation (SHAP) methods. XGBoost is used to predict CIN status from miRNA data, whereas SHAP is used to select miRNA features that have strong relationships with CIN. Furthermore, SHAP's rich visualization strategies enhance the interpretability of the entire model at the global and local levels. The performance of GL_XGBoost is validated on the TCGA-BRCA dataset, and it is shown to have an accuracy of 78.57% and an area under the curve value of 0.87. Rich visual analysis is used to explain the relationships between miRNAs and CIN status from different perspectives. Our study demonstrates an intuitive way of exploring the relationship between CIN and cancer from a microscopic perspective.

Keywords: Breast cancer; Chromosomal instability; SHAP; XGBoost; miRNAs.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Figure 1
Figure 1
The architecture of GL_XGBoost.
Figure 2
Figure 2
ROC curves obtained by five classifiers.
Figure 3
Figure 3
Confusion matrices obtained by five classifiers, which are DT(a), SVM(b), RF(c), AdaBoost(d) and GL_XGBoost(e) respectively.
Figure 4
Figure 4
Comparison of three feature selection strategies of XGBoost model.
Figure 5
Figure 5
Feature importance obtained by SHAP method.
Figure 6
Figure 6
The relationships between the feature values of the top 20 important miRNA features and their SHAP values for all samples in the XGBoost model prediction process.
Figure 7
Figure 7
Single predictive interpretation for a patient with id “TCGA-AN-A0FZ”.
Figure 8
Figure 8
Overall visual analysis, interpreting the classification predictions of all samples in the test set.
Figure 9
Figure 9
Single feature dependence plot of hsa-mir-1224.
Figure 10
Figure 10
Dual feature interaction of hsa-mir-1224 and hsa-mir-93.
See this image and copyright information in PMC

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