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. 2022 Jul 28:2022:1289445.
doi: 10.1155/2022/1289445. eCollection 2022.

Establishment and Analysis of an Individualized EMT-Related Gene Signature for the Prognosis of Breast Cancer in Female Patients

Wei Xue  1   2   3 , Chenyu Sun  4 , Hui Yuan  5 , Xin Yang  2 , Qiuping Zhang  2   6 , Yunnuo Liao  1   2 , Hongwei Guo  1   2
Affiliations

Establishment and Analysis of an Individualized EMT-Related Gene Signature for the Prognosis of Breast Cancer in Female Patients

Wei Xue et al. Dis Markers. .

Abstract

Background: The current high mortality rate of female breast cancer (BC) patients emphasizes the necessity of identifying powerful and reliable prognostic signatures in BC patients. Epithelial-mesenchymal transition (EMT) was reported to be associated with the development of BC. The purpose of this study was to identify prognostic biomarkers that predict overall survival (OS) in female BC patients by integrating data from TCGA database.

Method: We first downloaded the dataset in TCGA and identified gene signatures by overlapping candidate genes. Differential analysis was performed to find differential EMT-related genes. Univariate regression analysis was then performed to identify candidate prognostic variables. We then developed a prognostic model by multivariate analysis to predict OS. Calibration curves, receiver operating characteristics (ROC) curves, C-index, and decision curve analysis (DCA) were used to test the veracity of the prognostic model.

Result: In this study, we identified and validated a prognostic model integrating age and six genes (CD44, P3H1, SDC1, COL4A1, TGFβ1, and SERPINE1). C-index values for BC patients were 0.672 (95% CI 0.611-0.732) and 0.692 (95% CI 0.586-0.798) in the training cohort and test set, respectively. The calibration curve and the DCA curve show the good predictive performance of the model.

Conclusion: This study offered a robust predictive model for OS prediction in female BC patients and may provide a more accurate treatment strategy and personalized therapy in the future.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Flow chart of this study.
Figure 2
Figure 2
Heat map (a) and volcano map (b) of differentially expressed gene related to EMT.
Figure 3
Figure 3
Forest plot analyzed by univariate Cox regression.
Figure 4
Figure 4
Nomogram for predicting 1-, 3-, and 5-year overall survival (OS) for BC patients in the training cohort.
Figure 5
Figure 5
(a, c, e) Distribution of risk score in patients with BC. The black dotted line serves as the dividing line between the high-risk group and the low-risk group. (b, d, f) Diagram of the relationship between risk score and patient survival time. The result of (a, b) is based on training set, the result of (c, d) is based on test set, and the result of (e, f) is based on the overall internal validation set.
Figure 6
Figure 6
Overall survival (OS) Kaplan-Meier curves for patients in the low- and high-risk groups: (a) training set; (b) test set; (c) overall internal validation set.
Figure 7
Figure 7
(a–c) Calibration plots to predict 1-, 3-, and 5-year overall survival (OS) in the training set; (d–f) calibration plots to predict 1-, 3-, and 5-year; overall survival (OS) in the test set; (g–i) calibration plots to predict 1-, 3-, and 5-year overall survival (OS) in the overall internal validation set.
Figure 8
Figure 8
(a–c) ROC curves to predict 1-, 3-, and 5-year overall survival (OS) in the training set; (d–f) ROC curves to predict 1-, 3-, and 5-year; overall survival (OS) in the test set; (g–i) ROC curves to predict 1-, 3-, and 5-year overall survival (OS) in the overall internal validation set.
Figure 9
Figure 9
(a–c) DCA analysis predicting 1-, 3-, and 5-year overall survival (OS) in the training set; (d–f) DCA analysis predicting 1-, 3-, and 5-year; overall survival (OS) in the test set; (g–i) DCA analysis predicting 1-, 3-, and 5-year overall survival (OS) in the overall internal validation set.
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