AI Article Synopsis
- The study highlights the urgent need for reliable prognostic markers in breast cancer due to high mortality rates among female patients.
- Researchers utilized TCGA data to identify gene signatures linked to epithelial-mesenchymal transition and developed a prognostic model based on multiple analyses.
- The validated model, which includes age and six specific genes, demonstrated good predictive accuracy for overall survival, suggesting potential for improved personalized treatment strategies in breast cancer care.
Article 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, -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, TGF1, and SERPINE1). -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.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9352481 | PMC |
| http://dx.doi.org/10.1155/2022/1289445 | DOI Listing |
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