Research Paper Volume 13, Issue 12 pp 16577—16599
Identification of differentially expressed genes-related prognostic risk model for survival prediction in breast carcinoma patients
- 1 Department of Breast Oncology, The Second Hospital of Dalian Medical University, Dalian, Liaoning 116023, China
- 2 Department of Respiratory Medicine, The Second Hospital of Dalian Medical University, Dalian, Liaoning 116023, China
- 3 Institute for Genome Engineered Animal Models of Human Diseases, Dalian Medical University, Dalian, Liaoning 116044, China
- 4 Institute of Cancer Stem Cell, Dalian Medical University, Dalian, Liaoning 116044, China
- 5 Department of Breast Surgery, The Second Hospital of Dalian Medical University, Dalian, Liaoning 116023, China
Received: November 19, 2020 Accepted: May 31, 2021 Published: June 26, 2021https://doi.org/10.18632/aging.203178
How to Cite
Copyright: © 2021 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Since the imbalance of gene expression has been demonstrated to tightly related to breast cancer (BRCA) genesis and growth, common genes expressed of BRCA were screened to explore the essence in-between. In current work, most common differentially expressed genes (DEGs) in various subtypes of BRCA were identified. Functional enrichment analysis illustrated the driving factor of deactivation of the cell cycle and the oocyte meiosis, which critically triggers the development of BRCA. Herein, we constructed a 12-gene prognostic risk model relative to differential gene expression. Subsequently, the K-M curves, analysis on time-ROC curve and Cox regression were performed to assess this risk model by determining the respective prognostic value, and the prediction performance were ascertained for both training and validation cohorts. In addition, multivariate Cox regression was analysed to reveal the independence between risk score and prognostic stage, and the accuracy and sensitivity of prognosis are particularly improved after clinical indicators are included into the analysis. In summary, this study offers novel insights into the imbalance of gene expression within BRCA, and highlights 12 selected genes associated with patient prognosis. The risk model can help individualize treatment for patients at different risks, and propose precise strategies and treatments for BRCA therapy.