Literature DB >> 33090721

Identification of a 9-gene prognostic signature for breast cancer.

Zelin Tian1, Jianing Tang1, Xing Liao1, Qian Yang1, Yumin Wu1, Gaosong Wu1.   

Abstract

Breast cancer (BRCA) is the most common cancer among women and is the second leading cause of cancer death in women. In this study, we developed a 9-gene prognostic signature to predict the prognosis of patients with BRCA. GSE20685, GSE42568, GSE20711, and GSE88770 were used as training sets. The Kaplan-Meier plot was constructed to assess survival differences and log-rank test was performed to evaluate the statistical significance. The overall survival (OS) of patients in the low-risk group was significantly higher than that in the high-risk group. ROC analysis indicated that this 9-gene signature shows good diagnostic efficiency both in OS and disease-free survival (DFS). The 9-gene signature was further validated through GSE16446, GSE7390, and TCGA-BRCA datasets. We also established a nomogram that integrates clinicopathological features and 9-gene signature. The analysis of the calibration plot showed that the nomogram has good prognostic performance. More convincingly, real-time reverse transcription-polymerase chain reaction (RT-PCR) results indicated that the protective prognostic factors in BRCA patients were downregulated, whereas the dangerous prognostic factors were upregulated. The innovation of this article is not only constructing a prognostic gene signature, but also combining with clinical information to further establish a nomogram to better predict the survival probability of patients. It is worth mentioning that this signature also does not depend on other clinical factors or variables.
© 2020 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.

Entities:  

Keywords:  breast cancer; gene expression omnibus; nomogram; prognostic signature

Year:  2020        PMID: 33090721      PMCID: PMC7774725          DOI: 10.1002/cam4.3523

Source DB:  PubMed          Journal:  Cancer Med        ISSN: 2045-7634            Impact factor:   4.452


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1.  A Novel Risk Model Based on Lipid Metabolism-Associated Genes Predicts Prognosis and Indicates Immune Microenvironment in Breast Cancer.

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2.  Identification of a 9-gene prognostic signature for breast cancer.

Authors:  Zelin Tian; Jianing Tang; Xing Liao; Qian Yang; Yumin Wu; Gaosong Wu
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