Identification of a 9‐gene prognostic signature for breast cancer. (14th October 2020)

• Record Type: Journal Article
• Authors: Tian, Zelin; Tang, Jianing; Liao, Xing; Yang, Qian; Wu, Yumin; Wu, Gaosong
• Is Part Of: Cancer medicine. Volume 9:Number 24(2020)
• Page Start: 9471
• Page End: 9484
• Publication Date: 2020-10-14
• Subjects: breast cancer -- gene expression omnibus -- nomogram -- prognostic signature; 616.994005
• DOI: 10.1002/cam4.3523
• Languages: English
• ISSNs: 2045-7634

Abstract

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. Abstract : This study developed a novel 9‐gene signature prediction model to predicting the prognosis of BRCA patients. The results show that the prediction model is a powerful predictor of OS and DFS. The establishment of this model may help BRCA patients to formulate more accurate treatment plans and improve the prognosis of BRCA.