Breast Cancer Research and Treatment

, Volume 175, Issue 3, pp 691–699 | Cite as

Integrating of genomic and transcriptomic profiles for the prognostic assessment of breast cancer

  • Chengxiao Yu
  • Na Qin
  • Zhening Pu
  • Ci Song
  • Cheng Wang
  • Jiaping Chen
  • Juncheng Dai
  • Hongxia Ma
  • Tao JiangEmail author
  • Yue JiangEmail author



To evaluate the prognostic effect of the integration of genomic and transcriptomic profiles in breast cancer.


Eight hundred and ten samples from the Cancer Genome Atlas (TCGA) data sets were randomly divided into the training set (540 subjects) and validation set (270 subjects). We first selected single-nucleotide polymorphism (SNPs) and genes associated with breast cancer prognosis in the training set to construct the prognostic prediction model, and then replicated the prediction efficiency in the validation set.


Four SNPs and three genes associated with the prognosis of breast cancer in the training set were included in the prognostic model. Patients were divided into the high-risk group and low-risk group based on the four SNPs and three genes signature-based genetic prognostic index. High-risk patients showed a significant worse overall survival [Hazard Ratio (HR) 9.43, 95% confidence interval (CI) 3.81–23.33, P < 0.001] than the low-risk group. Compared to the model constructed with only gene expression, the C statistics for the signature-based genetic prognostic index [area under curves (AUC) = 0.79, 95% CI 0.72–0.86] showed a significant increase (P < 0.001). Additionally, we further replicated the prognostic prediction model in the validation set as patients in the high-risk group also showed a significantly worse overall survival (HR 4.55, 95% CI 1.50–13.88, P < 0.001), and the C statistics for the signature-based genetic prognostic index was 0.76 (95% CI 0.65–0.86). The following time-dependent ROC revealed that the mean of AUCs were 0.839 and 0.748 in the training set and the validation set, respectively.


Our findings suggested that integrating genomic and transcriptomic profiles could greatly improve the predictive efficiency of the prognosis of breast cancer patients.


Breast cancer Prognostic model Genomic Transcriptomic 



We thank the study participants and research staff for their contributions and commitment to this study.

Author contributions

YJ and TJ conceived the project; CXY analyzed the data and drafted the manuscript; NQ modify the manuscript; ZNP, CS, and CW contributed to the interpretation of the results; JPC reviewed the manuscript; JCD and HXM supervised the research. All authors read and approved the final manuscript.


This work was supported by Science Fund for Creative Research Groups of the National Natural Science Foundation of China (81521004), Cheung Kong Scholars Programme of China, the Priority Academic Program for the Development of Jiangsu Higher Education Institutions (Public Health and Preventive Medicine), and Top-notch Academic Programs Project of Jiangsu Higher Education Institutions (PPZY2015A067).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no competing interests.

Supplementary material

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Supplementary material 4 (TIF 34993 KB)
10549_2019_5177_MOESM5_ESM.doc (58 kb)
Supplementary material 5 (DOC 58 KB)


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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Epidemiology, Center for Global Health, School of Public HealthNanjing Medical UniversityNanjingChina
  2. 2.State Key Laboratory of Reproductive MedicineNanjing Medical UniversityNanjingChina
  3. 3.Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center of Cancer MedicineNanjing Medical UniversityNanjingChina
  4. 4.Department of Bioinformatics, School of Biomedical Engineering and InformaticsNanjing Medical UniversityNanjingChina
  5. 5.Center of Clinical Research, Wuxi Institute of Translational MedicineWuxi People’s Hospital of Nanjing Medical UniversityWuxiChina

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