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01-06-2019 | Breast Cancer | Epidemiology

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

Authors: Chengxiao Yu, Na Qin, Zhening Pu, Ci Song, Cheng Wang, Jiaping Chen, Juncheng Dai, Hongxia Ma, Tao Jiang, Yue Jiang

Published in: Breast Cancer Research and Treatment | Issue 3/2019

Abstract

Purpose

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

Methods

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.

Results

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.

Conclusions

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

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Title: Integrating of genomic and transcriptomic profiles for the prognostic assessment of breast cancer
Authors: Chengxiao Yu
Na Qin
Zhening Pu
Ci Song
Cheng Wang
Jiaping Chen
Juncheng Dai
Hongxia Ma
Tao Jiang
Yue Jiang
Publication date: 01-06-2019
Publisher: Springer US
Keywords: Breast Cancer
Breast Cancer
Published in: Breast Cancer Research and Treatment / Issue 3/2019
Print ISSN: 0167-6806
Electronic ISSN: 1573-7217
DOI: https://doi.org/10.1007/s10549-019-05177-0

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