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Journal of Translational Medicine
Published in: Journal of Translational Medicine 1/2021

Open Access 01-12-2021 | Breast Cancer | Research

Detecting prognostic biomarkers of breast cancer by regularized Cox proportional hazards models

Authors: Lingyu Li, Zhi-Ping Liu

Published in: Journal of Translational Medicine | Issue 1/2021

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Abstract

Background

The successful identification of breast cancer (BRCA) prognostic biomarkers is essential for the strategic interference of BRCA patients. Recently, various methods have been proposed for exploring a small prognostic gene set that can distinguish the high-risk group from the low-risk group.

Methods

Regularized Cox proportional hazards (RCPH) models were proposed to discover prognostic biomarkers of BRCA from gene expression data. Firstly, the maximum connected network with 1142 genes by mapping 956 differentially expressed genes (DEGs) and 677 previously BRCA-related genes into the gene regulatory network (GRN) was constructed. Then, the 72 union genes of the four feature gene sets identified by Lasso-RCPH, Enet-RCPH, \(L_{0}\)-RCPH and SCAD-RCPH models were recognized as the robust prognostic biomarkers. These biomarkers were validated by literature checks, BRCA-specific GRN and functional enrichment analysis. Finally, an index of prognostic risk score (PRS) for BRCA was established based on univariate and multivariate Cox regression analysis. Survival analysis was performed to investigate the PRS on 1080 BRCA patients from the internal validation. Particularly, the nomogram was constructed to express the relationship between PRS and other clinical information on the discovery dataset. The PRS was also verified on 1848 BRCA patients of ten external validation datasets or collected cohorts.

Results

The nomogram highlighted that the importance of PRS in guiding significance for the prognosis of BRCA patients. In addition, the PRS of 301 normal samples and 306 tumor samples from five independent datasets showed that it is significantly higher in tumors than in normal tissues (\(P<0.05\)). The protein expression profiles of the three genes, i.e., ADRB1, SAV1 and TSPAN14, involved in the PRS model demonstrated that the latter two genes are more strongly stained in tumor specimens. More importantly, external validation illustrated that the high-risk group has worse survival than the low-risk group (\(P<0.05\)) in both internal and external validations.

Conclusions

The proposed pipelines of detecting and validating prognostic biomarker genes for BRCA are effective and efficient. Moreover, the proposed PRS is very promising as an important indicator for judging the prognosis of BRCA patients.
Appendix
Available only for authorised users
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Metadata
Title
Detecting prognostic biomarkers of breast cancer by regularized Cox proportional hazards models
Authors
Lingyu Li
Zhi-Ping Liu
Publication date
01-12-2021
Publisher
BioMed Central
Published in
Journal of Translational Medicine / Issue 1/2021
Electronic ISSN: 1479-5876
DOI
https://doi.org/10.1186/s12967-021-03180-y

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Keynote webinar | Spotlight on medication adherence

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Live: Thursday 27th June 2024, 18:00-19:30 (CEST)

WHO estimates that half of all patients worldwide are non-adherent to their prescribed medication. The consequences of poor adherence can be catastrophic, on both the individual and population level.

Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.

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