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Updated: Mar 21, 2026
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
Identification of breast cancer candidate genes using gene co-expression and protein-protein interaction information
Zhenyu Yue1,2, Hai-Tao Li3, Yabing Yang1
1School of Life Sciences, Anhui University, Hefei, Anhui 230601, China.
Abstract:
Breast cancer (BC) is one of the most common malignancies that could threaten female health. As the molecular mechanism of BC has not yet been completely discovered, identification of related genes of this disease is an important area of research that could provide new insights into gene function as well as potential treatment targets. Here we used subnetwork extraction algorithms to identify novel BC related genes based on the known BC genes (seed genes), gene co-expression profiles and protein-protein interaction network. We computationally predicted seven key genes (EPHX2, GHRH, PPYR1, ALPP, KNG1, GSK3A and TRIT1) as putative genes of BC. Further analysis shows that six of these have been reported as breast cancer associated genes, and one (PPYR1) as cancer associated gene. Lastly, we developed an expression signature using these seven key genes which significantly stratified 1660 BC patients according to relapse free survival (hazard ratio [HR], 0.55; 95% confidence interval [CI], 0.46-0.65; Logrank p = 5.5e-13). The 7-genes signature could be established as a useful predictor of disease prognosis in BC patients. Overall, the identified seven genes might be useful prognostic and predictive molecular markers to predict the clinical outcome of BC patients.
Insights
Researchers identified seven key genes associated with breast cancer (BC) using network analysis. A signature based on these genes accurately predicts relapse-free survival in BC patients, offering potential prognostic markers.
Area of Science:
- Genomics
- Bioinformatics
- Molecular Oncology
Background:
- Breast cancer (BC) remains a leading cause of mortality in women, with its molecular underpinnings incompletely understood.
- Identifying novel genes linked to BC is crucial for advancing gene function knowledge and discovering therapeutic targets.
Purpose of the Study:
- To identify novel breast cancer-related genes using computational methods.
- To develop a gene expression signature for predicting breast cancer patient prognosis.
Main Methods:
- Subnetwork extraction algorithms were applied to known BC genes (seed genes), gene co-expression data, and protein-protein interaction networks.
- Seven key genes (EPHX2, GHRH, PPYR1, ALPP, KNG1, GSK3A, TRIT1) were computationally predicted as putative BC genes.
- An expression signature was constructed using the seven identified genes.
Main Results:
- Six of the seven predicted genes were previously reported as breast cancer-associated, and one (PPYR1) as cancer-associated.
- The 7-gene expression signature significantly stratified 1660 BC patients based on relapse-free survival (HR, 0.55; 95% CI, 0.46-0.65; Logrank p = 5.5e-13).
Conclusions:
- The identified seven genes show promise as novel prognostic and predictive molecular markers for breast cancer.
- The developed 7-gene signature can serve as a valuable tool for predicting clinical outcomes and disease prognosis in breast cancer patients.
