Research Paper Volume 11, Issue 24 pp 12131—12146
Identification of breast cancer risk modules via an integrated strategy
- 1 College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
- 2 Molecular Imaging Research Center (MIRC), Harbin Medical University, Harbin, China
- 3 TOF-PET/CT/MR Center, the Fourth Hospital of Harbin Medical University, Harbin, China
Received: September 10, 2019 Accepted: November 19, 2019 Published: December 20, 2019https://doi.org/10.18632/aging.102546
How to Cite
Copyright © 2019 Li et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Breast cancer is one of the most common malignant cancers among females worldwide. This complex disease is not caused by a single gene, but resulted from multi-gene interactions, which could be represented by biological networks. Network modules are composed of genes with significant similarities in terms of expression, function and disease association. Therefore, the identification of disease risk modules could contribute to understanding the molecular mechanisms underlying breast cancer. In this paper, an integrated disease risk module identification strategy was proposed according to a multi-objective programming model for two similarity criteria as well as significance of permutation tests in Markov random field module score, function consistency score and Pearson correlation coefficient difference score. Three breast cancer risk modules were identified from a breast cancer-related interaction network. Genes in these risk modules were confirmed to play critical roles in breast cancer by literature review. These risk modules were enriched in breast cancer-related pathways or functions and could distinguish between breast tumor and normal samples with high accuracy for not only the microarray dataset used for breast cancer risk module identification, but also another two independent datasets. Our integrated strategy could be extended to other complex diseases to identify their risk modules and reveal their pathogenesis.