Abstract
Breast cancer has been the leading cause of cancer-associated mortality in women worldwide. Perturbation of oncogene and tumor suppressor gene expression is generally considered as the fundamental cause of cancer initiation and progression. In the present study, three gene expression datasets containing information of breast cancer and adjacent normal tissues that were detected using traditional gene microarrays were downloaded and batch effects were removed with R programming software. The differentially expressed genes between breast cancer and normal tissue groups were closely associated with cancer development pathways. Interestingly, five pathways, including 'extracellular matrix-receptor interaction', 'peroxisome proliferator-activated receptors signaling pathway', 'propanoate metabolism', 'pyruvate metabolism' and 'regulation of lipolysis in adipocytes', were thoroughly connected by 10 genes. Patients with upregulation of six of these hub genes (acetyl-CoA carboxylase β, acyl-CoA dehydrogenase medium chain, adiponectin, C1Q and collagen domain containing, acyl-CoA synthetase short chain family member 2, phosphoenolpyruvate carboxykinase 1 and perilipin 1) exhibited improved breast cancer prognosis. Additionally, breast cancer-specific network analysis identified several gene-gene interaction modules. These gene clusters had strong interactions according to the scoring in the whole network, which may be important to the development of breast cancer. In conclusion, the present study may improve the understanding of the mechanisms of breast cancer and provide several valuable prognosis and treatment signatures.