Biomarker identification in breast cancer: Beta-adrenergic receptor signaling and pathways to therapeutic response

• Published in: Computational and structural biotechnology journal Vol. 6; no. 7; p. e201303003
• Main Authors: Kafetzopoulou, Liana E, Boocock, David J, Dhondalay, Gopal Krishna R, Powe, Desmond G, Ball, Graham R
• Format: Journal Article
• Language: English
• Published: Netherlands Research Network of Computational and Structural Biotechnology (RNCSB) Organization 01.03.2013; Elsevier
• Subjects: Artificial Neural Networks; Beta-2-Adrenergic Receptor; beta-blockers; Microarray Data; Beta-2-Adrenergic Receptor; beta-blockers; Microarray Data; Artificial Neural Networks
• ISSN: 2001-0370; 2001-0370
• DOI: 10.5936/csbj.201303003

Recent preclinical studies have associated beta-adrenergic receptor (β-AR) signaling with breast cancer pathways such as progression and metastasis. These findings have been supported by clinical and epidemiological studies which examined the effect of beta-blocker therapy on breast cancer metastasis, recurrence and mortality. Results from these studies have provided initial evidence for the inhibition of cell migration in breast cancer by beta-blockers and have introduced the beta-adrenergic receptor pathways as a target for therapy. This paper analyzes gene expression profiles in breast cancer patients, utilising Artificial Neural Networks (ANNs) to identify molecular signatures corresponding to possible disease management pathways and biomarker treatment strategies associated with beta-2-adrenergic receptor (ADRB2) cell signaling. The adrenergic receptor relationship to cancer is investigated in order to validate the results of recent studies that suggest the use of beta-blockers for breast cancer therapy. A panel of genes is identified which has previously been reported to play an important role in cancer and also to be involved in the beta-adrenergic receptor signaling.