Using network and functional enrichment clustering analyses to find therapeutic targets for breast cancer: The role of cyclin‐dependent kinase 2. (12th February 2018)

• Record Type: Journal Article
• Authors: Valenzuela, Luis; Assar, Rodrigo
• Other Names: Anguelov Roumen guestEditor.; Banasiak Jacek guestEditor.; Debbouche Amar guestEditor.
• Is Part Of: Mathematical methods in the applied sciences. Volume 41:Number 18(2018)
• Page Start: 8514
• Page End: 8527
• Publication Date: 2018-02-12
• Subjects: breast cancer biomarkers -- functional enrichment analysis -- gene expression clustering -- gene network analysis; Mathematics -- Periodicals; Technology -- Mathematics -- Periodicals; 519
• DOI: 10.1002/mma.4788
• Languages: English
• ISSNs: 0170-4214

Abstract

Abstract : Population aging is rapidly increasing in developing countries; thus, covering medical needs for breast cancer diagnosis and treatment is a priority in Latin America. We describe an approach for integrating differential expression analysis, biological pathway enrichment, in silico transcription‐binding sites and network topology, to find key genes that may be used as biomarkers or therapeutic targets. This approach is based on publicly available data from microarrays of the MCF‐7 breast cancer cell line treated with estrogen. We found significant estrogen‐responsive genes, which were used as nodes to construct networks based on protein‐protein interactions reported in the literature. Then, we conducted a topology analysis of the networks, revealing the most‐connected nodes, ie, those responsible for maintaining the network structure corresponding to genes with well‐acknowledged functions in G1/S cell cycle transition, such as cyclin‐dependent kinase 2 (CDK2), which has been proposed as a therapeutic target in classical biochemical studies. In addition, analyses of biological pathway enrichment and in silico transcription‐binding sites support the biological meaning and importance of these key genes and help to decide the best target genes. Therefore, we postulate that the integrative bioinformatics approach shown here, unlike the classical bioinformatics approach that only includes differentially expressed genes and enriched biological pathways, can be applied as an approach for finding novel biomarkers and/or therapeutic target genes for nonresponsive treatments.