An Association Rule Mining Approach to Discover lncRNAs Expression Patterns in Cancer Datasets. (27th July 2015)

• Record Type: Journal Article
• Authors: Cremaschi, Paolo; Carriero, Roberta; Astrologo, Stefania; Colì, Caterina; Lisa, Antonella; Parolo, Silvia; Bione, Silvia
• Other Names: Jordan Peter Academic Editor.
• Is Part Of: BioMed research international. Volume 2015(2015)
• Publication Date: 2015-07-27
• Subjects: Medicine -- Periodicals; Biology -- Periodicals; Biotechnology -- Periodicals; Life sciences -- Periodicals; 610.5
• DOI: 10.1155/2015/146250
• Languages: English
• ISSNs: 2314-6133

Abstract

Abstract : In the past few years, the role of long noncoding RNAs (lncRNAs) in tumor development and progression has been disclosed although their mechanisms of action remain to be elucidated. An important contribution to the comprehension of lncRNAs biology in cancer could be obtained through the integrated analysis of multiple expression datasets. However, the growing availability of public datasets requires new data mining techniques to integrate and describe relationship among data. In this perspective, we explored the powerness of the Association Rule Mining (ARM) approach in gene expression data analysis. By the ARM method, we performed a meta-analysis of cancer-related microarray data which allowed us to identify and characterize a set of ten lncRNAs simultaneously altered in different brain tumor datasets. The expression profiles of the ten lncRNAs appeared to be sufficient to distinguish between cancer and normal tissues. A further characterization of this lncRNAs signature through a comodulation expression analysis suggested that biological processes specific of the nervous system could be compromised.