An Association Rule Mining Approach to Discover lncRNAs Expression Patterns in Cancer Datasets

被引:6
作者
Cremaschi, Paolo [1 ]
Carriero, Roberta [1 ]
Astrologo, Stefania [1 ]
Coli, Caterina [1 ]
Lisa, Antonella [1 ]
Parolo, Silvia [1 ]
Bione, Silvia [1 ]
机构
[1] CNR, Inst Mol Genet, Computat Biol Unit, I-27100 Pavia, Italy
关键词
LONG NONCODING RNA; POSTTRANSCRIPTIONAL GENE-REGULATION; SIGNATURES; GENCODE; CELLS; TOOL;
D O I
10.1155/2015/146250
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
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.
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页数:13
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