Abstract 2987: Regulatory network discovery using 3-way integration of high-dimensional mRNA, miRNA and lncRNA expression data from the entire NCI60 cancer cell line panel

• Published in: Cancer research (Chicago, Ill.) Ill.), 2012-04, Vol.72 (8_Supplement), p.2987-2987
• Main Authors: Mestdagh, Pieter, Hellemans, Jan, D'haene, Barbara, Derveaux, Stefaan, Wong, Kee, Kotturi, Paul, Ginzinger, David, Vandesompele, Jo
• Format: Article
• Language: English
• ISSN: 0008-5472; 1538-7445
• DOI: 10.1158/1538-7445.AM2012-2987

With the discovery of different non-coding RNA species, the complexity of the transcriptome and its regulation has increased dramatically. Next to the well-studied small non-coding miRNAs, several thousands of long non-coding RNAs (lncRNAs) have recently been described. Like miRNAs, lncRNAs appear to predominantly function as regulators of gene expression and are implicated in various regulatory networks involving both miRNAs and protein-coding genes. In order to facilitate the search for miRNA-lncRNA-mRNA regulatory networks in cancer we have profiled the expression of each of these RNA information layers using the high-throughput SmartChip RT-qPCR technology on the entire NCI60 cancer cell line panel. In total, three SmartChip Panels with 1050 miRNAs, 1250 cancer-focused mRNAs, and 1718 lncRNAs, respectively, were quantified with a minimum of 3 technical replicates against each cell line. Here, we present the results of an unprecedented integrated analysis aimed at identifying networks of highly co-regulated miRNA-lncRNA-mRNA clusters. In brief, individual clusters are annotated using a pathway enrichment approach whereby network edges are evaluated using miRNA target, lncRNA target, and transcription factor target predictions. MiRNA-lncRNA-mRNA clusters centered around key cancer genes are annotated and the complex interplay is described. This unique and extensive high quality dataset, comprised of three major information layers of the NCI60 cell line transcriptome offers numerous opportunities towards a better understanding of complex regulatory networks in cancer. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 2987. doi:1538-7445.AM2012-2987