Annotating function to differentially expressed LincRNAs in myelodysplastic syndrome using a network-based method

• Published in: Bioinformatics (Oxford, England) Vol. 33; no. 17; pp. 2622 - 2630
• Main Authors: Liu, Keqin, Beck, Dominik, Thoms, Julie A I, Liu, Liang, Zhao, Weiling, Pimanda, John E, Zhou, Xiaobo
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 01.09.2017
• Subjects: Aged; Gene Expression Profiling - methods; Gene Expression Regulation; Humans; MicroRNAs - genetics; Middle Aged; Molecular Sequence Annotation - methods; Myelodysplastic Syndromes - genetics; Myelodysplastic Syndromes - metabolism; Neoplasms - genetics; Neoplasms - metabolism; Oligonucleotide Array Sequence Analysis - methods; Original Papers; RNA, Long Noncoding - genetics; RNA, Long Noncoding - metabolism; RNA, Messenger - genetics; Sequence Analysis, RNA - methods; Signal Transduction; Software
• ISSN: 1367-4803; 1367-4811
• DOI: 10.1093/bioinformatics/btx280

Long non-coding RNAs (lncRNAs) have been implicated in the regulation of diverse biological functions. The number of newly identified lncRNAs has increased dramatically in recent years but their expression and function have not yet been described from most diseases. To elucidate lncRNA function in human disease, we have developed a novel network based method (NLCFA) integrating correlations between lncRNA, protein coding genes and noncoding miRNAs. We have also integrated target gene associations and protein-protein interactions and designed our model to provide information on the combined influence of mRNAs, lncRNAs and miRNAs on cellular signal transduction networks. We have generated lncRNA expression profiles from the CD34+ haematopoietic stem and progenitor cells (HSPCs) from patients with Myelodysplastic syndromes (MDS) and healthy donors. We report, for the first time, aberrantly expressed lncRNAs in MDS and further prioritize biologically relevant lncRNAs using the NLCFA. Taken together, our data suggests that aberrant levels of specific lncRNAs are intimately involved in network modules that control multiple cancer-associated signalling pathways and cellular processes. Importantly, our method can be applied to prioritize aberrantly expressed lncRNAs for functional validation in other diseases and biological contexts. The method is implemented in R language and Matlab. xizhou@wakehealth.edu. Supplementary data are available at Bioinformatics online.