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Cancer Informatics

Synopsis: An open access, peer reviewed electronic journal that covers the role of computational biology and bioinformatics in cancer treatment.


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Journal: 248492
Most read article: 4114
Editor in chief:
James Willey
ISSN: 1176-9351


 
 
 


An Integrative Analysis of microRNA and mRNA Expression - A Case Study

Authors: Li-Xuan Qin
Publication Date: 17 Jun 2008
Cancer Informatics 2008:6 369-379

Li-Xuan Qin

Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, New York, U.S.A.

Abstract

Background: MicroRNAs are believed to play an important role in gene expression regulation. They have been shown to be involved in cell cycle regulation and cancer. MicroRNA expression profiling became available owing to recent technology advancement. In some studies, both microRNA expression and mRNA expression are measured, which allows an integrated analysis of microRNA and mRNA expression.

Results: We demonstrated three aspects of an integrated analysis of microRNA and mRNA expression, through a case study of human cancer data. We showed that (1) microRNA expression efficiently sorts tumors from normal tissues regardless of tumor type, while gene expression does not; (2) many microRNAs are down-regulated in tumors and these microRNAs can be clustered in two ways: microRNAs similarly affected by cancer and microRNAs similarly interacting with genes; (3) taking let-7f as an example, targets genes can be identified and they can be clustered based on their relationship with let-7f expression.

Discussion: Our findings in this paper were made using novel applications of existing statistical methods: hierarchical clustering was applied with a new distance measure—the co-clustering frequency—to identify sample clusters that are stable; microRNA-gene correlation profiles were subject to hierarchical clustering to identify microRNAs that similarly interact with genes and hence are likely functionally related; the clustering of regression models method was applied to identify microRNAs similarly related to cancer while adjusting for tissue type and genes similarly related to microRNA while adjusting for disease status. These analytic methods are applicable to interrogate multiple types of -omics data in general.



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