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Whole miRNome-wide Differential Co-expression of MicroRNAs
Authors:Cord FStehler  Andreas Keller  Petra Leidinger  Christina Backes  Anoop Chandran  Jerg Wischhusen  Benjamin Meder  Eckart Meese
Institution:Cord F.St(a)ehler (Siemens Healthcare, Strategy, 91052 Erlangen, Germany);Andreas Keller (Siemens Healthcare, Strategy, 91052 Erlangen, Germany;Department of Human Genetics, Saarland University, 66421 Homburg, Germany);Petra Leidinger (Department of Human Genetics, Saarland University, 66421 Homburg, Germany);Christina Backes (Department of Human Genetics, Saarland University, 66421 Homburg, Germany);Anoop Chandran (Interdisciplinary Center for Clinical Research, University of Wuerzburg, 97070 Wuerzburg, Germany);J(o)erg Wischhusen (Interdisciplinary Center for Clinical Research, University of Wuerzburg, 97070 Wuerzburg, Germany);Benjamin Meder (Department of Internal Medicine Ⅲ, Heidelberg University, 69115 Heidelberg, Germany);Eckart Meese (Department of Human Genetics, Saarland University, 66421 Homburg, Germany);
Abstract:Co-regulation of genes has been extensively analyzed, however, rather limited knowledge is available on co-regulations within the miRNome. We investigated differential co-expression of microRNAs (miRNAs) based on miRNome profiles of whole blood from 540 individuals. These include patients suffering from different cancer and non-cancer diseases, and unaffected controls. Using hierarchical clustering, we found 9 significant clusters of co-expressed miRNAs containing 2–36 individual miRNAs. Through analyzing multiple sequencing alignments in the clusters, we found that co-expression of miRNAs is associated with both sequence similarity and genomic co-localization. We calculated correlations for all 371,953 pairs of miRNAs for all 540 individuals and identified 184 pairs of miRNAs with high correlation values. Out of these 184 pairs of miRNAs, 16 pairs (8.7%) were differentially co-expressed in unaffected controls, cancer patients and patients with non-cancer diseases. By computing correlated and anti-correlated miRNA pairs, we constructed a network with 184 putative co-regulations as edges and 100 miRNAs as nodes. Thereby, we detected specific clusters of miRNAs with high and low correlation values. Our approach represents the most comprehensive co-regulation analysis based on whole miRNome-wide expression profiling. Our findings further decrypt the interactions of miRNAs in normal and human pathological processes.
Keywords:Co-expression  Microarray  MicroRNA  Network analysis
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