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Clin Cancer Res. 2011 Dec 1;17(23):7402-12. doi: 10.1158/1078-0432.CCR-11-0596. Epub 2011 Sep 2.

The reconstruction of transcriptional networks reveals critical genes with implications for clinical outcome of multiple myeloma.

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Department of Medical Sciences, University of Milan and Hematology, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Italy.



The combined use of microarray technologies and bioinformatics analysis has improved our understanding of biological complexity of multiple myeloma (MM). In contrast, the application of the same technology in the attempt to predict clinical outcome has been less successful with the identification of heterogeneous molecular signatures. Herein, we have reconstructed gene regulatory networks in a panel of 1,883 samples from MM patients derived from publicly available gene expression sets, to allow the identification of robust and reproducible signatures associated with poor prognosis across independent data sets.


Gene regulatory networks were reconstructed by using Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe) and microarray data from seven MM data sets. Critical analysis of network components was applied to identify genes playing an essential role in transcriptional networks, which are conserved between data sets.


Network critical analysis revealed that (i) CCND1 and CCND2 were the most critical genes; (ii) CCND2, AIF1, and BLNK had the largest number of connections shared among the data sets; (iii) robust gene signatures with prognostic power were derived from the most critical transcripts and from shared primary neighbors of the most connected nodes. Specifically, a critical-gene model, comprising FAM53B, KIF21B, WHSC1, and TMPO, and a neighbor-gene model, comprising BLNK shared neighbors CSGALNACT1 and SLC7A7, predicted survival in all data sets with follow-up information.


The reconstruction of gene regulatory networks in a large panel of MM tumors defined robust and reproducible signatures with prognostic importance, and may lead to identify novel molecular mechanisms central to MM biology.

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