title: The reconstruction of transcriptional networks reveals critical genes with implications for clinical outcome of multiple myeloma.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  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.
authors: Department of Medical Sciences, University of Milan and Hematology, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Italy.
authors: Luca
authors: L
authors: Agnelli
authors: Mattia
authors: M
authors: Forcato
authors: Francesco
authors: F
authors: Ferrari
authors: Giacomo
authors: G
authors: Tuana
authors: Katia
authors: K
authors: Todoerti
authors: Brian A
authors: BA
authors: Walker
authors: Gareth J
authors: GJ
authors: Morgan
authors: Luigia
authors: L
authors: Lombardi
authors: Silvio
authors: S
authors: Bicciato
authors: Antonino
authors: A
authors: Neri
mesh_terms: D048868:Adaptor Proteins, Signal Transducing / Q000235:genetics; D000465:Algorithms; D002135:Calcium-Binding Proteins; D019938:Cyclin D1 / Q000235:genetics; D056742:Cyclin D2 / Q000235:genetics; D004268:DNA-Binding Proteins / Q000235:genetics; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D006801:Humans; D008840:Microfilament Proteins; D008957:Models, Genetic; D009101:Multiple Myeloma / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis; D016896:Treatment Outcome
chemical_list: C112986:AIF1 protein, human; D048868:Adaptor Proteins, Signal Transducing; C113939:B cell linker protein; C530104:CCND1 protein, human; C080336:CCND2 protein, human; D002135:Calcium-Binding Proteins; D056742:Cyclin D2; D004268:DNA-Binding Proteins; D008840:Microfilament Proteins; D019938:Cyclin D1