New potential ligand-receptor signaling loops in ovarian cancer identified in multiple gene expression studies

Giancarlo Castellano, James F. Reid, Paola Alberti, Maria Luisa Carcangiu, Antonella Tomassetti, Silvana Canevari

Research output: Contribution to journalArticlepeer-review


Based on the hypothesis that gene products involved in the same biological process would be coupled at transcriptional level, a previous study analyzed the correlation of the gene expression patterns of ligand-receptor (L-R) pairs to discover potential autocrine/paracrine signaling loops in different cancers (Graeber and Eisenberg. Nat Genet 2001; 29:295). By refining the starting database, a list of 511 L-R pairs was compiled, combined to eight data sets from a single pathology, epithelial ovarian cancer, and examined as a proof-of-principle of the statistical and biological validity of the correlation of the L-R gene expression patterns in cancer. Analysis revealed a Bonferroni-corrected significant correlation of 105 L-R pairs in at least one data set and, by systematic analysis, identified 39 more frequently correlated L-R pairs, 7 of which were already biologically confirmed. In four data sets examined for an L-R correlation associated with patient survival time, 15 L-R pairs were significantly correlated in short surviving patients in two of the data sets. Immunohistochemical analysis of one of the newly identified correlated L-R pairs (i.e., EFNB3-EPHB4) revealed the correlated expression of ephrin-B3 and EphB4 proteins in 45 of 55 epithelial ovarian tumor samples (P <0.0001). Together, these data not only support the validity of cross-comparison analysis of gene expression data because known and expected correlations were confirmed but also point to the promise of such analysis in identifying new L-R signaling loops in cancer.

Original languageEnglish
Pages (from-to)10709-10719
Number of pages11
JournalCancer Research
Issue number22
Publication statusPublished - Nov 15 2006

ASJC Scopus subject areas

  • Cancer Research
  • Oncology


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