Bioinformatic identification of potential autocrine signaling loops in cancers from gene expression profiles

Many biological signaling pathways involve autocrine ligand-receptor loops; misregulation of these signaling loops can contribute to cancer phenotypes . Here we present an algorithm for detecting such loops from gene expressio n profiles. Our method is based on the hypothesis that for some autocrine p athways, the ligand and receptor are regulated by coupled mechanisms at the level of transcription, and thus ligand-receptor pairs comprising such a l oop should have correlated mRNA expression. Using our database of experimen tally known ligand-receptor signaling partners, we found examples of ligand -receptor pairs with significantly correlated expression in five cancer-bas ed gene expression datasets. The correlated ligand-receptor pairs we identi fied are consistent with known autocrine signaling events in cancer cells. In addition, our algorithm predicts new autocrine signaling loops that can be verified experimentally. Chemokines were commonly members of these poten tial autocrine pathways. Our analysis also revealed ligand-receptor pairs w ith expression patterns that may indicate cellular mechanisms for preventin g autocrine signaling.