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 expression profiles. Our method is based on the hypothesis that for some autocrine pathways, the ligand and receptor are regulated by coupled mechanisms at the level of transcription, and thus ligand–receptor pairs comprising such a loop should have correlated mRNA expression. Using our database of experimentally known ligand–receptor signaling partners, we found examples of ligand–receptor pairs with significantly correlated expression in five cancer-based gene expression datasets. The correlated ligand–receptor pairs we identified 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 potential autocrine pathways. Our analysis also revealed ligand–receptor pairs with expression patterns that may indicate cellular mechanisms for preventing autocrine signaling.