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

Abstract

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.

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Figure 1: Bioinformatic identification of potential autocrine ligand–receptor signaling loops.
Figure 2: Identification of a previously reported autocrine signaling loop.
Figure 3: Correlated expression of CC chemokines, their receptors and integrins in DLBCLs.

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Acknowledgements

This paper is dedicated to the memory of Nathan B. Friedman, his inspiring spirit, and his contributions to pathology. We thank the authors of the gene expression papers cited here for making their data publicly available. We also thank M. Balbirnie, R. Grothe, G. Kleiger, R. Landgraf, P. Mallick, E. Marcotte, M. Pellegrini, L. Salwinski, and I. Xenarios for insights and helpful discussions. This work was supported by grants from the US Department of Energy (DOE) and National Institutes of Health. T.G.G. was supported by an Alfred P. Sloan Foundation/DOE postdoctoral fellowship.

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Correspondence to David Eisenberg.

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Graeber, T., Eisenberg, D. Bioinformatic identification of potential autocrine signaling loops in cancers from gene expression profiles. Nat Genet 29, 295–300 (2001). https://doi.org/10.1038/ng755

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