miRNA and mRNA cancer signatures determined by analysis of expression levels in large cohorts of patients

Abstract

Toward identifying a cancer-specific gene signature we applied surprisal analysis to the RNAs expression behavior for a large cohort of breast, lung, ovarian, and prostate carcinoma patients. We characterize the cancer phenotypic state as a shared response of a set of mRNA or microRNAs (miRNAs) in cancer patients versus noncancer controls. The resulting signature is robust with respect to individual patient variability and distinguishes with high fidelity between cancer and noncancer patients. The mRNAs and miRNAs that are implicated in the signature are correlated and are known to contribute to the regulation of cancer-signaling pathways. The miRNA and mRNA networks are common to the noncancer and cancer patients, but the disease modulates the strength of the connectivities. Furthermore, we experimentally assessed the cancer-specific signatures as possible therapeutic targets. Specifically we restructured a single dominant connectivity in the cancerspecific gene network in vitro. We find a deflection from the cancer phenotype, significantly reducing cancer cell proliferation and altering cancer cellular physiology. Our approach is grounded in thermodynamics augmented by information theory. The thermodynamic reasoning is demonstrated to ensure that the derived signature is bias-free and shows that the most significant redistribution of free energy occurs in programming a system between the noncancer and cancer states. This paper introduces a platform that can elucidate miRNA and mRNA behavior on a systems level and provides a comprehensive systematic view of both the energetics of the expression levels of RNAs and of their changes during tumorigenicity. microarray | deep sequencing | network connectivity | biomarker | maximal entropy

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