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 cancer-specific 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.

Keywords: biomarker; deep sequencing; maximal entropy; microarray; network connectivity.

Fig. 1.

The cancer-specific mRNA signature distinguishes diseased and healthy patients. (A) The heat map shows the twenty most up-regulated and twenty most down-regulated mRNAs in four different carcinomas. Each row in the heat map is a different gene. Each vertical column in the heat map shows the disease signature for a particular patient. There are 8 healthy and 13 diseased patients shown for ovarian cancer, 37 healthy and 140 diseased patients for prostate cancer, 20 healthy and 20 diseased patients for breast cancer, and 15 healthy and 15 diseased patients for lung cancer. For all cancers and for a sample of 268 patients, an mRNA that is up-regulated in diseased patients is found to be down-regulated in healthy patients and vice versa. Similar thermodynamic behavior is observed across patients; however, the greatest patient-specific variability in the cancer state came from prostate patients. (B) The plots show the first and dominant Lagrange multiplier, , as described in Eq. 1, plotted versus the same scale of patient index, n, as the heat map in A. The sign of is opposite for healthy and diseased patients, thereby providing a disease signature.

Fig. 2.

The cancer-specific miRNA signature distinguishes diseased and healthy patients. (A) Heat maps showing in the vertical direction the wide gap between healthy and diseased patients and, in the horizontal direction, the patient variability. Shown for the twenty most up-regulated and the twenty most down-regulated miRNAs in four carcinomas. The labeling of miRNA has been shortened to the number following hsa-mir. In the case of the ovarian data, UL70-3p should be preceded by hcmv-miR, 19-3p should be preceded by ebv-mir-BART, and K12-3 should be preceded by kshv-mir . Each column represents a healthy or a diseased patient. There are 8 healthy and 8 diseased patients for the ovarian data, 10 healthy and 10 diseased patients for the prostate data (SI Appendix, group 2 of Table S12), 17 healthy and 17 diseased patients for the breast data, and 35 healthy and 58 diseased patients for the lung data shown. (B) Quantitative representation of the distinction between healthy and diseased patients. Shown is the multiplier (Eq. 1) for the cancer signature versus the patient index n. Also in this way of representing the data it is clear that patient variability in the values of is moderate compared with the change in sign between the healthy and diseased patients. Both healthy and disease signatures are deviations in opposite direction from the balance state.

Fig. 3.

The disease signature accounts for the expression level of both miRNAs and mRNAs. Unlike the balance state alone, including the disease signature as in Eq. 1 provides an accurate description of the expression level of both miRNAs and mRNAs of any particular patient over several orders of magnitude of the data. Shown is a scatter plot of RNAseq-measured miRNA data represented in terms of surprisal analysis graphed versus the actual raw data for a prostate diseased patient and for a lung-healthy patient.

Fig. 4.

Modulation of the cancer state. (A) Cell proliferation assay in the presence of scramble or siRNA. Cell counts were conducted at 1, 2, 3, and 4 d. (B) Real-time bright-field images of lung A549 and ovarian OVCAR cancer cell line in the presence or absence of scramble or target siRNA 40 μM. (C) Single-cell microscopy of 22RV1 prostate cancer cells treated with or without scramble or target siRNA. (Scale bar, 20 μm.)