Research Article: A Mathematical Model for MicroRNA in Lung Cancer

Date Published: January 24, 2013

Publisher: Public Library of Science

Author(s): Hye-Won Kang, Melissa Crawford, Muller Fabbri, Gerard Nuovo, Michela Garofalo, S. Patrick Nana-Sinkam, Avner Friedman, Elad Katz. http://doi.org/10.1371/journal.pone.0053663

Abstract

Lung cancer is the leading cause of cancer-related deaths worldwide. Lack of early detection and limited options for targeted therapies are both contributing factors to the dismal statistics observed in lung cancer. Thus, advances in both of these areas are likely to lead to improved outcomes. MicroRNAs (miRs or miRNAs) represent a class of non-coding RNAs that have the capacity for gene regulation and may serve as both diagnostic and prognostic biomarkers in lung cancer. Abnormal expression patterns for several miRNAs have been identified in lung cancers. Specifically, let-7 and miR-9 are deregulated in both lung cancers and other solid malignancies. In this paper, we construct a mathematical model that integrates let-7 and miR-9 expression into a signaling pathway to generate an in silico model for the process of epithelial mesenchymal transition (EMT). Simulations of the model demonstrate that EGFR and Ras mutations in non-small cell lung cancers (NSCLC), which lead to the process of EMT, result in miR-9 upregulation and let-7 suppression, and this process is somewhat robust against random input into miR-9 and more strongly robust against random input into let-7. We elected to validate our model in vitro by testing the effects of EGFR inhibition on downstream MYC, miR-9 and let-7a expression. Interestingly, in an EGFR mutated lung cancer cell line, treatment with an EGFR inhibitor (Gefitinib) resulted in a concentration specific reduction in c-MYC and miR-9 expression while not changing let-7a expression. Our mathematical model explains the signaling link among EGFR, MYC, and miR-9, but not let-7. However, very little is presently known about factors that regulate let-7. It is quite possible that when such regulating factors become known and integrated into our model, they will further support our mathematical model.

Partial Text

Lung cancer is the leading cause of cancer-related deaths worldwide. In the U.S. the number of new occurrences is approximately annually, and the number of deaths is , representing of all cancer related deaths [1]. Lack of early detection and limited options for target therapies are both contributing factors to the dismal statistics observed in lung cancer. Thus, advances in both of these areas are likely to lead to improved outcomes.

Lung cancer is the leading cause of cancer-related deaths worldwide. The majority of cases are diagnosed at later stages thus limiting therapeutic options and contributing to poor outcome. As a result, investigators have sought to identify lung cancer specific biomarkers that may be utilized for early detection and to better understand the metastatic process. Such biomarkers may significantly improve prognosis and reduce mortality. In this paper, we have proposed a mathematical model that integrates the miRNAs let-7 and miR-9 into the process of EMT. miR-9 has been shown to be significantly upregulated and let-7 downregulated in NSCLC.

In this model, we assume that the EGF-EGFR complex is at steady state and set it as a constant. Brown et al. (2004) modeled EGFR signaling with negative feedback of ERK to SOS [32]. We simplified some parts of their model to obtain the equations for SOS, Ras, and ERK. We denote by , , and the concentrations of active SOS, inactive SOS, and total SOS, respectively. Assuming that the total number of SOS is conserved, we have(9)

Source:

http://doi.org/10.1371/journal.pone.0053663