Computational Modeling And Analysis Of Micro RNA-Mediated TGF-β Signaling Pathway In Prostate Cancer

TGF-β, a multifunctional cytokine, appears to function paradoxically as a tumor suppressor in normal cells and as a tumor promoter in cancer cells. Abnormal expression patterns for mi RNAs are closely related to tumorigenesis and development. Furthermore, mi RNAs can lead to prostate cancer by inhibiting TGF-β signaling. In prostate cancer, we found about 100 aberrant mi RNAs by data collection from reported literatures. Finally, we got six mi RNAs regulating TGF-β signaling through pathway enrichment, namely mi R-21, mi R-106 b, mi R-20 a, mi R-155, mi R-26 a and mi R-92 a. Compared with localized prostate cancer, mi R-21, mi R-106 b andmi R-20 a were over expressed to varying degrees in metastatic conditions. We also found that the main targets in TGF-β signaling pathway are primarily TGF-β receptors and Smad proteins though target analysis and reported literatures. Based on these observations, we propose a mathematical model which integrates mi RNAs in TGF-β signaling pathway from system and overall level. Results shows that the intensity and duration nuclear p S2S4 complex in cancer is significantly weakened compared with normal conditions. Sensitivity analysis proves that processes related to ligand-receptor complex, nuclear p S2S4 complex and shuttling play important role in the pathway. Three of four mi RNA parameters have high sensitivity coefficients. Simulations of the model demonstrate that TGF-β signaling is somewhat robust against random input into mi R-21 and mi R-106 b and strongly robust against random input into mi R-155 andmi R-20 a. Our study reveals that the important role of mi RNA on TGF-β signaling pathway in prostate cancer from a perspective of dynamics, which provides a reference for in-depth study of prostate cancer.