RESUMEN
Most p53 mutations in human cancers are missense mutations resulting in a full-length mutant p53 protein. Besides losing tumor suppressor activity, some hotspot p53 mutants gain oncogenic functions. This effect is mediated in part, through gene expression changes due to inhibition of p63 and p73 by mutant p53 at their target gene promoters. Here, we report that the tumor suppressor microRNA let-7i is downregulated by mutant p53 in multiple cell lines expressing endogenous mutant p53. In breast cancer patients, significantly decreased let-7i levels were associated with missense mutations in p53. Chromatin immunoprecipitation and promoter luciferase assays established let-7i as a transcriptional target of mutant p53 through p63. Introduction of let-7i to mutant p53 cells significantly inhibited migration, invasion and metastasis by repressing a network of oncogenes including E2F5, LIN28B, MYC and NRAS. Our findings demonstrate that repression of let-7i expression by mutant p53 has a key role in enhancing migration, invasion and metastasis.
Asunto(s)
Redes Reguladoras de Genes , MicroARNs/genética , Neoplasias/genética , Neoplasias/patología , Proteína p53 Supresora de Tumor/genética , Proteína p53 Supresora de Tumor/metabolismo , Animales , Línea Celular Tumoral , Movimiento Celular , Análisis Mutacional de ADN , Regulación hacia Abajo , Femenino , Redes Reguladoras de Genes/efectos de los fármacos , Humanos , Ratones , Ratones Desnudos , MicroARNs/antagonistas & inhibidores , Mutación Missense , Invasividad Neoplásica/genética , Invasividad Neoplásica/patología , Metástasis de la Neoplasia/genética , Metástasis de la Neoplasia/patología , Trasplante de Neoplasias , ARN Interferente Pequeño/farmacología , Factores de Transcripción/metabolismo , Proteínas Supresoras de Tumor/metabolismoRESUMEN
Topological properties of "scale-free" networks are investigated by determining their spectral dimensions d(S), which reflect a diffusion process in the corresponding graphs. Data bases for citation networks and metabolic networks together with simulation results from the growing network model [A.-L. Barabasi and R. Albert, Science 286, 509 (1999)] are probed. For completeness and comparisons lattice, random and small-world models are also investigated. We find that d(S) is around 3 for citation and metabolic networks, which is significantly different from the growing network model, for which d(S) is approximately 7.5. This signals a substantial difference in network topology despite the observed similarities in vertex-order distributions. In addition, the diffusion analysis indicates that the citation networks are treelike in structure, whereas the metabolic networks contain many loops.