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http://repositorio.pediatria.gob.mx:8180/handle/20.500.12103/2248
Título : | Data mining for identification of molecular targets in ovarian cancer |
Creador: | Villegas Ruiz, Vanessa |
Nivel de acceso: | Open access |
Palabras clave : | Neoplasias Ováricas - clasificación Neoplasias Ováricas - genética Neoplasias Ováricas - mortalidad Neoplasias Ováricas - metabolismo Neoplasias Ováricas - patología Análisis de Secuencia por Matrices de Oligonucleótidos Ciclo Celular Biología de Sistemas Expresión Génica Neoplasias Ováricas - genética Ovarian Neoplasms - classification Ovarian Neoplasms - genetics Ovarian Neoplasms - mortality Ovarian Neoplasms - metabolism Oligonucleotide Array Sequence Analysis Cell Cycle Systems Biology Gene Expression Ovarian Neoplasms - genetics Biología de cáncer de ovario - redes - sistemas Ovarian cancer - networks - systems biology |
Descripción : | Ovarian cancer is possibly the sixth most common malignancy worldwide, in Mexico representing the fourth leading cause of gynecological cancer death more than 70% being diagnosed at an advanced stage and the survival being very poor. Ovarian tumors are classified according to histological characteristics, epithelial ovarian cancer as the most common (~80%). We here used high-density microarrays and a systems biology approach to identify tissue-associated deregulated genes. Non-malignant ovarian tumors showed a gene expression profile associated with immune mediated inflammatory responses (28 genes), whereas malignant tumors had a gene expression profile related to cell cycle regulation (1,329 genes) and ovarian cell lines to cell cycling and metabolism (1,664 genes). |
Colaborador(es) u otros Autores: | Juarez-Mendez Sergio |
Fecha de publicación : | 2016 |
Tipo de publicación: | Artículo |
Formato: | |
Identificador del Recurso : | 10.7314/APJCP.2016.17.4.1691 |
Fuente: | Asian Pacific Journal of Cancer Prevention 17(4):1691 - 1699 |
URI : | http://repositorio.pediatria.gob.mx:8180/handle/20.500.12103/2248 |
Idioma: | eng |
Aparece en las colecciones: | Artículos |
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