Experimental Oncology Laboratory, Research Department, National Institute of Pediatrics, Mexico
Abstract
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).
Villegas-Ruiz, V., & Juarez-Mendez, S. (2016). Data Mining for Identification of Molecular Targets in Ovarian Cancer. Asian Pacific Journal of Cancer Prevention, 17(4), 1691-1699.
MLA
Vanessa Villegas-Ruiz; Sergio Juarez-Mendez. "Data Mining for Identification of Molecular Targets in Ovarian Cancer". Asian Pacific Journal of Cancer Prevention, 17, 4, 2016, 1691-1699.
HARVARD
Villegas-Ruiz, V., Juarez-Mendez, S. (2016). 'Data Mining for Identification of Molecular Targets in Ovarian Cancer', Asian Pacific Journal of Cancer Prevention, 17(4), pp. 1691-1699.
VANCOUVER
Villegas-Ruiz, V., Juarez-Mendez, S. Data Mining for Identification of Molecular Targets in Ovarian Cancer. Asian Pacific Journal of Cancer Prevention, 2016; 17(4): 1691-1699.
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