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Data Mining for Identification of Molecular Targets in Ovarian Cancer

  • Villegas-Ruiz, Vanessa (Experimental Oncology Laboratory, Research Department, National Institute of Pediatrics) ;
  • Juarez-Mendez, Sergio (Experimental Oncology Laboratory, Research Department, National Institute of Pediatrics)
  • Published : 2016.06.01

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).

Keywords

Ovarian cancer;networks;systems biology

Acknowledgement

Supported by : CONACyT, National Institute of Pediatrics

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