title: Data Mining for Identification of Molecular Targets in Ovarian Cancer.
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).
authors.affiliation: Experimental Oncology Laboratory, Research Department, National Institute of Pediatrics, Mexico E-mail : ser.mend@gmail.com.
authors.forename: Vanessa
authors.initials: V
authors.lastname: Villegas-Ruiz
authors.forename: Sergio
authors.initials: S
authors.lastname: Juarez-Mendez
mesh_terms: D014408:Biomarkers, Tumor / Q000235:genetics*; D057225:Data Mining / Q000379:methods*; D005260:Female; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis; D010051:Ovarian Neoplasms / Q000175:diagnosis / Q000235:genetics*
chemical_list: D014408:Biomarkers, Tumor