Data Mining for Identification of Molecular Targets in Ovarian Cancer

Asian Pac J Cancer Prev. 2016;17(4):1691-9. doi: 10.7314/apjcp.2016.17.4.1691.

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).

MeSH terms

  • Biomarkers, Tumor / genetics*
  • Data Mining / methods*
  • Female
  • Gene Expression Profiling*
  • Gene Expression Regulation, Neoplastic*
  • Gene Regulatory Networks
  • Humans
  • Oligonucleotide Array Sequence Analysis
  • Ovarian Neoplasms / diagnosis
  • Ovarian Neoplasms / genetics*

Substances

  • Biomarkers, Tumor