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Systematic analysis of ovarian cancer platinum-resistance mechanisms via text mining

Haixia Li1, Jinghua Li1, Wanli Gao1

  • 1Department of Obstetrics & Gynecology, Beijing TianTan Hospital, Capital Medical University, Bejing, 100050, China.

Journal of Ovarian Research
|March 13, 2020
PubMed
Abstract

Insights

Platinum resistance in ovarian cancer involves complex pathways. Different ovarian cancer subtypes may possess distinct platinum resistance mechanisms, identified through text mining and bioinformatics.

Area of Science:

  • Genomics
  • Bioinformatics
  • Oncology

Background:

  • Platinum resistance is a major cause of ovarian cancer recurrence and mortality.
  • Understanding the molecular basis of platinum resistance is crucial for improving patient outcomes.

Purpose of the Study:

  • To systematically explore molecular mechanisms of platinum resistance in ovarian cancer.
  • To identify key regulatory genes and pathways involved in platinum resistance using text mining and bioinformatics.

Main Methods:

  • Literature mining to identify relevant genes.
  • Gene ontology and protein-protein interaction (PPI) network analysis.
  • Co-occurrence analysis of genes with ovarian cancer subtypes and cluster analysis.

Main Results:

  • High-frequency genes are implicated in DNA repair, apoptosis, metal transport, and drug detoxification.
  • Key hub proteins in the PPI network include TP53, HSP90, ESR1, AKT1, BRCA1, EGFR, and CTNNB1.
  • Cluster analysis revealed subtype-specific genes, suggesting diverse resistance mechanisms across ovarian cancer subtypes.

Conclusions:

  • Platinum resistance in ovarian cancer is driven by complex signaling pathways.
  • Ovarian cancer subtypes exhibit distinct platinum resistance mechanisms.
  • Text mining combined with bioinformatics offers an effective approach for systematic analysis of complex biological data.
Keywords:
Ovarian cancerPlatinum resistanceText mining

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