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Topological network analysis of differentially expressed genes in cancer cells with acquired gefitinib resistance

Young Seok Lee1, Sun Goo Hwang2, Jin Ki Kim1

  • 1Department of Biochemistry, School of Medicine, Konkuk University, Seoul, Republic of Korea.

Cancer Genomics & Proteomics
|May 16, 2015
PubMed
Abstract

Insights

This study identifies key genes involved in acquired gefitinib resistance (AGR) by analyzing gene expression data. These findings offer new insights into cancer chemoresistance mechanisms.

Area of Science:

  • Oncology
  • Genomics
  • Bioinformatics

Background:

  • Acquired gefitinib resistance (AGR) remains a significant challenge in cancer chemotherapy.
  • The precise mechanisms and genetic factors underlying AGR are not fully understood.

Purpose of the Study:

  • To elucidate the complex mechanisms and genetic factors contributing to acquired gefitinib resistance (AGR).
  • To identify novel gene expression signatures for potential clinical applications in cancer treatment.

Main Methods:

  • Cross-platform meta-analysis of three public microarray datasets for cancer with AGR.
  • Identification and clustering of differentially expressed genes (DEGs) using gene co-expression and protein-protein interaction networks.
  • Functional enrichment analysis of DEGs using Gene Ontology (GO) and KEGG pathways.

Main Results:

  • Identified 1,033 DEGs (510 up-regulated, 523 down-regulated, 109 novel).
  • Selected six hub DEGs (PBX1, TRPC1, AXL, S100A9, S100A8, NOD2) associated with key biological processes like calcium homeostasis, apoptosis, and chemoresistance.
  • Confirmed correlation of expression for these hub genes in microarray data.

Conclusions:

  • The study provides comprehensive insights into the intricate mechanisms of acquired gefitinib resistance.
  • Identified novel gene expression signatures that may be valuable for future clinical studies in cancer therapy.
Keywords:
DEGsMeta-analysisacquired drug resistancedifferentially expressed genesgefitinibmicroarray

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