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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
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The Robustness of Pathway Analysis in Identifying Potential Drug Targets in Non-Small Cell Lung Carcinoma

Andrew Dalby1, Ian Bailey2

  • 1Faculty of Science and Technology, University of Westminster, Westminster W1W 6UW, UK. A.Dalby@westminster.ac.uk.

Microarrays (Basel, Switzerland)
|September 8, 2016
PubMed

Insights

Identifying cancer-causing genes from gene expression data is challenging. Network models offer a more robust method for detecting lung cancer pathways, revealing disease heterogeneity and suggesting multi-pathway therapeutic targets.

Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Gene expression analysis for cancer gene identification has yielded inconsistent results.
  • Methodological variations in data processing (normalization, filtering) impact gene detection.
  • Gene set enrichment analysis (GSEA) can incorporate biological context to improve differential gene/pathway detection.

Purpose of the Study:

  • To evaluate network models as a robust method for detecting differentially overrepresented pathways in lung cancer.
  • To investigate the genotypic heterogeneity within non-small cell lung carcinoma (NSCLC).
  • To provide insights for novel therapeutic strategies and explain challenges in NSCLC treatment.

Main Methods:

  • Utilized network models for pathway analysis on lung cancer gene expression data.
  • Applied gene set enrichment analysis principles.
  • Compared network model performance against other methods, considering normalization challenges.

Main Results:

  • Network models demonstrated robustness in identifying differentially overrepresented pathways in lung cancer data, despite normalization issues.
  • Evidence suggests significant genotypic heterogeneity within non-small cell lung carcinoma.
  • This heterogeneity challenges traditional phenotype classification of NSCLC.

Conclusions:

  • Network models offer a more reliable approach for detecting biologically relevant pathways in cancer gene expression data.
  • The identified genotypic diversity in NSCLC explains therapeutic development hurdles.
  • Future drug development for NSCLC should consider targeting multiple pathways due to disease heterogeneity.
Keywords:
NSCLCReactomeadenocarcinomapathway analysisrobustnesssquamous

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