Pathway-based analysis using genome-wide association data from a Korean non-small cell lung cancer study

Area of Science:

• Genomics
• Cancer Research
• Bioinformatics

Background:

• Genome-wide association studies (GWAS) are crucial for identifying genetic variants associated with complex diseases.
• Pathway-based analysis enhances GWAS by detecting subtle, systematic genetic patterns related to disease susceptibility.
• Lung cancer, a complex disease, requires advanced analytical methods to understand its genetic underpinnings.

Purpose of the Study:

• To investigate the utility of pathway-based analysis in post-GWAS research for lung cancer.
• To identify specific biological pathways associated with non-small cell lung cancer (NSCLC) susceptibility.
• To explore how multiple genetic association signals orchestrate to reveal disease-related pathways.

Main Methods:

• Utilized single-nucleotide polymorphism (SNP) array data from 869 NSCLC cases and 1,533 controls.
• Mapped SNPs to genes, including coding and regulatory regions (±20 kbp).
• Employed multivariate logistic regression and pathway analysis methods including Gene Set Enrichment Analysis (GSEA) and Adaptive Rank Truncated Product (ARTP).

Main Results:

• Identified 11 significant pathways out of 880 analyzed pathways.
• Top-ranked pathways associated with lung cancer susceptibility include ABC Transporters, VEGF Signaling Pathway, G1/S Check Point, and NRAGE Signals Death through JNK.
• Validated candidate pathways using ARTP and assessed pathway similarities.

Conclusions:

• Pathway-based analysis is a valuable tool for post-GWAS research in complex diseases like lung cancer.
• This approach can uncover potential molecular targets for cancer susceptibility.
• The identified pathways provide insights into the genetic architecture of lung cancer.