Pathway-Based Analysis Using Genome-wide Association Data from a Korean Non-Small Cell Lung Cancer Study

<div><p>Pathway-based analysis, used in conjunction with genome-wide association study (GWAS) techniques, is a powerful tool to detect subtle but systematic patterns in genome that can help elucidate complex diseases, like cancers. Here, we stepped back from genetic polymorphisms at a single locus and examined how multiple association signals can be orchestrated to find pathways related to lung cancer susceptibility. We used single-nucleotide polymorphism (SNP) array data from 869 non-small cell lung cancer (NSCLC) cases from a previous GWAS at the National Cancer Center and 1,533 controls from the Korean Association Resource project for the pathway-based analysis. After mapping single-nucleotide polymorphisms to genes, considering their coding region and regulatory elements (±20 kbp), multivariate logistic regression of additive and dominant genetic models were fitted against disease status, with adjustments for age, gender, and smoking status. Pathway statistics were evaluated using Gene Set Enrichment Analysis (GSEA) and Adaptive Rank Truncated Product (ARTP) methods. Among 880 pathways, 11 showed relatively significant statistics compared to our positive controls (P_GSEA≤0.025, false discovery rate≤0.25). Candidate pathways were validated using the ARTP method and similarities between pathways were computed against each other. The top-ranked pathways were <i>ABC Transporters</i> (P_GSEA<0.001, P_ARTP = 0.001), <i>VEGF Signaling Pathway</i> (P_GSEA<0.001, P_ARTP = 0.008), <i>G1/S Check Point</i> (P_GSEA = 0.004, P_ARTP = 0.013), and <i>NRAGE Signals Death through JNK</i> (P_GSEA = 0.006, P_ARTP = 0.001). Our results demonstrate that pathway analysis can shed light on post-GWAS research and help identify potential targets for cancer susceptibility.</p></div

Overview of the Study.

<p>Overview of the Study.</p

SNP Associations of Genes in “<i>ABC Transporters</i>.”

*<p>P-values<5×10⁻⁴ was considered genome-wide level significant and marked in bold.</p

Summary of Positive Control Tests.

1<p>IL1B, MTHFR, AKAP9, CAMKK1, SEZ6L, FAS, FASLG, TP53, TP53BP1, EGFR, KRAS, ERBB2, ALK, BRAF, PIK3CA, AKT1, MAP2K1, MET, ROS1, NRAS, C3ORF21, TP63, TERT, CLPTM1L, BAT3, MSH5, CHRNA3, CHRNA4, CHRNA5, XRCC1, RRM1, ERCC1.</p>2<p>3q28-29 Genes: C3ORF21, TP63.</p>3<p>5p15 Genes: TERT, CLPTM1L.</p>4<p>6p21 Genes: BAT3, MSH5.</p>5<p>15q25 Genes: CHRNA3, CHRNA4, CHRNA5.</p>6<p>DNA Repair Genes: XRCC1, RRM1, ERCC1.</p>*<p>GSEA P-values≤0.025 and FDRs≤0.25, ARTP P-values≤0.01 are marked in bold.</p

Demographic Features of Study Population.

<p>Demographic Features of Study Population.</p

Candidate Pathways with P-value≤0.025 and FDR≤0.25.

*<p>GSEA P-values≤0.025 and FDRs≤0.25, ARTP P-values≤0.01 are marked in bold.</p

Characteristics of 3 cases with discrepancy in PT & LN in major mutations.

<p>Characteristics of 3 cases with discrepancy in PT & LN in major mutations.</p

Baseline characteristics of 74 patients.

<p>Baseline characteristics of 74 patients.</p

Results of 31 patients with EGFR mutation(s) and response to tyrosine kinase inhibitor.

<p>Results of 31 patients with EGFR mutation(s) and response to tyrosine kinase inhibitor.</p

Cox proportional hazards regression analysis of gastrointestinal bleeding for each statin group during the observation period.

<p>Cox proportional hazards regression analysis of gastrointestinal bleeding for each statin group during the observation period.</p