Cross-Cancer Pleiotropic Analysis Reveals Novel Susceptibility Loci for Lung Cancer.

Genome-wide association studies (GWASs) have identified hundreds of single nucleotide polymorphisms (SNPs) associated with cancer risk, several of which have shown pleiotropic effects across cancers. Therefore, we performed a systematic cross-cancer pleiotropic analysis to detect the effects of GWAS-identified variants from non-lung cancers on lung cancer risk in 12,843 cases and 12,639 controls from four lung cancer GWASs. The overall association between variants in each cancer and risk of lung cancer was explored using sequential kernel association test (SKAT) analysis. For single variant analysis, we combined the result of specific study using fixed-effect meta-analysis. We performed functional exploration of significant associations based on features from public databases. To further detect the biological mechanism underlying identified observations, pathway enrichment analysis were conducted with R package "clusterProfiler." SNP-set analysis revealed the overall associations between variants of 8 cancer types and lung cancer risk. Single variant analysis identified 6 novel SNPs related to lung cancer risk after multiple correction ( < 0.10), including rs1707302 (1p34.1, OR = 0.93, 95% CI: 0.90-0.97, = 7.60 × 10), rs2516448 (6p21.33, OR = 1.07, 95% CI: 1.03-1.11, = 1.00 × 10), rs3869062 (6p22.1, OR = 0.91, 95% CI: 0.86-0.96, = 7.10 × 10), rs174549 (11q12.2, OR = 0.90, 95% CI: 0.87-0.94, = 1.00 × 10), rs7193541 (16q23.1, OR = 0.93, 95% CI: 0.90-0.96, = 1.20 × 10), and rs8064454 (17q12, OR = 1.07, 95% CI: 1.03-1.11, = 4.30 × 10). The eQTL analysis and functional annotation suggested that these variants might modify lung cancer susceptibility through regulating the expression of related genes. Pathway enrichment analysis showed that genes modulated by these variants play important roles in cancer carcinogenesis. Our findings demonstrate the pleiotropic associations between non-lung cancer susceptibility loci and lung cancer risk, providing important insights into the shared mechanisms of carcinogenesis across cancers.