Wang Lijuan, Zhu Meng, Wang Yuzhuo, Fan Jingyi, Sun Qi, Ji Mengmeng, Fan Xikang, Xie Junxing, Dai Juncheng, Jin Guangfu, Hu Zhibin, Ma Hongxia, Shen Hongbing
Department of Epidemiology and Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China.
Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China.
Front Oncol. 2020 Jan 15;9:1492. doi: 10.3389/fonc.2019.01492. eCollection 2019.
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.
全基因组关联研究（GWAS）已鉴定出数百个与癌症风险相关的单核苷酸多态性（SNP），其中一些在多种癌症中显示出多效性作用。因此，我们进行了一项系统性的跨癌多效性分析，以检测来自非肺癌的GWAS鉴定变异对4项肺癌GWAS中12843例病例和12639例对照的肺癌风险的影响。使用序列核关联检验（SKAT）分析探索了每种癌症中的变异与肺癌风险之间的总体关联。对于单变异分析,我们使用固定效应荟萃分析合并了特定研究的结果。我们基于公共数据库的特征对显著关联进行了功能探索。为了进一步检测已鉴定观察结果背后的生物学机制，使用R包“clusterProfiler”进行了通路富集分析。SNP集分析揭示了8种癌症类型的变异与肺癌风险之间的总体关联。单变异分析在多次校正后（<0.10）鉴定出6个与肺癌风险相关的新SNP，包括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：0.9~0.96，= 1.20×10）和rs8064454（17q12，OR = 1.07，95%CI：1.03 - 1.11，= 4.30×10）。eQTL分析和功能注释表明，这些变异可能通过调节相关基因的表达来改变肺癌易感性。通路富集分析表明，受这些变异调节的基因在癌症发生过程中起重要作用。我们的研究结果证明了非肺癌易感位点与肺癌风险之间的多效性关联，为跨癌症的共同致癌机制提供了重要见解。
