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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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6974684/
https://www.ncbi.nlm.nih.gov/pubmed/32010612
http://dx.doi.org/10.3389/fonc.2019.01492
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author 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
author_facet 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
author_sort Wang, Lijuan
collection PubMed
description 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 (P(fdr) < 0.10), including rs1707302 (1p34.1, OR = 0.93, 95% CI: 0.90–0.97, P = 7.60 × 10(−4)), rs2516448 (6p21.33, OR = 1.07, 95% CI: 1.03–1.11, P = 1.00 × 10(−3)), rs3869062 (6p22.1, OR = 0.91, 95% CI: 0.86–0.96, P = 7.10 × 10(−4)), rs174549 (11q12.2, OR = 0.90, 95% CI: 0.87–0.94, P = 1.00 × 10(−7)), rs7193541 (16q23.1, OR = 0.93, 95% CI: 0.90–0.96, P = 1.20 × 10(−4)), and rs8064454 (17q12, OR = 1.07, 95% CI: 1.03–1.11, P = 4.30 × 10(−4)). 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.
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spelling pubmed-69746842020-01-31 Cross-Cancer Pleiotropic Analysis Reveals Novel Susceptibility Loci for Lung Cancer 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 Front Oncol Oncology 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 (P(fdr) < 0.10), including rs1707302 (1p34.1, OR = 0.93, 95% CI: 0.90–0.97, P = 7.60 × 10(−4)), rs2516448 (6p21.33, OR = 1.07, 95% CI: 1.03–1.11, P = 1.00 × 10(−3)), rs3869062 (6p22.1, OR = 0.91, 95% CI: 0.86–0.96, P = 7.10 × 10(−4)), rs174549 (11q12.2, OR = 0.90, 95% CI: 0.87–0.94, P = 1.00 × 10(−7)), rs7193541 (16q23.1, OR = 0.93, 95% CI: 0.90–0.96, P = 1.20 × 10(−4)), and rs8064454 (17q12, OR = 1.07, 95% CI: 1.03–1.11, P = 4.30 × 10(−4)). 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. Frontiers Media S.A. 2020-01-15 /pmc/articles/PMC6974684/ /pubmed/32010612 http://dx.doi.org/10.3389/fonc.2019.01492 Text en Copyright © 2020 Wang, Zhu, Wang, Fan, Sun, Ji, Fan, Xie, Dai, Jin, Hu, Ma and Shen. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
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
Cross-Cancer Pleiotropic Analysis Reveals Novel Susceptibility Loci for Lung Cancer
title Cross-Cancer Pleiotropic Analysis Reveals Novel Susceptibility Loci for Lung Cancer
title_full Cross-Cancer Pleiotropic Analysis Reveals Novel Susceptibility Loci for Lung Cancer
title_fullStr Cross-Cancer Pleiotropic Analysis Reveals Novel Susceptibility Loci for Lung Cancer
title_full_unstemmed Cross-Cancer Pleiotropic Analysis Reveals Novel Susceptibility Loci for Lung Cancer
title_short Cross-Cancer Pleiotropic Analysis Reveals Novel Susceptibility Loci for Lung Cancer
title_sort cross-cancer pleiotropic analysis reveals novel susceptibility loci for lung cancer
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6974684/
https://www.ncbi.nlm.nih.gov/pubmed/32010612
http://dx.doi.org/10.3389/fonc.2019.01492
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