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Pleiotropic Analysis of Lung Cancer and Blood Triglycerides.

ABSTRACT: Epidemiologically related traits may share genetic risk factors, and pleiotropic analysis could identify individual loci associated with these traits. Because of their shared epidemiological associations, we conducted pleiotropic analysis of genome-wide association studies of lung cancer (12 160 lung cancer case patients and 16 838 control subjects) and cardiovascular disease risk factors (blood lipids from 188 577 subjects, type 2 diabetes from 148 821 subjects, body mass index from 123 865 subjects, and smoking phenotypes from 74 053 subjects). We found that 6p22.1 (rs6904596, ZNF184) was associated with both lung cancer (P = 5.50x10(-6)) and blood triglycerides (P = 1.39x10(-5)). We replicated the association in 6097 lung cancer case patients and 204 657 control subjects (P = 2.40?×?10(-4)) and in 71 113 subjects with triglycerides data (P = .01). rs6904596 reached genome-wide significance in lung cancer meta-analysis (odds ratio = 1.15, 95% confidence interval = 1.10 to 1.21 ,: Pcombined = 5.20x10(-9)). The large sample size provided by the lipid GWAS data and the shared genetic risk factors between the two traits contributed to the uncovering of a hitherto unidentified genetic locus for lung cancer.

SUBMITTER: Zuber V

PROVIDER: S-EPMC5241892 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature

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Pleiotropic Analysis of Lung Cancer and Blood Triglycerides.

Zuber Verena V Marconett Crystal N CN Shi Jianxin J Hua Xing X Wheeler William W Yang Chenchen C Song Lei L Dale Anders M AM Laplana Marina M Risch Angela A Witoelar Aree A Thompson Wesley K WK Schork Andrew J AJ Bettella Francesco F Wang Yunpeng Y Djurovic Srdjan S Zhou Beiyun B Borok Zea Z van der Heijden Henricus F M HF de Graaf Jacqueline J Swinkels Dorine D Aben Katja K KK McKay James J Hung Rayjean J RJ Bikeböller Heike H Stevens Victoria L VL Albanes Demetrius D Caporaso Neil E NE Han Younghun Y Wei Yongyue Y Panadero Maria Angeles MA Mayordomo Jose I JI Christiani David C DC Kiemeney Lambertus L Andreassen Ole A OA Houlston Richard R Amos Christopher I CI Chatterjee Nilanjan N Laird-Offringa Ite A IA Mills Ian G IG Landi Maria Teresa MT

Journal of the National Cancer Institute 20160826 12

Epidemiologically related traits may share genetic risk factors, and pleiotropic analysis could identify individual loci associated with these traits. Because of their shared epidemiological associations, we conducted pleiotropic analysis of genome-wide association studies of lung cancer (12 160 lung cancer case patients and 16 838 control subjects) and cardiovascular disease risk factors (blood lipids from 188 577 subjects, type 2 diabetes from 148 821 subjects, body mass index from 123 865 sub ...[more]

PMID: 27565901

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Project 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.

Project description:BackgroundGastric cancer (GC) is common and its prognosis is often poor due to difficulties in early diagnosis and optimal treatment strategies. TNM staging system is useful in predicting prognosis but only possible after surgery. Therefore, it is desirable to investigate prognostic factors/markers that may predict prognosis before surgery by which helps appropriate management decisions preoperatively.MethodsA total of 320 GC patients were consecutively recruited from 2004 to 2013 and followed up for 127 months (10.6 years) after surgery. These patients' were examined for body mass index (BMI) and blood levels of albumin, triglyceride, total cholesterol, low density lipoprotein cholesterol (LDL-C), and high density lipoprotein cholesterol (HDL-C). Kaplan-Meier method and log rank test were used to analyze long-term survival using the above potential risk markers. We first employed medians of these variables to reveal maximal potentials of the above prognostic predictors.ResultsThree major findings were obtained: (1) Preoperative BMI was positively correlated with albumin (r = 0.144, P<0.05) and triglyceride (r = 0.365, P<0.01), but negatively correlated with TNM staging (r = -0.265, P<0.05). Preoperative albumin levels were positively correlated with triglyceride (r = 0.173, P<0.05) but again, negatively correlated with TNM staging (r = -0.137, P<0.05); (2) Poor survival was observed in GC patients with lower levels of BMI (P = 0.028), albumin (P = 0.004), and triglyceride (P = 0.043), respectively. Receiver operating characteristic (ROC) curve analyses suggested BMI, albumin and triglyceride to have survival-predictor powers similar to TNM system; and (3) Cox multi-factorial analyses demonstrated that age (P = 0.049), BMI (P = 0.016), cell differentiation (P = 0.001), and TNM staging (P = 0.011) were independent overall survival-predictors for GC patients.ConclusionsPreoperative BMI, albumin, and triglyceride levels are capable of predicting survival for GC patients superior to postoperative TNM system in terms of timing for management. As potential survival-predictors, preoperative tests of BMI, albumin and triglyceride, combined with clinical imaging, may help personalized management for GC patients including planning surgical strategy, optimal radio-chemotherapy and appropriate follow-up intervals after surgery.

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Pleiotropic Analysis of Lung Cancer and Blood Triglycerides.

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Pleiotropic Analysis of Lung Cancer and Blood Triglycerides.

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