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Published in final edited form as: Mol Carcinog. 2016 Nov 15;56(4):1227–1238. doi: 10.1002/mc.22585

Susceptibility loci of CNOT6 in the general mRNA degradation pathway and lung cancer risk - a re-analysis of eight GWASs

Fei Zhou 1,2,3,4,5,*, Yanru Wang 1,2,*, Hongliang Liu 1,2, Neal Ready 1,2, Younghun Han 6, Rayjean J Hung 7, Yonathan Brhane 7, John McLaughlin 8, Paul Brennan 9, Heike Bickeböller 10, Albert Rosenberger 10, Richard S Houlston 11, Neil Caporaso 12, Maria Teresa Landi 12, Irene Brüske 13, Angela Risch 14, Yuanqing Ye 15, Xifeng Wu 15, David C Christiani 16, Gary Goodman 17,18, Chu Chen 17, Christopher I Amos 6, Wei Qingyi 1,2,**; Transdisciplinary Research in Cancer of the Lung (TRICL) Research Team.
PMCID: PMC5354966  NIHMSID: NIHMS845206  PMID: 27805284

Abstract

Purpose

mRNA degradation is an important regulatory step for controlling gene expression and cell functions. Genetic abnormalities of the genes involved in mRNA degradation were found to be associated with cancer risks. Therefore, we systematically investigated the roles of genetic variants of genes in the general mRNA degradation pathway in lung cancer risk.

Experimental design

Meta-analyses were conducted in six lung cancer genome-wide association studies (GWASs) from the Transdisciplinary Research in Cancer of the Lung and additional two GWASs from Harvard University and deCODE in the International Lung Cancer Consortium. Expression quantitative trait loci analysis (eQTL) was used for in silico functional validation of the identified significant susceptibility loci.

Results

This pathway-based analysis included 4,603 single nucleotide polymorphisms (SNP) in 68 genes in 14,463 lung cancer cases and 44,188 controls, of which 20 SNPs were found to be associated with lung cancer risk with a false discovery rate threshold of <0.05. Among the 11 newly identified SNPs in CNOT6, which were in high linkage disequilibrium, the rs2453176 with a RegulomDB score “1f” was chosen as the tag SNP for further analysis. We found that the rs2453176 T allele was significantly associated with lung cancer risk (odds ratio=1.11, 95% confidence interval=1.04–1.18, P=0.001) in the eight GWASs. In the eQTL analysis, we found that levels of CNOT6 mRNA expression were significantly correlated with the rs2453176 T allele, which provided additional biological basis for the observed positive association.

Conclusion

The CNOT6 rs2453176 SNP may be a new functional susceptible locus for lung cancer risk.

Keywords: lung cancer risk, pathway analysis, molecular epidemiology

Introduction

Lung cancer is one of the most frequently diagnosed cancers with about 1.8 million new lung cancer cases reported in 2012 worldwide, accounting for about 13% of total cancer diagnoses [1]. In the United States, 224,390 new lung cancer cases are estimated to occur in 2016 [2]. In addition to other factors, such as occupational and environmental carcinogens, cigarette smoking is the major risk factor for lung cancer [3,4], but not all smokers develop lung cancer, which suggests that genetic predisposition play an essential role in the lung carcinogenesis [5].

In recent years, some genome-wide association studies (GWASs) of lung cancer have been conducted, and a number of genetic variants, i.e., single nucleotide polymorphisms (SNPs), have been found to be associated with lung cancer risk. For example, the significant susceptibility loci associated with lung cancer risk include 5p15.3 (rs401681, rs4975616 and rs472010 in CLPTM1L and rs2736100 in TERT) [6–11], 6p21.3 (rs3117582 in BAG6 or APOM and rs2395185 in HLA-DRB5 or HLA-DRB9) [6,8,9,11], 6q22.1 (rs9387478 in RAP1BP3 or DCBLD1) [11] and 15q25.1 (rs8034191 in HYKK and rs1051730 in CHRNA3) [6,8,9,12–15]. Among these SNPs, rs1051370, rs3117582 and rs2731600 were found to be specifically associated with risk of lung adenocarcinoma (AD) [9], whereas rs12296850 (mapped to 12q23.1) in SLC17A8 or NR1H4 was found to be a susceptibility locus for risk of squamous cell carcinoma (SC) [16]. Interestingly, the vast majority of the SNPs identified by GWASs are in introns or intergenic regions, and their functional evidence is limited. In the present study, we employed the pathway-based strategy that dramatically decreases the number of SNPs to be analyzed and thus significantly reduced multiple testing with the aim to identify possible lung cancer risk-associated functional SNPs that may have not been revealed by previous lung cancer GWASs.

The degradation of mRNA is an important regulatory step for controlling gene expression and cell functions [17,18]. The general cytoplasmic mRNA decay pathway usually begins with the deadenylation, which removes the poly(A) tail Ccr4-Not complex [19], followed by degradation of mRNA proceeding in two directions of 5’-3’ or 3’-5’. The 5’-3’ mRNA degradation initiates with decapping N7-methylguanosine (m7G) cap mainly by DCP1/DCP2 proteins and subsequently degraded by the exoribonuclease Xrn1, while the 3’-5’ mRNA degradation is mainly catalyzed by 10–12 subunit exosome [20].

Some studies suggest that genetic abnormalities of genes involved in the general mRNA degradation pathway may be associated with lung cancer. For example, various genetic variants in LSM2-LSM8, which encode cofactors for mRNA decapping, were recently found in lung cancer cell lines [21]. Therefore, we hypothesize that genetic variants of the general mRNA degradation pathway are associated with lung cancer risk. To test the hypothesis, we conducted the comprehensive meta-analysis of the eight published lung cancer GWASs from the ILCCO (International Lung Cancer Consortium)-TRICL (Transdisciplinary Research in Cancer of the Lung) consortia, focusing on the SNPs of the genes in the general mRNA degradation pathway.

Materials and Methods

Study populations

The first part of the study populations came from the TRICL consortium, which included 12,160 lung cancer cases and 16,838 controls (all Europeans) of six previously published GWASs from: the MD Anderson Cancer Center (MDACC), the Institute of Cancer Research (ICR), the National Cancer Institute (NCI), the International Agency for Research on Cancer (IARC), Toronto study from Samuel Lunenfeld Research Institute study (Toronto), and the German Lung Cancer Study (GLC) [22]. The second part of the study populations included GWASs of European ancestry from Harvard Lung Cancer Study (984 cases and 970 controls) [23] and Icelandic Lung Cancer Study (deCODE) (1,319 cases and 26,380 controls) [15] of the ILCCO. Written informed consents were achieved for all participants, and the present study was approved by each institutional review board of the participating institutions.

GWAS genotyping and imputation

Genotyping in the eight GWASs was performed by Illumina HumanHap 317, 317+240S, 370Duo, 550, 610 or 1M arrays. The imputation was conducted by IMPUTE2 v2.1.1 or MaCH v1.0 software using the reference panel from the 1000 Genomes Project (phase I integrated release 3, March 2012). Standard quality control on samples was performed on all scans in the analysis, excluding any participants with low call rate (< 90%), extremely high or low heterozygosity (P < 1.0×10−4), non-European (with the HapMap phase II CEU, JPT/CHB and YRI populations as a reference) and imputed SNPs with an information score < 0.40 in IMPUTE2 or r2 < 0.30 in MaCH.

Gene and SNP selection

Genes in the general mRNA degradation pathway were identified from the Molecular Signatures Database [24] and the literature [19]. Overall, 75 genes located on autosomal chromosomes were selected. Among them, seven genes were pseudogenes or duplicates or withdrawn from updated NCBI. After removal of these genes, genotypes of 68 genes were abstracted from the GWAS datasets (detailed in Supplementary Table S1). The final meta-analysis contained 4,603 SNPs with the following standards: genotyping rate ≥ 90%, minor allele frequency ≥ 5%, and Hardy Weinberg Equilibrium exact P value ≥ 10−5. The overall workflow is shown in Figure 1.

Figure 1. Study workflow.

Figure 1

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SNP: single nucleotide polymorphism; FDR: false discovery rate; TRICL: Transdisciplinary Research in Cancer of the Lung; GWAS: genome-wide association study; eQTL: expression quantitative trait loci.

In silico functional validation

Two in silico tools, SNPinfo (http://snpinfo.niehs.nih.gov/snpinfo/snpfunc.htm) [25], RegulomeDB (http://regulomedb.org/) [26], were used to predict potential functions. Expression quantitative trait loci (eQTL) analysis was performed by using the expression data of lymphoblastoid cell lines from 373 Europeans available in the 1000 Genomes Project (http://www.1000genomes.org/category/frequently-asked-questions/gene-expression) [27] and The Cancer Genome Atlas (TCGA)( https://tcga-data.nci.nih.gov/tcga/)[28].

Statistical analysis

Logistic regression model was used to calculate the odds ratios (ORs) and their 95% confidence intervals (CIs) in an additive genetic model with PLINK (v1.06) software. A meta-analysis with the inverse variance method was employed on the 4,603 SNPs with Stata software (v12, State College, Texas, US). Cochran's Q statistic was applied to test for heterogeneity and the I2 statistic for the proportion of the total variation in the meta-analysis [29]. The fixed-effects model was used when there was no heterogeneity among GWASs (Q-test P > 0.100 and I2 < 50%); otherwise, the random-effects model was used. Multiple testing correction was conducted with false discovery rate (FDR) with a threshold < 0.050 [30]. A linear regression model was also performed to evaluate the correlation between SNPs and mRNA expression levels of the corresponding genes. A paired t-test was used to compare the mRNA expression levels of genes in the lung cancer and normal adjacent tissue from the TCGA database. LocusZoom (http://locuszoom.sph.umich.edu/locuszoom/) was applied to construct regional association plots using Europeans from the 1000 Genomes Project as the reference (phase I integrated release 3, March 2012) [31]. Haploview v4.2 was used to generate the Manhattan plot and LD plots [32]. All analyses were conducted with SAS (version 9.4; SAS Institute, Cary, NC, USA) except for those specified otherwise.

Results

Associations of the SNPs with lung cancer risk

We first performed a meta-analysis in the TRICL database consisted of six previously published GWAS datasets with 12,160 cases and 16,838 controls. The basic information of these six studies is presented in Supplemental Table S2. A total of 4,603 SNPs in the pathway were extracted, of which 318 SNPs were associated with lung cancer risk at P < 0.05 in the additive model and 20 SNPs on LSM2SKIV2L and CNOT6 remained significantly associated with lung cancer risk with FDR < 0.05 after multiple testing corrections (Figure 2A and Table 1). Among these SNPs, we excluded those of LSM2 and SKIV2L, because they were mapped to and in high LD with previously GWAS-reported locus at 6p21.33 [6,8]. As a result, 11 SNPs of CNOT6 located at 5q35.3 were left for further analysis. In the LD analysis, these 11 SNPs shared moderate to high LD (r2 ≥ 0.60, Figure 2B and 2C). We finally chose rs2453176 as the tag SNP, because it was significantly associated with lung cancer risk (OR = 1.13, 95% CI = 1.06–1.19, P = 4.33×10−5) (Table 1) and potentially functional according to function prediction and its imputation quality was the best among the 11 SNPs (Table 2). We used the forest plot to illustrate the association between rs2453176 and lung cancer risk in the six GWASs (Figure 3), and the rs2453176 T allele was associated with an increased lung cancer risk in five GWASs, except for the GLC GWAS.

Figure 2. Screening of SNPs in the general mRNA degradation pathway.

Figure 2

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A, Manhattan Plot of genome-wide association results from the general mRNA degradation pathway in TRICL. The x-axis shows SNPs’ positions on each chromosome. The y-axis shows the association P values with lung cancer risk (as –log10 P values). The FDR threshold of 0.05 was shown by a horizontal blue line. The P value of 0.05 was shown by a horizontal red line. B, Regional association plot for SNP rs2453176 in 500 kb up- and downstream region. The left-hand y-axis shows P values of the SNPs, which are transformed as −log10 (P) against chromosomal base pair positions. The right-hand y-axis shows the recombination rate estimated from HapMap Data Rel 22/phase II European population; C, The linkage disequilibrium plots of 11 SNPs in CNOT6. The value within each diamond represents the pairwise correlation between SNPs (measured as r2) defined by the upper left and the upper right sides of the diamond.

Table 1.

Associations between SNPs in the general mRNA degradation pathway and lung cancer risk with FDR < 0.050 in TRICL GWASs

SNP Gene Chr. Position (hg19) Allelea EAF Qb I2 Effectc OR (95% CI) P FDR
rs115834633 LSM2 6 31765984 G/A 0.11 0.200 30.79 ++++++ 1.20 (1.14–1.27) 3.92E-11 <.0001
rs114312980 LSM2 6 31768799 A/C 0.11 0.230 26.77 ++++++ 1.20 (1.14–1.27) 4.39E-11 <.0001
rs115801685 LSM2 6 31772093 C/A 0.11 0.220 27.36 ++++++ 1.20 (1.14–1.27) 4.54E-11 <.0001
rs115489726 LSM2 6 31766660 C/T 0.11 0.240 25.69 ++++++ 1.20 (1.14–1.27) 8.76E-11 <.0001
rs114637560 LSM2 6 31765864 T/A 0.15 0.260 22.42 +−++++ 1.14 (1.08–1.20) 3.04E-07 0.0003
rs114984862 SKIV2L 6 31936668 C/T 0.27 0.290 18.64 ++++++ 1.09 (1.05–1.13) 1.87E-05 0.013
rs9800264 CNOT6 5 179940091 G/A 0.10 0.750 0.00 +++++− 1.13 (1.07–1.19) 3.12E-05 0.013
rs2387281 CNOT6 5 179988283 T/C 0.10 0.743 0.00 +++++− 1.13 (1.07–1.19) 3.28E-05 0.013
rs6877400 CNOT6 5 179996111 T/C 0.10 0.747 0.00 +++++− 1.13 (1.07–1.19) 3.41E-05 0.013
rs116188106 SKIV2L 6 31927342 G/A 0.27 0.298 17.82 ++++++ 1.09 (1.04–1.13) 3.70E-05 0.013
rs114011334 SKIV2L 6 31928799 C/T 0.27 0.297 17.92 ++++++ 1.09 (1.04–1.13) 3.77E-05 0.013
rs115002281 SKIV2L 6 31929014 C/A 0.27 0.297 17.95 ++++++ 1.08 (1.04–1.13) 3.79E-05 0.013
rs10052782 CNOT6 5 179975104 C/T 0.10 0.723 0.00 +++++− 1.13 (1.06–1.19) 4.09E-05 0.013
rs6422334 CNOT6 5 179982151 C/T 0.10 0.734 0.00 +++++− 1.13 (1.06–1.19) 4.14E-05 0.013
rs2453176 CNOT6 5 179975792 C/T 0.10 0.723 0.00 +++++− 1.13 (1.06–1.19) 4.33E-05 0.013
rs2387285 CNOT6 5 179982278 A/G 0.10 0.700 0.00 +++++− 1.12 (1.06–1.19) 4.92E-05 0.014
rs2447734 CNOT6 5 179968674 G/C 0.10 0.720 0.00 +++++− 1.12 (1.06–1.19) 4.97E-05 0.014
rs76820754 CNOT6 5 179936737 G/A 0.10 0.735 0.00 +++++− 1.12 (1.06–1.19) 8.06E-05 0.021
rs780126 CNOT6 5 179963034 C/T 0.13 0.758 0.00 +++++− 1.12 (1.06–1.18) 8.67E-05 0.021
rs812956 CNOT6 5 179953048 G/C 0.10 0.653 0.00 +++++− 1.12 (1.06–1.18) 1.18E-04 0.027
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SNP: single nucleotide polymorphism; FDR: false discovery rate; TRICL: Transdisciplinary Research in Cancer of the Lung; GWAS: genome-wide association study; Chr.: chromosome; EAF: effect allele frequency; OR: odds ratio; CI: confidence interval;

a

Reference allele/effect allele;

b

Fixed effect models were used when no heterogeneity was found between studies (Q-test P > 0.100 and I2 < 50.0%); otherwise, random effect models were used;

c

“+” means a positive association, and “−“ means a negative association.

Table 2.

Linkage disequilibrium between the 11 SNPs of CNOT6 in European populations included in the 1000 Genomes Project and imputation quality scores

SNP Position
(hg19)		 D' r2 Function prediction Imputation quality
SNPinfoa Regulome DBb Info
ICR		 Rsq
MDACC		 Rsq
IARC		 Info
NCI		 Info
Toronto		 Rsq
GLC
rs2453176 179975792 -- 1f 1.000 0.999 1.000 1.000 1.000 1.000
rs780126 179963034 1.00 0.71 -- -- 0.874 0.751 0.703 0.859 0.857 0.784
rs2387281 179988283 1.00 0.97 -- -- 0.998 0.972 0.969 0.996 0.990 0.967
rs6877400 179996111 1.00 0.97 Splicing site 5 0.998 0.964 0.965 0.996 0.990 0.953
rs2387285 179982278 1.00 0.97 -- 4 0.990 0.966 0.923 0.988 0.981 0.964
rs812956 179953048 1.00 0.97 -- 6 0.991 0.961 0.962 0.988 0.978 0.976
rs9800264 179940091 1.00 0.99 -- -- 0.999 0.970 0.977 0.998 0.993 1.000
rs6422334 179982151 1.00 0.99 -- 5 0.999 0.982 0.976 0.997 0.994 0.979
rs10052782 179975104 1.00 1.00 -- 6 1.000 0.998 1.000 1.000 1.000 1.000
rs76820754 179936737 1.00 1.00 -- 6 1.000 0.970 0.971 0.999 0.998 0.999
rs2447734 179968674 1.00 1.00 -- -- 1.000 0.994 0.999 0.999 0.997 0.999
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SNP: single nucleotide polymorphism;

Imputation quality: Rsq: MaCH r-squared; Info: IMPUTE2 information score;

Figure 3. Forest plots for associations between CNOT6 rs2453176 and lung cancer risk for all participants.

Figure 3

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(P = 0.0013).

We expanded our analysis to include additional two independent lung cancer GWASs (Supplemental Table S2). The deCODE GWAS validated our result of the CNOT6 rs2453176 tag SNP (OR = 1.14, 95% CI = 1.01–1.28, P = 0.032), while the GWAS from Harvard University displayed the same trend as the GLC GWAS (OR = 0.85, 95% CI = 0.68–1.05, P = 0.133) (Figure 3 and Table 3).

Table 3.

Associations between of CNOT6 rs2453176 (C >T) and lung cancer risk stratified by histologic types in all eight lung cancer GWASs from ILCCO-TRICL

Study Overall AD SC
Case Control OR (95% CI) P Case Control OR (95% CI) P Case Control OR (95% CI) P
ICR 1952 5200 1.15 (1.02–1.28) 0.020 465 5200 1.38 (1.12–1.70) 0.002 611 5200 1.14 (0.95–1.38) 0.158
MDACC 1150 1134 1.25 (1.03–1.52) 0.027 619 1134 1.06 (0.92–1.47) 0.206 306 1134 1.45 (1.08–1.94) 0.013
IARC 2533 3791 1.14 (1.00–1.29) 0.053 517 2824 1.13 (0.90–1.42) 0.301 911 2968 1.18 (0.98–1.41) 0.081
NCI 5713 5736 1.10 (1.01–1.21) 0.025 1841 5736 1.17 (1.03–1.33) 0.016 1447 5736 1.04 (0.91–1.20) 0.543
Toronto 331 499 1.12 (0.76–1.63) 0.057 90 499 1.48 (0.83–2.64) 0.186 50 499 1.00 (0.44–2.25) 0.998
GLC 481 478 0.93 (0.69–1.26) 0.064 186 478 1.25 (0.86–1.83) 0.240 97 478 0.90 (0.52–1.54) 0.695
Discovery combined 12160 16838 1.13 (1.06–1.19) 4.33E-05 3818 15871 1.21 (1.11–1.32) 2.04E-05 3424 16015 1.13 (1.03–1.23) 0.009
Harvard 984 970 0.85 (0.68–1.05) 0.133 597 970 0.79 (0.62–1.01) 0.130 216 970 1.08 (0.75–1.56) 0.678
deCODE 1319 26380 1.14 (1.01–1.28) 0.032 547 26380 1.02 (0.85–1.21) 0.858 259 26380 1.11 (0.86–1.43) 0.436
Replication combined 2303 27350 1.00 (0.75–1.32) 0.098 1144 27350 0.91 (0.71–1.16) 0.449 475 27350 1.10 (0.89–1.36) 0.381
Overall 14463 44188 1.11 (1.04–1.18) 0.001 4862 43221 1.13 (1.00–1.27) 0.050 3897 43365 1.12 (1.03–1.22) 0.006
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GWAS: genome-wide association study; ILCCO: International Lung Cancer Consortium; TRICL: Transdisciplinary Research in Cancer of the Lung; AD, adenocarcinoma; SC, squamous cell carcinoma; OR: odds ratio; CI: confidence interval

Homogeneity tests suggest that there is no heterogeneity between the subgroups of AD and SC in each GWAS and overall result (P > 0.05).

As we combined all the data from the eight GWASs, the functional CNOT6 rs2453176 tag SNP was found to be significantly associated with an increased risk of lung cancer (OR = 1.11, 95% CI = 1.04–1.18, P = 0.001) after the FDR correction (Figure 3 and Table 3).

Stratified analyses by lung cancer histology

Since lung cancer has different histological types that could have distinct biological behaviors, we performed AD and SC subgroup analysis and found that the rs2453176 T allele was associated with a borderlinely increased risk in AD (OR = 1.13, 95% CI = 1.00–1.27, P = 0.050, Table 3), but it was significantly associated with SC risk (OR = 1.12, 95% CI = 1.03–1.22, P = 0.006, Table 3). Because smoking is a major risk factor for lung cancer, we further stratified the data into smokers and non-smokers and found that that the rs2453176 T allele was associated with a significantly increased risk in smokers (OR = 1.09, 95% CI = 1.02–1.17, P = 0.011, Table 4), while the allele was not statistically significant in non-smokers (OR = 1.10, 95% CI = 0.89–1.36, P = 0.363, Table 4). Homogeneity tests suggested that there was no heterogeneity between strata either in subgroups of histologic types or smoking status (Table 3 and Table 4, all P > 0.05).

Table 4.

Associations between of CNOT6 rs2453176 (C >T) and lung cancer risk stratified by smoking status in six lung cancer GWASs from ILCCO-TRICL Consortia

Study Smoker Non-smoker
Case Control OR (95% CI) P Case Control OR (95% CI) P
MDACC 1150 1134 1.25 (1.03–1.52) 0.027
IARC 2367 2508 1.12 (0.97–1.29) 0.131 159 1253 1.40 (0.94–2.09) 0.096
NCI 5342 4336 1.10 (1.00–1.22) 0.058 350 1379 0.99 (0.72–1.37) 0.972
Toronto 236 272 1.14 (0.70–1.86) 0.606 95 217 1.13 (0.61–2.11) 0.702
GLC 433 258 1.00 (0.67–1.49) 0.995 35 220 1.64 (0.71–3.82) 0.250
Harvard 892 809 0.87 (0.70–1.09) 0.221 92 161 0.71 (0.38–1.33) 0.288
Overall 10420 9317 1.09 (1.02–1.17) 0.011 731 3230 1.10 (0.89–1.36) 0.363
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GWAS: genome-wide association study; ILCCO: International Lung Cancer Consortium; TRICL: Transdisciplinary Research in Cancer of the Lung; OR: odds ratio; CI: confidence interval.

Homogeneity tests suggest there is no heterogeneity between the subgroups of smoker and non-smoker in each GWAS and overall result (P > 0.05).

Functional validation by eQTL analysis

Because the CNOT6 rs2453176 SNP was predicted with a score of "1f", suggesting the most confident functional annotation by regulomeDB [26], we further explored the underlying molecular mechanism by performing the eQTL analysis. With mRNA expression data of lymphoblastoid cell lines from 373 Europeans available from the 1000 Genomes Project, We found that expected mRNA expression levels of CNOT6 were significantly decreased with an increased number of the rs2453176 T allele in both the additive (P = 0.008) (Figure 4A) and dominant (P = 0.007) (Figure 4B) models but not the recessive model (Figure 4C). We also used the 105 normal adjacent tissue samples in the TCGA to further explore the correlation between the rs2453176 genotypes and their corresponding mRNA expression levels, but we did not observe a statistical significance (P > 0.05) (Supplemental Figure S1A-S1C). We also compared the mRNA expression level of CNOT6 in the 107 paired samples and did not find a statistically significant difference (P > 0.05) (Supplemental Figure S1D).

Figure 4. The eQTL analysis of CNOT6 mRNA expression for rs2453176 with lymphoblastoid cell data of 373 Europeans from 1000 Genomes Project.

Figure 4

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A. additive model, P = 0.008; B. dominant model, P = 0.007; C. recessive model, P = 0.634.

Discussion

In the present study, we found that a novel potentially functional susceptibility locus rs2453176 C>T of CNOT6 in the general mRNA degradation pathway was associated with an increased lung cancer risk in 14,463 cases and 44,188 controls. This association was further supported by a significant correlation between a decreased mRNA expression level and an increasing number of the A allele in the eQTL analysis.

Gene expression disorder is one of cancer hallmarks, and instability of mRNA may result in altered transcprit/protein levels of oncogenes and tumor repressor genes [33]. The degradation of mRNA is a key step in controlling the expression of genes related to cell proliferation. For example, the CCR4-Not complex consists of highly conserved exoribonucleases and adaptor proteins that hydrolyze and shorten the poly(A) tail, which starts the initial and the rate-limiting step of mRNA degradation [19]. Located at 5q35.3, CNOT6 encodes a protein that has a 3'-5' RNase activity and acts as a catalytic subunit of the CCR4-Not deadenylation complex [34]. Although it remains unclear how the catalytic subunit works during the deadenylation process, some studies reported that its expression level was associated with carcinogenesis or prognosis. For example, one study of lung cancer found that the CNOT6 overexpression in lung SC predicted a significantly less metastasis [33]. Another study of acute leukemia discovered that CNOT6 had a significantly lower expression in patients than in controls [35]. These two studies suggest that high expression levels of CNOT6 may promote the degradation of mRNA of some oncogenes and the suppression of cell proliferation in carcinogenesis.

In the present study, we identified that the CNOT6 rs2453176 T allele was associated with an increased risk of lung cancer, which was supported by the association of CNOT6 rs2453176 T allele with a decreased mRNA expression level in lymphoblastoid cell lines from 373 Europeans. This finding is consistent with the role of CNOT6 in lung cancer prognosis as previously described [33]. The ENCODE project data from University of California Santa Cruz show that the CNOT6 rs2453176 locus is located at the DNase I hypersensitive region (Supplemental Figure S2). Usually such an area has a loose chromatin structure and renders it a region with a high affinity for transcription factors (TFs). As a result, some TFs, including MAFK and MAFF, bond to this region in many cell types (Supplemental Figure S2). For example, MAFK and MAFF were found to form heterodimers with a series of TFs and suppressed gene transcriptions [36,37]. Based on these, we speculate that the rs2453176 T allele may have a relatively high affinity with MAFK or MAFF and thus leads to the decreased mRNA expression of CNOT6. It is likely that a reduced quantity of CNOT6 may not be optimal in the mRNA degradation of some aberrant genes, which may in turn increases lung cancer risk, but these speculations need to be further investigated.

In the stratification analysis, rs2453176 was associated with lung cancer risk in both AD and SC subtypes, which is not surprising, because smoking has been established as a predominant risk factor for developing lung cancer, regardless histologic types [38]. Genetic susceptibility to smoking-related lung cancer risk may determine smoking behavior and tobacco metabolism [39]. Indeed, we found that the rs2453176 T allele was associated with a higher risk of lung cancer in smokers than in non-smokers. One study reported that smoking would enhance the activity of the GATA family [40], and another study reported that nicotine would increase the expression of EP300 and promote the lung cancer growth [41]. From the Supplemental Figure S2, GATA1, GATA2 and EP300 are the TFs that bind to the rs2453176 locus, possibly explaining why carriers of the rs2453176 T allele may have an increased risk of lung cancer in smokers than non-smokers.

There are some limitations in the present study. First, we employed the gene set enrichment analysis with a collection of annotated gene sets to define the general mRNA degradation pathway to be investigated, but we may have missed some newly discovered genes in the pathway. However, we searched the literatures and added genes as many as possible. Second, due to the data limitation, we had no access to family history and others factors that may have an impact on lung cancer risk. Third, we used the eQTL analyses from lymphoblastoid cell lines and normal adjacent tissue in TCGA database to validate the risk association. Although the results from the cell lines support our identified association, they may only reflect the baseline or genetically determined expression levels without exposure to smoking. The gene expressions in the normal adjacent lung tissues may be in some degree different from the normal lung tissue and did not support the association.

Overall, the present study of eight published GWASs identified a novel CNOT6 rs2453176 SNP in the general mRNA degradation pathway to be significantly associated with lung cancer risk in European populations, and the risk was more evident in smokers than in non-smokers. Although we used the publically available gene expression database from blood to confirm the biological significance of the variant, further functional evaluations in normal lung tissue are warranted to validate our findings.

Supplementary Material

SF1. Supplemental Figure S1 A-C. The eQTL analysis of CNOT6 mRNA expressions for rs2453176 in the 105 adjacent normal lung cancer tissue samples from the TCGA database.

A. additive model, P = 0.491, B. dominant model, P = 0.990, C. recessive model, P = 0.667; D, The mRNA expression of CNOT6 in the 107 paired lung cancer and normal adjacent tissue samples from the TCGA database (P = 0.237).

SF2. Supplementary Figure S2. The ENCODE project data of rs2453176 from UCSC browser (NCBI137/hg19).
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Acknowledgments

As Duke Cancer Institute members, QW, KO and NR acknowledge support from the Duke Cancer Institute as part of the P30 Cancer Center Support Grant (Grant ID: NIH CA014236). QW was also supported by the start-up funds from Duke Cancer Institute, Duke University Medical Center.

TRICL

This work was supported by the Transdisciplinary Research in Cancer of the Lung (TRICL) Study and, U19-CA148127 on behalf of the Genetic Associations and Mechanisms in Oncology (GAME-ON) Network. The Toronto study was supported by Canadian Cancer Society Research Institute (020214), Ontario Institute of Cancer and Cancer Care Ontario Chair Award to RH The ICR study was supported by Cancer Research UK (C1298/A8780 andC1298/A8362—Bobby Moore Fund for Cancer Research UK) and NCRN, HEAL and Sanofi-Aventis. Additional funding was obtained from NIH grants (5R01CA055769, 5R01CA127219, 5R01CA133996, and 5R01CA121197). The Liverpool Lung Project (LLP) was supported by The Roy Castle Lung Cancer Foundation, UK. The ICR and LLP studies made use of genotyping data from the Wellcome Trust Case Control Consortium 2 (WTCCC2); a full list of the investigators who contributed to the generation of the data is available from www.wtccc.org.uk. Sample collection for the Heidelberg lung cancer study was in part supported by a grant (70–2919) from the Deutsche Krebshilfe. The work was additionally supported by a Helmholtz-DAAD fellowship (A/07/97379 to MNT) and by the NIH (U19CA148127). The KORA Surveys were financed by the GSF, which is funded by the German Federal Ministry of Education, Science, Research and Technology and the State of Bavaria. The Lung Cancer in the Young study (LUCY) was funded in part by the National Genome Research Network (NGFN), the DFG (BI576/2-1; BI 576/2-2), the Helmholtzgemeinschaft (HGF) and the Federal office for Radiation Protection (BfS: STSch4454). Genotyping was performed in the Genome Analysis Center (GAC) of the Helmholtz Zentrum Muenchen. Support for the Central Europe, HUNT2/Tromsø and CARET genome-wide studies was provided by Institut National du Cancer, France. Support for the HUNT2/Tromsø genome-wide study was also provided by the European Community (Integrated Project DNA repair, LSHG-CT- 2005–512113), the Norwegian Cancer Association and the Functional Genomics Programme of Research Council of Norway. Support for the Central Europe study, Czech Republic, was also provided by the European Regional Development Fund and the State Budget of the Czech Republic (RECAMO, CZ.1.05/2.1.00/03.0101). Support for the CARET genome-wide study was also provided by grants from the US National Cancer Institute, NIH (R01 CA111703 and UO1 CA63673), and by funds from the Fred Hutchinson Cancer Research Center. Additional funding for study coordination, genotyping of replication studies and statistical analysis was provided by the US National Cancer Institute (R01 CA092039). The lung cancer GWAS from Estonia was partly supported by a FP7 grant (REGPOT245536), by the Estonian Government (SF0180142s08), by EU RDF in the frame of Centre of Excellence in Genomics and Estoinian Research Infrastructure’s Roadmap and by University of Tartu (SP1GVARENG). The work reported in this paper was partly undertaken during the tenure of a Postdoctoral Fellowship from the IARC (for MNT). The Environment and Genetics in Lung Cancer Etiology (EAGLE), the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study (ATBC), and the Prostate, Lung, Colon, Ovary Screening Trial (PLCO)studies and the genotyping of ATBC, the Cancer Prevention Study II Nutrition Cohort (CPS-II) and part of PLCO were supported by the Intramural Research Program of NIH, NCI, Division of Cancer Epidemiology and Genetics. ATBC was also supported by US Public Health Service contracts (N01-CN-45165, N01-RC-45035 and N01-RC-37004) from the NCI. PLCO was also supported by individual contracts from the NCI to the University of Colorado Denver (NO1-CN-25514), Georgetown University(NO1-CN-25522), Pacific Health Research Institute (NO1-CN-25515), Henry Ford Health System (NO1-CN-25512), University of Minnesota(NO1-CN-25513), Washington University(NO1-CN-25516), University of Pittsburgh (NO1-CN-25511), University of Utah (NO1-CN-25524), Marshfield Clinic Research Foundation (NO1-CN-25518), University of Alabama at Birmingham (NO1-CN-75022, Westat, Inc. NO1-CN-25476), University of California, Los Angeles (NO1-CN-25404). The Cancer Prevention Study II Nutrition Cohort was supported by the American Cancer Society. The NIH Genes, Environment and Health Initiative (GEI) partly funded DNA extraction and statistical analyses (HG-06-033-NCI-01 andRO1HL091172-01), genotyping at the Johns Hopkins University Center for Inherited Disease Research (U01HG004438 and NIH HHSN268200782096C) and study coordination at the GENEVA Coordination Center (U01 HG004446) for EAGLE and part of PLCO studies. Funding for the MD Anderson Cancer Study was provided by NIH grants (P50 CA70907, R01CA121197, R01CA127219, U19 CA148127, R01 CA55769, and K07CA160753) and CPRIT grant (RP100443). Genotyping services were provided by the Center for Inherited Disease Research (CIDR). CIDR is funded through a federal contract from the NIH to The Johns Hopkins University (HHSN268200782096C). The Harvard Lung Cancer Study was supported by the NIH (National Cancer Institute) grants CA092824, CA090578, and CA074386.

deCODE

The project was funded in part by GENADDICT: LSHMCT-2004-005166), the National Institutes of Health (R01-DA017932)

Abbreviations

AD

Adenocarcinoma

CI

confidence interval

eQTL

expression quantitative trait loci

FDR

false discovery rate

GWAS

genome-wide association study

ILCCO

International Lung Cancer Consortium

LD

linkage disequilibrium

OR

odds ratio

SC

squamous cell carcinoma

SNP

single nucleotide polymorphisms

TCGA

The Cancer Genome Atlas

TRICL

Transdisciplinary Research in Cancer of the Lung

Footnotes

Conflict of interest:

The authors disclose no potential conflicts of interest.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

SF1. Supplemental Figure S1 A-C. The eQTL analysis of CNOT6 mRNA expressions for rs2453176 in the 105 adjacent normal lung cancer tissue samples from the TCGA database.

A. additive model, P = 0.491, B. dominant model, P = 0.990, C. recessive model, P = 0.667; D, The mRNA expression of CNOT6 in the 107 paired lung cancer and normal adjacent tissue samples from the TCGA database (P = 0.237).

NIHMS845206-supplement-SF1.tif (6MB, tif)
SF2. Supplementary Figure S2. The ENCODE project data of rs2453176 from UCSC browser (NCBI137/hg19).
NIHMS845206-supplement-SF2.tif (3.9MB, tif)
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NIHMS845206-supplement-ST1.docx (15.4KB, docx)
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NIHMS845206-supplement-ST2.docx (16KB, docx)

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