Skip to main content
NCBI home page
American Journal of Cancer Research logoLink to American Journal of Cancer Research
. 2020 Jul 1;10(7):2128–2144.

Associations of novel variants in PIK3C3, INSR and MAP3K4 of the ATM pathway genes with pancreatic cancer risk

Ling-Ling Zhao 1,2,3, Hong-Liang Liu 2,3, Sheng Luo 4, Kyle M Walsh 2,5, Wei Li 1, Qingyi Wei 2,3,6
PMCID: PMC7407350  PMID: 32775006

Abstract

The ATM serine/threonine kinase (ATM) pathway plays important roles in pancreatic cancer (PanC) development and progression, but the roles of genetic variants of the genes in this pathway in the etiology of PanC are unknown. In the present study, we assessed associations between 31,499 single nucleotide polymorphisms (SNPs) in 198 ATM pathway-related genes and PanC risk using genotyping data from two previously published PanC genome-wide association studies (GWASs) of 15,423 subjects of European ancestry. In multivariable logistic regression analysis, we identified three novel independent SNPs to be significantly associated with PanC risk [PIK3C3 rs76692125 G>A: odds ratio (OR)=1.26, 95% confidence interval (CI)=1.12-1.43 and P=2.07×10-4, INSR rs11668724 G>A: OR=0.89, 95% CI=0.84-0.94 and P=4.21×10-5 and MAP3K4 rs13207108 C>T: OR=0.83, 95% CI=0.75-0.92, P=2.26×10-4]. The combined analysis of these three SNPs exhibited an increased PanC risk in a dose-response manner as the number of unfavorable genotypes increased (P trend<0.0001). The risk-associated rs76692125 A allele was correlated with decreased PIK3C3 mRNA expression levels, while the protective-associated rs11668724 A allele was correlated with increased INSR mRNA expression levels, but additional mechanistic studies of these SNPs are warranted. Once validated, these SNPs may serve as biomarkers for PanC risk in populations of European ancestry.

Keywords: Pancreatic cancer, single nucleotide polymorphism, risk analysis, ATM pathway

Introduction

Pancreatic cancer (PanC) is one of the deadliest cancers because of its rapid metastasis, accounting for 3.2% of all new cancer diagnoses [1]. An estimated 53% of PanCs are diagnosed at an advanced stage as a result of the lack of early symptoms and tumor-specific diagnostic tests, and the 5-year survival for metastatic PanC is only 2.9% [1]. It is estimated that PanC will be the second leading cause of cancer-related deaths in the United States by 2030 [2]. Hence, early prevention and detection may be the key to reduce PanC mortality.

Currently, several risk factors for PanC have been identified, including age, sex, smoking status, alcohol consumption, obesity, dietary factors, ethnicity, diabetes mellitus, family history of PanC and genetic susceptibility [3]. While the high-penetrant germline mutations are associated with an increased PanC risk, common genetic variants may also be the risk factors [4-9]. Over the past decade, although more than twenty single nucleotide polymorphisms (SNPs) have been identified as PanC-associated risk variants through genome-wide association studies (GWASs) that require a stringent p value of <5.0×10-8 due to the multiple-test inherent in this study method. Thus, some important functional susceptibility genes with a week effect may still remain unidentified. Recently, the pathway-based analyses of existing GWAS datasets have emerged as a hypothesis-driven approach of identifying novel functional SNPs within a biological context [10].

The ATM serine/threonine kinase (ATM) pathway is best known for its role in DNA damage response and plays important roles in maintenance of genomic stability and suppression of tumorigenesis [11]. The ATM gene, also known as the ataxia telangiectasia mutated gene, is located on chromosome 11q22-23 and encodes a serine/threonine kinase member of the phosphatidylinositol 3 kinase family that is activated by DNA double-strand breaks; once activated, ATM phosphorylates multiple downstream effectors, including those involved in DNA damage repair, cell-cycle checkpoint arrest and apoptosis [12].

Recent studies have identified the link between the ATM pathway and PanC. For example, the activation of ATM and CHK1 has been reported to be associated with cell cycle arrest and apoptosis in human pancreatic cancer cells [13]. It has been reported that reduced levels of ATM coupled with oncogenic KRAS activation result in a higher number of dysplastic pancreatic lesions [14]. Accumulated evidence suggests that ATM can be activated independently from DNA damage through oxidative stress involved in autophagy induction [15]. The phosphoinositide-3-kinase class III (PIK3C3), a critical membrane marker for the autophagosome, has been reported to be associated with mediation of the autophagy in pancreatic cancer cells [16]. In addition, the ATM pathway genes can also affect insulin signaling function and glucose metabolism by regulating intracellular levels of reactive oxygen species, a known contributor to the onset of diabetes that is also a well-known risk factor of PanC [17].

In accordance with these findings, we hypothesize that genetic variants in the ATM pathway-related genes are associated with PanC risk. To test our hypothesis, we performed a comprehensive pathway gene-set-based analysis to identify potential functional SNPs that were associated with PanC risk by using the case and control subjects and genotyping data from two available GWAS datasets of pancreatic ductal adenocarcinoma in populations of European ancestry.

Material and methods

Study participants

We used the study participants with genotyping data from the two available PanC GWAS datasets, i.e., the Pancreatic Cancer Cohort Consortium (PanScan) and the Pancreatic Cancer Case Control Consortium (PanC4) studies that included 15,423 individuals of European ancestry. The PanScan GWAS dataset included 17 cohort studies and 11 case-control studies and had three phases: PanScan I (1,760 cases and 1,780 controls), PanScan II (1,457 cases and 1,666 controls) and PanScan III (1,538 cases and 0 controls) [4-6]. Because PanScan III lacks study-specific controls, the data from PanScan II and PanScan III were analyzed jointly, and the joint dataset was denoted as PanScan II/III (2,995 cases and 1,666 controls). The PanC4 GWAS dataset included nine studies from North America, Central Europe and Australia (3,722 cases and 3,500 controls) [7,18,19] (Table S1 and Figure S1).

All the studies obtained a written informed consent from study participants. The present study protocol was also approved by Duke University Health System Institutional Review Board (Pro00054575) and by the administration of the database of Genotypes and Phenotypes (dbGaP). The PanScan and PanC4 GWAS datasets are available from the dbGaP (accession #: phs000206.v5.p3 and phs000648.v1.p1, respectively).

Gene and SNP selection

We used the keyword “ATM” for the search in Molecular Signatures Database (MSigDB, v7.0) [20] and PathCards: multi-source consolidation of human biological pathways [21]. As a result, we obtained a total of 198 ATM pathway-related genes located in the autosomes from the online databases of BIOCARTA, PID and PathCards (Table S2), which were used for SNP extraction from the available GWAS datasets. The Illumina HumanHap550v3.0, the Human610_Quadv1_B, the HumanOmniExpress-12v1.0 and the HumanOmniExpressExome-8v1 arrays [4-6] were used for genotyping in the GWAS datasets made available to the present study. We performed imputation by using IMPUTE2 with a buffer region of 500-kb up- and down-stream of these pathway genes and a reference panel from the 1000 Genomes Project (phase 3 release v5) [22]. We extracted the SNPs within the three GWAS datasets by using a boundary of 2-kb up- and down-stream of selected genes and performed quality assurance by using the following criteria: a SNP call rate of ≥ 95%, a minor allele frequency (MAF) of ≥ 0.01, a Hardy-Weinberg Equilibrium (HWE) test of P value ≥ 1×10-5 among controls, and an imputation quality (INFO) score of ≥ 0.5 (Figures 1 and S2).

Figure 1.

Figure 1

Open in a new tab

Flowchart of the present study. Abbreviations: kb, kilobase; SNP, single nucleotide polymorphism; MAF, minor allele frequency; HWE, Hardy-Weinberg equilibrium; PanC, pancreatic cancer; FDR, false discovery rate; PIK3C3, phosphatidylinositol 3-kinase catalytic subunit type 3; INSR, insulin receptor; MAP3K4, mitogen-activated protein kinase kinase kinase 4; eQTL, expression quantitative trait loci.

Statistical analysis

We performed single-locus analysis between SNPs of the ATM pathway-related genes and PanC risk by using a multivariable logistic regression model with PLINK 1.9 [23] for each of the GWAS datasets separately. We constructed the models with adjustment for age, sex, and the top ancestry-informative principal components (PCs) from the genotyping data in PanScan I/II/III and PanC4. We estimated the effect sizes of SNPs by calculating odds ratio (OR) and 95% confidence interval (CI) accordingly. Then, we conducted a meta-analysis of the results of these three datasets by using the generic inverse variance method [24] with PLINK 1.9. We used Cochran’s Q test and I2 to assess the heterogeneity between the datasets [25]. If the Cochran’s Q-test P-value >0.100 and the I2<50.0% were observed, a fixed-effects model was employed; otherwise, a random-effects model was adopted.

The false discovery rate (FDR) cutoff value was set at ≤ 0.10 to correct for multiple comparisons, which is sufficient for the tested SNPs largely in linkage disequilibrium (LD) [26]. We assessed the association between each SNP and PanC risk by using an additive genetic model. To identify independent-effect SNPs for PanC risk, we performed a stepwise logistic regression with adjustment for age, sex and the top five significant PCs as well as the 13 previously reported SNPs from the same datasets [27-30]. We constructed Manhattan plots by using Haploview v4.2 and the regional association plots to show all SNPs in LD by using LocusZoom [31]. To assess the joint effect of the SNPs, we also used the number of unfavorable genotypes (NUG) to calculate the cumulative effects of the independent SNPs and divided all the individuals into groups by the number of NUG for further analyses. Then, we used a general linear regression model in the expression quantitative trait loci (eQTL) analysis to estimate the correlations between the independent SNPs and corresponding mRNA expression levels of the genes with the R software (v3.6.2). We performed all other statistical analyses with SAS software (v9.4; SAS Institute, Cary, NC, USA), if not specified otherwise.

Functional prediction and validation

We used three online bioinformatics tools, SNPinfo, RegulomeDB and HaploReg, to predict potential functions of the significant SNPs. SNPinfo integrates GWAS and candidate gene information into functional SNP selection for genetic association studies [32]; RegulomeDB helps identify DNA features and regulatory elements in non-coding regions of the human genome [33] and HaploReg is a tool for exploring annotations of the noncoding genome at variants on haplotype blocks, such as candidate regulatory SNPs at disease-associated loci [34]. We also performed the eQTL analysis to estimate the associations between the genotypes of SNPs and the mRNA expression levels of the corresponding gene by using the mRNA expression data in the Genotype-Tissue Expression (GTEx) Project [35], the lymphoblastoid cells of 373 Europeans available from the 1,000 Genomes Project [36] and The Cancer Genome Atlas (TCGA) Project [37]. We used the PERFECTOS-APE [38,39] and the PROMO online tools [40,41] to predict the transcription factors in the promoter regions. We also obtained differences in the mRNA expression between tumor and adjacent normal tissues for the identified genes in TCGA dataset from the UALCAN web site [42,43]. Finally, we conducted the overall survival analysis by using the TCGA dataset with the GEPIA web-portal tool [44,45] and perform the mutations analysis by using publicly available data in the database of the cBioportal for Cancer Genomics [46,47].

Results

Association analysis

The distributions of demographic characteristics between the three GWAS datasets are summarized in Table S1, including 8,477 cases and 6,946 controls of European ancestry [4-7,18,19]. As portrayed in Figure 1, after imputation and quality control, there were 36,854, 41,415 and 36,290 SNPs for PanScan I, PanScan II/III and PanC4, respectively. The single-locus analysis for the association between each of SNPs in the 198 ATM pathway-related genes (Table S2) and PanC risk was employed with the adjustment for age, sex and the top significant principle components (PCs) for each of the three datasets. A total of 2,177, 2,608 and 2,285 SNPs had a nominal P<0.050 in each dataset, respectively (Figure S3). In the meta-analysis of 31,499 SNPs (978 genotyped and 30,521 imputed) of the three datasets, we observed 1,901 SNPs to be associated with PanC risk with a nominal P<0.050, 68 of which were in eight genes (WNT2B, MAP3K4, SMC2, ERBB2, TP53, PIK3C3, INSR and CHEK2) with FDR ≤ 0.10 (Figure 2A). Then, we assessed these 68 SNPs in a multivariable stepwise logistic regression model to identify novel independent functional SNPs. As a result, we found that seven SNPs (i.e., rs13207108 in MAP3K4, rs76692125 in PIK3C3, rs11668724 in INSR, rs3838412 in WNT2B, rs2417487 in SMC2, rs2517955 in ERBB2 and rs2236141 in CHEK2,) to be significantly associated with PanC risk (Table S3). Because some susceptibility loci located in WNT2B, SMC2, ERBB2 and CHEK2 had been reported to be associated with PanC risk through GWAS datasets analyses [6,7,9,48], we used the three PanC risk loci [i.e., 18q12.3 (PIK3C3 rs76692125 G>A), 19p13.2 (INSR rs11668724 G>A) and 6q26 (MAP3K4 rs13207108 C>T)] newly identified in the present study for further analyses (Table 1 and Figure 2A-D). As shown in Table 2, the PIK3C3 rs76692125 A allele was associated with an increased risk of PanC (OR=1.26, 95% CI=1.12-1.43, P=2.07×10-4), while the INSR rs11668724 A allele and the MAP3K4 rs13207108 T allele were associated with a reduced risk of PanC (OR=0.89, 95% CI=0.84-0.94, P=4.21×10-5 and OR=0.83, 95% CI=0.75-0.92, P=2.26×10-4, respectively) in the meta-analysis of the three GWAS datasets. Heterogeneity in these associations was not observed among the three datasets.

Figure 2.

Figure 2

Open in a new tab

Association results of SNPs in the ATM-related pathway genes. (A) Manhattan plot of the association results. The statistical values across the autosomes of associations between 31,499 SNPs and PanC risk are plotted as -log10 p values in the meta-analysis of the three datasets. The red horizontal line indicates P=0.050. The blue horizontal line indicates FDR=0.10. There are 1,901 SNPs with P<0.050 and 68 SNPs with FDR ≤ 0.10. The three SNPs (i.e., rs76692125, rs11668724 and rs13207108) shown in the red bold are the novel findings in the present study. The five genes (i.e., WNT2B, SMC2, ERBB2, TP53 and CHEK2) shown in blue are previously reported genes associated with PanC risk. Regional association plots for the three newly identified SNPs in the ATM pathway genes. SNPs in the region of 200 kb up- and down-stream of (B) PIK3C3, (C) INSR and (D) MAP3K4.

Table 1.

The three novel independent SNPs identified from the stepwise logistic regression analysis in the pooling PanC data

SNP Allele1 Position Gene Region OR (95% CI)2 P2
rs76692125 A/G 39562230 PIK3C3 18q12.3 1.31 (1.16-1.49) <.0001
rs11668724 A/G 7208526 INSR 19p13.2 0.89 (0.84-0.94) <.0001
rs13207108 T/C 161417626 MAP3K4 6q26 0.82 (0.74-0.91) 0.0002
Open in a new tab

Abbreviations: SNP, single nucleotide polymorphism; PanC, pancreatic cancer; OR, odds ratio; CI, confidence interval.

1

Effect (minor) allele/reference allele.

2

Stepwise analysis adjusted for age, sex, the top five principal components and 13 SNPs (rs35075084, rs2727572, rs34852782, rs62068300, rs3751936, rs3124761, rs17458086, rs1630747, rs5757573, rs6001516, rs79447092, rs9895829 and rs3818626) previously reported in the same dataset (PMID: 29168174, 30794721, 30972876 and 30997723, respectively).

Table 2.

Associations between the three independent SNPs and PanC risk in the three PanC datasets

SNP Allele1 Position Gene PanScan I 1,760/1,7802 PanScan II/III 2,995/1,6662 PanC4 3,722/3,5002 Meta-analysis 8,477/6,9462 Heterogeneity5 FDR
MAF OR (95% CI) P 3 MAF OR (95% CI) P 3 MAF OR (95% CI) P 3 OR (95% CI) P 4 Q I 2
rs76692125 A/G 39562230 PIK3C3 0.04 1.09 (0.86-1.39) 0.4808 0.03 1.22 (0.96-1.54) 0.0989 0.03 1.40 (1.17-1.67) 0.0003 1.26 (1.12-1.43) 2.07×10-4 0.263 25.2 0.10
rs11668724 A/G 7208526 INSR 0.23 0.94 (0.84-1.05) 0.2812 0.24 0.83 (0.75-0.92) 0.0003 0.22 0.91 (0.84-0.98) 0.0185 0.89 (0.84-0.94) 4.21×10-5 0.211 35.8 0.03
rs13207108 T/C 161417626 MAP3K4 0.05 0.93 (0.75-1.15) 0.5113 0.07 0.84 (0.70-1.00) 0.0597 0.06 0.78 (0.67-0.90) 0.0007 0.83 (0.75-0.92) 2.26×10-4 0.395 0.0 0.10
Open in a new tab

Abbreviations: SNP, single nucleotide polymorphism; PanC, pancreatic cancer; GWAS, genome-wide association study; MAF, minor allele frequency; OR, odds ratio; CI, confidence interval; FDR, false discovery rate.

1

Effect (minor) allele/reference allele.

2

Number of case/number of control.

3

Adjusted for age, sex and significant principal components in each dataset.

4

Meta-analysis in the three datasets.

5

Heterogeneity assessed by Q-test or I 2: fixed-effects model if Q test P>0.100 and I 2<50.0%; otherwise random-effects model.

Combined and stratified analyses

As shown in Table 3, PIK3C3 rs76692125 GA+AA genotypes were associated with an increased PanC risk (OR=1.31, 95% CI=1.16-1.48, P<0.0001), while INSR rs11668724 GA+AA genotypes and MAP3K4 rs13207108 CT+TT genotypes were associated with a reduced PanC risk (OR=0.88, 95% CI=0.82-0.94, P<0.0001 and OR=0.84, 95% CI=0.76-0.93, P=0.0007, respectively). Therefore, we combined the unfavorable genotypes of rs76692125 GA+AA, rs11668724 GG and rs13207108 CC to assess the cumulative effect of these three SNPs. The joint analysis suggested that the NUG was significantly associated with risk of PanC in a dose-response manner (P trend<0.0001, Table 3). The multivariable logistic regression model incorporating the NUG exhibited that individuals with two-three NUGs had a higher risk of PanC, compared with those with zero-one NUGs (OR=1.22, 95% CI=1.15-1.30, P<0.0001, Table 3).

Table 3.

Combined analysis of the three independent SNPs and PanC risk in the three PanC datasets

Genotype Case (%) Control (%) OR (95% CI)1 P 1
PIK3C3 rs76692125
    GG 7778 (91.8) 6490 (93.5) 1.0
    GA 682 (8.0) 444 (6.4) 1.30 (1.14-1.47) <0.0001
    AA 15 (0.2) 5 (0.1) 2.62 (0.95-7.25) 0.0627
    Trend test <0.0001
    Dominant model
    GG 7778 (91.8) 6940 (93.5) 1.0
    GA+AA 697 (8.2) 449 (6.5) 1.31 (1.16-1.48) <0.0001
INSR rs11668724
    GG 5322 (62.8) 4142 (59.6) 1.0
    GA 2774 (32.7) 2440 (35.1) 0.89 (0.83-0.95) 0.0005
    AA 379 (4.5) 364 (5.2) 0.81 (0.69-0.94) 0.0050
    Trend test <0.0001
    Dominant model
    GG 5322 (62.8) 4142 (59.6) 1.0
    GA+AA 3153 (37.2) 2804 (40.4) 0.88 (0.82-0.94) <0.0001
    Reversed model
    GA+AA 3153 (37.2) 2804 (40.4) 1.0
    GG 5322 (62.8) 4142 (59.6) 1.14 (1.06-1.22) <0.0001
MAP3K4 rs13207108
    CC 7637 (90.1) 6130 (88.3) 1.0
    CT 818 (9.6) 793 (11.4) 0.84 (0.76-0.93) 0.0009
    TT 22 (0.3) 23 (0.3) 0.79 (0.44-1.41) 0.4209
    Trend test 0.0007
    Dominant model
    CC 7637 (90.1) 6130 (88.3) 1.0
    CT+TT 840 (9.9) 816 (11.7) 0.84 (0.76-0.93) 0.0007
    Reversed model
    CT+TT 840 (9.9) 816 (11.7) 1.0
    CC 7637 (90.1) 6130 (88.3) 1.19 (1.08-1.32) 0.0007
NUG2
    0 279 (3.3) 295 (4.3) 1.0
    1 3099 (36.6) 2813 (40.5) 1.14 (0.96-1.35) 0.1393
    2 4734 (55.9) 3593 (51.8) 1.36 (1.15-1.61) 0.0004
    3 361 (4.3) 238 (3.4) 1.59 (1.26-2.01) <0.0001
    Trend test <0.0001
    0-1 3378 (39.9) 3108 (44.8) 1.0
    2-3 5095 (60.1) 3831 (55.2) 1.22 (1.15-1.30) <0.0001
Open in a new tab

Abbreviations: SNP, single nucleotide polymorphism; PanC, pancreatic cancer; OR, odds ratio; CI, confidence interval; NUG, number of unfavorable genotypes.

1

Adjusted by age, sex and the top five significant principal components.

2

Unfavorable genotypes were rs76692125 GA+AA, rs11668724 GG and rs13207108 CC.

Furthermore, we employed stratified analysis for risk modification by age and sex in the present study, and no interaction was observed between the subgroups within the strata (P inter>0.050, Table S4).

Function analysis

We performed the eQTL analysis for the three identified independent SNPs on mRNA expression by using the expression and genotyping data of 305 normal pancreatic tissues and 670 whole blood samples from the GTEx Project [35], the RNA-Seq and genotyping data of lymphoblastoid cell lines from 373 European descendants from the 1,000 Genomes Project [36], and the expression and genotyping data of 180 pancreatic tumor samples from the TCGA Project [37]. We found that the PIK3C3 rs76692125 A allele was correlated with a decreased mRNA expression level in the whole blood cells, but not in the normal pancreatic tissues (Figure 3A), form the GTEx Project (n=670, P=1.13×10-5, Figure S4A), and in the lymphoblastoid cells from the 1,000 Genomes Project (n=373, P=5.26×10-3, Figure 3B). As shown in Figure 3C, the rs11668724 A allele was associated with an increased INSR mRNA expression level in normal pancreas tissues from the GTEx Project (n=305, P=5.97×10-6). This finding was not observed either in the whole blood cells from the GTEx Project (Figure S4B) or in the lymphoblastoid cells from the 1,000 Genomes Project (Figure 3D). No significant eQTL results for MAP3K4 rs13207108 was observed either from the GTEx or from the 1,000 Genomes Projects (Figures 3E, 3F and S4C). However, the TCGA eQTL data of these three SNPs were not available for further analyses.

Figure 3.

Figure 3

Open in a new tab

Functional analyses of the three identified SNPs. Expression quantitative trait loci (eQTL) analysis of SNP rs76692125 and PIK3C3 mRNA expression levels (A) in the normal pancreatic tissues (n=305, P=0.83) from the GTEx Project and (B) in the transformed lymphoblastoid cells (n=373, P=5.26×10-3) from the 1000 Genomes Project; the rs11668724 genotypes and INSR mRNA expression levels (C) in the normal pancreatic tissues (n=305, P=5.97×10-6) from the GTEx Project and (D) in the transformed lymphoblastoid cells (n=373, P=0.700) from the 1000 Genomes Project; the rs13207108 genotypes and MAP3K4 mRNA expression levels (E) the normal pancreatic tissues (n=305, P=0.69) from the GTEx Project and (F) in the transformed lymphoblastoid cells (n=373, P=0.814) from the 1000 Genomes Project. (G-I) Location and functional prediction of the three novel SNPs in the ENCODE Project. The H3K4Me1, H3K4Me3 and H3K27Ac tracks are associated with the enhancer and promoter regions. The Txn Factor ChIP tracks show regions of transcription factor binding of DNA; Effect analyses of the three SNPs on TF motifs predicated by using HOCOMOCO-11 collection from the PERFECTOS-APE online tools: (J) the rs76692125 A allele may alter the predicted TF-binding motifs for TBP; (K) the rs11668724 A allele may alter the predicted TF-binding motifs for HNF4A and (L) the rs13207108 T allele may alter the predicted TF-binding motif for SRY. The putative binding sites in promoter predicted by PROMO online tools: (M) the TBP binding site in PIK3C3 promoter; (N) the HNF4A binding site in INSR promoter and (O) the SRY binding sites in MAP3K4 promoter. Abbreviations: SNP, single nucleotide polymorphism; GTEx, Genotype-Tissue Expression; ChIP, Chromatin Immunoprecipitation; Txn Factor ChIP, Transcription Factor ChIP-seq from ENCODE; TF, transcriptional factor.

We assessed potential functions of the three independent PanC risk-associated SNPs by using in silico bioinformatics tools (Table S5). These three SNPs are located in intron regions, where active histone marks are enriched, including histone H3 mono methyl K4 (H3K4me1) and histone H3 lysine 27 acetylation (H3K27ac) (Figure 3G-I). We then evaluated the effects of these SNPs on the predicted transcription factor binding sites using the PERFECTOS-APE [38,39] and the PROMO online tools [40,41]. Several transcription factors, including TBP, HNF4A and SRY, are predicted to bind to DNA fragments containing these SNPs with different affinities (Figure 3J-O).

Furthermore, we performed differential mRNA expression analysis of PIK3C3, INSR and MAP3K4 between pancreatic tumors and adjacent normal tissues and overall survival analysis from the TCGA database by using the UALCAN [42,43] and GEPIA [44,45] web-portal tools. The mRNA expression levels of MAP3K4, but not PIK3C3 and INSR, were significantly decreased in pancreatic tumors (P=0.047) (Figure S4D-F), compared with that of normal pancreatic tissues. High mRNA expression levels of MAP3K4, but not PIK3C3 and INSR, were associated with a better survival of PanC patients, compared with low mRNA expression levels (P=0.045) (Figure S4G-I).

Mutation analysis

Finally, we assessed the mutation status of PIK3C3, INSR and MAP3K4 in PanC tissues using the cBioPortal database for Cancer Genomics [46,47]. The three genes had low somatic mutation rates in PanC: PIK3C3 [0.56% (1/179), 0.52% (2/383), 0% (0/99) and 0% (0/109)]; INSR [1.12% (2/179), 0.26% (1/383), 0% (0/99) and 0% (0/109)]; and MAP3K4 [0.56% (1/179), 0% (0/383), 0% (0/99), and 0% (0/109)] from TCGA PanCan 2018, Queensland Centre of Medical Genomics 2016, the International Cancer Genome Consortium and The University of Texas Southwest studies, respectively (Figure S5A-C).

Discussion

In the present study, we identified three independent and potentially functional SNPs (i.e., PIK3C3 rs76692125 G>A, INSR rs11668724 G>A and MAP3K4 rs13207108 C>T) that were associated with PanC risk in populations of European ancestry. The risk-associated rs76692125 A allele was correlated with decreased PIK3C3 mRNA expression levels, while the protective-associated rs11668724 A allele was correlated with increased INSR mRNA expression levels. Because the ATM pathway-related genes play important roles in diverse biological processes and pathological disorders, such as DNA damage repair, autophagy regulation, metabolic disorders and cancers, including PanC [11-17], these genotype-mRNA expression correlations provided biological plausibility for the observed genotype-associated risk of PanC.

PIK3C3 is located at chromosome region 18q12.3 and encodes a phosphatidylinositol 3-kinase catalytic subunit type 3 (PIK3C3) protein, the member of the class III PI3K family that plays a positive role in autophagy through complex formation by generating phosphatidylinositol 3P. We found that the PIK3C3 rs76692125A risk allele was significantly associated with an increased risk of PanC, possibly by a mechanism of decreasing PIK3C3 mRNA expression levels. Studies showed that activation of ATM contributed to phosphorylation of Beclin1, followed by the complex formation with PIK3C3 [15,16]. At the early stage of tumorigenesis, an inhibition of autophagy might contribute to continuous growth of precancerous cells, and the autophagy might act as a suppressor [49]. Although no reported study has demonstrated the role of PIK3C3 in malignant transformation, the autophagy-associated beclin1 gene as part of the PIK3C3 complex inhibit tumorigenesis in mice and expresses at decreased levels in human tumors [50,51]. Another study indicated that the combined loss of autophagy and p53 dramatically promoted progression from early pancreatic Intraepithelial neoplastic lesions towards adenocarcinoma [52]. According to the functional analyses, the SNP rs76692115 may participate in modulating the interaction with DNA binding motifs and thus altering the PIK3C3 expression, which may further affect the autophagy activity to increase the risk of PanC. However, this speculation deserves further investigation and additional functional studies.

INSR is located at chromosome region 19p13.2 and encodes an insulin receptor (INSR) that plays an essential role in the insulin signaling. In the present study, we found that the INSR rs11668724 A protective allele was associated with a significantly decreased risk for PanC and also was correlated with an increased mRNA expression level of INSR in normal pancreatic tissues. Because the mutation rate of INSR in PanC was as low as 1.12%, the expression levels of INSR in pancreatic tissues were likely to be effected by SNPs in the gene. For example, the INSR rs11668724 was predicted to change the DNA binding capacities with the HNF4A motif that may be associated with transcriptional regulation of INSR expression and beta cell function in pancreatic tissues [53,54]. One study identified that the pancreatic beta cell INSR knockout mice or cell lines lost insulin secretion, suggesting that INSRs in beta cells may play a critical role in beta cell dysfunction that is associated with insulin resistance and type 2 diabetes [55-57]. Epidemiological evidence also points to a link between some syndromes (e.g., insulin resistance and type 2 diabetes) and PanC risk [3]. Therefore, these results suggest that the INSR SNP rs11668724 may be involved in dysfunction of beta cells by regulating the INSR mRNA expression levels in the pancreas, leading to PanC risk. In addition, several previous GWASs identified that the INSR SNP rs7248104, which was in moderate linkage disequilibrium (LD) with rs11668724 (r2=0.22), was associated with elevated blood triglycerides and fasting glucose levels [58] and an increased risk of thyroid cancer [59]. However, all these were of an indirect evidence, and more specific investigations are still required to investigate the mechanisms underlying the observed associations.

MAP3K4 is located at chromosome region 6q26 and encodes a mitogen-activated protein kinase kinase kinase 4 (MAP3K4) that is a member of the mitogen-activated protein kinase superfamily. In the present study, we identified that the MAP3K4 rs13207108 T protective allele was associated with a significantly decreased risk of PanC, while a suggestive risk locus (PARK2 rs3016539) was also identified at 6q26 in a PanC GWAS performed in a Japanese population [60]. Although there is no LD between rs3016539 and rs13207108 identified in the present study, it is likely indicative that the 6q26 region may be a potential PanC susceptibility locus in both populations of European ancestry and Japanese populations.

MAP3K4 acts as a mediator in the environmental stress-induced activation of the p38 and JNK signaling to regulate cell proliferation, apoptosis, and migration [61]. The MAP3K4 genes have been reported to be associated with a variety of cancers, including PanC [62-64]. Studies have identified that MAP3K4 may act as a suppressor in some cancers [63,64]. One study reported that continuous activation of p38 through the Smad/GADD45β/MAP3K4 cascade might contribute to the tumor-suppressive effect in pancreatic cells [63]. This suppressor role has been supported by human intrahepatic cholangiocarcinoma [64]. Although no significant eQTL result was observed for this genetic variant, the data from TCGA study indicated that the PanC patients had lower MAP3K4 expression levels that were associated with a worse survival. Taken together, multiple levels of evidence suggest that MAP3K4 may be a candidate tumor suppressor for PanC.

Although the present study identified three potential susceptibility loci for PanC risk, it has some limitations. First, all the genotyping data used in the present study were exclusively from populations of European ancestry. Therefore, the findings may not be generalized to other ethnic populations. Secondly, except for age and sex, other clinical variables (e.g. treatment history, family history, smoking status, alcohol intake) were not available for further adjustment and stratified analyses. Finally, additional functional studies are required to verify the hypothesized biological mechanisms underlying our observed associations.

In summary, the present ATM pathway-based study analyzed genetic variants to be associated with PanC risk in populations of European ancestry. We identified three novel independent and potentially functional SNPs (i.e., PIK3C3 rs76692125 G>A, INSR rs11668724 G>A and MAP3K4 rs13207108 C>T) that were significantly associated with PanC risk. Additional larger population-based and functional studies are needed to validate our findings.

PanScan

The PanScan study was funded in whole or in part with federal funds from the National Cancer Institute (NCI) and National Institutes of Health (NIH), contract number HHSN261200800001E. Additional support was received from NIH/NCI K07CA140790, the American Society of Clinical Oncology Conquer Cancer Foundation, the Howard Hughes Medical Institute, the Lustgarten Foundation, the Robert T. and Judith B. Hale Fund for Pancreatic Cancer Research and Promises for Purple. A full list of acknowledgments for each participating study is provided in the Supplementary Note of the manuscript with PubMed ID: 25086665. The dbGaP accession number for this study is phs000206.v5.p3.

PanC4

The cases and controls for the PanC4 study were drawn from the following studies: Johns Hopkins National Familial Pancreas Tumor Registry, Mayo Clinic Biospecimen Resource for Pancreas Research, Ontario Pancreas Cancer Study (OPCS), Yale University, MD Anderson Case Control Study, Queensland Pancreatic Cancer Study, University of California San Francisco Molecular Epidemiology of Pancreatic Cancer Study, International Agency of Cancer Research and Memorial Sloan Kettering Cancer Center. The PanC4 study was supported by NCI R01CA154823. Genotyping services were provided by the Center for Inherited Disease Research (CIDR). CIDR was funded by a federal contract from the NIH to The Johns Hopkins University, contract number HHSN2682011000111. The dbGaP accession number for this study is phs000648.v1.p1.

TCGA

The results published here are in whole or part based on data generated by the TCGA Project established by the NCI and the National Human Genome Research Institute (NHGRI). Information about TCGA and the investigators and institutions that constitute TCGA Research Network can be found at “http://cancergenome.nih.gov”. The TCGA SNP data analyzed here are requested through dbGaP, accession number phs000178.v1.p1.

Acknowledgements

The authors acknowledge all the participants of the PanScan Study, PanC4 Study and the dbGaP repository for providing the cancer genotyping datasets that are described as follows. Qing-yi Wei was supported by the P30 Cancer Center Support Grant from the Duke Cancer Institute (Grant ID: NIH CA014236). Ling-Ling Zhao was supported by funds from The First Hospital of Jilin University in China.

Disclosure of conflict of interest

None.

Supporting Information

ajcr0010-2128-f4.pdf (1.3MB, pdf)

References

  • 1.Howlader N, Noone AM, Krapcho M, Miller D, Brest A, Yu M, Ruhl J, Tatalovich Z, Mariotto A, Lewis DR, Chen HS, Feuer EJ, Cronin KA, editors. National Cancer Institute. SEER Cancer Statistics Review, 1975-2016. Bethesda, MD: https://seer.cancer.gov/csr/1975_2016/, based on November 2018 SEER data submission, posted to the SEER web site, April 2019. [Google Scholar]
  • 2.Rahib L, Smith BD, Aizenberg R, Rosenzweig AB, Fleshman JM, Matrisian LM. Projecting cancer incidence and deaths to 2030: the unexpected burden of thyroid, liver, and pancreas cancers in the United States. Cancer Res. 2014;74:2913–2921. doi: 10.1158/0008-5472.CAN-14-0155. [DOI] [PubMed] [Google Scholar]
  • 3.Rawla P, Sunkara T, Gaduputi V. Epidemiology of pancreatic cancer: global trends, etiology and risk factors. World J Oncol. 2019;10:10–27. doi: 10.14740/wjon1166. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Amundadottir L, Kraft P, Stolzenberg-Solomon RZ, Fuchs CS, Petersen GM, Arslan AA, Bueno-de-Mesquita HB, Gross M, Helzlsouer K, Jacobs EJ, LaCroix A, Zheng W, Albanes D, Bamlet W, Berg CD, Berrino F, Bingham S, Buring JE, Bracci PM, Canzian F, Clavel-Chapelon F, Clipp S, Cotterchio M, de Andrade M, Duell EJ, Fox JW Jr, Gallinger S, Gaziano JM, Giovannucci EL, Goggins M, González CA, Hallmans G, Hankinson SE, Hassan M, Holly EA, Hunter DJ, Hutchinson A, Jackson R, Jacobs KB, Jenab M, Kaaks R, Klein AP, Kooperberg C, Kurtz RC, Li D, Lynch SM, Mandelson M, McWilliams RR, Mendelsohn JB, Michaud DS, Olson SH, Overvad K, Patel AV, Peeters PHM, Rajkovic A, Riboli E, Risch HA, Shu XO, Thomas G, Tobias GS, Trichopoulos D, Van Den Eeden SK, Virtamo J, Wactawski-Wende J, Wolpin BM, Yu H, Yu K, Zeleniuch-Jacquotte A, Chanock SJ, Hartge P, Hoover RN. Genome-wide association study identifies variants in the ABO locus associated with susceptibility to pancreatic cancer. Nat Genet. 2009;41:986–990. doi: 10.1038/ng.429. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Petersen GM, Amundadottir L, Fuchs CS, Kraft P, Stolzenberg-Solomon RZ, Jacobs KB, Arslan AA, Bueno-de-Mesquita HB, Gallinger S, Gross M, Helzlsouer K, Holly EA, Jacobs EJ, Klein AP, LaCroix A, Li D, Mandelson MT, Olson SH, Risch HA, Zheng W, Albanes D, Bamlet WR, Berg CD, Boutron-Ruault MC, Buring JE, Bracci PM, Canzian F, Clipp S, Cotterchio M, de Andrade M, Duell EJ, Gaziano JM, Giovannucci EL, Goggins M, Hallmans G, Hankinson SE, Hassan M, Howard B, Hunter DJ, Hutchinson A, Jenab M, Kaaks R, Kooperberg C, Krogh V, Kurtz RC, Lynch SM, McWilliams RR, Mendelsohn JB, Michaud DS, Parikh H, Patel AV, Peeters PH, Rajkovic A, Riboli E, Rodriguez L, Seminara D, Shu XO, Thomas G, Tjønneland A, Tobias GS, Trichopoulos D, Van Den Eeden SK, Virtamo J, Wactawski-Wende J, Wang Z, Wolpin BM, Yu H, Yu K, Zeleniuch-Jacquotte A, Fraumeni JF Jr, Hoover RN, Hartge P, Chanock SJ. A genome-wide association study identifies pancreatic cancer susceptibility loci on chromosomes 13q22.1, 1q32.1 and 5p15.33. Nat Genet. 2010;42:224–8. doi: 10.1038/ng.522. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Wolpin BM, Rizzato C, Kraft P, Kooperberg C, Petersen GM, Wang Z, Arslan AA, Beane-Freeman L, Bracci PM, Buring J, Canzian F, Duell EJ, Gallinger S, Giles GG, Goodman GE, Goodman PJ, Jacobs EJ, Kamineni A, Klein AP, Kolonel LN, Kulke MH, Li D, Malats N, Olson SH, Risch HA, Sesso HD, Visvanathan K, White E, Zheng W, Abnet CC, Albanes D, Andreotti G, Austin MA, Barfield R, Basso D, Berndt SI, Boutron-Ruault MC, Brotzman M, Büchler MW, Bueno-de-Mesquita HB, Bugert P, Burdette L, Campa D, Caporaso NE, Capurso G, Chung C, Cotterchio M, Costello E, Elena J, Funel N, Gaziano JM, Giese NA, Giovannucci EL, Goggins M, Gorman MJ, Gross M, Haiman CA, Hassan M, Helzlsouer KJ, Henderson BE, Holly EA, Hu N, Hunter DJ, Innocenti F, Jenab M, Kaaks R, Key TJ, Khaw KT, Klein EA, Kogevinas M, Krogh V, Kupcinskas J, Kurtz RC, LaCroix A, Landi MT, Landi S, Le Marchand L, Mambrini A, Mannisto S, Milne RL, Nakamura Y, Oberg AL, Owzar K, Patel AV, Peeters PHM, Peters U, Pezzilli R, Piepoli A, Porta M, Real FX, Riboli E, Rothman N, Scarpa A, Shu XO, Silverman DT, Soucek P, Sund M, Talar-Wojnarowska R, Taylor PR, Theodoropoulos GE, Thornquist M, Tjønneland A, Tobias GS, Trichopoulos D, Vodicka P, Wactawski-Wende J, Wentzensen N, Wu C, Yu H, Yu K, Zeleniuch-Jacquotte A, Hoover R, Hartge P, Fuchs C, Chanock SJ, Stolzenberg-Solomon RS, Amundadottir LT. Genome-wide association study identifies multiple susceptibility loci for pancreatic cancer. Nat Genet. 2014;46:994–1000. doi: 10.1038/ng.3052. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Childs EJ, Mocci E, Campa D, Bracci PM, Gallinger S, Goggins M, Li D, Neale RE, Olson SH, Scelo G, Amundadottir LT, Bamlet WR, Bijlsma MF, Blackford A, Borges M, Brennan P, Brenner H, Bueno-de-Mesquita HB, Canzian F, Capurso G, Cavestro GM, Chaffee KG, Chanock SJ, Cleary SP, Cotterchio M, Foretova L, Fuchs C, Funel N, Gazouli M, Hassan M, Herman JM, Holcatova I, Holly EA, Hoover RN, Hung RJ, Janout V, Key TJ, Kupcinskas J, Kurtz RC, Landi S, Lu L, Malecka-Panas E, Mambrini A, Mohelnikova-Duchonova B, Neoptolemos JP, Oberg AL, Orlow I, Pasquali C, Pezzilli R, Rizzato C, Saldia A, Scarpa A, Stolzenberg-Solomon RZ, Strobel O, Tavano F, Vashist YK, Vodicka P, Wolpin BM, Yu H, Petersen GM, Risch HA, Klein AP. Common variation at 2p13.3, 3q29, 7p13 and 17q25.1 associated with susceptibility to pancreatic cancer. Nat Genet. 2015;47:911. doi: 10.1038/ng.3341. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Amundadottir LT. Pancreatic cancer genetics. Int J Biol Sci. 2016;12:314–325. doi: 10.7150/ijbs.15001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Klein AP, Wolpin BM, Risch HA, Stolzenberg-Solomon RZ, Mocci E, Zhang M, Canzian F, Childs EJ, Hoskins JW, Jermusyk A. Genome-wide meta-analysis identifies five new susceptibility loci for pancreatic cancer. Nat Commun. 2018;9:1–11. doi: 10.1038/s41467-018-02942-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Cantor RM, Lange K, Sinsheimer JS. Prioritizing GWAS results: a review of statistical methods and recommendations for their application. Am J Hum Genet. 2010;86:6–22. doi: 10.1016/j.ajhg.2009.11.017. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Bartkova J, Hořejší Z, Koed K, Krämer A, Tort F, Zieger K, Guldberg P, Sehested M, Nesland JM, Lukas C, Ørntoft T, Lukas J, Bartek J. DNA damage response as a candidate anti-cancer barrier in early human tumorigenesis. Nature. 2005;434:864–870. doi: 10.1038/nature03482. [DOI] [PubMed] [Google Scholar]
  • 12.Armstrong SA, Schultz CW, Azimi-Sadjadi A, Brody JR, Pishvaian MJ. ATM dysfunction in pancreatic adenocarcinoma and associated therapeutic implications. molecular cancer Therapeutics. 2019;18:1899. doi: 10.1158/1535-7163.MCT-19-0208. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Sahu RP, Batra S, Srivastava SK. Activation of ATM/Chk1 by curcumin causes cell cycle arrest and apoptosis in human pancreatic cancer cells. Br J Cancer. 2009;100:1425–1433. doi: 10.1038/sj.bjc.6605039. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Russell R, Perkhofer L, Liebau S, Lin Q, Lechel A, Feld FM, Hessmann E, Gaedcke J, Güthle M, Zenke M, Hartmann D, von Figura G, Weissinger SE, Rudolph KL, Möller P, Lennerz JK, Seufferlein T, Wagner M, Kleger A. Loss of ATM accelerates pancreatic cancer formation and epithelial-mesenchymal transition. Nat Commun. 2015;6:7677–7677. doi: 10.1038/ncomms8677. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Stagni V, Cirotti C, Barilà D. Ataxia-telangiectasia mutated kinase in the control of oxidative stress, mitochondria, and autophagy in cancer: a maestro with a large orchestra. Front Oncol. 2018;8:73–73. doi: 10.3389/fonc.2018.00073. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Maertin S, Elperin JM, Lotshaw E, Sendler M, Speakman SD, Takakura K, Reicher BM, Mareninova OA, Grippo PJ, Mayerle J, Lerch MM, Gukovskaya AS. Roles of autophagy and metabolism in pancreatic cancer cell adaptation to environmental challenges. Am J Physiol Gastrointest Liver Physiol. 2017;313:G524–G536. doi: 10.1152/ajpgi.00138.2017. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Ditch S, Paull TT. The ATM protein kinase and cellular redox signaling: beyond the DNA damage response. Trends Biochem Sci. 2012;37:15–22. doi: 10.1016/j.tibs.2011.10.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Borgida AE, Ashamalla S, Al-Sukhni W, Rothenmund H, Urbach D, Moore M, Cotterchio M, Gallinger S. Management of pancreatic adenocarcinoma in Ontario, Canada: a population-based study using novel case ascertainment. Can J Surg. 2011;54:54. doi: 10.1503/cjs.026409. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.McWilliams RR, Bamlet WR, De Andrade M, Rider DN, Cunningham JM, Petersen GM. Nucleotide excision repair pathway polymorphisms and pancreatic cancer risk: evidence for role of MMS19L. Cancer Epidemiol Biomarkers Prev. 2009;18:1295–1302. doi: 10.1158/1055-9965.EPI-08-1109. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Liberzon A, Birger C, Thorvaldsdóttir H, Ghandi M, Mesirov JP, Tamayo P. The molecular signatures database hallmark gene set collection. Cell Syst. 2015;1:417–425. doi: 10.1016/j.cels.2015.12.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Belinky F, Nativ N, Stelzer G, Zimmerman S, Iny Stein T, Safran M, Lancet D. PathCards: multi-source consolidation of human biological pathways. Database. 2015;2015 doi: 10.1093/database/bav006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Howie BN, Donnelly P, Marchini J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 2009;5:e1000529. doi: 10.1371/journal.pgen.1000529. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, Maller J, Sklar P, De Bakker PIW, Daly MJ. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81:559–575. doi: 10.1086/519795. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Begum F, Ghosh D, Tseng GC, Feingold E. Comprehensive literature review and statistical considerations for GWAS meta-analysis. Nucleic Acids Res. 2012;40:3777–3784. doi: 10.1093/nar/gkr1255. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Higgins JPT, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ. 2003;327:557–560. doi: 10.1136/bmj.327.7414.557. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Benjamini Y, Drai D, Elmer G, Kafkafi N, Golani I. Controlling the false discovery rate in behavior genetics research. Behav Brain Res. 2001;125:279–284. doi: 10.1016/s0166-4328(01)00297-2. [DOI] [PubMed] [Google Scholar]
  • 27.Duan B, Hu J, Liu H, Wang Y, Li H, Liu S, Xie J, Owzar K, Abbruzzese J, Hurwitz H. Genetic variants in the platelet-derived growth factor subunit B gene associated with pancreatic cancer risk. Int J Cancer. 2018;142:1322–1331. doi: 10.1002/ijc.31171. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Feng Y, Liu H, Duan B, Liu Z, Abbruzzese J, Walsh KM, Zhang X, Wei Q. Potential functional variants in SMC2 and TP53 in the AURORA pathway genes and risk of pancreatic cancer. Carcinogenesis. 2019;40:521–528. doi: 10.1093/carcin/bgz029. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Yang W, Liu H, Duan B, Xu X, Carmody D, Luo S, Walsh KM, Abbruzzese JL, Zhang X, Chen X. Three novel genetic variants in NRF2 signaling pathway genes are associated with pancreatic cancer risk. Cancer Sci. 2019;110:2022. doi: 10.1111/cas.14017. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Xu X, Qian D, Liu H, Cruz D, Luo S, Walsh KM, Abbruzzese JL, Zhang X, Wei Q. Genetic variants in the liver kinase B1-AMP-activated protein kinase pathway genes and pancreatic cancer risk. Mol Carcinog. 2019;58:1338–1348. doi: 10.1002/mc.23018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Pruim RJ, Welch RP, Sanna S, Teslovich TM, Chines PS, Gliedt TP, Boehnke M, Abecasis GR, Willer CJ. LocusZoom: regional visualization of genome-wide association scan results. Bioinformatics. 2010;26:2336–2337. doi: 10.1093/bioinformatics/btq419. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Xu Z, Taylor JA. SNPinfo: integrating GWAS and candidate gene information into functional SNP selection for genetic association studies. Nucleic Acids Res. 2009;37:W600–W605. doi: 10.1093/nar/gkp290. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Boyle AP, Hong EL, Hariharan M, Cheng Y, Schaub MA, Kasowski M, Karczewski KJ, Park J, Hitz BC, Weng S. Annotation of functional variation in personal genomes using RegulomeDB. Genome Res. 2012;22:1790–1797. doi: 10.1101/gr.137323.112. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Ward LD, Kellis M. HaploReg v4: systematic mining of putative causal variants, cell types, regulators and target genes for human complex traits and disease. Nucleic Acids Res. 2015;44:D877–D881. doi: 10.1093/nar/gkv1340. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Consortium GT. The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science. 2015;348:648–660. doi: 10.1126/science.1262110. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Lappalainen T, Sammeth M, Friedländer MR, Ac‘t Hoen P, Monlong J, Rivas MA, Gonzalez-Porta M, Kurbatova N, Griebel T, Ferreira PG. Transcriptome and genome sequencing uncovers functional variation in humans. Nature. 2013;501:506. doi: 10.1038/nature12531. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Cancer Genome Atlas Research Network. Comprehensive molecular profiling of lung adenocarcinoma. Nature. 2014;511:543–550. doi: 10.1038/nature13385. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Vorontsov IE, Kulakovskiy IV, Khimulya G, Nikolaeva DD, Makeev VJ. “PERFECTOS-APE-predicting regulatory functional effect of SNPs by approximate P-value estimation”. SCITEPRESS) :102–108. [Google Scholar]
  • 39.PERFECTOS-APE. http://opera.autosome.ru/perfectosape/
  • 40.Messeguer X, Escudero R, Farré D, Nuñez O, Martínez J, Albà MM. PROMO: detection of known transcription regulatory elements using species-tailored searches. Bioinformatics. 2002;18:333–334. doi: 10.1093/bioinformatics/18.2.333. [DOI] [PubMed] [Google Scholar]
  • 41.PROMO. http://alggen.lsi.upc.es/cgi-bin/promo_v3/promo/promoinit.cgi?dirDB=TF_8.3.
  • 42.Chandrashekar DS, Bashel B, Balasubramanya SAH, Creighton CJ, Ponce-Rodriguez I, Chakravarthi BVSK, Varambally S. UALCAN: a portal for facilitating tumor subgroup gene expression and survival analyses. Neoplasia. 2017;19:649–658. doi: 10.1016/j.neo.2017.05.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.UALCAN. http://ualcan.path.uab.edu/index.html.
  • 44.Tang Z, Li C, Kang B, Gao G, Li C, Zhang Z. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res. 2017;45:W98–W102. doi: 10.1093/nar/gkx247. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.GEPIA. http://gepia.cancer-pku.cn/detail.php?gene.
  • 46.Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, Jacobsen A, Byrne CJ, Heuer ML, Larsson E, Antipin Y, Reva B, Goldberg AP, Sander C, Schultz N. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012;2:401–404. doi: 10.1158/2159-8290.CD-12-0095. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 47.cBioPortal for Cancer Genomics. 2015. https://www.cbioportal.org/
  • 48.Walsh N, Zhang H, Hyland PL, Yang Q, Mocci E, Zhang M, Childs EJ, Collins I, Wang Z, Arslan AA, Beane-Freeman L, Bracci PM, Brennan P, Canzian F, Duell EJ, Gallinger S, Giles GG, Goggins M, Goodman GE, Goodman PJ, Hung RJ, Kooperberg C, Kurtz RC, Malats N, LeMarchand L, Neale RE, Olson SH, Scelo G, Shu XO, Van Den Eeden SK, Visvanathan K, White E, Zheng W PanScan and PanC4 consortia. Albanes D, Andreotti G, Babic A, Bamlet WR, Berndt SI, Borgida A, Boutron-Ruault MC, Brais L, Brennan P, Bueno-de-Mesquita B, Buring J, Chaffee KG, Chanock S, Cleary S, Cotterchio M, Foretova L, Fuchs C, M Gaziano JM, Giovannucci E, Goggins M, Hackert T, Haiman C, Hartge P, Hasan M, Helzlsouer KJ, Herman J, Holcatova I, Holly EA, Hoover R, Hung RJ, Janout V, Klein EA, Kurtz RC, Laheru D, Lee IM, Lu L, Malats N, Mannisto S, Milne RL, Oberg AL, Orlow I, Patel AV, Peters U, Porta M, Real FX, Rothman N, Sesso HD, Severi G, Silverman D, Strobel O, Sund M, Thornquist MD, Tobias GS, Wactawski-Wende J, Wareham N, Weiderpass E, Wentzensen N, Wheeler W, Yu H, Zeleniuch-Jacquotte A, Kraft P, Li D, Jacobs EJ, Petersen GM, Wolpin BM, Risch HA, Amundadottir LT, Yu K, Klein AP. Agnostic pathway/gene set analysis of genome-wide association data identifies associations for pancreatic cancer. J Natl Cancer Inst. 2019;111:557–567. doi: 10.1093/jnci/djy155. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Shintani T, Klionsky DJ. Autophagy in health and disease: a double-edged sword. Science. 2004;306:990–995. doi: 10.1126/science.1099993. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Liang XH, Jackson S, Seaman M, Brown K, Kempkes B, Hibshoosh H, Levine B. Induction of autophagy and inhibition of tumorigenesis by beclin 1. Nature. 1999;402:672–676. doi: 10.1038/45257. [DOI] [PubMed] [Google Scholar]
  • 51.Galluzzi L, Pietrocola F, Bravo-San Pedro JM, Amaravadi RK, Baehrecke EH, Cecconi F, Codogno P, Debnath J, Gewirtz DA, Karantza V. Autophagy in malignant transformation and cancer progression. EMBO J. 2015;34:856–880. doi: 10.15252/embj.201490784. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Rosenfeldt MT, O’Prey J, Morton JP, Nixon C, MacKay G, Mrowinska A, Au A, Rai TS, Zheng L, Ridgway R. p53 status determines the role of autophagy in pancreatic tumour development. Nature. 2013;504:296–300. doi: 10.1038/nature12865. [DOI] [PubMed] [Google Scholar]
  • 53.Payankaulam S, Raicu AM, Arnosti DN. Transcriptional regulation of INSR, the insulin receptor gene. Genes. 2019;10:984. doi: 10.3390/genes10120984. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Hansen SK, Párrizas M, Jensen ML, Pruhova S, Ek J, Boj SF, Johansen A, Maestro MA, Rivera F, Eiberg H, Andel M, Lebl J, Pedersen O, Ferrer J, Hansen T. Genetic evidence that HNF-1alpha-dependent transcriptional control of HNF-4alpha is essential for human pancreatic beta cell function. J Clin Invest. 2002;110:827–833. doi: 10.1172/JCI15085. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Okada T, Liew CW, Hu J-J, Hinault C, Michael M, Krützfeldt J, Yin C, Holzenberger M, Stoffel MH, Kulkarni R. Insulin receptors in β-cells are critical for islet compensatory growth response to insulin resistance. Proc Natl Acad Sci U S A. 2007;104:8977–8982. doi: 10.1073/pnas.0608703104. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 56.Kulkarni RN, Brüning JC, Winnay JN, Postic C, Magnuson MA, Kahn CR. Tissue-specific knockout of the insulin receptor in pancreatic β cells creates an insulin secretory defect similar to that in type 2 diabetes. Cell. 1999;96:329–339. doi: 10.1016/s0092-8674(00)80546-2. [DOI] [PubMed] [Google Scholar]
  • 57.Wang J, Gu W, Chen C. Knocking down insulin receptor in pancreatic beta cell lines with lentiviral-small hairpin RNA reduces glucose-stimulated insulin secretion via decreasing the gene expression of insulin, GLUT2 and Pdx1. Int J Mol Sci. 2018;19:985. doi: 10.3390/ijms19040985. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 58.Willer CJ, Schmidt EM, Sengupta S, Peloso GM, Gustafsson S, Kanoni S, Ganna A, Chen J, Buchkovich ML, Mora S. Discovery and refinement of loci associated with lipid levels. Nat Genet. 2013;45:1274. doi: 10.1038/ng.2797. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.Son HY, Hwangbo Y, Yoo SK, Im SW, Kwak SJ, Park MS, Kwak SH, Cho SW, Ryu JS, Kim J. Genome-wide association and expression quantitative trait loci studies identify multiple susceptibility loci for thyroid cancer. Nat Commun. 2017;8:15966. doi: 10.1038/ncomms15966. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.Low SK, Kuchiba A, Zembutsu H, Saito A, Takahashi A, Kubo M, Daigo Y, Kamatani N, Chiku S, Totsuka H. Genome-wide association study of pancreatic cancer in Japanese population. PLoS One. 2010;5:e11824. doi: 10.1371/journal.pone.0011824. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61.Takekawa M, Posas F, Saito H. A human homolog of the yeast Ssk2/Ssk22 MAP kinase kinase kinases, MTK1, mediates stress-induced activation of the p38 and JNK pathways. EMBO J. 1997;16:4973–4982. doi: 10.1093/emboj/16.16.4973. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 62.Liang Q, Yao X, Tang S, Zhang J, Yau TO, Li X, Tang CM, Kang W, Lung RWM, Li JW. Integrative identification of Epstein-Barr virus-associated mutations and epigenetic alterations in gastric cancer. Gastroenterology. 2014;147:1350–1362. doi: 10.1053/j.gastro.2014.08.036. [DOI] [PubMed] [Google Scholar]
  • 63.Takekawa M, Tatebayashi K, Itoh F, Adachi M, Imai K, Saito H. Smad-dependent GADD45β expression mediates delayed activation of p38 MAP kinase by TGF-β. EMBO J. 2002;21:6473–6482. doi: 10.1093/emboj/cdf643. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 64.Yang LX, Gao Q, Shi JY, Wang ZC, Zhang Y, Gao PT, Wang XY, Shi YH, Ke AW, Shi GM. Mitogen-activated protein kinase kinase kinase 4 deficiency in intrahepatic cholangiocarcinoma leads to invasive growth and epithelial-mesenchymal transition. Hepatology. 2015;62:1804–1816. doi: 10.1002/hep.28149. [DOI] [PubMed] [Google Scholar]

Associated Data

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

Supplementary Materials

ajcr0010-2128-f4.pdf (1.3MB, pdf)

Articles from American Journal of Cancer Research are provided here courtesy of e-Century Publishing Corporation

RESOURCES