Functional genomics annotation of a statistical epistasis network associated with bladder cancer susceptibility
© Hu et al.; licensee BioMed Central Ltd. 2014
Received: 3 December 2013
Accepted: 5 April 2014
Published: 11 April 2014
Several different genetic and environmental factors have been identified as independent risk factors for bladder cancer in population-based studies. Recent studies have turned to understanding the role of gene-gene and gene-environment interactions in determining risk. We previously developed the bioinformatics framework of statistical epistasis networks (SEN) to characterize the global structure of interacting genetic factors associated with a particular disease or clinical outcome. By applying SEN to a population-based study of bladder cancer among Caucasians in New Hampshire, we were able to identify a set of connected genetic factors with strong and significant interaction effects on bladder cancer susceptibility.
To support our statistical findings using networks, in the present study, we performed pathway enrichment analyses on the set of genes identified using SEN, and found that they are associated with the carcinogen benzo[a]pyrene, a component of tobacco smoke. We further carried out an mRNA expression microarray experiment to validate statistical genetic interactions, and to determine if the set of genes identified in the SEN were differentially expressed in a normal bladder cell line and a bladder cancer cell line in the presence or absence of benzo[a]pyrene. Significant nonrandom sets of genes from the SEN were found to be differentially expressed in response to benzo[a]pyrene in both the normal bladder cells and the bladder cancer cells. In addition, the patterns of gene expression were significantly different between these two cell types.
The enrichment analyses and the gene expression microarray results support the idea that SEN analysis of bladder in population-based studies is able to identify biologically meaningful statistical patterns. These results bring us a step closer to a systems genetic approach to understanding cancer susceptibility that integrates population and laboratory-based studies.
KeywordsEpistasis Gene-gene interactions Statistical epistasis networks Benzo[a]pyrene Gene-drug association Bladder cancer
Tobacco consumption has been implicated as the most relevant risk factor for the development of bladder cancer. Among various carcinogens identified in tobacco smoke, the polycyclic aromatic hydrocarbons (PAHs) are well characterized and their risk associations with bladder cancer are well studied [1–4]. In addition to these strong environmental risk factors, recent genome-wide association studies have identified several highly significant genetic factors with small effects [5, 6]. Some of these have been shown to modify the effects of smoking on risk of bladder cancer .
Most existing genetic association studies have focused on the independent effects of individual genes. That is, they have by design ignored the context of human ecology and the extensive variability in the human genome. As a result, much of the heritability of common human diseases such as bladder cancer remains unexplained. Multiple approaches have been proposed to account for this missing heritability including sequencing to identify causative rare variants and advanced analytical strategies to characterize those genetic effects that are context-dependent . The characterization of gene-gene interaction effects, i.e. epistasis, provides a promising research avenue to understand the complex genetic architecture underlying common human diseases in population-based studies [9–11]. Previously, we developed a network-based framework, statistical epistasis networks (SEN), to characterize the global epistatic interaction space of bladder cancer [12, 13]. SEN were able to identify a large set of interacting single-nucleotide polymorphisms (SNPs) that had strong associations with bladder cancer. In addition, these SNPs were organized in the form of a connected network structure that was highly significant and nonrandom compared to the null distribution of networks generated using permutation tests.
The goal of the present study was to determine whether the SEN identified for bladder cancer have any biological relevance in the context of the most prevalent risk factor. Because the majority of bladder cancer cases in the United States can be attributed to tobacco exposure, we hypothesized that many of the SEN genes would have a biological role related to biological response to PAH exposure. Here, we applied pathway enrichment analysis of the bladder cancer SEN and found that it is enriched for genes that have strong associations with benzo[a]pyrene. We carried out a gene expression microarray experiment in cell lines from normal bladder and bladder cancer cells treated with and without benzo[a]pyrene. The goal of this experiment was to provide a functional genomics annotation of the SEN.
Methods and results
The 29 genes identified in our previous genetic interaction study on bladder cancer using SEN
Entrez Gene ID
Aryl-hydrocarbon receptor repressor
Aldo-keto reductase family 1, member C3
B-cell CLL/lymphoma 6
Baculoviral IAP repeat containing 3
Nucleotide-binding oligomerization domain containing 2
Chemokine (C-C motif) ligand 5
Aldehyde dehydrogenase 1 family, member L1
GATA binding protein 3
Glutathione S-transferase mu 3 (brain)
Hydroxysteroid (17-beta) dehydrogenase 4
Insulin-like growth factor 1 receptor
Interleukin 1, alpha
Mannan-binding lectin serine peptidase 1 (C4/C2 activating component of Ra-reactive factor)
Methyl-CpG binding domain protein 2
Matrix metallopeptidase 1 (interstitial collagenase)
V-myb myeloblastosis viral oncogene homolog (avian)-like 2
Opioid receptor, mu 1
Poly (ADP-ribose) polymerase family, member 4
RAS-like, estrogen-regulated, growth inhibitor
Tankyrase, TRF1-interacting ankyrin-related ADP-ribose polymerase
Tumor protein p53 inducible protein 3
Xeroderma pigmentosum, complementation group C
Next, we performed a gene expression microarray experiment by exposing both normal bladder cells and bladder cancer cells to benzo[a]pyrene. Four plates each of the normal urothelial cell line UROtsa and the tumorigenic cell line J82 with and without the treatment of 10 uM benzo[a]pyrene, were cultured. mRNAs were purified and transcription levels were measured using the HumanHT-12 v3 Expression BeadChip (Illumina, Inc., San Diego, CA). There were 47,230 probes on the array of which 29,785 were kept after filtering for detectable probes (detection p-value ≤ 0.05). A quantile normalization method was applied to normalize the data . To test for genes that were differentially expressed in cell lines with and without benzo[a]pyrene treatment, we used a random variance t-test as described by Wright et al. . Genes with a parametric p ≤ 0.05 were considered differentially expressed.
It is interesting to observe that the normal UROtsa cell and the tumorigenic J82 cells only had two differentially expressed genes in common. The SEN was derived from a population-based case–control study. Genes in the network were those that contained SNPs associated with bladder cancer risk. The fact that their expression levels changed differently in normal and tumorigenic cells supports a biological role for these genes in response to tobacco, that potentially the cells to have greater susceptibility to transformation or carcinogenic progression. The differential responses to benzo[a]pyrene in the normal and cancer cells indicate the possible contribution of interactions between genetic factors and the tobacco carcinogen to bladder cancer risk.
In this study, we used functional genomics data to validate genetic interaction network that was constructed purely based on statistical analysis. We performed an enrichment analysis on a set of genes previously identified by our SEN methodology that had strong and significant epistatic interactions associated with bladder cancer. We tested the hypothesis that these susceptibility genes would be related to tobacco response, since this is the major bladder carcinogen. The enrichment analysis results suggested our network had more genes that respond to a tabacco carcinogen benzo[a]pyrene than would be expected by chance. We then carried out a microarray experiment culturing normal bladder and bladder cancer cells in the presence or absence of benzo[a]pyrene. These results showed that there were more genes in our SEN that respond to benzo[a]pyrene than would be expected by chance given the size of the network. These gene set enrichment and functional genomics annotation analysis results suggest that the SEN methodology may be able to capture biological effects at the heart of the interplay between environmental factors such as smoking and the complex gene-gene interactions that likely influence risk of bladder cancer. Although not functional proof, this study demonstrates how integrating population-based statistical results with cellular experiments can assist with interpretation of associations to provide a systems genetics approach to understanding cancer susceptibility.
Polycyclic aromatic hydrocarbon
Statistical epistasis networks
This work was supported by NIH grants R01 LM010098, R01 LM009012, R01 AI59694, P20 GM103506 and P20 GM103534.
- Hoffmann D, Hoffmann I, EI-Bayoumy K: The less harmful cigarette: a controversial issue. A tribute to Ernst L. Wynder. Chem Res Toxicol. 2001, 14 (7): 767-790. 10.1021/tx000260u.View ArticlePubMedGoogle Scholar
- Wolf A, Kutz A, Plottner S, Behm C, Bolt HM, Follmann W, Kuhlmann J: The effect of benzo[a]pyrene on porcine urinary bladder epithelial cells analyzed for the expression of selected genes and cellular toxicological endpoints. Toxicology. 2005, 207: 255-269. 10.1016/j.tox.2004.09.006.View ArticlePubMedGoogle Scholar
- Smith CJ, Perfetti TA, Rumple MA, Rodgman A, Doolittle DJ: “IARC group 2A carcinogens” reported in cigarette mainstream smoke. Food Chem Toxicol. 2000, 38 (4): 371-383. 10.1016/S0278-6915(99)00156-8.View ArticlePubMedGoogle Scholar
- Smith CJ, Perfetti TA, Mullens MA, Rodgman A, Doolittle DJ: “IARC group 2B carcinogens” reported in cigarette mainstream smoke. Food Chem Toxicol. 2000, 38 (9): 825-848. 10.1016/S0278-6915(00)00066-1.View ArticlePubMedGoogle Scholar
- Rothman N, Garcia-Closas M, Chatterjee N, Malats N, Wu X, Figueroa JD, Real FX, Van Den Berg D, Matullo G, Baris D, Thun M, Kiemeney LA, Vineis P, De Vivo I, Albanes D, Purdue MP, Rafnar T, Hildebrandt MA, Kiltie AE, Cussenot O, Golka K, Kumar R, Taylor JA, Mayordomo JI, Jacobs KB, Kogevinas M, Hutchinson A, Wang Z, Fu YP, Prokunina-Olsson L: A multi-stage genome-wide association study of bladder cancer identifies multiple susceptibility loci. Nat Genet. 2010, 42: 978-984. 10.1038/ng.687.View ArticlePubMedPubMed CentralGoogle Scholar
- Garcia-Closas M, Ye Y, Rothman N, Figueroa JD, Malats N, Dinney CP, Chatterjee N, Prokunina-Olsson L, Wang Z, Lin J, Real FX, Jacobs KB, Baris D, Thun M, De Vivo I, Albanes D, Purdue MP, Kogevinas M, Kamat AM, Lerner SP, Grossman HB, Gu J, Pu X, Hutchinson A, Fu YP, Burdett L, Yeager M, Tang W, Tardón A, Serra C: A genome-wide association study of bladder cancer identifies a new susceptibility locus within SLC14A1, a urea transporter gene on chromosome 18q12.3. Hum Mol Genet. 2011, 20: 4282-4289. 10.1093/hmg/ddr342.View ArticlePubMedPubMed CentralGoogle Scholar
- Garcia-Closas M, Rothman N, Figueroa JD, Prokunina-Olsson L, Han SS, Baris D, Jacobs EJ, Malats N, De Vivo I, Albanes D, Purdue MP, Sharma S, Fu YP, Kogevinas M, Wang Z, Tang W, Tardón A, Serra C, Carrato A, García-Closas R, Lloreta J, Johnson A, Schwenn M, Karagas MR, Schned A, Andriole G, Grubb R, Black A, Gapstur SM, Thun M: Common genetic polymorphisms modify the effect of smoking on absolute risk of bladder cancer. Canc Res. 2013, 73: 2211-2220. 10.1158/0008-5472.CAN-12-2388.View ArticleGoogle Scholar
- Eichler EE, Flint J, Gibson G, Kong A, Leal SM, Moore JH, Nadeau JH: Missing heritability and strategies for finding the underlying causes of complex disease. Nat Rev Genet. 2010, 11: 446-450. 10.1038/nrg2809.View ArticlePubMedPubMed CentralGoogle Scholar
- Moore JH: The ubiquitous nature of epistasis in determining susceptibility to common human diseases. Hum Hered. 2003, 56 (1–3): 73-82.View ArticlePubMedGoogle Scholar
- Carlborg O, Haley CS: Epistasis: too often neglected in complex trait studies?. Nat Rev Genet. 2004, 5 (8): 618-625. 10.1038/nrg1407.View ArticlePubMedGoogle Scholar
- Cordell HJ: Detecting gene-gene interactions that underline human diseases. Nat Rev Genet. 2009, 10 (6): 392-404. 10.1038/nrg2579.View ArticlePubMedPubMed CentralGoogle Scholar
- Hu T, Sinnott-Armstrong NA, Kiralis JW, Andrew AS, Karagas MR, Moore JH: Characterizing genetic interactions in human disease association studies using statistical epistasis networks. BMC Bioinforma. 2011, 12: 364-10.1186/1471-2105-12-364.View ArticleGoogle Scholar
- Hu T, Chen Y, Kiralis JW, Moore JH: ViSEN: methodology and software for visualization of statistical epistasis networks. Genet Epidemiol. 2013, 37 (3): 283-285. 10.1002/gepi.21718.View ArticlePubMedPubMed CentralGoogle Scholar
- Karagas MR, Tosteson TD, Blum J, Morris JS, Baron JA, Klaue B: Design of an epidemiologic study of drinking water arsenic exposure and skin and bladder cancer risk in a U.S. population. Environ Health Perspect. 1998, 106 (Suppl 4): 1047-1050. 10.1289/ehp.98106s41047.View ArticlePubMedPubMed CentralGoogle Scholar
- Moore JH, Gilbert JC, Tsai CT, Chiang FT, Holden T, Barney H, White BC: A flexible computational framework for detecting, characterizing, and interpreting statistical patterns of epistasis in genetic studies of human disease susceptibility. J Theor Biol. 2006, 241 (2): 252-261. 10.1016/j.jtbi.2005.11.036.View ArticlePubMedGoogle Scholar
- Fan R, Zhong M, Wang S, Zhang Y, Andrew A, Karagas M, Chen H, Amos CI, Xiong M, Moore JH: Entropy-based information gain approaches to detect and to characterize gene-gene and gene-environment interactions/correlations of complex diseases. Genet Epidemiol. 2011, 35: 706-721. 10.1002/gepi.20621.View ArticlePubMedPubMed CentralGoogle Scholar
- Hu T, Chen Y, Kiralis JW, Collins RL, Wejse C, Sirugo G, Williams SM, Moore JH: An information-gain approach to detecting three-way epistatic interactions in genetic association studies. J Am Med Inform Assoc. 2013, 20: 603-636. 10.1136/amiajnl-2012-001574.View ArticleGoogle Scholar
- Chen J, Bardes EE, Aronow BJ, Jegga AG: ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res. 2009, 37 (suppl 2): W305-W311.View ArticlePubMedPubMed CentralGoogle Scholar
- Schmid R, Baum P, Ittrich C, Fundel-Clemens K, Huber W, Brors B, Eils R, Weith A, Mennerich D, Quast K: Comparison of normalization methods for illumina BeadChip HumanHT-12 v3. BMC Genomics. 2010, 11: 349-10.1186/1471-2164-11-349.View ArticlePubMedPubMed CentralGoogle Scholar
- Wright GW, Simon RA: A random variance model for detection of differential gene expression in small microarray experiments. Bioinformatics. 2003, 19: 2448-2455. 10.1093/bioinformatics/btg345.View ArticlePubMedGoogle Scholar
- Shenker NS, Ueland PM, Polidoro S, van Veldhoven K, Ricceri F, Brown R, Flanagan JM, Vineis P: DNA methylation as a long-term biomarker of exposure to tobacco smoke. Epidemiology. 2013, 24 (5): 712-716. 10.1097/EDE.0b013e31829d5cb3.View ArticlePubMedGoogle Scholar
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