- Open Access
Post genome-wide association studies functional characterization of prostate cancer risk loci
© Jiang et al.; licensee BioMed Central Ltd. 2013
- Published: 9 December 2013
Over the last decade, genome-wide association studies (GWAS) have discovered many risk associated single nucleotide polymorphisms (SNPs) of prostate cancer (PCa). However, the majority of the associated PCa SNPs, including those in linkage disequilibrium (LD) blocks, are generally not located in protein coding regions. The systematical investigation of the functional roles of these SNPs, especially the non-coding SNPs, becomes very necessary and helpful to the understanding of the molecular mechanism of PCa.
In this work, we proposed a comprehensive framework at network level to integrate the SNP annotation, target gene assignment, gene ontology (GO) classification, pathway enrichment analysis and regulatory network reconstruction to illustrate the molecular functions of PCa associated SNPs. By LD expansion, we first identified 1828 LD SNPs using 49 reported GWAS SNPs as a start. We carefully annotated these 1828 LD SNPs via either UCSC known genes, UCSC regulation elements, or expression Quantitative Trait Loci (eQTL) data. As a result, we found 1154 SNPs were functionally annotated and obtained 205 unique PCa genes for further enrichment analysis. The enriched GO biological processes and pathways were found mainly related to regulation of cell death, apoptosis, cell proliferation, and metabolic process, which have been proved essential to cancer development. We constructed PCa genes specific transcription regulatory networks, finding several important genetic regulators for PCa, such as IGF-1/IGF-2 receptors, SP1, CREB1, and androgen receptor (AR).
A comprehensive framework was proposed for integrative and systematic analysis of PCa SNPs, the analysis can provide essential information for the understanding of the regulatory function of GWAS SNPs in PCa, and will facilitate the discovery of novel candidate biomarkers for diagnosis and prognosis of PCa.
- Genome-wide association study
- prostate cancer
- gene ontology
As one of the most common but complex malignancy in men of developed countries, prostate cancer (PCa) has been the second death-leading one among various cancers [1–4]. However, the pathophysiology and molecular mechanism for PCa have remained poorly understood. According to the National Human Genome Research Institute (NHGRI) Catalog of published genome-wide association studies (GWAS) , there have been 49 SNPs reported in 14 publications (Caucasian population, as of June 3, 2011) relevant to PCa. Although these comprehensive studies have elucidated the mechanism of incidence of PCa to a certain extent, limited conclusions have been made regarding the causal correlation between the identified SNPs and the molecular carcinogenesis of PCa . Moreover, findings from GWAS cannot directly lead to the identification of disease associated genes. One way is to consider genes overlapped by the originally identified SNPs as functional markers to complex disease traits . Through this approach, several genes have been reported to be associated with PCa, such as TCF2 , HNF1B , MSMB , and EHBP1 . However, most of the PCa GWAS SNPs were found to be located in intergenic region [5, 11–16], making it hard to characterize the biological function at the gene level.
Gene expression has been reported to play essential roles in numerous important biological process and is highly heritable . Considering the SNPs may have functional impacts on gene expression, the expression Quantitative Trait Loci (eQTL) approach has been proposed and commonly used to facilitate the identification of associations between intergenic SNPs and traits [18–20]. To date, several studies have demonstrated the great power of the eQTL approach to detect SNPs with stronger effects on gene expression from various human samples, including lymphoblastoid cell lines (LCLs) [19, 21–28], monocytes , lymphocytes , adipose , brain , and liver . However, those eQTL SNPs are also reported to act in a tissue-specific manner [34, 35]. In this study, we mainly chose eQTLs of LCLs or those reported similar to LCLs [36–38], such as monocytes , and lymphocytes , which may provide much more information than LCLs alone.
Despite the significant power of genetic mapping in complex traits using eQTLs approach, there are many other factors may affect gene expression, such as the transcript stability, epigenetic effects, environmental stimuli, drug exposure, populations, and clinical covariates [17, 36, 39, 40]. So far, most of PCa marker studies mainly focus on single genes, while how the discovered genes interact together to exert a significant combined effect on PCa still remains elusive. Recent studies [41, 42] indicate that genes with altered expression levels may individually contribute a moderate risk to disease, but act in a synergistic mode at biological pathway or gene-network level [43, 44]. Methods that focus on pathway/network rather than individual genes can detect significant coordinated changes. A representative analytic approach is the Gene Set Enrichment Analysis (GSEA), which has been commonly adapted [3, 4, 45–49] to sort the collected genes into predefined pathways or functional categories.
In this study, first, we performed a comprehensive assessment of the potential function of PCa related SNPs, utilizing the Encyclopedia of DNA Elements (ENCODE) genomic annotation databases, the annotation systems from University of California Santa Cruz database (UCSC table browser; http://genome.ucsc.edu/), and knowledge of PCa specific transcription factor binding sites (TFBS), e.g., AR, ER, and FoxA1, defined by previous studies [50, 51]. Then, we collected the Pca related genes by either overlaying the SNPs or eQTL mapping. Functional enrichment analysis of the collected genes was then performed using Gene Ontology (GO) and predefined canonical pathways encoded in MetaCore™ (GeneGo, Inc.), a commercial integrated knowledge database. Finally, PCa-specific transcription regulatory networks were constructed from the inferred gene set. Our work may provide a practical framework for integrative genomics analysis of PCa at system level, which may provide a better insight into PCa and other complex diseases.
Identification and annotation of SNPs in LD with GWAS PCa risk SNPs
Enrichment analysis for PCa candidate genes
The top 10 terms of GO functional ontologies.
GO Biological Processes
Regulation of cell death
4.944 × 10-9
Regulation of apoptosis
5.506 × 10-9
Regulation of programmed cell death
6.945 × 10-9
Response to external stimulus
1.237 × 10-8
2.357 × 10-8
Epithelial cell proliferation
7.699 × 10-8
Response to organic cyclic compound
1.153 × 10-7
Negative regulation of biological process
1.650 × 10-7
2.056 × 10-7
Branching morphogenesis of a tube
2.070 × 10-7
GO Molecular Functions
Insulin-like growth factor receptor binding
6.617 × 10-18
Insulin receptor binding
1.501 × 10-12
1.685 × 10-11
1.574 × 10-10
1.593 × 10-9
Protein complex binding
2.310 × 10-7
4.671 × 10-6
3.102 × 10-5
Oxidoreductase activity, acting on NADH or NADPH, quinone or similar compound as acceptor
6.596 × 10-5
4 iron, 4 sulfur cluster binding
8.215 × 10-5
1.009 × 10-6
2.992 × 10-6
9.733 × 10-6
1.480 × 10-5
2.505 × 10-5
3.432 × 10-5
Mitochondrial inner membrane
7.758 × 10-5
Mitochondrial respiratory chain complex I
7.937 × 10-5
NADH dehydrogenase complex
7.937 × 10-5
Respiratory chain complex I
7.937 × 10-5
The enriched GeneGO canonical pathways.
GeneGO canonical pathways
3.187 × 10-4
4.790 × 10-4
7.620 × 10-4
WNT signaling pathway. Part 1. Degradation of beta-catenin in the absence WNT signaling
5.859 × 10-4
1.119 × 10-3
Role of SCF complex in cell cycle regulation
2.073 × 10-3
ESR1 regulation of G1/S transition
3.018 × 10-3
Regulation of G1/S transition (part 1)
4.521 × 10-3
PCa-specific transcription regulatory networks
Although PCa risk-associated SNPs identified from GWAS have been extensively investigated, the study of their synergistic function of the SNPs still remains elusive. One practical approach is to map the SNPs to related genes, which can be utilized for further systematic studies. Preliminary annotation of all 1828 LD SNPs indicated that nearly 50% of the SNPs were located in gene desert region. After collecting the 205 unique genes by gene overlapping or eQTL mapping, we further studied their biological functions by performing GO and pathway enrichment test, and constructing regulatory networks. The whole pipeline may offer us new insights into the function of PCa SNPs, genes, and relevant regulatory networks.
Our GO enrichment analysis of the collected genes revealed the significant biological associations with PCa. The enriched GO biological process terms were mostly related with cellular regulation and metabolic process. In terms of biological process, we found that some genes of these GO enriched PCa genes have already been proved important in PCa, such as NADH dehydrogenase (NDUF) subcomplexes  (NDUFA11, NDUFA13, NDUFA3, NDUFS8, NDUFV1), IGF-2  and CTCF  in the cellular regulation process, and SOCS2 , HNF1B , CCND1 , and INS  in the metabolic process. While top enriched terms in molecular function indicated that our PCa genes might actively involve in transcription factor binding activities. Although not surprising, the identified significant GeneGO pathways were previously reported important pathways in PCa, such as phosphorylation , metabolism , CREB signaling pathway [68–70], Wnt signaling pathway [71–73], and ECM remodeling .
Networks allowed us to explore the systematic gene interactions involved in cell signaling and metabolism, from initial receptor-ligand interactions to second messenger and signal transduction cascades. Interestingly, we observed a strong enrichment in transcription regulation and transcription factor networks, involving important genetic regulators, such as IGF-1, IGF-2, SP1, c-Myc, AR, and p53. In summary, our approach was efficient to discover the putative PCa associated genes using the reported GWAS SNPs as a start and public annotation data, e.g., known genes and eQTLs. Another advantage of our work is that we do not require the raw genotyping data, thus less computational burden.
Nevertheless, our works still has a few limitations. First, the number of reported GWAS SNPs is increasing according to the NHGRI website, our analysis need to be updated in the future with the changing. Second, we paid more attention to PCa genes and involved regulatory networks rather than the SNPs, which might ignore the importance of SNPs themselves, e.g., corresponding mutations at SNP sites in gene region. Third, we used LCL related eQTLs data for SNPs mapping in this study, while eQTLs derived from prostate tissue should be more comprehensive, which will be considered if data are available in the future. Fourth, the PCa specific networks were constructed based on GeneGO database. Although the quality of the databases has been validated, the interactions in the network are still with the accumulation of the scientific findings. Fifth, our work was carried out based on our computational strategies, which required further experimental validation. However, the work here explains a part of the intergenic SNPs, and therefore provides a practical and effective framework to the annotation of disease associated SNPs, especially the intergenic SNPs, at systems biology or network level.
We developed a systems biology framework to evaluate the function of PCa GWAS SNPs and their synergistic biological function in PCa. We explored well defined annotation data from UCSC tracks and eQTL from publications to collect the putative PCa specific genes. Our approach offered a comprehensive analysis including GO enrichment, pathway enrichment, and network construction, providing informative insights for further study of PCa, and could be applied to other complex diseases.
Definition and annotation of SNPs in linkage disequilibrium (LD) with the GWAS PCa risk associated SNPs
All SNPs in linkage disequilibrium (LD) (r2 ≥ 0.5) with the 49 GWAS PCa SNPs (reached genome-wide significance level with a p-value ≤ 10-5) were obtained from SNAP database (http://www.broadinstitute.org/mpg/snap/ldsearchpw.php; proxy search; CEU genotype; 1000 Genomes Pilot 1 data set) . We mapped all identified SNPs to UCSC known genes (NCBI36/hg18 assembly, Mar. 2006) using ANNOVAR . Predefined regulatory regions for Yale TFBS, histone modifications defined by ENCODE project, and 11 regulatory elements were extracted from the UCSC database (Additional file 3). Transcription factors (AR, ER and FoxA1) binding sites from previous studies [50, 51] were also applied for functional annotation .
Functional enrichment of the PCa candidate gene set
The PCa associated gene list was obtained according to annotation results as followed, if the SNP was located in the gene region, then the corresponding gene was selected. Otherwise, we mapped the SNPs to collected eQTL data, which was built based on a set of previously published papers [18, 19, 21–26, 29, 30, 34] and a web-based database, SCAN , to discover the target genes.
To study the functional roles of the gene list, GeneGO database was used for Gene Ontology (GO) and pathway enrichment analysis. The significance of the enrichment (p-value) was determined by hypergeometric distribution for the probability of finding a set of genes within a given GO term or pathway, in which lower p-value indicated higher potential of non-randomness of the finding. The p-value was then adjusted by false discover rate (FDR) with a value of 0.05.
Construction of PCa-specific transcription regulatory networks
To construct the PCa-specific transcription regulatory networks, the algorithms implemented in GeneGO were applied to the PCa associated genes. In our study, transcription regulation and transcription factor networks were constructed, and the generated networks were ranked by statistical significance of enrichment (the p-values). For transcription regulation network construction, the transcription factors were added into the initial gene list to build a separate network around each transcription factor. Additional nodes from GeneGO database were extracted in order to make the target network interconnected. Transcription factor network with shortest paths between the transcription factor and the direct receptor was built using the PCa associated genes as seed nodes.
We gratefully acknowledge financial support from the National Natural Science Foundation of China grants (91230117, 31170795, 91029703), the Specialized Research Fund for the Doctoral Program of Higher Education of China (20113201110015), International S&T Cooperation Program of Suzhou (SH201120) and the National High Technology Research and Development Program of China (863 program, Grant No. 2012AA02A601).
The publication costs for this article were funded by the above grants.
This article has been published as part of BMC Genomics Volume 14 Supplement 8, 2013: Selected articles from the International Conference on Intelligent Biology and Medicine (ICIBM 2013): Genomics. The full contents of the supplement are available online at http://0-www.biomedcentral.com.brum.beds.ac.uk/bmcgenomics/supplements/14/S8.
- Crawford ED: Epidemiology of prostate cancer. Urology. 2003, 62 (6 Suppl 1): 3-12.PubMedView ArticleGoogle Scholar
- Chen J, Zhang D, Yan W, Yang D, Shen B: Translational bioinformatics for diagnostic and prognostic prediction of prostate cancer in the next-generation sequencing era. BioMed research international. 2013, 2013: 901578-PubMedPubMed CentralGoogle Scholar
- Tang Y, Yan W, Chen J, Luo C, Kaipia A, Shen B: Identification of novel microRNA regulatory pathways associated with heterogeneous prostate cancer. BMC systems biology. 2013, 7 (Suppl 3): S6-10.1186/1752-0509-7-S3-S6.PubMedPubMed CentralView ArticleGoogle Scholar
- Wang Y, Chen J, Li Q, Wang H, Liu G, Jing Q, Shen B: Identifying novel prostate cancer associated pathways based on integrative microarray data analysis. Comput Biol Chem. 2011, 35 (3): 151-158. 10.1016/j.compbiolchem.2011.04.003.PubMedView ArticleGoogle Scholar
- Hindorff LA, Sethupathy P, Junkins HA, Ramos EM, Mehta JP, Collins FS, Manolio TA: Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci USA. 2009, 106 (23): 9362-9367. 10.1073/pnas.0903103106.PubMedPubMed CentralView ArticleGoogle Scholar
- Drake TA, Schadt EE, Davis RC, Lusis AJ: Integrating genetic and gene expression data to study the metabolic syndrome and diabetes in mice. Am J Ther. 2005, 12 (6): 503-511. 10.1097/01.mjt.0000178775.39149.64.PubMedView ArticleGoogle Scholar
- Botstein D, Risch N: Discovering genotypes underlying human phenotypes: past successes for mendelian disease, future approaches for complex disease. Nat Genet. 2003, 33 Suppl: 228-237.PubMedView ArticleGoogle Scholar
- Gudmundsson J, Sulem P, Steinthorsdottir V, Bergthorsson JT, Thorleifsson G, Manolescu A, Rafnar T, Gudbjartsson D, Agnarsson BA, Baker A, et al: Two variants on chromosome 17 confer prostate cancer risk, and the one in TCF2 protects against type 2 diabetes. Nat Genet. 2007, 39 (8): 977-983. 10.1038/ng2062.PubMedView ArticleGoogle Scholar
- Thomas G, Jacobs KB, Yeager M, Kraft P, Wacholder S, Orr N, Yu K, Chatterjee N, Welch R, Hutchinson A, et al: Multiple loci identified in a genome-wide association study of prostate cancer. Nat Genet. 2008, 40 (3): 310-315. 10.1038/ng.91.PubMedView ArticleGoogle Scholar
- Gudmundsson J, Sulem P, Rafnar T, Bergthorsson JT, Manolescu A, Gudbjartsson D, Agnarsson BA, Sigurdsson A, Benediktsdottir KR, Blondal T, et al: Common sequence variants on 2p15 and Xp11.22 confer susceptibility to prostate cancer. Nat Genet. 2008, 40 (3): 281-283. 10.1038/ng.89.PubMedPubMed CentralView ArticleGoogle Scholar
- Lu Y, Zhang Z, Yu H, Zheng SL, Isaacs WB, Xu J, Sun J: Functional annotation of risk loci identified through genome-wide association studies for prostate cancer. Prostate. 2011, 71: 955-963. 10.1002/pros.21311.PubMedPubMed CentralView ArticleGoogle Scholar
- Eeles RA, Kote-Jarai Z, Giles GG, Olama AA, Guy M, Jugurnauth SK, Mulholland S, Leongamornlert DA, Edwards SM, Morrison J, et al: Multiple newly identified loci associated with prostate cancer susceptibility. Nat Genet. 2008, 40 (3): 316-321. 10.1038/ng.90.PubMedView ArticleGoogle Scholar
- Gudmundsson J, Sulem P, Manolescu A, Amundadottir LT, Gudbjartsson D, Helgason A, Rafnar T, Bergthorsson JT, Agnarsson BA, Baker A, et al: Genome-wide association study identifies a second prostate cancer susceptibility variant at 8q24. Nat Genet. 2007, 39 (5): 631-637. 10.1038/ng1999.PubMedView ArticleGoogle Scholar
- Yeager M, Orr N, Hayes RB, Jacobs KB, Kraft P, Wacholder S, Minichiello MJ, Fearnhead P, Yu K, Chatterjee N, et al: Genome-wide association study of prostate cancer identifies a second risk locus at 8q24. Nat Genet. 2007, 39 (5): 645-649. 10.1038/ng2022.PubMedView ArticleGoogle Scholar
- Lou H, Yeager M, Li H, Bosquet JG, Hayes RB, Orr N, Yu K, Hutchinson A, Jacobs KB, Kraft P, et al: Fine mapping and functional analysis of a common variant in MSMB on chromosome 10q11.2 associated with prostate cancer susceptibility. Proc Natl Acad Sci USA. 2009, 106 (19): 7933-7938. 10.1073/pnas.0902104106.PubMedPubMed CentralView ArticleGoogle Scholar
- Chang BL, Cramer SD, Wiklund F, Isaacs SD, Stevens VL, Sun J, Smith S, Pruett K, Romero LM, Wiley KE, et al: Fine mapping association study and functional analysis implicate a SNP in MSMB at 10q11 as a causal variant for prostate cancer risk. Hum Mol Genet. 2009, 18 (7): 1368-1375. 10.1093/hmg/ddp035.PubMedPubMed CentralView ArticleGoogle Scholar
- Storey JD, Madeoy J, Strout JL, Wurfel M, Ronald J, Akey JM: Gene-expression variation within and among human populations. Am J Hum Genet. 2007, 80 (3): 502-509. 10.1086/512017.PubMedPubMed CentralView ArticleGoogle Scholar
- Stranger BE, Nica AC, Forrest MS, Dimas A, Bird CP, Beazley C, Ingle CE, Dunning M, Flicek P, Koller D, et al: Population genomics of human gene expression. Nat Genet. 2007, 39 (10): 1217-1224. 10.1038/ng2142.PubMedPubMed CentralView ArticleGoogle Scholar
- Dixon AL, Liang L, Moffatt MF, Chen W, Heath S, Wong KC, Taylor J, Burnett E, Gut I, Farrall M, et al: A genome-wide association study of global gene expression. Nat Genet. 2007, 39 (10): 1202-1207. 10.1038/ng2109.PubMedView ArticleGoogle Scholar
- Schadt EE, Monks SA, Drake TA, Lusis AJ, Che N, Colinayo V, Ruff TG, Milligan SB, Lamb JR, Cavet G, et al: Genetics of gene expression surveyed in maize, mouse and man. Nature. 2003, 422 (6929): 297-302. 10.1038/nature01434.PubMedView ArticleGoogle Scholar
- Cheung VG, Spielman RS, Ewens KG, Weber TM, Morley M, Burdick JT: Mapping determinants of human gene expression by regional and genome-wide association. Nature. 2005, 437 (7063): 1365-1369. 10.1038/nature04244.PubMedPubMed CentralView ArticleGoogle Scholar
- Kwan T, Benovoy D, Dias C, Gurd S, Provencher C, Beaulieu P, Hudson TJ, Sladek R, Majewski J: Genome-wide analysis of transcript isoform variation in humans. Nat Genet. 2008, 40 (2): 225-231. 10.1038/ng.2007.57.PubMedView ArticleGoogle Scholar
- Petretto E, Mangion J, Dickens NJ, Cook SA, Kumaran MK, Lu H, Fischer J, Maatz H, Kren V, Pravenec M, et al: Heritability and tissue specificity of expression quantitative trait loci. PLoS Genet. 2006, 2 (10): e172-10.1371/journal.pgen.0020172.PubMedPubMed CentralView ArticleGoogle Scholar
- Stranger BE, Forrest MS, Dunning M, Ingle CE, Beazley C, Thorne N, Redon R, Bird CP, de Grassi A, Lee C, et al: Relative impact of nucleotide and copy number variation on gene expression phenotypes. Science. 2007, 315 (5813): 848-853. 10.1126/science.1136678.PubMedPubMed CentralView ArticleGoogle Scholar
- Min JL, Taylor JM, Richards JB, Watts T, Pettersson FH, Broxholme J, Ahmadi KR, Surdulescu GL, Lowy E, Gieger C, et al: The use of genome-wide eQTL associations in lymphoblastoid cell lines to identify novel genetic pathways involved in complex traits. PloS one. 2011, 6 (7): e22070-10.1371/journal.pone.0022070.PubMedPubMed CentralView ArticleGoogle Scholar
- Veyrieras JB, Kudaravalli S, Kim SY, Dermitzakis ET, Gilad Y, Stephens M, Pritchard JK: High-resolution mapping of expression-QTLs yields insight into human gene regulation. PLoS Genet. 2008, 4 (10): e1000214-10.1371/journal.pgen.1000214.PubMedPubMed CentralView ArticleGoogle Scholar
- Gamazon ER, Zhang W, Konkashbaev A, Duan S, Kistner EO, Nicolae DL, Dolan ME, Cox NJ: SCAN: SNP and copy number annotation. Bioinformatics. 2010, 26 (2): 259-262. 10.1093/bioinformatics/btp644.PubMedPubMed CentralView ArticleGoogle Scholar
- Emilsson V, Thorleifsson G, Zhang B, Leonardson AS, Zink F, Zhu J, Carlson S, Helgason A, Walters GB, Gunnarsdottir S, et al: Genetics of gene expression and its effect on disease. Nature. 2008, 452 (7186): 423-428. 10.1038/nature06758.PubMedView ArticleGoogle Scholar
- Zeller T, Wild P, Szymczak S, Rotival M, Schillert A, Castagne R, Maouche S, Germain M, Lackner K, Rossmann H, et al: Genetics and beyond--the transcriptome of human monocytes and disease susceptibility. PloS one. 2010, 5 (5): e10693-10.1371/journal.pone.0010693.PubMedPubMed CentralView ArticleGoogle Scholar
- Goring HH, Curran JE, Johnson MP, Dyer TD, Charlesworth J, Cole SA, Jowett JB, Abraham LJ, Rainwater DL, Comuzzie AG, et al: Discovery of expression QTLs using large-scale transcriptional profiling in human lymphocytes. Nat Genet. 2007, 39 (10): 1208-1216. 10.1038/ng2119.PubMedView ArticleGoogle Scholar
- Chen Y, Zhu J, Lum PY, Yang X, Pinto S, MacNeil DJ, Zhang C, Lamb J, Edwards S, Sieberts SK, et al: Variations in DNA elucidate molecular networks that cause disease. Nature. 2008, 452 (7186): 429-435. 10.1038/nature06757.PubMedPubMed CentralView ArticleGoogle Scholar
- Myers AJ, Gibbs JR, Webster JA, Rohrer K, Zhao A, Marlowe L, Kaleem M, Leung D, Bryden L, Nath P, et al: A survey of genetic human cortical gene expression. Nat Genet. 2007, 39 (12): 1494-1499. 10.1038/ng.2007.16.PubMedView ArticleGoogle Scholar
- Schadt EE, Molony C, Chudin E, Hao K, Yang X, Lum PY, Kasarskis A, Zhang B, Wang S, Suver C, et al: Mapping the genetic architecture of gene expression in human liver. PLoS Biol. 2008, 6 (5): e107-10.1371/journal.pbio.0060107.PubMedPubMed CentralView ArticleGoogle Scholar
- Dimas AS, Deutsch S, Stranger BE, Montgomery SB, Borel C, Attar-Cohen H, Ingle C, Beazley C, Gutierrez Arcelus M, Sekowska M, et al: Common regulatory variation impacts gene expression in a cell type-dependent manner. Science. 2009, 325 (5945): 1246-1250. 10.1126/science.1174148.PubMedPubMed CentralView ArticleGoogle Scholar
- Heinzen EL, Ge D, Cronin KD, Maia JM, Shianna KV, Gabriel WN, Welsh-Bohmer KA, Hulette CM, Denny TN, Goldstein DB: Tissue-specific genetic control of splicing: implications for the study of complex traits. PLoS Biol. 2008, 6 (12): e1-10.1371/journal.pbio.1000001.PubMedView ArticleGoogle Scholar
- Cookson W, Liang L, Abecasis G, Moffatt M, Lathrop M: Mapping complex disease traits with global gene expression. Nat Rev Genet. 2009, 10 (3): 184-194. 10.1038/nrg2537.PubMedPubMed CentralView ArticleGoogle Scholar
- Ding J, Gudjonsson JE, Liang L, Stuart PE, Li Y, Chen W, Weichenthal M, Ellinghaus E, Franke A, Cookson W, et al: Gene expression in skin and lymphoblastoid cells: Refined statistical method reveals extensive overlap in cis-eQTL signals. Am J Hum Genet. 2010, 87 (6): 779-789. 10.1016/j.ajhg.2010.10.024.PubMedPubMed CentralView ArticleGoogle Scholar
- Nica AC, Parts L, Glass D, Nisbet J, Barrett A, Sekowska M, Travers M, Potter S, Grundberg E, Small K, et al: The architecture of gene regulatory variation across multiple human tissues: the MuTHER study. PLoS Genet. 2011, 7 (2): e1002003-10.1371/journal.pgen.1002003.PubMedPubMed CentralView ArticleGoogle Scholar
- Spielman RS, Bastone LA, Burdick JT, Morley M, Ewens WJ, Cheung VG: Common genetic variants account for differences in gene expression among ethnic groups. Nat Genet. 2007, 39 (2): 226-231. 10.1038/ng1955.PubMedPubMed CentralView ArticleGoogle Scholar
- Innocenti F, Cooper GM, Stanaway IB, Gamazon ER, Smith JD, Mirkov S, Ramirez J, Liu W, Lin YS, Moloney C, et al: Identification, replication, and functional fine-mapping of expression quantitative trait loci in primary human liver tissue. PLoS Genet. 2011, 7 (5): e1002078-10.1371/journal.pgen.1002078.PubMedPubMed CentralView ArticleGoogle Scholar
- Guo AY, Sun J, Jia P, Zhao Z: A novel microRNA and transcription factor mediated regulatory network in schizophrenia. BMC systems biology. 2010, 4: 10-10.1186/1752-0509-4-10.PubMedPubMed CentralView ArticleGoogle Scholar
- Jia P, Zheng S, Long J, Zheng W, Zhao Z: dmGWAS: dense module searching for genome-wide association studies in protein-protein interaction networks. Bioinformatics. 2011, 27 (1): 95-102. 10.1093/bioinformatics/btq615.PubMedPubMed CentralView ArticleGoogle Scholar
- Sun J, Jia P, Fanous AH, van den Oord E, Chen X, Riley BP, Amdur RL, Kendler KS, Zhao Z: Schizophrenia gene networks and pathways and their applications for novel candidate gene selection. PloS one. 2010, 5 (6): e11351-10.1371/journal.pone.0011351.PubMedPubMed CentralView ArticleGoogle Scholar
- Jia P, Ewers JM, Zhao Z: Prioritization of epilepsy associated candidate genes by convergent analysis. PloS one. 2011, 6 (2): e17162-10.1371/journal.pone.0017162.PubMedPubMed CentralView ArticleGoogle Scholar
- Mootha VK, Lindgren CM, Eriksson KF, Subramanian A, Sihag S, Lehar J, Puigserver P, Carlsson E, Ridderstrale M, Laurila E, et al: PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet. 2003, 34 (3): 267-273. 10.1038/ng1180.PubMedView ArticleGoogle Scholar
- Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, et al: Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005, 102 (43): 15545-15550. 10.1073/pnas.0506580102.PubMedPubMed CentralView ArticleGoogle Scholar
- Wang K, Li M, Bucan M: Pathway-based approaches for analysis of genomewide association studies. Am J Hum Genet. 2007, 81 (6): 1278-1283. 10.1086/522374.PubMedPubMed CentralView ArticleGoogle Scholar
- Chen J, Wang Y, Shen B, Zhang D: Molecular signature of cancer at gene level or pathway level? Case studies of colorectal cancer and prostate cancer microarray data. Computational and mathematical methods in medicine. 2013, 2013: 909525-PubMedPubMed CentralGoogle Scholar
- Chen J, Zhang D, Zhang W, Tang Y, Yan W, Guo L, Shen B: Clear cell renal cell carcinoma associated microRNA expression signatures identified by an integrated bioinformatics analysis. Journal of translational medicine. 2013, 11: 169-10.1186/1479-5876-11-169.PubMedPubMed CentralView ArticleGoogle Scholar
- Wang Q, Li W, Zhang Y, Yuan X, Xu K, Yu J, Chen Z, Beroukhim R, Wang H, Lupien M, et al: Androgen receptor regulates a distinct transcription program in androgen-independent prostate cancer. Cell. 2009, 138 (2): 245-256. 10.1016/j.cell.2009.04.056.PubMedPubMed CentralView ArticleGoogle Scholar
- Carroll JS, Meyer CA, Song J, Li W, Geistlinger TR, Eeckhoute J, Brodsky AS, Keeton EK, Fertuck KC, Hall GF, et al: Genome-wide analysis of estrogen receptor binding sites. Nat Genet. 2006, 38 (11): 1289-1297. 10.1038/ng1901.PubMedView ArticleGoogle Scholar
- Pavlides S, Tsirigos A, Vera I, Flomenberg N, Frank PG, Casimiro MC, Wang C, Fortina P, Addya S, Pestell RG, et al: Loss of stromal caveolin-1 leads to oxidative stress, mimics hypoxia and drives inflammation in the tumor microenvironment, conferring the "reverse Warburg effect": a transcriptional informatics analysis with validation. Cell Cycle. 2010, 9 (11): 2201-2219. 10.4161/cc.9.11.11848.PubMedView ArticleGoogle Scholar
- Tombal B: What is the pathophysiology of a hormone-resistant prostate tumour?. Eur J Cancer. 2011, 47 (Suppl 3): S179-188.PubMedView ArticleGoogle Scholar
- Vesely DL: Metabolic targets of cardiac hormones' therapeutic anti-cancer effects. Curr Pharm Des. 2010, 16 (9): 1159-1166. 10.2174/138161210790963887.PubMedView ArticleGoogle Scholar
- Redig AJ, Munshi HG: Metabolic syndrome after hormone-modifying therapy: risks associated with antineoplastic therapy. Oncology (Williston Park). 2010, 24 (9): 839-844.Google Scholar
- Russell PJ, Bennett S, Stricker P: Growth factor involvement in progression of prostate cancer. Clin Chem. 1998, 44 (4): 705-723.PubMedGoogle Scholar
- Sankpal UT, Goodison S, Abdelrahim M, Basha R: Targeting Sp1 transcription factors in prostate cancer therapy. Med Chem. 2011, 7 (5): 518-525. 10.2174/157340611796799203.PubMedView ArticleGoogle Scholar
- Djavan B, Waldert M, Seitz C, Marberger M: Insulin-like growth factors and prostate cancer. World J Urol. 2001, 19 (4): 225-233. 10.1007/s003450100220.PubMedView ArticleGoogle Scholar
- Heinlein CA, Chang C: Androgen receptor in prostate cancer. Endocr Rev. 2004, 25 (2): 276-308. 10.1210/er.2002-0032.PubMedView ArticleGoogle Scholar
- Chi SG, deVere White RW, Meyers FJ, Siders DB, Lee F, Gumerlock PH: p53 in prostate cancer: frequent expressed transition mutations. J Natl Cancer Inst. 1994, 86 (12): 926-933. 10.1093/jnci/86.12.926.PubMedView ArticleGoogle Scholar
- Kaltz-Wittmer C, Klenk U, Glaessgen A, Aust DE, Diebold J, Lohrs U, Baretton GB: FISH analysis of gene aberrations (MYC, CCND1, ERBB2, RB, and AR) in advanced prostatic carcinomas before and after androgen deprivation therapy. Lab Invest. 2000, 80 (9): 1455-1464. 10.1038/labinvest.3780152.PubMedView ArticleGoogle Scholar
- Filippova GN: Genetics and epigenetics of the multifunctional protein CTCF. Curr Top Dev Biol. 2008, 80: 337-360.PubMedView ArticleGoogle Scholar
- Zhu JG, Dai QS, Han ZD, He HC, Mo RJ, Chen G, Chen YF, Wu YD, Yang SB, Jiang FN, et al: Expression of SOCSs in human prostate cancer and their association in prognosis. Mol Cell Biochem. 2013, 381: 51-59. 10.1007/s11010-013-1687-6.PubMedView ArticleGoogle Scholar
- Sun J, Zheng SL, Wiklund F, Isaacs SD, Purcell LD, Gao Z, Hsu FC, Kim ST, Liu W, Zhu Y, et al: Evidence for two independent prostate cancer risk-associated loci in the HNF1B gene at 17q12. Nat Genet. 2008, 40 (10): 1153-1155. 10.1038/ng.214.PubMedPubMed CentralView ArticleGoogle Scholar
- Neuhausen SL, Slattery ML, Garner CP, Ding YC, Hoffman M, Brothman AR: Prostate cancer risk and IRS1, IRS2, IGF1, and INS polymorphisms: strong association of IRS1 G972R variant and cancer risk. Prostate. 2005, 64 (2): 168-174. 10.1002/pros.20216.PubMedView ArticleGoogle Scholar
- Xiao D, Powolny AA, Moura MB, Kelley EE, Bommareddy A, Kim SH, Hahm ER, Normolle D, Van Houten B, Singh SV: Phenethyl isothiocyanate inhibits oxidative phosphorylation to trigger reactive oxygen species-mediated death of human prostate cancer cells. J Biol Chem. 2010, 285 (34): 26558-26569. 10.1074/jbc.M109.063255.PubMedPubMed CentralView ArticleGoogle Scholar
- Taylor BS, Pal M, Yu J, Laxman B, Kalyana-Sundaram S, Zhao R, Menon A, Wei JT, Nesvizhskii AI, Ghosh D, et al: Humoral response profiling reveals pathways to prostate cancer progression. Mol Cell Proteomics. 2008, 7 (3): 600-611.PubMedView ArticleGoogle Scholar
- Shaywitz AJ, Greenberg ME: CREB: a stimulus-induced transcription factor activated by a diverse array of extracellular signals. Annu Rev Biochem. 1999, 68: 821-861. 10.1146/annurev.biochem.68.1.821.PubMedView ArticleGoogle Scholar
- Unni E, Sun S, Nan B, McPhaul MJ, Cheskis B, Mancini MA, Marcelli M: Changes in androgen receptor nongenotropic signaling correlate with transition of LNCaP cells to androgen independence. Cancer Res. 2004, 64 (19): 7156-7168. 10.1158/0008-5472.CAN-04-1121.PubMedView ArticleGoogle Scholar
- Mantamadiotis T, Papalexis N, Dworkin S: CREB signalling in neural stem/progenitor cells: Recent developments and the implications for brain tumour biology. Bioessays. 2012Google Scholar
- Moon RT, Miller JR: The APC tumor suppressor protein in development and cancer. Trends Genet. 1997, 13 (7): 256-258. 10.1016/S0168-9525(97)01196-7.PubMedView ArticleGoogle Scholar
- Spink KE, Polakis P, Weis WI: Structural basis of the Axin-adenomatous polyposis coli interaction. EMBO J. 2000, 19 (10): 2270-2279. 10.1093/emboj/19.10.2270.PubMedPubMed CentralView ArticleGoogle Scholar
- Kharaishvili G, Simkova D, Makharoblidze E, Trtkova K, Kolar Z, Bouchal J: Wnt signaling in prostate development and carcinogenesis. Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub. 2011, 155 (1): 11-18. 10.5507/bp.2011.016.PubMedView ArticleGoogle Scholar
- Tuxhorn JA, Ayala GE, Smith MJ, Smith VC, Dang TD, Rowley DR: Reactive stroma in human prostate cancer: induction of myofibroblast phenotype and extracellular matrix remodeling. Clin Cancer Res. 2002, 8 (9): 2912-2923.PubMedGoogle Scholar
- Johnson AD, Handsaker RE, Pulit SL, Nizzari MM, O'Donnell CJ, de Bakker PI: SNAP: a web-based tool for identification and annotation of proxy SNPs using HapMap. Bioinformatics. 2008, 24 (24): 2938-2939. 10.1093/bioinformatics/btn564.PubMedPubMed CentralView ArticleGoogle Scholar
- Lu Y, Zhang Z, Yu H, Zheng SL, Isaacs WB, Xu J, Sun J: Functional annotation of risk loci identified through genome-wide association studies for prostate cancer. Prostate. 2011, 71 (9): 955-963. 10.1002/pros.21311.PubMedPubMed CentralView ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.