Volume 14 Supplement 8
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.
KeywordsGenome-wide association study prostate cancer gene ontology pathway network
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.
Materials and methods
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://www.biomedcentral.com/bmcgenomics/supplements/14/S8.
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