Figures
Abstract
Prostate cancer (PCa) is a heterogeneous trait for which several susceptibility loci have been implicated by genome-wide linkage and association studies. The genomic region 13q14 is frequently deleted in tumour tissues of both sporadic and familial PCa patients and is consequently recognised as a possible locus of tumour suppressor gene(s). Deletions of this region have been found in many other cancers. Recently, we showed that homozygous carriers for the T442C variant of the ARLTS1 gene (ADP-ribosylation factor-like tumour suppressor protein 1 or ARL11, located at 13q14) are associated with an increased risk for both unselected and familial PCa. Furthermore, the variant T442C was observed in greater frequency among malignant tissue samples, PCa cell lines and xenografts, supporting its role in PCa tumourigenesis. In this study, 84 PCa cases and 15 controls were analysed for ARLTS1 expression status in blood-derived RNA. A statistically significant (p = 0.0037) decrease of ARLTS1 expression in PCa cases was detected. Regulation of ARLTS1 expression was analysed with eQTL (expression quantitative trait loci) methods. Altogether fourteen significant cis-eQTLs affecting the ARLTS1 expression level were found. In addition, epistatic interactions of ARLTS1 genomic variants with genes involved in immune system processes were predicted with the MDR program. In conclusion, this study further supports the role of ARLTS1 as a tumour suppressor gene and reveals that the expression is regulated through variants localised in regulatory regions.
Citation: Siltanen S, Fischer D, Rantapero T, Laitinen V, Mpindi JP, Kallioniemi O, et al. (2013) ARLTS1 and Prostate Cancer Risk - Analysis of Expression and Regulation. PLoS ONE 8(8): e72040. https://doi.org/10.1371/journal.pone.0072040
Editor: Amanda Ewart Toland, Ohio State University Medical Center, United States of America
Received: April 4, 2013; Accepted: July 3, 2013; Published: August 5, 2013
Copyright: © 2013 Siltanen et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by the Tampere Graduate Program in Biomedicine and Biotechnology salary and Orion-Farmos Research Foundation grant to S.S. and by the Sigrid Juselius Foundation, the Academy of Finland (251074), the Finnish Cancer Organisations, and the Competitive Research Funding of the Pirkanmaa Hospital District (9N069) grants to J.S. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Prostate cancer (PCa) is a heterogeneous trait, and it is the most common malignancy among men in western countries, including Finland. It is a multifactorial disease, and definitive risk factors include age, ethnic origin and family history. In Finland, the incidence of PCa is 89.4/100,000, and in 2010, 4697 new prostate cancer cases were diagnosed (http://www.cancer.fi/syoparekisteri/en/). Despite extensive research over the last decade, the etiological risk factors and genes that cause genetic susceptibility remain largely unknown. This lack of knowledge has hampered effective cancer prevention and development of better treatments.
Mutations in the known high-penetrance PCa predisposition genes explain only a small fraction of PCa cases. A polygenic model for familial aggregation of cancer has been proposed where several low-penetrance alleles may have a multiplicative and/or modifying effect. One low-penetrant candidate gene is ARLTS1 (ARL11), ADP-ribosylation factor like tumour suppressor protein 1, a putative tumour-suppressor gene on chromosome 13q14, which has been shown to function in many human cancers [1]–[5]. ARLTS1 is a member of the ADP-ribosylation factor family that plays a role in apoptotic signalling. The same chromosomal area on 13q has been indicated in a multi-centre genome-wide linkage study in families with at least five affected members [6]. In another recent study, the immediate adjacent region 13q13 showed a suggestive linkage to PCa, with a HLOD>1.9 [7]. In addition, the locus 13q14 is among the most frequently deleted chromosomal regions in somatic tumour tissues in both unselected and hereditary prostate cancers [8], [9], suggesting that ARLTS1 could be a target for both germline and somatic mutations. We previously reported a significant association of ARLTS1 T442C (rs3803185) homozygote carriers with PCa [10]. This risk genotype was also associated with decreased ARLTS1 expression in the lymphoblastoid cell line samples of PCa patients, and ARLTS1 co-expression signatures from data mining revealed that ARLTS1 expression was strongly associated with immune system processes. These processes are of interest because a link between chronic inflammation and PCa progression has been repeatedly proposed, and multiple genes acting in inflammatory pathways have been linked to PCa susceptibility [11]–[13].
Complex diseases, such as cancer, are caused by a combination of multiple genetic interactions and environmental factors, which are hard to detect and link to each other. To determine true causal associations using statistical methods and phenotype information, it is advisable to organise individual markers into groups according to meaningful biological criteria, such as inflammation. By composing variant sets, it is possible to reduce the number of hypotheses being tested, which allows the association between a genomic feature and a phenotype to be more easily detected.
Epistasis in genotype level is defined as the interaction among multiple genes or loci, and this joint genetic effect may be the factor behind “missing heritability”, a phenomenon linked to the unexplained portion of hereditary cancer susceptibility, which is observed in PCa. The genome-wide expression quantitative trait loci, eQTL, analysis is a method for studying epistasis in complex traits that is able to detect associations between genotypic and expression data. The eQTL analysis is a widely used approach to gain insight into the role of single nucleotide polymorphisms (SNPs) affecting transcript levels.
In this study, we investigated the mechanisms behind the previously observed association of ARLTS1 and PCa by focusing on finding gene/expression interactions that dispose patients to PCa, including interactions involving the ARLTS1 gene. To further investigate the ARLTS1 expression differences seen previously in tumor samples, prostate cancer and lymphoblastoid cell lines [10], we performed functional eQTL analysis from whole blood derived total RNA from PCa patients. The previously reported ARLTS1 co-expression with immune system processes [10] was tested by MDR analysis. To our knowledge, this is the first study reporting the findings of ARLTS1 interacting variants and prostate cancer eQTLs at the 13q14 region.
Materials and Methods
Study population
All the samples were of Finnish origin. The identification and collection of the Finnish HPC families has been described elsewhere [14]. The familial samples analysed in this study had at least two affected first or second degree relatives. Altogether, 102 prostate cancer cases and 33 healthy male family members belonging to 31 families were initially taken into the study population. The clinical characteristics of the familial patients used in RNA sequencing (n = 84) are referred to in Table 1. Average age at diagnosis was 63.0 y.
Patient information and samples were obtained with full written informed consent. The study was performed under appropriate research permissions from the Ethics Committees of the Tampere University Hospital, Finland, as well as the Ministry of Social Affairs and Health in Finland.
Genome-wide SNP Genotyping
Genome-wide SNP genotyping was performed using the HumanOmniExpress BeadChip microarray (Illumina, Inc., San Diego, CA, USA) by the Technology Centre, Institute for Molecular Medicine Finland (FIMM), University of Helsinki. This array covers more than 700,000 markers with an average spacing of 4 kb across the entire genome.
DNA Sequencing
The ARLTS1 variant status of the PCa patients used in Illumina genotyping was examined by direct sequencing. Sequencing was performed in an Applied Biosystems 3130xl Genetic Analyzer (Life Technologies Corporation, Carlsbad, CA, USA) according to the manufacturer's instructions. Primers and PCR conditions used in the mutation screening are available upon request.
RNA Extraction and sequencing
Total RNA was extracted from 84 PCa cases and 15 healthy male relatives. All subjects belonged to the 31 Finnish HPC families mentioned above. Total RNA was purified from whole blood collected in PAXgene® Blood RNA Tubes (PreAnalytiX GmbH, Switzerland/Qiagen/BD) using the MagMAX™ for Stabilized Blood Tubes RNA Isolation Kit (Ambion®/Life Technologies, Carlsbad, CA, USA) and the PAXgene Blood miRNA Kit (PreAnalytiX GmbH, Switzerland/Qiagen/BD). The RNA quality was assessed using the Agilent 2100 Bioanalyzer and the Agilent RNA 6000 Nano Kit (Agilent Technologies, Santa Clara, CA, USA).
Library preparation, target enrichment and massively parallel paired-end sequencing of expressed transcripts was performed by Beijing Genomics Institute (BGI Hong Kong Co., Ltd., Tai Po, Hong Kong) using Illumina HiSeq2000 technology (Illumina Inc., San Diego, CA, USA).
eQTL analysis
To obtain ARLTS1 expression values, first, the reads from RNA sequencing were aligned with tophat2 using hg19 as the reference genome [15]. The raw read count for ARLTS1 was calculated using HTseq, and read counts were transformed to normalised expression values using DESeq (www-huber.embl.de/users/anders/HTSeq/, [16]). The linear regression model implemented in PLINK was used to detect transcript specific variants. Associations in cis were delineated by a 1 Mb window upstream or downstream of the ARLTS1 SNP rs9526582, as most of the cis-eQTLs are located within or close to the gene of interest [17]. In addition, we applied a new method based on probabilistic indices to test for eQTL. The new method is a non-parametric directional test (similar to the well-known Jonckheere-Terpstra test), implemented in our R-Package GeneticTools that is available on the Comprehensive R Archive Network (http://cran.r-project.org/package=GeneticTools), and a package description is under development (unpublished data). P-values were calculated using permutation tests and were adjusted for multiple testing by applying the Benjamini-Hochberg correction. We also calculated for each test size α in [0,0.1] the ratio of the amount of expected test rejections and the amount of observed rejections. The α for which the ratio of these two values was maximal, we chose also as an optimal test size.
Gene expression dataset
All microarray gene expression data on cell lines (n = 1445) included in these analyses are publicly available via the Gene Expression Omnibus (GEO) (http://www.ncbi.nlm.nih.gov/geo/, accession numbers; GSE36133, GSE7127, GSE8332, GSE10843, GSE10890, GSE12777, GSE15455, GSE18773, GSE20126, GSE21654 and GSE24795) and GSK Cancer Cell Line Genomic Profiling Data (https://cabig.nci.nih.gov/tools/caArray_GSKdata) (2008) [18] (GlaxoSmithKline). The bulk of the gene expression data was acquired from the Cancer Cell Line Encyclopedia (CCLE) (GSE36133) [19]. We used samples from the most recent and widely cited Affymetrix microarray platform, HGU133_plus 2.0, to perform these analyses.
Gene expression data Normalisation
Gene expression data normalisation was performed from the raw CEL files using the Aroma Affymetrix (Version 1.3.0) R package (http://www.aroma-project.org) based on custom CDF files (version 16) found at http://brainarray.mbni.med.umich.edu [20]. We processed expression for 19,003 distinct genes. All computations were performed in the R statistical environment, employing the BioConductor suite of packages.
Co-expression analysis of ARLTS1.
The co-expression analysis method was described previously [10]. The meta cell line data (n = 1445) originated from over 48 human anatomical parts. A correlation value >0.30 and a p-value <0.05 were used as determinants for a statistically significant association. We performed multiple test corrections using Benjamini Hochberg and Bonferroni methods. We decided to use the Benjamini Hochberg (BH) method for selecting significantly co-expressed genes because it was moderately strict at calling a gene pair correlation a false positive.
In silico functionality prediction
RegulomeDB (http://regulome.stanford.edu/) and HaploReg (http://www.broadinstitute.org/mammals/haploreg/haploreg.php) [21], [22] databases were used to further elucidate the role of eQTLs in gene regulation. RegulomeDB allows he features of DNA and regulatory elements of non-coding regions to be assessed, and HaploReg is a tool for developing mechanistic hypotheses of the impact of candidate regulatory non-coding variants on clinical phenotypes and normal variation.
MDR analysis
The multifactor dimensionality reduction (MDR) program is publicly available from the internet (www.epistasis.org). We used version 2.0_beta_8.4 to examine gene-gene interactions. MDR detects interactions in relatively small sample sizes. MDR is a non-parametric, model-free data mining approach constructive induction algorithm that transforms the high-dimensional data into one-dimensional variables by pooling genotypes into high and low risk groups based on the ratio of cases to controls that have the genotype in question [23]. MDR selects one genetic model (a one, two, three or four stage locus) that most successfully predicts the phenotype or disease status, e.g., cancer. Data are then divided into ten equal parts to perform a 10-fold cross-validation. The model creates a training set (9/10 of the data) and testing set (1/10 data) to evaluate the prediction ability. The procedure repeats this protocol ten times and calculates the cross-validation consistency (CVC). CVC depicts the number of times that particular model is chosen as the best one of those ten intervals. From the MDR results section, testing balanced accuracy (TBA) shows how many instances are correctly classified. The ARLTS1 genotypes from 102 PCa cases and 33 controls were combined with the GWAS data of 700,000 SNPs.
Selection of genes and SNPs for MDR
The genes functioning in immune system processes and inflammation pathways were selected from a previously published comprehensive collection made by Loza MJ et al. [24] because MDR is not able to run data sets of thousands of SNPs within reasonable time limits. In short, the SNPs selected included those involved in apoptosis, cytokine signalling, Toll-like receptor signalling, leukocyte signalling, complement, adhesion and natural killer cell signalling. We also ran MDR with a subset of genes gathered from our previous ARLTS1 co-expression studies (e.g., twelve genes within B-cell receptor [BCR] signalling pathway). The total amount of SNPs was 12,011 of which 4,764 were found in our Illumina Human OmniExpress GWAS data.
Results
RNA expression
The ARLTS1 RNA expression levels were analysed from total RNA of 84 PCa cases and 15 controls. A significant decrease of the ARLTS1 expression level in PCa cases was detected (p = 0.0037, Fig. 1). This is in concordance with our previous results from cell line, benign prostatic hyperplasia (BPH) and tumour specimen RNA expression data [10].
Relative ARLTS1 RNA expression was determined by RNA sequencing analysis. Columns represent means of individuals; bars represent SD.
eQTL analysis
To identify possible cis-acting genetic variants associated with ARLTS1 transcript levels, we performed an eQTL analysis within a special area of the 13q14 region. By a linear regression model (PLINK), we were able to detect 5 eSNPs affecting ARLTS1 expression (Table 2). When the calculation window was diminished from 1 Mb to 200 kb, only one SNP, rs7997377, remained. With the directional test performed by the R-Package analysis tool, 11 statistically significant eSNPs were found (Table 2), and two were in concordance with the PLINK linear regression model results. For both test procedures a test size of 0.01 was chosen. The eSNPs found by the directional test were located mainly in non-coding regions (Table 2). The genomic locations of the eSNPs found in the 13q14 region are visualised in Figure 2. Altogether, 468 genomic variants within the 1 Mb region originating from ARLTS1 SNP rs9526582 were tested by a linear regression model and directional test.
The gene symbols are as follows: CYSLTR2 (cysteinyl leukotriene receptor 2), FNCD3A (fibronectin type III domain containing 3A), MLNR (motilin receptor), CDADC1 (cytidine and dCMP deaminase domain containing 1), CAB39L (calcium binding protein 39-like), SETDB2 (SET domain, bifurcated 2/CLLD8), PHF11 (PHD finger protein 11/NY-REN-34 antigen), RCBTB1 (regulator of chromosome condensation [RCC1] and BTB [POZ] domain containing protein 1/CLLD7), ARLTS1 (/ARL11, ADP-ribosylation factor-like 11), EBPL (emopamil binding protein-like), KPNA3 (karyopherin alpha 3, importin alpha 4), SPRYD7 (SPRY domain containing 7/C13orf1, chromosome open reading frame 1), MIR3613 (microRNA 3613), TRIM13 (tripartite motif containing 13), KCNRG (potassium channel regulator), MIR-15A and MIR16-1 (microRNA genes 15a and 16-1) and DLEU2 (deleted in lymphocytic leukemia 2).
After adjusting for multiple testing, a FDR of 39% in the linear model and approximately 32% in the directional test has to be accepted to keep the significant test results from the marginal p-values. For a more common FDR level of 10% one significant eSNP from the directional test remained significant (rs9568354). When we considered the ratio of expected and observed significant tests, a maximum ratio for α = 0.011 in directional test was identified. For that α approximately 2.5 times more significant test results appeared than expected. Hence, we report also the above mentioned non-adjusted p-values for a significance level 0.01. With linear regression model, the amount of observed significant tests matched the amount of expected test rejections under the null hypothesis.
The association of the eSNP genotypes with the ARLTS1 expression level was calculated. A statistically significant correlation was naturally observed between all the 14 SNPs and ARLTS1 transcript levels. The eSNP genotype - ARLTS1 expression association box plots are depicted in Figure 3.
A, rs2075610; B, rs7997737; C, rs1543513; D, rs9568232; E, rs9568354; F, rs1886014; G, rs7337547; H, rs7995192; I, rs9562905; J, rs2580189; K, rs2532975; L, rs1262781; M, rs1262774 and N, rs17074618.
The functionality and possible transcriptional regulatory effect of the 14 eSNPs within genes found by eQTL was evaluated using ENCODE-data in the RegulomeDB and HaploReg databases. Altogether nine of the fourteen eQTLs are reported in RegulomeDB. The findings in the GM12878 lymphoblastoid cell line are emphasized below because this cell line resembles the tissue type from which the RNA sequencing data was retrieved.
The most substantial evidence for the regulation of ARLTS1 was found for SNP rs2532975. Its regulatory role is supported by its location in a regulatory active region. According to the Chip-Seq data from the GM12878 cell line, rs2532975 resides in a BATF (basic leucine zipper transcription factor) binding site. In addition, using position weight matrix (PWM) matching, a CDC5 (cell cycle serine/threonine-protein kinase) binding motif has been identified that spans the genomic position of this variant. Furthermore, a promyelocytic leukemia zinc finger (PLZF) motif is reported in HaploReg. As reported by HaploReg, CDC5 binding efficiency decreases while PLZF binding increases. Additionally, the chromatin state in the region surrounding rs2532975 might adopt weak enhancer characteristics. This prediction is further strengthened by the presence of two histone marks in this region, H3k4me1 and H3k4me2, identified in the GM12878 cells.
Another possible candidate for ARLTS1 regulation is rs9562905. In a Chip-Seq study, a POLA2 binding site was identified in a human embryonic stem cell line, H1-hESC, in the region surrounding rs9562905. HaploReg predicts active enhancer characteristics in the chromatin surrounding rs9562905 in the lymphoblastoid GM12878 cells, similar to rs2532975. This interpretation is supported by the presence of two enhancer associated histone marks, H3k4me1 and H3k4me2. In addition, a FAIRE-sequencing study conducted for the GM12878 cells also implies that this region is in an open chromatin state and is therefore likely to have regulatory activity.
The variants rs1543513, rs7997737 and rs9568354 share similar chromatin structural features in the GM12878 cells. According to HaploReg, the chromatin state associates with the weakly transcribed region. This prediction is confirmed by the elongation of histone mark H3k36me3 in the surrounding regions of the variants rs1543513, rs7997737 and rs9568354. According to RegulomeDB, rs1543513 is located within the Irx3 and Irx6 motifs. However, in HaploReg, the presence of these motifs is not reported. Similarly, the transcription factor binding motif of AIRE (autoimmune regulator) is reported for rs1262781 in RegulomeDB but not in HaploReg. A MZF1 (myeloid zinc finger 1) motif surrounding rs7997737 is reported in both databases. HaploReg reports a negative LOD score difference for rs7997737, which can be interpreted as lowered binding efficiency of MZF. Compared to the three variants mentioned above, rs9568232 shares similar characteristics regarding its chromatin state. According to HaploReg, this chromatin state is related to elongated transcription.
The variants rs2075610 and rs1262774 are located within regions most likely under the control of epigenetic regulation by the polycomb-group proteins in the GM12878 cells. In addition, DNase-Seq studies performed for several cell lines indicate an open chromatin state surrounding rs2075610. However, two repressed state chromatin histone marks, H3k27me3 and H3k9me3, have also been identified. Variant rs1262774 is not reported in the RegulomeDB.
The remaining variants are located in heterochromatin regions, according to HaploReg. For rs7995192, rs2580189 and rs1262781, this prediction is further confirmed by the presence of H3k27me3. In addition, another repressive state associated histone mark, namely H3k9me3, is present in the rs7995192 and rs2580189 regions. Although there is evidence of repressed state chromatin, RegulomeDB reports that transcription factor binding motifs do in fact span the genomic positions of rs2580189 and rs1262781.
Two motifs for XBP-1 (X-box binding protein 1) and ATF6 (activating transcription factor 6) span the region of rs1262781, according to RegulomeDB. HaploReg confirms the presence of XBP-1 and ATF6 motifs and an additional motif for SOX-17 (SRY [sex determining region Y] box 17). HaploReg reports lower predicted binding efficiencies for XBP-1 and ATF6 and a slightly increased binding efficiency for SOX-17.
RegulomeDB reports a ZNF143 (zinc finger protein 143) motif in the region surrounding SNP rs2580189. However, HaploReg does not report the presence of this motif. Taken together, the results gathered from RegulomeDB and HaploReg indicate that the ARLTS1 eQTLs are located within regulatory areas of the 13q14 region.
Co-expression analysis
The GeneSapiens mRNA expression database data, including ARLTS1 expression, from 1445 cell lines (48 cancer subtypes) and prostate cancer tumours was available for interaction studies. Altogether 1381 genes with correlation value >0.30 and p-value <0.05 was found to be positively correlating with the ARLTS1 gene. Using DAVID GO functional clustering, (http://david.abcc.ncifcrf.gov/tools.jsp) [25], [26] a strong association with nucleus and zinc-finger protein processes was illustrated when all the genes positively correlating with ARLTS1 expression (n = 36) in the PCa cell line cohort were taken into account (with a more stringent correlation value >0.50). The group of nuclear processes (nucleus, intracellular organelles, transcription and DNA-binding) was enriched when data of PCa cell lines was studied. The enrichment score was 13.49 with a p-value 1.1E-32. The adjusted Benjamin score was 5.1E-30, and the cluster of zinc-finger binding proteins revealed a p-value of 9.0E-22. The same phenomenon was observed within the whole data of cell lines (meta cohort) with genes showing a correlation value >0.30. ARLTS1 co-expression genes (with correlation value >0.50) from the meta cell line data revealed a category of immune system processes (B-/T-cell activation, leukocyte/lymphocyte differentiation and activation), with an enrichment score of 2.94 (p-value 5.57E-7, adjusted Benjamin score 2.9E-5).
ARLTS1 co-expression data of genes negatively correlated to ARLTS1 identified a strong gene ontology of glycoprotein and plasma membrane protein genes in PCa cell lines (n = 2722, correlation value<−0.50, enrichment score 52.13, p-value 3.4E-72 and 38.35, p-value 7.9E-32, respectively). Within the negatively correlating genes, a cluster of immunoglobulin domain containing proteins harboured an enrichment score of 12.23 with a p-value of 5.4E-24. The GO term “cytokine activity” revealed an enrichment score of 10.43 with a p-value of 6.5E-10 within negatively correlating genes. In addition to the result of ARLTS1 negatively correlating genes, we also identified clusters of G-protein coupled receptors and cell-cell signalling.
Top five positively and negatively ARLTS1 correlating genes in cell line data are presented in Table 3 (upper panel). Additionally, results of the ARLTS1 co-expression with genes (SETDB2, PHF11, SPRYD7, MLNR) that harbored eSNPs are presented in the lower part of the Table 3, from the co-expression analysis performed in the meta cell line, prostate cell line data and prostate tumor data.
The expression of ARLTS1 was positively correlated with the expression of SETBD2, in both the whole data of cell lines (meta cell line cohort) (correlation value 0.64, p-value 0.000) and in the specific PCa cell lines (correlation value 0.61, p-value 0.00009) (Table 3). Expression of the PHF11 gene was positively correlated with ARLTS1 expression in the meta cell line data (correlation value 0.44, p-value 0.000). Expression of MLNR and SPRYD7 was negatively correlated with ARLTS1 expression. ARLTS1 and MLNR had a correlation value of −0.35 (p-value 0.04) in PCa cell lines, and ARLTS1 and SPRYD7 had a correlation value of −0.34 (p-value 0.003) in prostate tumour specimens (Table 3). The strong negative correlation of ARLTS1 and SPRYD7 expression levels was also validated in our transcriptome data of 84 PCa cases and 15 controls.
MDR analysis
By direct sequencing, we were able to detect six ARLTS1 variants at the same amplicon from PCa patients included in the Illumina genotyping (n = 135). All the variants were previously known (rs117251022, rs3803186, rs147120792, rs3803185, rs138452698 and G446A [Trp149Stop]). To elucidate the genotypic ARLTS1 interactions we used multifactor dimensionality reduction (MDR). Gene-gene interaction status between ARLTS1 and genes functioning in immune system processes within 102 PCa cases and 33 controls was calculated, but we were not able to find any statistically significant ARLTS1 interactions in this study cohort (data not shown).
Discussion
ARLTS1 is a cancer-predisposing gene with proven tumour suppressor properties. However, very little evidence on function, especially on pathways, is currently available. In this study, we were able to verify downregulated ARLTS1 expression in the blood-derived RNA of PCa patient samples, demonstrating for the first time the effect of germline alteration on ARLTS1 expression levels. Tumour suppressor function of the ARLTS1 gene has been previously proven by Calin GA et al., who found that transduction of full-length ARLTS1 to A549 cells in Nu/Nu mice decreased tumour growth when compared to empty vector [1]. The ability of ARLTS1 to suppress tumour formation in preclinical models has also been observed with ovarian [27] and lung cancer cells [28]. However, these results are based on somatic mutations in cancerous cell lines, whereas our result reveals a novel expression difference at the germline level, supporting the role of ARLTS1 as a tumour suppressor gene. In the future, these types of findings could be used to enable screening and detection of at-risk patients even before clinical diagnoses.
We have previously genotyped ARLTS1 variants in prostate, breast and colorectal cancer [29] and produced a prostate cancer follow-up study [10]. In the first study [29], we reported a statistically significant association with ARLTS1 variants T442C, G194T and prostate cancer risk. However, after adjusting for multiple testing, none of the results were significant. In the follow-up study with larger sample size, we reported a statistically significant association with T442C variant and prostate cancer risk [10]. We reported also a decreased or lost ARLTS1 RNA or protein expression in clinical prostate tumors, prostate cancer cell lines and xenografts, supporting the role of ARLTS1 as a tumor suppressor gene. Thus, there is no conflict between the previous publications and this study that is confirming the tumor suppressor role of ARLTS1.
Here, 14 eSNPs located at the 13q14 region were shown to significantly influence ARLTS1 transcript levels in PCa patients. The eQTL analysis was performed with two methods, a linear model approach and a directional test. One advantage of our directional test method over the commonly used linear model approach is its robustness against outliers and the weaker model assumptions. In the linear model approach, the expression values of the different genotype groups are considered to follow the same normal distribution up to a location shift parameter. If the location shift parameter is not equal to zero, it generates a testing problem. The directional test only assumes that the different expression values follow certain distribution functions, so a testing problem only occurs if the distributions of the genotype groups are stochastically ordered.
Interestingly, 5 of the eSNPs are located within the protein-coding genes SETDB2, PHF11, SPRYD7 and MLNR. Two eQTLs were positioned in the SETDB2 (SET domain, bifurcated 2) gene, also known as the CLLD8 (chronic lymphocytic leukemia deletion region gene 8 protein) gene, which functions mainly in epigenetic regulation [30]. The PHF11 gene, another hit for the eSNPs, is a positive regulator of Th1-type cytokine gene expression through nuclear factor kappa B, (NF-kB) [30], [31]. The third eSNP gene, SPRYD7, (SPRY domain containing 7/chromosome 13 open reading frame 1 [C13or1]) also known as chronic lymphocytic deletion region gene 6 protein, (CLLD6) [32], is proposed to have a tumour suppressor function because it has been detected to be downregulated in B-CLL patients [33]. The SPRY/B30.2 protein domain occurs in a variety of cellular proteins, mediates protein-protein interactions and negatively regulates cytokine activities [34], [35]. The fourth eSNP gene, MLNR/MTLR1, (G-protein-coupled receptor 38, GPR38) is a member of the G-protein coupled receptor 1 family and is identified as the motilin receptor [36]. The found eSNPs were not affecting the expression of the gene they resided in, suggesting the role of these eSNPs as specific regulators targeting ARLTS1 expression. However, no explicit conclusions about the actual causal interaction between these variants and ARLTS1 can be made without further functional validation. The eQTL result gathered in this study was from a relatively small sample set in which analyses encompassed 1 Mb up- and downstream of the ARLTS1 gene. Thus, further validation in larger sample sets and genomic areas are warranted. However, in our results, one eSNP remained significant even after adjustments for multiple testing. Those adjustments are intricate in the case of eQTL analysis and studies with small sample sizes often suffer from a low detection rate after multiple testing adjustment [37]. One possible strategy to deal with this is to accept a relatively high FDR for the sake of later validation in a larger population. Here, no potential candidate eSNPs were excluded from further studies because the α with largest ratio between observed and rejected tests was considered as optimal test size.
Lymphoblastoid cell lines (LCLs) are widely used in eQTL studies because they are an easily accessible source of patient samples of a single cell type. Particularly with prostate cancer, the tumour specimens of multiple foci are hard to collect in large amounts making it difficult to reliably detect disease-associated traits. By using prostate cancer specimens, it is possible to find tissue-specific genetic effects, but the use of lymphoblastoid cell lines or whole blood of familial prostate cancer patients enables one to detect the possible heritable germline differences which may contribute to prostate cancer susceptibility. It has been argued that SNP-transcript approaches using LCLs of small sample sets are underpowered [38], but many studies have been able to find overlapping eQTLs in cell lines and primary tissues [39]–[41]. Our finding of the eSNP in the SPRYD7 gene was endorsed in the co-expression data, in which the ARLTS1 expression levels were significantly correlated with SPRYD7 expression, and in the prostate tumour specimens (Table 3). Additionally, in the data from the ENCODE consortium, the significance and usage of LCLs are justified because the lymphoblastoid cell line (GM12878) is one of the three cell lines in the higher priority Tier 1 cohort [42].
There may be a joint effect between the 13q14 genes. For example, one larger transcript variant that consists of more than one gene has been proposed with SETDB2 and PHF11 [30]. Very little is known about the interactions in this area, particularly the genomic collaborators of the ARLTS1 gene. A recent study identified cellular retinoic acid binding protein 2 (CRABP2) and phosphoglycerate mutase 1 (PGAM1) as novel ARLTS1-binding proteins using the in-frame cDNA library technique [43]. These proteins were not observed in the expression or genotype level of our data, so further studies are needed to elucidate the actual ARLTS1 interacting genes and proteins.
Considering the interactions between ARLTS1 and inflammation pathway genes, it was our hypothesis that ARLTS1 would have interactions with inflammatory genes, as chronic inflammation has been proposed as a prostate cancer risk factor. With MDR analysis, we aimed to investigate the previously observed ARLTS1 connection with immune system processes [10]. Another aim was to test the hypothesis of genes functioning in inflammation processes affecting prostate cancer risk. However, we failed to substantiate our hypothesis, and statistically significant association between ARLTS1 T442C SNP and the inflammatory/immune system SNPs was not found within the 700,000 SNPs analysed. For the analysis, we extracted distinct SNP groups from 4,764 markers found from our data of 700,000 variants in total. One possible reason for the negative results may be the relatively limited sample set of 135 cases and controls because we were not able to generalise the results to the test data. We were also concerned about overfitting the data with MDR.
Our previously found association between ARLTS1 T442C and prostate cancer risk may be caused by the interaction network of different SNPs in the 13q14 region. Variant T442C may be included in a very rare haploblock, or there may still be a missing variant because the region has been connected to other cancers in addition to PCa.
In conclusion, in this study we have shown that the ARLTS1 expression level is influenced by changes in germline expression levels and that the ARLTS1 gene is a quantitative trait locus for 14 eSNPs in the 13q14 region. Thus, our results indicate a more complicated network of changes that increase PCa risk than merely one genetic variant in ARLTS1.
Acknowledgments
We thank all of the patients who participated in our study and acknowledge the contribution of Riina Liikala and Riitta Vaalavuo for technical assistance.
Author Contributions
Conceived and designed the experiments: SS TW DF JPM JS. Performed the experiments: SS VL DF JPM TR. Analyzed the data: SS DF TR JPM. Contributed reagents/materials/analysis tools: JS OK. Wrote the paper: SS TW JS.
References
- 1. Calin GA, Trapasso F, Shimizu M, Dumitru CD, Yendamuri S, et al. (2005) Familial cancer associated with a polymorphism in ARLTS1. N Engl J Med 352: 1667–1676.
- 2. Frank B, Hemminki K, Brenner H, Hoffmeister M, Chang-Claude J, et al. (2006) ARLTS1 variants and risk of colorectal cancer. Cancer Lett 244: 172–175.
- 3. Frank B, Hemminki K, Meindl A, Wappenschmidt B, Klaes R, et al. (2006) Association of the ARLTS1 Cys148Arg variant with familial breast cancer risk. Int J Cancer 118: 2505–2508.
- 4. Frank B, Meyer P, Boettger MB, Hemminki K, Stapelmann H, et al. (2006) ARLTS1 variants and melanoma risk. Int J Cancer 119: 1736–1737.
- 5. Sellick GS, Catovsky D, Houlston RS (2006) Relationship between ARLTS1 polymorphisms and risk of chronic lymphocytic leukemia. Leuk Res 30: 1573–1576.
- 6. Xu J, Dimitrov L, Chang BL, Adams TS, Turner AR, et al. (2005) A combined genomewide linkage scan of 1,233 families for prostate cancer-susceptibility genes conducted by the international consortium for prostate cancer genetics. Am J Hum Genet 77: 219–229.
- 7. Cropp CD, Simpson CL, Wahlfors T, Ha N, George A, et al. (2011) Genome-wide linkage scan for prostate cancer susceptibility in finland: Evidence for a novel locus on 2q37.3 and confirmation of signal on 17q21–q22. Int J Cancer 129: 2400–2407.
- 8. Visakorpi T, Kallioniemi AH, Syvanen AC, Hyytinen ER, Karhu R, et al. (1995) Genetic changes in primary and recurrent prostate cancer by comparative genomic hybridization. Cancer Res 55: 342–347.
- 9. Rokman A, Koivisto PA, Matikainen MP, Kuukasjarvi T, Poutiainen M, et al. (2001) Genetic changes in familial prostate cancer by comparative genomic hybridization. Prostate 46: 233–239.
- 10. Siltanen S, Wahlfors T, Schindler M, Saramaki OR, Mpindi JP, et al. (2011) Contribution of ARLTS1 Cys148Arg (T442C) variant with prostate cancer risk and ARLTS1 function in prostate cancer cells. PLoS One 6: e26595.
- 11. Sfanos KS, De Marzo AM (2012) Prostate cancer and inflammation: The evidence. Histopathology 60: 199–215.
- 12. Kwon EM, Salinas CA, Kolb S, Fu R, Feng Z, et al. (2011) Genetic polymorphisms in inflammation pathway genes and prostate cancer risk. Cancer Epidemiol Biomarkers Prev 20: 923–933.
- 13. De Marzo AM, Platz EA, Sutcliffe S, Xu J, Gronberg H, et al. (2007) Inflammation in prostate carcinogenesis. Nat Rev Cancer 7: 256–269.
- 14. Schleutker J, Matikainen M, Smith J, Koivisto P, Baffoe-Bonnie A, et al. (2000) A genetic epidemiological study of hereditary prostate cancer (HPC) in finland: Frequent HPCX linkage in families with late-onset disease. Clin Cancer Res 6: 4810–4815.
- 15. Trapnell C, Pachter L, Salzberg SL (2009) TopHat: Discovering splice junctions with RNA-seq. Bioinformatics 25: 1105–1111.
- 16. Anders S, Huber W (2010) Differential expression analysis for sequence count data. Genome Biol 11 R106-2010-11-10-r106. Epub 2010 Oct 27.
- 17. Veyrieras JB, Kudaravalli S, Kim SY, Dermitzakis ET, Gilad Y, et al. (2008) High-resolution mapping of expression-QTLs yields insight into human gene regulation. PLoS Genet 4: e1000214.
- 18. George RE, Attiyeh EF, Li S, Moreau LA, Neuberg D, et al. (2007) Genome-wide analysis of neuroblastomas using high-density single nucleotide polymorphism arrays. PLoS One 2: e255.
- 19. Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, et al. (2012) The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483: 603–607.
- 20. Hu R, Qiu X, Glazko G, Klebanov L, Yakovlev A (2009) Detecting intergene correlation changes in microarray analysis: A new approach to gene selection. BMC Bioinformatics 10 20-2105-10-20.
- 21. Ward LD, Kellis M (2012) HaploReg: A resource for exploring chromatin states, conservation, and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids Res 40: D930–4.
- 22. ENCODE Project Consortium (2011) A user's guide to the encyclopedia of DNA elements (ENCODE). PLoS Biol 9: e1001046.
- 23. Hahn LW, Ritchie MD, Moore JH (2003) Multifactor dimensionality reduction software for detecting gene-gene and gene-environment interactions. Bioinformatics 19: 376–382.
- 24. Loza MJ, McCall CE, Li L, Isaacs WB, Xu J, et al. (2007) Assembly of inflammation-related genes for pathway-focused genetic analysis. PLoS One 2: e1035.
- 25. Huang da W, Sherman BT, Lempicki RA (2009) Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc 4: 44–57.
- 26. Huang da W, Sherman BT, Lempicki RA (2009) Bioinformatics enrichment tools: Paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 37: 1–13.
- 27. Petrocca F, Iliopoulos D, Qin HR, Nicoloso MS, Yendamuri S, et al. (2006) Alterations of the tumor suppressor gene ARLTS1 in ovarian cancer. Cancer Res 66: 10287–10291.
- 28. Yendamuri S, Trapasso F, Ferracin M, Cesari R, Sevignani C, et al. (2007) Tumor suppressor functions of ARLTS1 in lung cancers. Cancer Res 67: 7738–7745.
- 29. Siltanen S, Syrjakoski K, Fagerholm R, Ikonen T, Lipman P, et al. (2008) ARLTS1 germline variants and the risk for breast, prostate, and colorectal cancer. Eur J Hum Genet 16: 983–991.
- 30. Zhang Y, Leaves NI, Anderson GG, Ponting CP, Broxholme J, et al. (2003) Positional cloning of a quantitative trait locus on chromosome 13q14 that influences immunoglobulin E levels and asthma. Nat Genet 34: 181–186.
- 31. Clarke E, Rahman N, Page N, Rolph MS, Stewart GJ, et al. (2008) Functional characterization of the atopy-associated gene PHF11. J Allergy Clin Immunol 121: 1148–1154.e3.
- 32. Mabuchi H, Fujii H, Calin G, Alder H, Negrini M, et al. (2001) Cloning and characterization of CLLD6, CLLD7, and CLLD8, novel candidate genes for leukemogenesis at chromosome 13q14, a region commonly deleted in B-cell chronic lymphocytic leukemia. Cancer Res 61: 2870–2877.
- 33. van Everdink WJ, Baranova A, Lummen C, Tyazhelova T, Looman MW, et al. (2003) RFP2, c13ORF1, and FAM10A4 are the most likely tumor suppressor gene candidates for B-cell chronic lymphocytic leukemia. Cancer Genet Cytogenet 146: 48–57.
- 34. Nicholson SE, Hilton DJ (1998) The SOCS proteins: A new family of negative regulators of signal transduction. J Leukoc Biol 63: 665–668.
- 35. Woo JS, Suh HY, Park SY, Oh BH (2006) Structural basis for protein recognition by B30.2/SPRY domains. Mol Cell 24: 967–976.
- 36. Feighner SD, Tan CP, McKee KK, Palyha OC, Hreniuk DL, et al. (1999) Receptor for motilin identified in the human gastrointestinal system. Science 284: 2184–2188.
- 37. Loo LW, Cheng I, Tiirikainen M, Lum-Jones A, Seifried A, et al. (2012) Cis-expression QTL analysis of established colorectal cancer risk variants in colon tumors and adjacent normal tissue. PLoS One 7: e30477.
- 38. Min JL, Taylor JM, Richards JB, Watts T, Pettersson FH, et al. (2011) The use of genome-wide eQTL associations in lymphoblastoid cell lines to identify novel genetic pathways involved in complex traits. PLoS One 6: e22070.
- 39. Bullaughey K, Chavarria CI, Coop G, Gilad Y (2009) Expression quantitative trait loci detected in cell lines are often present in primary tissues. Hum Mol Genet 18: 4296–4303.
- 40. Ding J, Gudjonsson JE, Liang L, Stuart PE, Li Y, et al. (2010) Gene expression in skin and lymphoblastoid cells: Refined statistical method reveals extensive overlap in cis-eQTL signals. Am J Hum Genet 87: 779–789.
- 41. Nica AC, Parts L, Glass D, Nisbet J, Barrett A, et al. (2011) The architecture of gene regulatory variation across multiple human tissues: The MuTHER study. PLoS Genet 7: e1002003.
- 42. ENCODE Project Consortium (2012) Dunham I, Kundaje A, Aldred SF, Collins PJ, et al. (2012) An integrated encyclopedia of DNA elements in the human genome. Nature 489: 57–74.
- 43. Lee S, Lee I, Jung Y, McConkey D, Czerniak B (2012) In-frame cDNA library combined with protein complementation assay identifies ARL11-binding partners. PLoS One 7: e52290.