Table of Contents  
Year : 2021  |  Volume : 23  |  Issue : 5  |  Page : 472-478

Genome-wide association analysis reveals regulation of at-risk loci by DNA methylation in prostate cancer

1 Department of Urology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China Department of Urology, Yale University School of Medicine, New Haven, CT 06510, USA Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China Institute of Estuarine and Coastal Research, East China Normal University, Shanghai 200062, China

Date of Submission18-Nov-2020
Date of Acceptance21-Jan-2021
Date of Web Publication23-Mar-2021

Correspondence Address:
Jian Kang
Department of Urology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200092
Jun Qi
Department of Urology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200092
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/aja.aja_20_21

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Epigenetic changes are potentially important for the ontogeny and progression of tumors but are not usually studied because of the complexity of analyzing transcript regulation resulting from epigenetic alterations. Prostate cancer (PCa) is characterized by variable clinical manifestations and frequently unpredictable outcomes. We performed an expression quantitative trait loci (eQTL) analysis to identify the genomic regions that regulate gene expression in PCa and identified a relationship between DNA methylation and clinical information. Using multi-level information published in The Cancer Genome Atlas, we performed eQTL-based analyses on DNA methylation and gene expression. To better interpret these data, we correlated loci and clinical indexes to identify the important loci for both PCa development and progression. Our data demonstrated that although only a small proportion of genes are regulated via DNA methylation in PCa, these genes are enriched in important cancer-related groups. In addition, single nucleotide polymorphism analysis identified the locations of CpG sites and genes within at-risk loci, including the 19q13.2–q13.43 and 16q22.2–q23.1 loci. Further, an epigenetic association study of clinical indexes detected risk loci and pyrosequencing for site validation. Although DNA methylation-regulated genes across PCa samples are a small proportion, the associated genes play important roles in PCa carcinogenesis.

Keywords: CpG sites; DNA methylation; expression quantitative trait loci; genome-wide association study; prostate cancer

How to cite this article:
Liu Q, Liu G, Martin DT, Xing YT, Weiss RM, Qi J, Kang J. Genome-wide association analysis reveals regulation of at-risk loci by DNA methylation in prostate cancer. Asian J Androl 2021;23:472-8

How to cite this URL:
Liu Q, Liu G, Martin DT, Xing YT, Weiss RM, Qi J, Kang J. Genome-wide association analysis reveals regulation of at-risk loci by DNA methylation in prostate cancer. Asian J Androl [serial online] 2021 [cited 2022 Aug 18];23:472-8. Available from:

Qiang Liu, Gang Liu
These authors contributed equally to this work.

  Introduction Top

Prostate cancer (PCa) is a common malignancy and leading cause of cancer death among men in the US where it is estimated that approximately 191 930 new PCa cases are expected to be diagnosed in 2020.[1] Further, according to a Chinese statistical report, 60 300 PCa cases were confirmed in 72 population-based cancer registries in China from 2009 to 2011, with a mortality of 26 600, resulting in rankings of sixth and eleventh place, respectively, among all cancers affecting men, in China.[2] As the Chinese population continues to age, both the incidence and mortality of PCa are expected to continue increasing over the next two decades. PCa cells are known to harbor a variety of genetic defects, including gene mutations and translocations, all of which provide the cells with new capabilities for dysregulated proliferation, immune system evasion, tissue invasion and destruction, inappropriate survival, and metastasis.[3] Furthermore, there is abundant evidence that along with genetic changes,[4] somatic epigenetic alterations also contribute to PCa carcinogenesis and metastasis.[5],[6],[7],[8] Epigenetic gene inactivation in cancer cells is largely based on transcriptional silencing mediated by the aberrant CpG methylation of CpG-rich promoter regions.[6],[9],[10],[11],[12] DNA methylation is a widely recognized epigenetic marker associated with diagnosis and prognosis in many malignancies.[6],[13],[14] Notably, abnormal DNA methylation has been reported to contribute to the occurrence and progression of PCa.[15],[16] Previous studies of DNA methylation and PCa risk have found that specific promoter sequences are hypermethylated at a higher frequency in PCa tumor tissues than those in nontumor tissues.[4],[6],[10],[17]

Quantitative genetics has made a significant progress in revealing the genetic bases of complex traits, especially in developing sophisticated tools to identify the location of genes that impact complex traits. A region of the genome contributing to the variation in a quantitative trait, also known as quantitative trait loci (QTLs), has been used to study gene expression phenotypes (expression quantitative trait loci [eQTLs]) on a massive scale. DNA methylation quantitative trait loci (meQTLs) have been identified in pathological and physiological contexts. The genome-wide gene expression studies can provide information on genetic variation that affects gene expression levels.[18] We can use linkage or association mapping to map cis- and trans-acting factors for many genes to explain the inheritance patterns. Previous reports showed that cis/trans-meQTLs could target different CpGs and clarified DNA methylation involvement in diseases and cancers.[19],[20],[21],[22],[23] Thus, eQTL analysis is a straightforward and popular method for discovering regulatory genome sites[24],[25],[26] and detecting underlying associations across the genome in PCa studies.[27],[28]

The Cancer Genome  Atlas More Details (TCGA, is a database that collects multiple types of “omics” from thousands of samples and provides public data access to researchers. Accordingly, modified eQTL methods involving calculations of correlations among gene expression, phenotype, DNA methylation, copy number variations, and single-nucleotide polymorphisms (SNPs) have proven to be a powerful tool.[29],[30],[31] However, genome-wide correlations between gene expression, DNA methylation, and clinical phenotypes in PCa are not yet understood.

Therefore, we performed a meQTL analysis of PCa samples from the TCGA database. Although genes regulated via methylation comprise a minor proportion of the genome (0.8%), these genes are enriched in some gene ontology (GO) groups and in important canonical cancer-related pathways. In our study, we mainly identified meQTL pairs on chromosomes 16q and 19q, which have been reported as high-risk regions for PCa, using SNP analyses. We also identified DNA methylation regions and genes associated with clinical indexes at 11q13 and 16q13 and found several genes regulated by DNA methylation that are important for prognosis. According to our meQTL analyses, we selected some androgen receptor (AR) gene-related CpG sites and some sites that are altered during PCa genesis, and correlated them with Gleason score.

  Materials and Methods Top

Data preanalysis

A transcriptome of a prostate adenocarcinoma (PRAD) gene evaluation was downloaded with the TCGA dataset. The nonprimary PCa samples were discarded. Furthermore, the primary tumor samples not providing either gene expression (evaluated with RNA sequencing [RNA-seq]) or DNA methylation information also were excluded from later analysis. As result, a total of 419 samples were used for meQTL identification. Methylation data also were downloaded for the TCGA dataset and were correlated with expression data. Methylation levels at the evaluated sites were estimated using an Illumina Human Infilium 450k BeadChip (Illumina, San Diego, CA, USA). After sample normalization, samples were combined to a methylation matrix according to the gene ID.

meQTL analysis

We used R software MatrixeQTL package[32] (version 2.15.1; R Project for Statistical Computing, Vienna, Austria) for eQTL analysis. Each methylated CpG level was regarded as a continuous variable rather than a discrete variable. Correlations between the methylation level of each CpG site and each gene were evaluated using MatrixeQTL. As DNA methylation mostly influences the expression of genes via promoter regions, we distinguished cis- and trans-regulation for further analysis. The gene and methylation CpG sites were extracted from Illumina Human Infilium 450k BeadChip annotation files and University of California, Santa Cruz (UCSC) reference gene list locations. For reference genes, the gene location was counted from the transcription start site to terminal site. We set a P value threshold of 1 × 10−5 for cis-regulated and 1 × 10−6 for trans-regulated eQTLs. Cis-regulated meQTLs were defined as interacting pairs with tested DNA methylation sites and genes <1 megabase (MB), whereas trans-regulated meQTLs were defined as pairs with genes >1 MB or located on other chromosomes.

Tissue specimens and bisulfite modification of DNA

A total of 70 PCa patients who underwent radical prostatectomy at Xinhua Hospital affiliated to Shanghai Jiao Tong University School of Medicine (Shanghai, China), between July 2014 and January 2017, were enrolled in this study. Informed consent was obtained from all subjects. All cases were histologically confirmed and had clinical stage II or III tumors, no clinical evidence of lymph node or distant metastasis, available pathology specimens, and complete clinical and serum prostate-specific antigen (PSA) data. Patients with missing any variable needed to accurately assign them to a risk group (PSA, tumor [T] stage, or Gleason score) were excluded. Patients with missing information on any available demographic variables were excluded. All samples were retrieved from the archive of the Institute of Pathology, Xinhua Hospital, and were anonymously analyzed in accordance with the guidelines of the Ethics Committee of Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine. We confirm that all experimental protocols were approved by the local ethics committee (approval No. XHEC-D-2016-005).

Patients were categorized as low-, moderate-, or high-risk PCa based on the 2015 National Comprehensive Cancer Network guidelines [Table 1]. All 70 cases included tumor tissues and adjacent nontumor tissues. Prostate tissue samples from these cases were obtained at the time of radical prostatectomy, and tumor cell contents were determined to exceed 70.0% of all tissues. Tissue microdissection yielded tumor and adjacent nontumor tissues from which DNA was extracted using an FFPE DNA Kit (Omega Bio-Tek, Norcross, GA, USA) according to the manufacturer's instructions. Extracted DNA concentrations were measured using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific, Wilmington, DE, USA) and subsequently adjusted to approximately 40 ng ml−1. Prepared DNA was subjected to bisulfite treatment using the EZ DNA Methylation-Gold kit (ZYMO, Orange, CA, USA); converted DNA was dissolved in Tris-EDTA (TE) buffer and stored at or below −20°C for later use.
Table 1: Patient baseline characteristics

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Primer design and PCR

Specific primers were designed for 30 loci using the publicly available MethPrimer software package ( A complete list of the primer pairs is available in [Supplementary Table 1 [Additional file 1]]. Biotinylated reverse primers were substituted with 5′-tailed unlabeled reverse primers (aaccttcaacaccccaaccatata), allowing single (expansive) biotinylated primers to be used for subsequent pyrosequencing. All primers and tag sequences were provided by Sangon (Shanghai, China).

PCR amplification of DNA was conducted using a nested-PCR protocol, using the primers shown in [Supplementary Table 1]. Two rounds of amplification reactions were performed. In the first round, a total reaction volume of 10 μl contains 1 mmol l−1 first primers (1 μl), bisulfite converted DNA (1 μl), ×2 PCR master mix (5 μl), and ddH2O (2 μl). In the second round, the total reaction volume was 30 μl: 4 μl of first-round PCR product as template, plus 10 mmol l−1 second primers (0.5 μl), ×2 PCR Master Mix (15 μl), and ddH2O (10 μl). PCR conditions were as follows: 94°C for 5 min, 30 cycles of denaturation at 94°C for 30 s, annealing at 55°C for 40 s, elongation at 72°C for 30 s, and an additional elongation step at 72°C for 7 min. Approximately 8 μl of each PCR product was separated by electrophoresis using a 3% agarose gel stained with GelRed (Invitrogen, Carlsbad, CA, USA) for 40 min at 120 V and visualized using a Gene Genius bio-imaging system (Syngene, Cambridge, UK).

Pyrosequencing™ methylation analysis

PCR products (20 μl) were added to a mix comprising Streptavidin Sepharose HP (3 μl; GE Healthcare, Dornstadt, Germany) and binding buffer (37 μl; Qiagen, Hilden, Germany). The contents were mixed at 2000g (Centrifuge 5810R, Eppendorf, Hamburg, Germany) for 10 min at room temperature. Using the Vacuum Prep Tool (Qiagen), according to the manufacturer's instructions, single-stranded PCR products were prepared. Sepharose beads with attached single-stranded templates were released into a PSQ 96 Plate Low (Qiagen) containing a mix of 40 μl annealing buffer (Qiagen) and the corresponding sequencing primer at 400 nmol l−1 [Supplementary Table 1]. Pyrosequencing™ reactions were performed in a PyroMark ID System (Qiagen), according to the manufacturer's instructions, using the PyroMark Gold 96 Reagent Kit (Qiagen). CpG site quantification was performed using Pyro Q-CpG methylation software (Qiagen).

Statistical analyses

The cis- and trans-meQTL sites were identified using R package “MatrixeQTL”. The P values were adjusted using Bonferroni method, and cis-meQTLs with false discovery rate (FDR) <1 × 10−5 and trans-meQTLs with FDR <1 × 10−6 were identified as meQTLs. Association significance between clinical indexes and gene expression/methylation levels was identified using analysis of variance (ANOVA) test (P < 0.05 being considered as significant). Gene expression comparison between recurrence and nonrecurrence group was performed using Student's t-test, and P < 0.05 was considered as statistically significant.

  Results Top

Methylated meQTLs in PCa

We subjected PCa samples including 419 samples from TCGA to a meQTL analysis using expression levels evaluated via RNA-seq and CpG site methylation levels in CpG island (CGI) regions. Among 20 321 genes, the methylation levels of 485 513 CpG loci were used for meQTL identification. We identified 5852 cis-regulation and 5 156 662 trans-regulation meQTL pairs in our dataset. Cis-regulation pairs included 1717 genes (8.4% of all genes tested) and 4895 corresponding CpG sites, with an FDR (Bonferroni) of <0.01. Among these pairs, 784 genes (45.7%) were regulated by multiple CpG sites, and 1661 CpG sites (33.9%) regulated multiple genes. We noticed that adenosine triphosphate (ATP)-binding cassette subfamily A member 17, pseudogene (ABCA17P), chromosome 11 open reading frame 85 (C11orf85), and cyclic adenosine monophosphate (cAMP) responsive element binding protein 3 like 3 (CREB3L3) were regulated according to the methylation levels of 135, 95, and 93 nearby CpG sites, respectively, which are the top three CpG sites-related genes, suggesting the importance of DNA methylation on these genes. We also found that significantly high numbers of CpG sites in meQTLs were located on 19q13.2–q13.43 (P = 1 × 10−11) and 16q22.2–q23.1 (P = 1 × 10−11), as shown in [Figure 1] and [Supplementary Table 2 [Additional file 2]], consistent with previous reports.[33],[34] According to GATHER (, the genes with significant involvement are located at 19q13, 16p13, 11q13, and 17q25 [Supplementary Table 3 [Additional file 3]]. Through a GO and pathway analysis using the database for annotation, visualization, and integration discovery (DAVID),[35] we discovered that although most GO groups appeared stochastic, several signaling pathways associated with PCa, including the Janus kinase/signal transducer and activator of tran-ions (JAK/STAT), vascular endothelial growth factor (VEGF), and cytokine–cytokine interactions, also are involved [Supplementary Table 4 [Additional file 4]] and [Supplementary Table 5 [Additional file 5]]. Finally, the analysis of trans-regulation pairs identified 18 999 genes (93.5% of tested genes) and 239 808 CpG sites, with an FDR of <1 × 10−6. In summary, SNP analysis identified the locations of CpG sites and genes within at-risk loci, including the 19q13.2–q13.43 and 16q22.2–q23.1 loci.
Figure 1: Circos plot showing the CpG density of eQTLs. The density of CpG sites associated with eQTLs (from inside to outside) across the genome. Each wedge represents a chromosome and different colors show different cytobands. In the inner track, from inner to outer, the fold line represents the values. Higher values mean that more meQTL-related CpG sites are over-present in these regions. eQTLs: expression quantitative trait loci; meQTL: methylation quantitative trait loci.

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Gene/CpG methylation and clinical information

To identify the mechanism by which these CpG sites affect carcinogenesis and development, we performed a correlation analysis and an ANOVA between gene expression levels and clinical indexes. The evaluated genes included 20 532 genes from the TCGA database that were evaluated via RNA-seq and correlated with clinical information, including primary and secondary Gleason score, node invasion stage, biochemical recurrence indicators, most recent PSA level, and clinical and pathological primary tumor stages. Each clinical index was associated with a number of genes, as shown in [Table 2].
Table 2: Number of genes identified significantly associated with clinical observations

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Genes associated with the same clinical information may affect each other. We further performed a similar analysis to compare methylation sites and clinical information and identified 57 719 CpG sites significantly correlated with the aforementioned clinical information [Table 3].
Table 3: Number of CpG sites significantly associated with clinical observations

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Methylation sites influence clinical performance by regulating gene expression

A canonical function of DNA methylation is the blockade of transcription factors from binding gene regulatory elements, thus inhibiting gene expression. To reduce the FDR and determine causality, we sought CpG sites that were significantly correlated with gene expression via cis-regulation according to methylated QTLs. These genes were positively correlated further with clinical information [Supplementary Table 6 [Additional file 6]], and the CpG sites also were positively correlated with the same clinical indexes. We identified 92 paired methylated QTLs that fulfilled the aforementioned criteria. Most methylated QTLs were associated with Gleason score; a DAVID-based GO analysis[36] revealed that these genes were significantly enriched for DNA repair and cell cytoskeleton. Most of the sites associated with Gleason score were located in 11q13 and 16q13, and their correlated genes included essential meiotic structure-specific endonuclease subunit 2 (EME2), potassium channel tetramerization domain containing 13 (KCTD13), kelch like family member 17 (KLHL17), and WD repeat domain 90 (WDR90). Of these, EME2 is associated with genomic stability maintenance, whereas reports of the other genes in the context of all types of cancer are scarce.

We identified that calcium/calmodulin-dependent serine protein kinase interacting protein 1 (CASKIN1) was enriched with CpGs and CASKIN1 itself was also significantly associated with genes related to biochemical cancer recurrence (P = 0.001; [Figure 2]). According to the methylated QTL algorithm, these CpG sites' methylation levels may act as cis-regulators of CASKIN1, suggesting that the methylation of nearby CpG sites probably influences the expression of CASKIN1 and thus affects the recurrence in these cases.
Figure 2: The violin plot of relative expression of CASKIN1 between biomedical recurrence and nonrecurrence subgroup evaluated by TCGA. The expression of CASKIN1 is significantly different between recurrence and nonrecurrence group (P = 0.001). CASKIN1: calcium/calmodulin-dependent serine protein kinase interacting protein 1; TCGA: The Cancer Genome Atlas.

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Methylated QTLs in PCa-related genes

An analysis of these regulation pairs should include the regulatory status in both prostate and PCa cells. To extract useful information for later analysis, we selected meQTLs concerning genes associated with PCa genesis and metastasis.[37],[38],[39],[40],[41],[42] Ninety-two genes were used for meQTL selection. In our meQTL dataset, 38 cis-regulated meQTLs existed near these sites, and 1753 trans-regulated eQTLs were identified. Genes of interest among cis-eQTLs included alanyl aminopeptidase, membrane (ANPEP), activating transcription factor 3 (ATF3), B cell leukemia/lymphoma 2 (BCL2) associated X (BAX), and early growth response 1/3 (EGR1/3), suggesting that the expression levels of these PCa-related genes are regulated by the methylation levels of nearby CpG sites. For further analysis, these gene and methylation findings were validated in 70 pairs of clinical tumorous and nontumorous tissue samples. The patients from whom samples were collected were classified as low, moderate, and high risk according to Gleason score, mean PSA, and tumor/node/metastasis (TNM) clinical staging; a variety of tissues from different risk levels were subjected to methylation quantification via pyrosequencing™.

In our results, the false discovery rate was relatively high because we did not detect the expression levels of the selected meQTL-related genes. However, we still detected several differences in methylation sites between normal and tumorous samples [Figure 3]. These sites included meQTL pairs involving FosB proto-oncogene, activator protein-1 (AP-1) transcription factor subunit (FOSB), ANPEP, and ras-related dexamethasone-induced 1 (RASD1). In tumorous samples, the methylation levels at these sites, particularly cg20664996 near RASD1, were low. We also noticed that in high- and moderate-risk samples, the methylation levels at this site reached 0. RASD1 has been reported as an apoptosis- and ras-related gene that prevents aberrant cell growth in several cell lines.[43] In short, an epigenetic association study of clinical indexes detected risk loci and pyrosequencing for site validation. Although the number of DNA methylation-regulated genes across PCa samples is a small proportion, the associated genes play essential roles in PCa carcinogenesis.
Figure 3: Four CpG loci that were significantly differently methylated according to the validated dataset. In each CpG locus, the left dots indicate the relative methylation level of the normal tissues and the right is the corresponding cancerous tissues, the P values between the normal tissues and cancerous tissues are shown from left to right.

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  Discussion Top

DNA methylation is one among the most common and well-characterized epigenetic changes in PCa.[5],[6],[7],[8],[9],[10],[11] The importance of large-scale methylome studies has increased in biomedical fields, and high-throughput sequencing technologies promise sensitive, quantitative, and high-resolution large-scale DNA methylation analyses. A genome-wide association study (GWAS) based on QTL analysis is an effective method with which cancer-related genes and risk loci throughout the genome can be studied;[14],[15],[44] similarly, the usefulness of methylation-based GWAS has been reported.[45] However, despite reports on the association of DNA methylation with carcinogenesis and development in the prostate,[46],[47],[48],[49] genome-wide analyses of the associations between gene expression, DNA methylation, and clinical information related to PCa continue to yield vague results. Here, we utilized TCGA to obtain DNA methylation data from more than 400 000 CpG sites, expression levels of over 20 000 genes, and clinical indexes for 419 PCa samples, to perform a methylated QTL analysis in which we identified 5852 cis-regulating pairs comprising 1712 genes. Compared with previous breast cancer studies, only 0.8% of genes associated with PCa are affected by DNA methylation, and these genes exhibit significant involvement in several pathways that previously have been reported to play important roles in PCa genesis, including the JAK/STAT and VEGF pathways.[50],[51] We also noticed that the associated genes and locations were significantly enriched at several genome locations, including 19q13.2–q13.43 and 16q22.2–q23.1. As these are cis-interactions within 1 MB, the regulated genes also are enriched at these sites. Previous reports of SNPs and PCa noted an association between SNPs at 19q13 and PCa risk.[52] Another GWAS of a Chinese population demonstrated 19q13 as a novel risk locus for PCa,[33] and a decade-long study detected chromosomal deletion and gene suppression at 16q22 in PCa.[53] It was worth noting that this region was reported to show a loss of heterogeneity in breast cancer.[54] In addition to somatic mutations, SNPs, and copy number variations, our data suggest that DNA methylation may also influence PCa genesis and probably plays important roles in this process by regulating the transcription of this gene nexus.

Although a previous study based on eQTL algorithms aimed to define the relationships between genomic, epigenomic, and transcriptomic changes,[26],[31] the link between the data obtained and clinical information remains unclear. Heyn et al.[55] reported a TCGA QTL analysis using 13 tumor types from TCGA, including PCa, but their study lacks correlations to PCa-specific clinical variables such as Gleason score and PSA. Their data sets (cancer samples and adjacent normal tissues) did not include control subjects without the disease; future studies are needed to determine if GWAS risk alleles exhibit similar relationships in cancer-unrelated donors.

To further investigate the relationships of DNA methylation and gene expression with clinical information, we performed an association study between the three pairs (expression-methylation, methylation-clinical information, and clinical information-expression) and identified 92 genes regulated by CpG site methylation. Furthermore, we associated the expression levels of these genes with clinical information, particularly the Gleason score. GO analysis revealed that these 92 genes were significantly enriched with respect to DNA repair and cell cytoskeleton, suggesting that the genes probably direct the development and prognosis of PCa. Of these genes, we noted that EME2 previously was reported to maintain genomic stability,[56] whereas the roles of the other genes were poorly elucidated. A locus analysis of these 92 genes demonstrated significant enrichment at 11q13 (P = 0.008, 3 genes) and 16q13 (P < 0.0001, 6 genes). The genes identified were enriched according to an in-house R script of the hypergeometric distribution, using the genes assayed for meQTLs as background. Enriched genomic loci with P < 0.01 were identified, and some genes associated with the mQTL were significantly enriched for DNA repair and cell cytoskeleton. Most of the sites associated with Gleason score were in 11q13 and 16q13, and their correlated genes included EME2,[56] KCTD13,[57] KLHL17,[58] and WDR90.[59] These special genes played critical roles in the biological and pathological characteristics of an organism.

Studies based on SNPs or somatic mutation profiling have shown that 11q13 and 16q13 loci are associated with aggressiveness, invasion, and poor prognosis of PCa. Our results show that epigenetic alterations in these regions are associated with genes considered necessary for the progression of PCa and suggest that methylation at 11q13 and 16q13 loci is essential to PCa progression. We were the first to prove that CASKIN1 was enriched with CpGs, and CASKIN1 itself was also significantly associated with genes related to biochemical cancer recurrence. Although the mechanism of how CASKIN1 affects PCa is unclear, its interacting protein, CASK has been associated with survival of patients with colorectal cancer.[60]

For validation, we collected 70 clinical tissue samples from 2014 to 2017. Although these patients are Asian, we used the 2020 National Comprehensive Cancer Network (NCCN) guidelines as a criterion to classify distinct groups. Some studies suggested racial differences in PCa. Non-Whites were associated with a significantly higher likelihood of presenting with high-risk PCa (22.9% of Hispanics, 23.8% of Blacks, and 23.3% of those of other races compared with 19.0% of Whites; all P < 0.001).[61] Age-adjusted PCa mortality in black men was more than double that of white men (42.0/100 000 vs 18.7/100 000). Asian men had a lower incidence of PCa and death than white men. Chinese and Japanese men have a relatively low risk of PCa than their European and North American counterparts.[62]

The method we describe is based on detecting methylation and expression differences between samples of PCa. Therefore, it aims to identify correlations that occur within particular subsets of cases. For example, we found that some genes of interest among cis-eQTLs have played essential roles in PCa, including ANPEP,[63],[64] ATF3,[65],[66] BAX,[67] and EGR1/3.[68] These genes are involved in tumorigenesis, metastasis, and recurrence of PCa.

We selected 30 high-confidence CpG sites that have been reported to associate with PCa-related genes and evaluated methylation at related CpG sites [Supplementary Table 7 [Additional file 7]]. Despite a relatively high FDR, we determined differences in the methylation levels of several CpG sites between cancerous and noncancerous tissues (associated genes included ANPEP and FOSB). FDR is a tremendous concern for this study. To reduce FDR, we first enrolled the samples using the inclusion criteria described in the methods part. Second, we selected the P-value of meQTL pairs as 1 × 10−5 instead of 0.01/0.05. Last, the FDR values were calculated using Bonferroni, which is the most rigorous method available, and 0.01 was used for cutoff.

We further identified a correlation between the Gleason score and methylation level at cg20664996, a CpG location associated with RASD1 expression, according to an eQTL analysis. RASD1 is a 30 kDa G-protein that belongs to the Ras superfamily of small GTPases. Several robustly upregulated bicalutamide-dependent genes were identified by micro-array in LNCaP-ARW741L cells that were not significantly enhanced by dihydrotestosterone (DHT) in the LNCaP-LacZ line, including RASD1.[69] Liu et al.[70] showed that formononetin inhibited cell proliferation and induced apoptosis in DU-145 cells throughout the RASD1/mitogen-activated protein kinase (MAPK)/BAX pathway in PCa. The selected high-confidence CpG sites are reported to associate with some PCa-related genes, including FOSB,[71] secretoglobin family 1A member 1 (SCGB1A1),[72] mucin 16 (MUC16),[73] alpha-methylacyl-coenzyme A racemase (AMACR),[74] and glycine N-methyltransferase (GNMT).[75] Emerging evidence has shown that these genes may be useful diagnostic and prognostic biomarkers for PCa.

  Conclusions Top

We demonstrated that novel meQTL pairs were associated with PCa, and for the first time, we identified the locations of CpG sites and genes within at-risk loci, including the 19q13.2–q13.43 and 16q22.2–q23.1 loci. We further used pyrosequencing for validation and identified several genes that may impact the development and prognosis of PCa.

  Author Contributions Top

QL conceived and participated in its design, searched databases, and extracted and assessed studies. QL and GL carried out the statistical analysis and interpretation of data and drafted the main manuscript. QL and DTM prepared the tables and figures. QL, YTX, and RMW prepared the figures and supplementary tables. QL, JK, and JQ participated in the conceptualization and design of the manuscript, performed the selection of studies, and drafted the manuscript. All authors reviewed and approved the final manuscript.

  Competing Interests Top

All authors declare no competing interests.

  Acknowledgments Top

We thank all the study participants. This study was supported by the Projects of National Science Foundation of China (No. 81070600 and 81570684) and Projects of the Shanghai Committee of Science and Technology, China (No. 14430720800, 134119a0600, and 11ZR1424100).

Supplementary Information is linked to the online version of the paper on the Asian Journal of Andrology website.

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  [Figure 1], [Figure 2], [Figure 3]

  [Table 1], [Table 2], [Table 3]


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