Differentially methylated CpG sites associated with the high-risk group of prostate cancer

Anastasiya Kobelyatskaya 1 , 2 , Elena Pudova 1 , Maria Fedorova 1 , Kirill Nyushko 3 , Boris Alekseev 3 , Andrey Kaprin 3 , Dmitry Trofimov 4 , Gennady Sukhikh 4 , Anastasia Snezhkina 1 , George Krasnov 1 , Sergey Razin 2 , and Anna Kudryavtseva 1
  • 1 Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia
  • 2 Institute of Gene Biology, Russian Academy of Sciences, Moscow, Russia
  • 3 National Medical Research Radiological Center, Ministry of Health of the Russian Federation, Moscow, Russia
  • 4 National Medical Research Center for Obstetrics, Gynecology and Perinatology named after Academician V.I. Kulakov, Ministry of Health of the Russian Federation, Moscow, Russia
Anastasiya Kobelyatskaya
  • Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia, http://www.eimb.ru/
  • Institute of Gene Biology, Russian Academy of Sciences, Moscow, Russia, http://www.genebiology.ru/
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, Elena Pudova
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  • Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia, http://www.eimb.ru/
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, Maria Fedorova
  • Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia, http://www.eimb.ru/
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, Kirill Nyushko
  • National Medical Research Radiological Center, Ministry of Health of the Russian Federation, Moscow, Russia, https://nmicr.ru/
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, Boris Alekseev
  • National Medical Research Radiological Center, Ministry of Health of the Russian Federation, Moscow, Russia, https://nmicr.ru/
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, Andrey Kaprin
  • National Medical Research Radiological Center, Ministry of Health of the Russian Federation, Moscow, Russia, https://nmicr.ru/
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, Dmitry Trofimov
  • National Medical Research Center for Obstetrics, Gynecology and Perinatology named after Academician V.I. Kulakov, Ministry of Health of the Russian Federation, Moscow, Russia, https://en.ncagp.ru/
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, Gennady Sukhikh
  • National Medical Research Center for Obstetrics, Gynecology and Perinatology named after Academician V.I. Kulakov, Ministry of Health of the Russian Federation, Moscow, Russia, https://en.ncagp.ru/
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, Anastasia Snezhkina
  • Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia, http://www.eimb.ru/
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, George Krasnov
  • Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia, http://www.eimb.ru/
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, Sergey Razin
  • Institute of Gene Biology, Russian Academy of Sciences, Moscow, Russia, http://www.genebiology.ru/
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and Anna Kudryavtseva
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  • Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, Moscow, Russia, http://www.eimb.ru/
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Abstract

Prostate cancer (PC) is one of the most common and socially significant oncological diseases among men. Bioinformatic analysis of omics data allows identifying molecular genetic changes associated with the disease development, as well as markers of prognosis and response to therapy. Alterations in DNA methylation and histone modification profiles widely occur in malignant tumors. In this study, we analyzed changes in DNA methylation in three groups of PC patients based on data from The Cancer Genome Atlas project (TCGA, https://portal.gdc.cancer.gov): (1) high- and intermediate-risk of the tumor progression, (2) favorable and unfavorable prognoses within the high-risk group, and (3) TMPRSS2-ERG-positive (tumors with TMPRSS2-ERG fusion transcript) and TMPRSS2-ERG-free cases within the high-risk group. We found eight CpG sites (cg07548607, cg13533340, cg16643088, cg18467168, cg23324953, cg23753247, cg25773620, and cg27148952) hypermethylated in the high-risk group compared with the intermediate-risk group of PC. Seven differentially methylated CpG sites (cg00063748, cg06834698, cg18607127, cg25273707, cg01704198, cg02067712, and cg02157224) were associated with unfavorable prognosis within the high-risk group. Six CpG sites (cg01138171, cg14060519, cg19570244, cg24492886, cg25605277, and cg26228280) were hypomethylated in TMPRSS2-ERG-positive PC compared to TMPRSS2-ERG-negative tumors within the high-risk group. The CpG sites were localized, predominantly, in regulatory genome regions belonging to promoters of the following genes: ARHGEF4, C6orf141, C8orf86, CLASP2, CSRNP1, GDA, GSX1, IQSEC1, MYOF, OR10A3, PLCD1, PLEC1, PRDM16, PTAFR, RP11-844P9.2, SCYL3, VPS13D, WT1, and ZSWIM2. For these genes, analysis of differential expression and its correlation with CpG site methylation (β-value level) was also performed. In addition, STK33 and PLCD1 had similar changes in colorectal cancer. As for the CSRNP1, the ARHGEF4, and the WT1 genes, misregulated expression levels were mentioned in lung, liver, pancreatic and androgen-independent prostate cancer. The potential impact of changed methylation on the mRNA level was determined for the CSRNP1, STK33, PLCD1, ARHGEF4, WT1, SCYL3, and VPS13D genes. The above CpG sites could be considered as potential prognostic markers of the high-risk group of PC.

1 Introduction

Prostate cancer (PC, MeSH - D011471) is a common malignant neoplasm in men worldwide [1]. Currently, to predict the course of PC, patients are stratified into appropriate risk groups based on the following criteria: pathological stage of the tumor (pT), prostate-specific antigen (PSA) level before surgery, and Gleason score [2]. However, these criteria often incorrectly reflect the aggressive tumor phenotype. The solution to this problem can be the study of tumor molecular genetic characteristics using modern approaches. Bioinformatic analysis of omics datasets (genome, transcriptome, and methylome) enables identifying molecular changes that can be associated with the tendency of a tumor to disseminate or can predict the time from radical prostatectomy to disease progression.

Epigenetic changes occur in all types of malignant tumors and include perturbation of both the DNA methylation and the histone modification patterns [3], [4]. These changes can be associated with various clinical and pathological characteristics and, in some cases, allow to conclude about the prognosis [3]. Aberrant CpG methylation was found in various malignant tumors even at the early stages [4]. However, it is necessary to clearly distinguish between the role of aberrant methylation of the promoter regions and global hyper/hypomethylation throughout the genome, including intergenic and intronic regions. Hypermethylation of CpG islands can contribute to genetic instability and enhance cell growth, proliferation, and invasion [4]. For PC, global DNA hypomethylation is almost always associated with the late stages of the disease and is usually found in metastatic tissues [5].

The most commonly described change of the methylation pattern in PC concerns the promoter of the GSTP1 gene [6], which is involved in DNA repair [7]. Its hypermethylation was detected in 90% of PC samples and 50% of hyperplasia prone to malignancy [8]. The GSTP1 [9], APC [10], RASSF1A [11], RARB [3], CCND2 [12], EphA5 [13], and PTGS2 [14] genes were detected to be hypermethylated in PC compared with adjacent normal prostate tissues. Promoter DNA methylation of GSTP1 [15], RARB [16], RASSF1 [17], and APC [18] was widely studied as a non-invasive marker for PC early diagnosis. Hypermethylated GSTP1 promoter detecting in blood or urine are associated with the presence of PC [17]. Tumors carrying a mutation in the IDH1 gene, which amount 1% of all PC cases, also have an increased level of DNA methylation [19].

In some cases, subgroups of malignant tumors are featured with the so-called CpG island methylator phenotype (CIMP) that is characterized by intense hypermethylation of the gene promoter regions and is associated with an unfavorable prognosis in colorectal cancer [20], [21]. The existence of the CIMP was firstly demonstrated for colorectal cancer and then was shown for bladder, breast, endometrial, gastric, hepatocellular, and lung cancer, as well as gliomas [21]. The presence of the TMPRSS2-ERG fusion transcript indicates one of the most common molecular subtypes of PC. The presence of this fusion transcript has been considered as a marker of unfavorable prognosis in PC [19]. CIMP has not been found in PC, however, higher overall genome methylation level was shown in the TMPRSS2-ERG-negative cases of PC [22]. It was reported that among TMPRSS2-ERG-positive samples methylation clusters were found; moreover one-third of TMPRSS2-ERG-positive samples of PC has been seen to be characterized by hypermethylated cluster [19]. However, the association of aberrant DNA methylation with the PC prognosis currently remains unclear [23].

The study aims to identify differentially methylated CpG sites associated with the high-risk group of PC, including unfavorable prognosis within the group and TMPRSS2-ERG molecular subtype, based on The Cancer Genome Atlas (TCGA) project data.

2 Materials and methods

2.1 Dataset

The present study includes PC methylation profiling data (Illumina 450k methylation arrays) and RNA-seq data from TCGA project (TCGA-PRAD) [24]. The cohort included PC patients belonging to the Caucasian population. The patients were not receiving neoadjuvant therapy. The cohort (n = 358) was divided into two PC groups, high (n = 251) and intermediate (n = 107) risk, according to the classification of D’Amico (Table 1) [2]. High-risk group (n = 251) was divided into favorable (n = 83) and unfavorable (n = 21) prognoses groups based on biochemical recurrence (postoperative PSA ≥ 0.2 ng/ml), and TMPRSS2-ERG-positive (n = 75) and TMPRSS2-ERG-negative (n = 79) groups.

Table 1:

Clinicopathologic characteristics of the cohort.

CriteriaParameterHigh risk, nIntermediate risk, n
Gleason score6813
78294
851
9108
102
Mean preoperative PSA (ng/ml)13.26.7
Biochemical recurrence (postoperative PSA ≥ 0.2 ng/ml)Yes452
No18387
Mean age (yr)6260
Pathologic tumor stage (pT)pT2a5
pT2b42
pT2c1996
pT3a118
pT3b102
pT47
Pathologic lymph nodes (pN)pN017281
pN163
Clinical distant metastases (cM)cM0251107
cM1
Molecular subtype1-ERG8034
2-ETV1156
3-ETV493
4-FLI111
5-SPOP137
6-FOXA141
7-IDH12
8-other3522
Total251107

2.2 Methods

The analysis of differential CpG methylation was carried out in the R statistical environment (v. 3.5.2) [25]. For comparison of β-value between groups, BiSeq (v.1.22.0) [26] package was used. The Mann–Whitney test, β-regression, and logistic regression modeling were applied. We considered CpG sites (Illumina CpG IDs – cg#) with p-value <0.05 in all three tests as differentially methylated. To retrieve CpG sites mostly differentiating two patient groups, fold-change (Log2FC) and Δβ-value between comparison groups were calculated. Spearman’s rank correlation (standart “cor.test” function) analysis of detected CpG sites with the high-risk group was fulfilled. CpG site annotation (genomic position, gene name, promoter or enhancer) was accomplished by Ensembl [27] and GeneHancer [28] databases, UCSC browser [29], and annotatr (v.1.8.0) [30]. When selecting top-ranked CpG sites the preference was given to ones located in regulatory genomic regions (promoters or enhancers).

Differential expression analysis was carried out on the same samples using edgeR package (v.3.24.3) [31]. The trimmed mean of M-values (TMM) normalization method of count matrix was used; Quasi-likelihood (QLF), Exact Fisher’s (ET), and Mann–Whitney tests were applied for detecting differences between comparison groups. In addition, changes in gene expression level between the comparison groups (Log2FC) and overall gene expression level in the cohort (Log2CPM) were calculated. Spearman’s rank correlation (standart “cor.test” function) analysis of identified CpG sites with their gene expression level was fulfilled. Differentially expressed genes were annotated by biomaRt package (v.2.38.0) [32], [33].

3 Results

3.1 Differentially methylated CpG sites associated with the high-risk group of PC

We identified eight hypermethylated CpG sites (p-value ≤0.05; FC >1; Δβ-value >0) under comparing high and intermediate-risk groups: cg07548607, cg13533340, cg16643088, cg18467168, cg23324953, cg23753247, cg25773620, and cg27148952 (Figure 1a). These CpG sites were located in the promoters of the following genes [27], [28], [29], [30]: ZSWIM2, GDA, CSRNP1, IQSEC1, PLEC1, STK33, PLCD1, and C6orf141, respectively (Table 2).

Figure 1:
Figure 1:

Manhattan plot of methylation level (β-value) of detected CpG sites among the studied groups of PC. (a) Differentially methylated CpG sites associated with the high-risk group of PC. (b) Differentially methylated CpG sites associated with the unfavorable prognosis within the high-risk group of PC. (c) Differentially methylated CpG sites associated the TMPRSS2-ERG molecular subtype within the high-risk group of PC.

Citation: Journal of Integrative Bioinformatics 17, 4; 10.1515/jib-2020-0031

Table 2:

Differentially methylated CpG sites associated with the high-risk group of PC.

CpG site ID (Illumina 450k)Position (hg19)Gene (region)Linear regression, p-valueLogistic regression, p-valueMann–Whitney, p-valueSpearman’s correlation coefficient, rsp-valueΔβ-valueFC
cg07548607chr2: 187713964ZSWIM2 (promoter)3.78E-02*2.36E-02*1.76E-07*0.244.28e-06*0.122.14
cg13533340chr9: 74764495GDA (promoter)2.77E-02*7.36E-03*1.80E-04*0.243.65e-06*0.102.02
cg16643088chr3: 39188743CSRNP1 (promoter)1.02E-02*6.14E-03*2.01E-07*0.288.89e-08*0.111.67
cg18467168chr3: 13114803IQSEC1 (promoter)4.65E-03*2.26E-03*5.34E-04*0.185.86e-04*0.071.70
cg23324953chr8: 145013728PLEC1 (promoter)3.33E-04*8.77E-03*7.37E-03*0.171.37e-03*0.071.85
cg23753247chr11: 8615842STK33 (promoter flank)1.97E-02*8.05E-05*1.03E-02*0.201.56e-04*0.061.81
cg25773620chr3: 38071309PLCD1 (promoter)4.47E-04*3.78E-03*5.32E-04*0.192.91e-04*0.071.68
cg27148952chr6: 49518347C6orf141 (promoter flank)5.14E-03*1.44E-02*1.56E-02*0.187.06e-04*0.061.63
cg00063748chr1: 3352986PRDM16 (promoter)4.85E-02*4.15E-02*1.38E-02*−0.222.70E-02−0.06−1.15
cg06834698chr11: 7961985OR10A3 (promoter)1.18E-02*1.51E-02*4.05E-02*−0.194.36E-02−0.09−1.18
cg18607127chr5: 175630310RP11-844P9.2 (promoter)1.24E-02*3.31E-02*4.83E-03*−0.241.36E-02−0.08−1.11
cg25273707chr11: 76037066TF binding site (ENSR00001009205)4.72E-02*4.31E-02*1.59E-03*−0.321.04E-03−0.09−1.18
cg01704198chr3: 33757893CLASP2 (promoter)6.15E-03*3.25E-02*1.03E-02*0.241.35E-020.081.15
cg02067712chr13: 28364724GSX1 (promoter flank)2.49E-02*2.94E-02*8.70E-03*0.259.61E-030.141.30
cg02157224chr8: 38368889C8orf86 (promoter flank)2.05E-02*2.53E-02*5.62E-04*−0.352.77E-04−0.10−1.12
cg01138171chr2: 131724244ARHGEF4 (intron)2.25E-06*1.73E-02*6.71E-15*−0.617.26E-17−0.26−1.46
cg14060519chr10: 95222867MYOF (promoter)3.25E-02*3.91E-02*6.53E-16*−0.692.43E-23−0.37−2.12
cg19570244chr11: 32457158WT1 (promoter)4.53E-02*8.92E-05*2.14E-10*−0.441.52E-08−0.16−3.65
cg24492886chr1: 28474511PTAFR (promoter flank)4.16E-03*9.70E-03*8.81E-12*−0.524.98E-12−0.19−1.51
cg25605277chr1: 169859761SCYL3 (promoter)6.20E-03*7.27E-03*5.64E-09*−0.441.25E-08−0.17−1.25
cg26228280chr1: 12514410VPS13D (enhancer)2.44E-02*1.30E-05*1.05E-11*−0.545.56E-13−0.18−1.31

*p-value ≤ 0.05.

The differential expression analysis showed that just CSRNP1, STK33, and PLCD1 genes were significantly downregulated (p-value ≤0.05) in the high-risk group (Table 3). Moreover, expression levels of the CSRNP1 and STK33 genes negatively correlated with β-values of their CpG sites; Spearman’s rank correlation coefficients were −0.19 and −0.13 respectively (Table 3).

Table 3:

Differentially expressed genes associated with the high-risk group of PC.

GeneFCLogCPMQuasi-likelihood test, p-valueExact Fisher’s test, p-valueMann–Whitney test, p-valueSpearman’s correlation coefficient, rsSpearman’s correlation coefficient, p-value
ZSWIM21.97−4.581.49E-04*2.88E-02*6.01E-020.018.46E-01
GDA1.081.027.68E-017.93E-012.20E-01−0.192.90E-04*
CSRNP1−1.306.491.20E-03*7.06E-04*2.86E-03*−0.193.70E-04*
IQSEC1−1.026.654.35E-014.44E-012.84E-010.062.90E-01
PLEC1−1.016.325.69E-015.68E-011.98E-010.039.00E-01
STK33−1.271.743.33E-03*2.15E-03*2.12E-04*−0.131.79E-02*
PLCD1−1.134.281.05E-03*1.03E-03*2.96E-04*−0.097.69E-02
C6orf1411.16−0.952.74E-012.60E-017.10E-01−0.062.51E-01
PRDM161.200.432.36E-012.19E-019.94E-010.222.55E-02*
OR10A3−1.38−4.143.51E-014.72E-012.42E-010.336.91E-04*
CLASP21.005.809.83E-019.71E-016.04E-010.103.13E-01
GSX16.15−4.426.19E-08*3.60E-03*3.84E-01−0.037.90E-01
C8orf861.40−2.771.52E-011.30E-018.45E-02−0.587.40E-11*
ARHGEF41.283.833.50E-03*2.92E-03*1.02E-03*−0.226.98E-03*
MYOF1.166.631.77E-011.61E-012.90E-01−0.036.89E-01
WT12.680.551.32E-05*1.63E-06*1.13E-07*−0.234.42E-03*
PTAFR−1.132.622.45E-012.37E-018.62E-010.092.50E-01
SCYL31.314.501.78E-09*9.24E-10*5.89E-09*−0.402.11E-07*
VPS13D1.166.941.68E-03*1.91E-03*3.22E-03*−0.251.56E-03*

*p-value < 0.05.

According to literature, cancer-associated hypermethylation was previously shown for the STK33 [34], [35], [36], IQSEC1 [37], and PLCD1 [38], [39], [40], [41], [42], [43] genes, however, a decrease in the expression was observed only for IQSEC1 [37] and PLCD1 [38], [39], [40], [41], [42], [43] (Table 4). CSRNP1 and C6orf141 were found to be downregulated with no studied methylation status.

Table 4:

Methylation and gene expression data reported for identified genes.

GenePathologyAlterationRelationReference
CSRNP1Hepatocellular carcinomaNo methylation data,

downregulated
Tumor progression[44]
Lung squamous cell carcinomaNo methylation data,

downregulated
Tumor progression[45]
IQSEC1Non-small cell lung cancerHypermethylated,

downregulated
Tumor progression[37]
PLEC1Pancreatic cancerNo methylation data,

upregulated
[46]
STK33Colorectal cancerHypermethylated,

no expression data
Tumor progression[34], [35]
Head and neck cancersHypermethylated,

no expression data
Tumor progression[36]
PLCD1Colorectal cancerHypermethylated,

downregulated
Tumor progression[40], [42]
Breast cancerHypermethylated,

downregulated
[38]
Gastric cancerHypermethylated,

downregulated
[39]
Chronic myeloid leukemiaHypermethylated,

downregulated
[41]
Endometrial cancerHypermethylated,

downregulated
[43]
C6orf141Oral squamous cell carcinomaNo methylation data,

downregulated
Tumor progression[47]
PRDM16Lung cancer cell lines (A549 and HTB-182)Hypermethylated,

downregulated
[48]
Non–small cell lung cancerHypermethylated,

downregulated
[49]
Gastric cancerNo methylation data,

downregulated
Unfavorable prognosis[50]
CLASP2Muscle-invasive bladder urothelial cancerNo methylation data,

upregulated
High-stage tumors,

lymph node metastases
[51]
ARHGEF4Pancreatic cancerNo methylation data,

upregulated
Unfavorable prognosis[52], [53]
MYOFPancreatic cancerNo methylation data,

upregulated
Poor survival outcome[54], [55]
Triple-negative breast cancerNo methylation data,

upregulated
Poor survival outcome[56]
WT1Prostate cancerNo methylation data,

upregulated
Androgen-independent stage[57], [58]
PTAFRBreast cancerNo methylation data,

Upregulated
Bone metastases[59]

3.2 Differentially methylated CpG sites associated with the unfavorable prognosis in the high-risk group of PC

We identified seven differentially methylated CpG sites (p-value ≤0.05) in the unfavorable prognosis group of PC compared with the favorable one: cg00063748, cg06834698, cg18607127, cg25273707, cg01704198, cg02067712, and cg02157224. Among them, the cg01704198 and cg02067712 sites were hypermethylated (FC >1; Δβ-value >0), when other CpG sites were characterized by the hypomethylation status (FC <1; Δβ-value <0) (Figure 1b). Six identified CpG sites were localized in the promoter regions of the PRDM16, OR10A3, RP11-844P9.2, CLASP2, GSX1, and C8orf86 genes; the cg25273707 CpG site belonged to the transcription factor (TF)-binding region (Table 2) [27], [28], [29], [30].

Differential expression analysis revealed no significant expression changes of the above genes between the unfavorable prognosis group and the favorable one within the high-risk group of PC (Table 3).

However, several studies noticed that the PRDM16 gene was hypermethylated and downregulated in lung cancer (Table 4) [48], [49], [50]. The CLASP2 gene showed differential expression levels in lung, gastric, and bladder cancers [51].

3.3 Differentially methylated CpG sites associated with the TMPRSS2-ERG molecular subtype in the high-risk group of PC

When studying the molecular subtype of TMPRSS2-ERG in the high-risk group, we identified six hypomethylated CpG sites (p-value ≤0.05; FC >1; Δβ-value >0) (cg01138171, cg14060519, cg19570244, cg24492886, cg25605277, and cg26228280) that were localized in the intron of ARHGEF4, and promoters of MYOF, WT1, PTAFR, SCYL3, and VPS13D, respectively (Figure 1c, Table 2) [27], [28], [29], [30].

Differential expression analysis showed that the ARHGEF4, WT1, SCYL3, and VPS13D genes were significantly upregulated (p-value ≤0.05) in TMPRSS2-ERG-positive tumors (Table 3). Furthermore, expression levels of the above genes negatively correlated with β-values of their CpG sites; Spearman’s rank correlation coefficients were −0.22, −0.23, −0.40, and −0.25 respectively (Table 3).

Presently, there are no data on the methylation status of ARHGEF4, MYOF, WT1, PTAFR, SCYL3, and VPS13D in the literature (Table 4). Nevertheless, ARHGEF4 [52], [53], MYOF [54], [55], [56], WT1 [57], [58], PTAFR [59] were upregulated in pancreatic, breast, and prostate cancers.

4 Discussion

DNA methylation is one of the main mechanisms of gene expression regulation. In adult normal somatic cells, oncogene silencing is maintained by the promoter methylation, when promoter methylation of tumor suppressor genes does not occur [4]. Altered DNA methylation leads to the deregulation of gene expression patterns and disruption of crucial cellular processes, such as DNA repair, cell adhesion, cell cycle control, and apoptosis, contributing to the development of cancer [4], [60]. Cancer-associated genome-wide hypomethylation more often occurs than individual gene hypomethylation [60]. At the same time, hypermethylation can be seen in promoters of individual genes in carcinogenesis a lot [60]. In this study, we found both hypermethylation and hypomethylation of CpG sites of individual genes associated with the high-risk group of PC. Identified genes have not been previously reported as oncogenes or tumor suppressor genes.

Comparison of the high- and intermediate-risk groups of PC revealed eight hypermethylated CpG sites in promoters of different genes. The decreased expression has been found only for three out of eight genes (CSRNP1, STK33, and PLCD1). For these genes, we observed a negative correlation of CpG site methylation status (β-value levels) and expression changes. Spearman’s rank correlation coefficients were statistically significant but had low values. Thus, we can conclude that there is a tendency of the impact of these CpG site hypermethylation on the gene expression. The hypermethylation of other identified CpG sites was not associated with expression alterations of corresponding genes. Notably, aberrant methylation of the STK33, and PLCD1 genes was observed in other cancers. In particular, often promoter hypermethylation of the PLCD1 gene was shown to be associated with its downregulation in breast [38], gastric [39], and colorectal cancers [40], as well as chronic myeloid leukemia [41]. In colorectal cancer, PLCD1 promoter hypermethylation and its decreased expression were correlated with tumor progression [42]. The hypermethylation of the STK33 gene promoter was associated with progression of colorectal [34], [35] and head and neck cancers [36]; no data on the altered gene expression were previously reported. For IQSEC1 gene, we did not observe a significant expression change correlated with the CpG methylation status. However, hypermethylation of the IQSEC1 gene promoter and its downregulation was reported in lung cancer [37]. Methylation status of CSRNP1 has not been earlier studied, however, the gene expression was decreased in hepatocellular [44] and lung cancers [45] correlating with tumor progression.

Seven differentially methylated CpG sites were found under comparison of the favorable and unfavorable prognosis within the high-risk group of PC. Additional analysis of differential expression of genes with identified CpG sites revealed no significant expression changes. Therefore, aberrant methylation of identified CpG sites does not influence the gene expression. Two genes (PRDM16 and CLASP2) genes have been previously shown to be involved in cancer. Promotor hypermethylation and downregulated expression of the PRDM16 gene was observed in lung cancer [49]. In gastric cancer, decreased PRDM16 expression was associated with an unfavorable prognosis [50]. Methylation status of the CLASP2 gene has not been studied; however, the gene upregulation was detected in bladder cancer [51].

The analysis of TMPRSS2-ERG-positive tumors within the high-risk group of PC revealed six hypomethylated CpG sites in different genes, among which significant upregulation was observed for ARHGEF4, WT1, SCYL3, and VPS13D. Expression changes in these genes were negatively correlated with the β-value levels of the identified CpG sites. Thus, hypomethylation of cg01138171, cg19570244, cg25605277, cg26228280 CpG sites can potentially upregulate the expression of the corresponding genes. In the literature, there are no data on the methylation status of the identified genes. However, the ARHGEF4 and WT1 genes were characterized by increased expression in pancreatic [52], [53] and prostate cancers [57], [58] that correlated with unfavorable prognosis and poor survival of patients.

Likewise our study, the STK33 and the PLCD1 genes had similar both methylation changes and expression signatures in colorectal cancer, indicating their potential effect on the gene expression. With regards to the CSRNP1, the ARHGEF4, and the WT1 genes, shifted expression were noticed in lung, liver, pancreatic and androgen-independent prostate cancer. However, methylation or expression changes in SCYL3 and VPS13D have never been marked in any cancer.

5 Conclusion

Thus, we found differential methylation of several CpG sites associated with the high-risk group of PC. Furthermore, aberrant methylation was related to individual CpG sites located predominantly in the gene promoter regions. CSRNP1, STK33, PLCD1, ARHGEF4, WT1, SCYL3, and VPS13D were also characterized by significant changes in the mRNA levels negatively correlated with the methylation status of identified CpG sites. Identified CpG sites could be considered as potential prognostic markers of the high-risk group of PC.

Acknowledgments

The authors thank the National Medical Research Center of Radiology and National Medical Research Center for Obstetrics, Gynecology and Perinatology named after Academician V.I. Kulakov for assistance in data interpretation and discussion. The authors thank the Institute of Gene Biology for assistance in data analysis. This work was performed using the equipment of EIMB RAS “Genome” center (http://www.eimb.ru/ru1/ckp/ccu_genome_c.php).

Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

Research funding: This work was financially supported by the Russian Foundation for Basic Research, grant no. 17-29-06083.

Conflict of interests: Authors state no conflict of interest. All authors have read the journal’s Publication ethics and publication malpractice statement available at the journal’s website and hereby confirm that they comply with all its parts applicable to the present scientific work.

References

  • 1.

    Bray, F, Ferlay, J, Soerjomataram, I, Siegel, RL, Torre, LA, Jemal, A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. Canc J Clin 2018;68:394–424. https://doi.org/10.3322/caac.21492.

    • Crossref
    • Export Citation
  • 2.

    D’Amico, AV, Whittington, R, Malkowicz, SB, Schultz, D, Blank, K, Broderick, GA, et al.. Biochemical outcome after radical prostatectomy, external beam radiation therapy, or interstitial radiation therapy for clinically localized prostate cancer. J Am Med Assoc 1998;280:969–74. https://doi.org/10.1001/jama.280.11.969.

    • Crossref
    • Export Citation
  • 3.

    Perry, AS, Watson, RW, Lawler, M, Hollywood, D. The epigenome as a therapeutic target in prostate cancer. Nat Rev Urol 2010;7:668–80. https://doi.org/10.1038/nrurol.2010.185.

    • Crossref
    • PubMed
    • Export Citation
  • 4.

    Shames, DS, Minna, JD, Gazdar, AF. DNA methylation in health, disease, and cancer. Curr Mol Med 2007;7:85–102. https://doi.org/10.2174/156652407779940413.

    • Crossref
    • PubMed
    • Export Citation
  • 5.

    Yegnasubramanian, S, Kowalski, J, Gonzalgo, ML, Zahurak, M, Piantadosi, S, Walsh, PC, et al.. Hypermethylation of CpG islands in primary and metastatic human prostate cancer. Canc Res 2004;64:1975–86. https://doi.org/10.1158/0008-5472.can-03-3972.

    • Crossref
    • Export Citation
  • 6.

    Millar, DS, Ow, KK, Paul, CL, Russell, PJ, Molloy, PL, Clark, SJ. Detailed methylation analysis of the glutathione S-transferase pi (GSTP1) gene in prostate cancer. Oncogene 1999;18:1313–24. https://doi.org/10.1038/sj.onc.1202415.

    • Crossref
    • PubMed
    • Export Citation
  • 7.

    Henrique, R, Ribeiro, FR, Fonseca, D, Hoque, MO, Carvalho, AL, Costa, VL, et al.. High promoter methylation levels of APC predict poor prognosis in sextant biopsies from prostate cancer patients. Clin Canc Res 2007;13:6122–9. https://doi.org/10.1158/1078-0432.ccr-07-1042.

    • Crossref
    • Export Citation
  • 8.

    Jeronimo, C, Usadel, H, Henrique, R, Oliveira, J, Lopes, C, Nelson, WG, et al.. Quantitation of GSTP1 methylation in non-neoplastic prostatic tissue and organ-confined prostate adenocarcinoma. J Natl Cancer Inst 2001;93:1747–52. https://doi.org/10.1093/jnci/93.22.1747.

    • Crossref
    • PubMed
    • Export Citation
  • 9.

    Meiers, I, Shanks, JH, Bostwick, DG. Glutathione S-transferase pi (GSTP1) hypermethylation in prostate cancer: review 2007. Pathology 2007;39:299–304. https://doi.org/10.1080/00313020701329906.

    • PubMed
    • Export Citation
  • 10.

    Li, LC. Epigenetics of prostate cancer. Front Biosci 2007;12:3377–97. https://doi.org/10.2741/2320.

    • Crossref
    • PubMed
    • Export Citation
  • 11.

    Goering, W, Kloth, M, Schulz, WA. DNA methylation changes in prostate cancer. Methods Mol Biol 2012;863:47–66. https://doi.org/10.1007/978-1-61779-612-8_4.

    • Crossref
    • PubMed
    • Export Citation
  • 12.

    Liu, L, Yoon, JH, Dammann, R, Pfeifer, GP. Frequent hypermethylation of the RASSF1A gene in prostate cancer. Oncogene 2002;21:6835–40. https://doi.org/10.1038/sj.onc.1205814.

    • Crossref
    • PubMed
    • Export Citation
  • 13.

    Gurioli, G, Salvi, S, Martignano, F, Foca, F, Gunelli, R, Costantini, M, et al.. Methylation pattern analysis in prostate cancer tissue: identification of biomarkers using an MS-MLPA approach. J Transl Med 2016;14:249. https://doi.org/10.1186/s12967-016-1014-6.

    • Crossref
    • PubMed
    • Export Citation
  • 14.

    Martignano, F, Gurioli, G, Salvi, S, Calistri, D, Costantini, M, Gunelli, R, et al.. GSTP1 methylation and protein expression in prostate cancer: diagnostic implications. Dis Markers 2016;2016:4358292. https://doi.org/10.1155/2016/4358292.

    • PubMed
    • Export Citation
  • 15.

    Sunami, E, Shinozaki, M, Higano, CS, Wollman, R, Dorff, TB, Tucker, SJ, et al.. Multimarker circulating DNA assay for assessing blood of prostate cancer patients. Clin Chem 2009;55:559–67. https://doi.org/10.1373/clinchem.2008.108498.

    • Crossref
    • PubMed
    • Export Citation
  • 16.

    Ahmed, H. Promoter methylation in prostate cancer and its application for the early detection of prostate cancer using serum and urine samples. Biomarkers Canc 2010;2:17–33. https://doi.org/10.4137/bic.s3187.

  • 17.

    Haluskova, J, Lachvac, L, Nagy, V. The investigation of GSTP1, APC and RASSF1 gene promoter hypermethylation in urine DNA of prostate-diseased patients. Bratisl Lek Listy 2015;116:79–82. https://doi.org/10.4149/bll_2015_014.

    • PubMed
    • Export Citation
  • 18.

    Jatkoe, TA, Karnes, RJ, Freedland, SJ, Wang, Y, Le, A, Baden, J. A urine-based methylation signature for risk stratification within low-risk prostate cancer. Br J Canc 2015;112:802–8. https://doi.org/10.1038/bjc.2015.7.

    • Crossref
    • Export Citation
  • 19.

    Cancer Genome Atlas Research Network. The molecular taxonomy of primary prostate cancer. Cell 2015;163:1011–25. https://doi.org/10.1016/j.cell.2015.10.025.

    • Crossref
    • PubMed
    • Export Citation
  • 20.

    Rhee, YY, Kim, KJ, Kang, GH. CpG island methylator phenotype-high colorectal cancers and their prognostic implications and relationships with the serrated neoplasia pathway. Gut Liver 2017;11:38–46. https://doi.org/10.5009/gnl15535.

    • Crossref
    • PubMed
    • Export Citation
  • 21.

    Hughes, LA, Melotte, V, de Schrijver, J, de Maat, M, Smit, VT, Bovee, JV, et al.. The CpG island methylator phenotype: what’s in a name? Canc Res 2013;73:5858–68. https://doi.org/10.1158/0008-5472.can-12-4306.

    • Crossref
    • Export Citation
  • 22.

    Borno, ST, Fischer, A, Kerick, M, Falth, M, Laible, M, Brase, JC, et al.. Genome-wide DNA methylation events in TMPRSS2-ERG fusion-negative prostate cancers implicate an EZH2-dependent mechanism with miR-26a hypermethylation. Canc Discov 2012;2:1024–35. https://doi.org/10.1158/2159-8290.cd-12-0041.

    • Crossref
    • Export Citation
  • 23.

    Massie, CE, Mills, IG, Lynch, AG. The importance of DNA methylation in prostate cancer development. J Steroid Biochem Mol Biol 2017;166:1–15. https://doi.org/10.1016/j.jsbmb.2016.04.009.

    • Crossref
    • PubMed
    • Export Citation
  • 24.

    TCGA Research Network. [Internet]; 2019. Available from: https://portal.gdc.cancer.gov. [Accessed 4 Apr 2020].

  • 25.

    R Core Team. [Internet]. Vienna; 2018. Available from: https://www.R-project.org/. [Accessed 4 Apr 2020].

  • 26.

    Hebestreit, K, Dugas, M, Klein, HU. Detection of significantly differentially methylated regions in targeted bisulfite sequencing data. Bioinformatics 2013;29:1647–53. https://doi.org/10.1093/bioinformatics/btt263.

    • Crossref
    • PubMed
    • Export Citation
  • 27.

    Zerbino, DR, Johnson, N, Juetteman, T, Sheppard, D, Wilder, SP, Lavidas, I, et al.. Ensembl regulation resources. Database (Oxford); 2016, vol 2016.

  • 28.

    Fishilevich, S, Nudel, R, Rappaport, N, Hadar, R, Plaschkes, I, Iny Stein, T, et al.. GeneHancer: genome-wide integration of enhancers and target genes in GeneCards. Database (Oxford); 2017, vol 2017.

  • 29.

    Haeussler, M, Zweig, AS, Tyner, C, Speir, ML, Rosenbloom, KR, Raney, BJ, et al.. The UCSC genome browser database: 2019 update. Nucleic Acids Res 2019;47:D853–D8. https://doi.org/10.1093/nar/gky1095.

    • Crossref
    • PubMed
    • Export Citation
  • 30.

    Cavalcante, RG, Sartor, MA. Annotatr: genomic regions in context. Bioinformatics 2017;33:2381–3. https://doi.org/10.1093/bioinformatics/btx183.

    • Crossref
    • PubMed
    • Export Citation
  • 31.

    Robinson, MD, McCarthy, DJ, Smyth, GK. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 2010;26:139–40. https://doi.org/10.1093/bioinformatics/btp616.

    • Crossref
    • PubMed
    • Export Citation
  • 32.

    Durinck, S, Spellman, PT, Birney, E, Huber, W. Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat Protoc 2009;4:1184–91. https://doi.org/10.1038/nprot.2009.97.

    • Crossref
    • Export Citation
  • 33.

    Durinck, S, Moreau, Y, Kasprzyk, A, Davis, S, De Moor, B, Brazma, A, et al.. BioMart and bioconductor: a powerful link between biological databases and microarray data analysis. Bioinformatics 2005;21:3439–40. https://doi.org/10.1093/bioinformatics/bti525.

    • Crossref
    • PubMed
    • Export Citation
  • 34.

    Yin, MD, Ma, SP, Liu, F, Chen, YZ. Role of serine/threonine kinase 33 methylation in colorectal cancer and its clinical significance. Oncol Lett 2018;15:2153–60. https://doi.org/10.3892/ol.2017.7614.

    • PubMed
    • Export Citation
  • 35.

    Moon, JW, Lee, SK, Lee, JO, Kim, N, Lee, YW, Kim, SJ, et al.. Identification of novel hypermethylated genes and demethylating effect of vincristine in colorectal cancer. J Exp Clin Canc Res 2014;33:4. https://doi.org/10.1186/1756-9966-33-4.

    • Crossref
    • Export Citation
  • 36.

    Bai, G, Song, J, Yuan, Y, Chen, Z, Tian, Y, Yin, X, et al.. Systematic analysis of differentially methylated expressed genes and site-specific methylation as potential prognostic markers in head and neck cancer. J Cell Physiol 2019;234:22687–702. https://doi.org/10.1002/jcp.28835.

    • Crossref
    • Export Citation
  • 37.

    Dmitriev, AA, Kashuba, VI, Haraldson, K, Senchenko, VN, Pavlova, TV, Kudryavtseva, AV, et al.. Genetic and epigenetic analysis of non-small cell lung cancer with NotI-microarrays. Epigenetics 2012;7:502–13. https://doi.org/10.4161/epi.19801.

    • Crossref
    • PubMed
    • Export Citation
  • 38.

    Xiang, T, Li, L, Fan, Y, Jiang, Y, Ying, Y, Putti, TC, et al.. PLCD1 is a functional tumor suppressor inducing G(2)/M arrest and frequently methylated in breast cancer. Canc Biol Ther 2010;10:520–7. https://doi.org/10.4161/cbt.10.5.12726.

    • Crossref
    • Export Citation
  • 39.

    Hu, XT, Zhang, FB, Fan, YC, Shu, XS, Wong, AH, Zhou, W, et al.. Phospholipase C delta 1 is a novel 3p22.3 tumor suppressor involved in cytoskeleton organization, with its epigenetic silencing correlated with high-stage gastric cancer. Oncogene 2009;28:2466–75. https://doi.org/10.1038/onc.2009.92.

    • Crossref
    • PubMed
    • Export Citation
  • 40.

    Xiang, Q, He, X, Mu, J, Mu, H, Zhou, D, Tang, J, et al.. The phosphoinositide hydrolase phospholipase C delta1 inhibits epithelial-mesenchymal transition and is silenced in colorectal cancer. J Cell Physiol 2019;234:13906–16. https://doi.org/10.1002/jcp.28073.

    • Crossref
    • PubMed
    • Export Citation
  • 41.

    Song, JJ, Liu, Q, Li, Y, Yang, ZS, Yang, L, Xiang, TX, et al.. Epigenetic inactivation of PLCD1 in chronic myeloid leukemia. Int J Mol Med 2012;30:179–84. https://doi.org/10.3892/ijmm.2012.970.

    • PubMed
    • Export Citation
  • 42.

    Danielsen, SA, Cekaite, L, Agesen, TH, Sveen, A, Nesbakken, A, Thiis-Evensen, E, et al.. Phospholipase C isozymes are deregulated in colorectal cancer–insights gained from gene set enrichment analysis of the transcriptome. PLoS One 2011;6:e24419. https://doi.org/10.1371/journal.pone.0024419.

    • Crossref
    • Export Citation
  • 43.

    Liu, J, Wan, Y, Li, S, Qiu, H, Jiang, Y, Ma, X, et al.. Identification of aberrantly methylated differentially expressed genes and associated pathways in endometrial cancer using integrated bioinformatic analysis. Cancer Med 2020;9:3522–36. https://doi.org/10.1002/cam4.2956.

    • Crossref
    • PubMed
    • Export Citation
  • 44.

    Xu, B, Lv, W, Li, X, Zhang, L, Lin, J. Prognostic genes of hepatocellular carcinoma based on gene coexpression network analysis. J Cell Biochem 2019;120:11616–23. https://doi.org/10.1002/jcb.28441.

    • Crossref
    • Export Citation
  • 45.

    Wang, Z, Wang, Z, Niu, X, Liu, J, Wang, Z, Chen, L, et al.. Identification of seven-gene signature for prediction of lung squamous cell carcinoma. OncoTargets Ther 2019;12:5979–88. https://doi.org/10.2147/ott.s198998.

    • Crossref
    • Export Citation
  • 46.

    Bausch, D, Thomas, S, Mino-Kenudson, M, Fernandez-del, CC, Bauer, TW, Williams, M, et al.. Plectin-1 as a novel biomarker for pancreatic cancer. Clin Canc Res 2011;17:302–9. https://doi.org/10.1158/1078-0432.ccr-10-0999.

    • Crossref
    • Export Citation
  • 47.

    Yang, CM, Chang, HS, Chen, HC, You, JJ, Liou, HH, Ting, SC, et al.. Low C6orf141 expression is significantly associated with a poor prognosis in patients with oral cancer. Sci Rep 2019;9:4520. https://doi.org/10.1038/s41598-019-41194-1.

    • Crossref
    • PubMed
    • Export Citation
  • 48.

    Tan, SX, Hu, RC, Liu, JJ, Tan, YL, Liu, WE. Methylation of PRDM2, PRDM5 and PRDM16 genes in lung cancer cells. Int J Clin Exp Pathol 2014;7:2305–11.

    • PubMed
    • Export Citation
  • 49.

    Tan, SX, Hu, RC, Xia, Q, Tan, YL, Liu, JJ, Gan, GX, et al.. The methylation profiles of PRDM promoters in non-small cell lung cancer. OncoTargets Ther 2018;11:2991–3002. https://doi.org/10.2147/ott.s156775.

    • Crossref
    • Export Citation
  • 50.

    Meng, X, Zhao, Y, Liu, J, Wang, L, Dong, Z, Zhang, T, et al.. Comprehensive analysis of histone modification-associated genes on differential gene expression and prognosis in gastric cancer. Exp Ther Med 2019;18:2219–30. https://doi.org/10.3892/etm.2019.7808.

    • PubMed
    • Export Citation
  • 51.

    Chen, L, Xiong, W, Guo, W, Su, S, Qi, L, Zhu, B, et al.. Significance of CLASP2 expression in prognosis for muscle-invasive bladder cancer patients: a propensity score-based analysis. Urol Oncol 2019;37:800–7. https://doi.org/10.1016/j.urolonc.2019.05.003.

    • Crossref
    • PubMed
    • Export Citation
  • 52.

    Taniuchi, K, Furihata, M, Naganuma, S, Saibara, T. ARHGEF4 predicts poor prognosis and promotes cell invasion by influencing ERK1/2 and GSK-3alpha/beta signaling in pancreatic cancer. Int J Oncol 2018;53:2224–40. https://doi.org/10.3892/ijo.2018.4549.

    • PubMed
    • Export Citation
  • 53.

    Taniuchi, K, Furihata, M, Naganuma, S, Sakaguchi, M, Saibara, T. Overexpression of PODXL/ITGB1 and BCL7B/ITGB1 accurately predicts unfavorable prognosis compared to the TNM staging system in postoperative pancreatic cancer patients. PLoS One 2019;14:e0217920. https://doi.org/10.1371/journal.pone.0217920.

    • Crossref
    • PubMed
    • Export Citation
  • 54.

    Shang, M, Zhang, L, Chen, X, Zheng, S. Identification of hub genes and regulators associated with pancreatic ductal adenocarcinoma based on integrated gene expression profile analysis. Discov Med 2019;28:159–72.

    • PubMed
    • Export Citation
  • 55.

    Li, H, Wang, X, Fang, Y, Huo, Z, Lu, X, Zhan, X, et al.. Integrated expression profiles analysis reveals novel predictive biomarker in pancreatic ductal adenocarcinoma. Oncotarget 2017;8:52571–83. https://doi.org/10.18632/oncotarget.16732.

    • Crossref
    • PubMed
    • Export Citation
  • 56.

    Blomme, A, Costanza, B, de Tullio, P, Thiry, M, Van Simaeys, G, Boutry, S, et al.. Myoferlin regulates cellular lipid metabolism and promotes metastases in triple-negative breast cancer. Oncogene 2017;36:2116–30. https://doi.org/10.1038/onc.2016.369.

    • Crossref
    • PubMed
    • Export Citation
  • 57.

    Jacobs, DI, Mao, Y, Fu, A, Kelly, WK, Zhu, Y. Dysregulated methylation at imprinted genes in prostate tumor tissue detected by methylation microarray. BMC Urol 2013;13:37. https://doi.org/10.1186/1471-2490-13-37.

    • Crossref
    • PubMed
    • Export Citation
  • 58.

    Devilard, E, Bladou, F, Ramuz, O, Karsenty, G, Dales, JP, Gravis, G, et al.. FGFR1 and WT1 are markers of human prostate cancer progression. BMC Canc 2006;6:272. https://doi.org/10.1186/1471-2407-6-272.

    • Crossref
    • Export Citation
  • 59.

    Hou, T, Lou, Y, Li, S, Zhao, C, Ji, Y, Wang, D, et al.. Kadsurenone is a useful and promising treatment strategy for breast cancer bone metastases by blocking the PAF/PTAFR signaling pathway. Oncol Lett 2018;16:2255–62. https://doi.org/10.3892/ol.2018.8935.

    • PubMed
    • Export Citation
  • 60.

    Kulis, M, Esteller, M. DNA methylation and cancer. Adv Genet 2010;70:27–56. https://doi.org/10.1016/b978-0-12-380866-0.60002-2.

    • Crossref
    • PubMed
    • Export Citation

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • 1.

    Bray, F, Ferlay, J, Soerjomataram, I, Siegel, RL, Torre, LA, Jemal, A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. Canc J Clin 2018;68:394–424. https://doi.org/10.3322/caac.21492.

    • Crossref
    • Export Citation
  • 2.

    D’Amico, AV, Whittington, R, Malkowicz, SB, Schultz, D, Blank, K, Broderick, GA, et al.. Biochemical outcome after radical prostatectomy, external beam radiation therapy, or interstitial radiation therapy for clinically localized prostate cancer. J Am Med Assoc 1998;280:969–74. https://doi.org/10.1001/jama.280.11.969.

    • Crossref
    • Export Citation
  • 3.

    Perry, AS, Watson, RW, Lawler, M, Hollywood, D. The epigenome as a therapeutic target in prostate cancer. Nat Rev Urol 2010;7:668–80. https://doi.org/10.1038/nrurol.2010.185.

    • Crossref
    • PubMed
    • Export Citation
  • 4.

    Shames, DS, Minna, JD, Gazdar, AF. DNA methylation in health, disease, and cancer. Curr Mol Med 2007;7:85–102. https://doi.org/10.2174/156652407779940413.

    • Crossref
    • PubMed
    • Export Citation
  • 5.

    Yegnasubramanian, S, Kowalski, J, Gonzalgo, ML, Zahurak, M, Piantadosi, S, Walsh, PC, et al.. Hypermethylation of CpG islands in primary and metastatic human prostate cancer. Canc Res 2004;64:1975–86. https://doi.org/10.1158/0008-5472.can-03-3972.

    • Crossref
    • Export Citation
  • 6.

    Millar, DS, Ow, KK, Paul, CL, Russell, PJ, Molloy, PL, Clark, SJ. Detailed methylation analysis of the glutathione S-transferase pi (GSTP1) gene in prostate cancer. Oncogene 1999;18:1313–24. https://doi.org/10.1038/sj.onc.1202415.

    • Crossref
    • PubMed
    • Export Citation
  • 7.

    Henrique, R, Ribeiro, FR, Fonseca, D, Hoque, MO, Carvalho, AL, Costa, VL, et al.. High promoter methylation levels of APC predict poor prognosis in sextant biopsies from prostate cancer patients. Clin Canc Res 2007;13:6122–9. https://doi.org/10.1158/1078-0432.ccr-07-1042.

    • Crossref
    • Export Citation
  • 8.

    Jeronimo, C, Usadel, H, Henrique, R, Oliveira, J, Lopes, C, Nelson, WG, et al.. Quantitation of GSTP1 methylation in non-neoplastic prostatic tissue and organ-confined prostate adenocarcinoma. J Natl Cancer Inst 2001;93:1747–52. https://doi.org/10.1093/jnci/93.22.1747.

    • Crossref
    • PubMed
    • Export Citation
  • 9.

    Meiers, I, Shanks, JH, Bostwick, DG. Glutathione S-transferase pi (GSTP1) hypermethylation in prostate cancer: review 2007. Pathology 2007;39:299–304. https://doi.org/10.1080/00313020701329906.

    • PubMed
    • Export Citation
  • 10.

    Li, LC. Epigenetics of prostate cancer. Front Biosci 2007;12:3377–97. https://doi.org/10.2741/2320.

    • Crossref
    • PubMed
    • Export Citation
  • 11.

    Goering, W, Kloth, M, Schulz, WA. DNA methylation changes in prostate cancer. Methods Mol Biol 2012;863:47–66. https://doi.org/10.1007/978-1-61779-612-8_4.

    • Crossref
    • PubMed
    • Export Citation
  • 12.

    Liu, L, Yoon, JH, Dammann, R, Pfeifer, GP. Frequent hypermethylation of the RASSF1A gene in prostate cancer. Oncogene 2002;21:6835–40. https://doi.org/10.1038/sj.onc.1205814.

    • Crossref
    • PubMed
    • Export Citation
  • 13.

    Gurioli, G, Salvi, S, Martignano, F, Foca, F, Gunelli, R, Costantini, M, et al.. Methylation pattern analysis in prostate cancer tissue: identification of biomarkers using an MS-MLPA approach. J Transl Med 2016;14:249. https://doi.org/10.1186/s12967-016-1014-6.

    • Crossref
    • PubMed
    • Export Citation
  • 14.

    Martignano, F, Gurioli, G, Salvi, S, Calistri, D, Costantini, M, Gunelli, R, et al.. GSTP1 methylation and protein expression in prostate cancer: diagnostic implications. Dis Markers 2016;2016:4358292. https://doi.org/10.1155/2016/4358292.

    • PubMed
    • Export Citation
  • 15.

    Sunami, E, Shinozaki, M, Higano, CS, Wollman, R, Dorff, TB, Tucker, SJ, et al.. Multimarker circulating DNA assay for assessing blood of prostate cancer patients. Clin Chem 2009;55:559–67. https://doi.org/10.1373/clinchem.2008.108498.

    • Crossref
    • PubMed
    • Export Citation
  • 16.

    Ahmed, H. Promoter methylation in prostate cancer and its application for the early detection of prostate cancer using serum and urine samples. Biomarkers Canc 2010;2:17–33. https://doi.org/10.4137/bic.s3187.

  • 17.

    Haluskova, J, Lachvac, L, Nagy, V. The investigation of GSTP1, APC and RASSF1 gene promoter hypermethylation in urine DNA of prostate-diseased patients. Bratisl Lek Listy 2015;116:79–82. https://doi.org/10.4149/bll_2015_014.

    • PubMed
    • Export Citation
  • 18.

    Jatkoe, TA, Karnes, RJ, Freedland, SJ, Wang, Y, Le, A, Baden, J. A urine-based methylation signature for risk stratification within low-risk prostate cancer. Br J Canc 2015;112:802–8. https://doi.org/10.1038/bjc.2015.7.

    • Crossref
    • Export Citation
  • 19.

    Cancer Genome Atlas Research Network. The molecular taxonomy of primary prostate cancer. Cell 2015;163:1011–25. https://doi.org/10.1016/j.cell.2015.10.025.

    • Crossref
    • PubMed
    • Export Citation
  • 20.

    Rhee, YY, Kim, KJ, Kang, GH. CpG island methylator phenotype-high colorectal cancers and their prognostic implications and relationships with the serrated neoplasia pathway. Gut Liver 2017;11:38–46. https://doi.org/10.5009/gnl15535.

    • Crossref
    • PubMed
    • Export Citation
  • 21.

    Hughes, LA, Melotte, V, de Schrijver, J, de Maat, M, Smit, VT, Bovee, JV, et al.. The CpG island methylator phenotype: what’s in a name? Canc Res 2013;73:5858–68. https://doi.org/10.1158/0008-5472.can-12-4306.

    • Crossref
    • Export Citation
  • 22.

    Borno, ST, Fischer, A, Kerick, M, Falth, M, Laible, M, Brase, JC, et al.. Genome-wide DNA methylation events in TMPRSS2-ERG fusion-negative prostate cancers implicate an EZH2-dependent mechanism with miR-26a hypermethylation. Canc Discov 2012;2:1024–35. https://doi.org/10.1158/2159-8290.cd-12-0041.

    • Crossref
    • Export Citation
  • 23.

    Massie, CE, Mills, IG, Lynch, AG. The importance of DNA methylation in prostate cancer development. J Steroid Biochem Mol Biol 2017;166:1–15. https://doi.org/10.1016/j.jsbmb.2016.04.009.

    • Crossref
    • PubMed
    • Export Citation
  • 24.

    TCGA Research Network. [Internet]; 2019. Available from: https://portal.gdc.cancer.gov. [Accessed 4 Apr 2020].

  • 25.

    R Core Team. [Internet]. Vienna; 2018. Available from: https://www.R-project.org/. [Accessed 4 Apr 2020].

  • 26.

    Hebestreit, K, Dugas, M, Klein, HU. Detection of significantly differentially methylated regions in targeted bisulfite sequencing data. Bioinformatics 2013;29:1647–53. https://doi.org/10.1093/bioinformatics/btt263.

    • Crossref
    • PubMed
    • Export Citation
  • 27.

    Zerbino, DR, Johnson, N, Juetteman, T, Sheppard, D, Wilder, SP, Lavidas, I, et al.. Ensembl regulation resources. Database (Oxford); 2016, vol 2016.

  • 28.

    Fishilevich, S, Nudel, R, Rappaport, N, Hadar, R, Plaschkes, I, Iny Stein, T, et al.. GeneHancer: genome-wide integration of enhancers and target genes in GeneCards. Database (Oxford); 2017, vol 2017.

  • 29.

    Haeussler, M, Zweig, AS, Tyner, C, Speir, ML, Rosenbloom, KR, Raney, BJ, et al.. The UCSC genome browser database: 2019 update. Nucleic Acids Res 2019;47:D853–D8. https://doi.org/10.1093/nar/gky1095.

    • Crossref
    • PubMed
    • Export Citation
  • 30.

    Cavalcante, RG, Sartor, MA. Annotatr: genomic regions in context. Bioinformatics 2017;33:2381–3. https://doi.org/10.1093/bioinformatics/btx183.

    • Crossref
    • PubMed
    • Export Citation
  • 31.

    Robinson, MD, McCarthy, DJ, Smyth, GK. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 2010;26:139–40. https://doi.org/10.1093/bioinformatics/btp616.

    • Crossref
    • PubMed
    • Export Citation
  • 32.

    Durinck, S, Spellman, PT, Birney, E, Huber, W. Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat Protoc 2009;4:1184–91. https://doi.org/10.1038/nprot.2009.97.

    • Crossref
    • Export Citation
  • 33.

    Durinck, S, Moreau, Y, Kasprzyk, A, Davis, S, De Moor, B, Brazma, A, et al.. BioMart and bioconductor: a powerful link between biological databases and microarray data analysis. Bioinformatics 2005;21:3439–40. https://doi.org/10.1093/bioinformatics/bti525.

    • Crossref
    • PubMed
    • Export Citation
  • 34.

    Yin, MD, Ma, SP, Liu, F, Chen, YZ. Role of serine/threonine kinase 33 methylation in colorectal cancer and its clinical significance. Oncol Lett 2018;15:2153–60. https://doi.org/10.3892/ol.2017.7614.

    • PubMed
    • Export Citation
  • 35.

    Moon, JW, Lee, SK, Lee, JO, Kim, N, Lee, YW, Kim, SJ, et al.. Identification of novel hypermethylated genes and demethylating effect of vincristine in colorectal cancer. J Exp Clin Canc Res 2014;33:4. https://doi.org/10.1186/1756-9966-33-4.

    • Crossref
    • Export Citation
  • 36.

    Bai, G, Song, J, Yuan, Y, Chen, Z, Tian, Y, Yin, X, et al.. Systematic analysis of differentially methylated expressed genes and site-specific methylation as potential prognostic markers in head and neck cancer. J Cell Physiol 2019;234:22687–702. https://doi.org/10.1002/jcp.28835.

    • Crossref
    • Export Citation
  • 37.

    Dmitriev, AA, Kashuba, VI, Haraldson, K, Senchenko, VN, Pavlova, TV, Kudryavtseva, AV, et al.. Genetic and epigenetic analysis of non-small cell lung cancer with NotI-microarrays. Epigenetics 2012;7:502–13. https://doi.org/10.4161/epi.19801.

    • Crossref
    • PubMed
    • Export Citation
  • 38.

    Xiang, T, Li, L, Fan, Y, Jiang, Y, Ying, Y, Putti, TC, et al.. PLCD1 is a functional tumor suppressor inducing G(2)/M arrest and frequently methylated in breast cancer. Canc Biol Ther 2010;10:520–7. https://doi.org/10.4161/cbt.10.5.12726.

    • Crossref
    • Export Citation
  • 39.

    Hu, XT, Zhang, FB, Fan, YC, Shu, XS, Wong, AH, Zhou, W, et al.. Phospholipase C delta 1 is a novel 3p22.3 tumor suppressor involved in cytoskeleton organization, with its epigenetic silencing correlated with high-stage gastric cancer. Oncogene 2009;28:2466–75. https://doi.org/10.1038/onc.2009.92.

    • Crossref
    • PubMed
    • Export Citation
  • 40.

    Xiang, Q, He, X, Mu, J, Mu, H, Zhou, D, Tang, J, et al.. The phosphoinositide hydrolase phospholipase C delta1 inhibits epithelial-mesenchymal transition and is silenced in colorectal cancer. J Cell Physiol 2019;234:13906–16. https://doi.org/10.1002/jcp.28073.

    • Crossref
    • PubMed
    • Export Citation
  • 41.

    Song, JJ, Liu, Q, Li, Y, Yang, ZS, Yang, L, Xiang, TX, et al.. Epigenetic inactivation of PLCD1 in chronic myeloid leukemia. Int J Mol Med 2012;30:179–84. https://doi.org/10.3892/ijmm.2012.970.

    • PubMed
    • Export Citation
  • 42.

    Danielsen, SA, Cekaite, L, Agesen, TH, Sveen, A, Nesbakken, A, Thiis-Evensen, E, et al.. Phospholipase C isozymes are deregulated in colorectal cancer–insights gained from gene set enrichment analysis of the transcriptome. PLoS One 2011;6:e24419. https://doi.org/10.1371/journal.pone.0024419.

    • Crossref
    • Export Citation
  • 43.

    Liu, J, Wan, Y, Li, S, Qiu, H, Jiang, Y, Ma, X, et al.. Identification of aberrantly methylated differentially expressed genes and associated pathways in endometrial cancer using integrated bioinformatic analysis. Cancer Med 2020;9:3522–36. https://doi.org/10.1002/cam4.2956.

    • Crossref
    • PubMed
    • Export Citation
  • 44.

    Xu, B, Lv, W, Li, X, Zhang, L, Lin, J. Prognostic genes of hepatocellular carcinoma based on gene coexpression network analysis. J Cell Biochem 2019;120:11616–23. https://doi.org/10.1002/jcb.28441.

    • Crossref
    • Export Citation
  • 45.

    Wang, Z, Wang, Z, Niu, X, Liu, J, Wang, Z, Chen, L, et al.. Identification of seven-gene signature for prediction of lung squamous cell carcinoma. OncoTargets Ther 2019;12:5979–88. https://doi.org/10.2147/ott.s198998.

    • Crossref
    • Export Citation
  • 46.

    Bausch, D, Thomas, S, Mino-Kenudson, M, Fernandez-del, CC, Bauer, TW, Williams, M, et al.. Plectin-1 as a novel biomarker for pancreatic cancer. Clin Canc Res 2011;17:302–9. https://doi.org/10.1158/1078-0432.ccr-10-0999.

    • Crossref
    • Export Citation
  • 47.

    Yang, CM, Chang, HS, Chen, HC, You, JJ, Liou, HH, Ting, SC, et al.. Low C6orf141 expression is significantly associated with a poor prognosis in patients with oral cancer. Sci Rep 2019;9:4520. https://doi.org/10.1038/s41598-019-41194-1.

    • Crossref
    • PubMed
    • Export Citation
  • 48.

    Tan, SX, Hu, RC, Liu, JJ, Tan, YL, Liu, WE. Methylation of PRDM2, PRDM5 and PRDM16 genes in lung cancer cells. Int J Clin Exp Pathol 2014;7:2305–11.

    • PubMed
    • Export Citation
  • 49.

    Tan, SX, Hu, RC, Xia, Q, Tan, YL, Liu, JJ, Gan, GX, et al.. The methylation profiles of PRDM promoters in non-small cell lung cancer. OncoTargets Ther 2018;11:2991–3002. https://doi.org/10.2147/ott.s156775.

    • Crossref
    • Export Citation
  • 50.

    Meng, X, Zhao, Y, Liu, J, Wang, L, Dong, Z, Zhang, T, et al.. Comprehensive analysis of histone modification-associated genes on differential gene expression and prognosis in gastric cancer. Exp Ther Med 2019;18:2219–30. https://doi.org/10.3892/etm.2019.7808.

    • PubMed
    • Export Citation
  • 51.

    Chen, L, Xiong, W, Guo, W, Su, S, Qi, L, Zhu, B, et al.. Significance of CLASP2 expression in prognosis for muscle-invasive bladder cancer patients: a propensity score-based analysis. Urol Oncol 2019;37:800–7. https://doi.org/10.1016/j.urolonc.2019.05.003.

    • Crossref
    • PubMed
    • Export Citation
  • 52.

    Taniuchi, K, Furihata, M, Naganuma, S, Saibara, T. ARHGEF4 predicts poor prognosis and promotes cell invasion by influencing ERK1/2 and GSK-3alpha/beta signaling in pancreatic cancer. Int J Oncol 2018;53:2224–40. https://doi.org/10.3892/ijo.2018.4549.

    • PubMed
    • Export Citation
  • 53.

    Taniuchi, K, Furihata, M, Naganuma, S, Sakaguchi, M, Saibara, T. Overexpression of PODXL/ITGB1 and BCL7B/ITGB1 accurately predicts unfavorable prognosis compared to the TNM staging system in postoperative pancreatic cancer patients. PLoS One 2019;14:e0217920. https://doi.org/10.1371/journal.pone.0217920.

    • Crossref
    • PubMed
    • Export Citation
  • 54.

    Shang, M, Zhang, L, Chen, X, Zheng, S. Identification of hub genes and regulators associated with pancreatic ductal adenocarcinoma based on integrated gene expression profile analysis. Discov Med 2019;28:159–72.

    • PubMed
    • Export Citation
  • 55.

    Li, H, Wang, X, Fang, Y, Huo, Z, Lu, X, Zhan, X, et al.. Integrated expression profiles analysis reveals novel predictive biomarker in pancreatic ductal adenocarcinoma. Oncotarget 2017;8:52571–83. https://doi.org/10.18632/oncotarget.16732.

    • Crossref
    • PubMed
    • Export Citation
  • 56.

    Blomme, A, Costanza, B, de Tullio, P, Thiry, M, Van Simaeys, G, Boutry, S, et al.. Myoferlin regulates cellular lipid metabolism and promotes metastases in triple-negative breast cancer. Oncogene 2017;36:2116–30. https://doi.org/10.1038/onc.2016.369.

    • Crossref
    • PubMed
    • Export Citation
  • 57.

    Jacobs, DI, Mao, Y, Fu, A, Kelly, WK, Zhu, Y. Dysregulated methylation at imprinted genes in prostate tumor tissue detected by methylation microarray. BMC Urol 2013;13:37. https://doi.org/10.1186/1471-2490-13-37.

    • Crossref
    • PubMed
    • Export Citation
  • 58.

    Devilard, E, Bladou, F, Ramuz, O, Karsenty, G, Dales, JP, Gravis, G, et al.. FGFR1 and WT1 are markers of human prostate cancer progression. BMC Canc 2006;6:272. https://doi.org/10.1186/1471-2407-6-272.

    • Crossref
    • Export Citation
  • 59.

    Hou, T, Lou, Y, Li, S, Zhao, C, Ji, Y, Wang, D, et al.. Kadsurenone is a useful and promising treatment strategy for breast cancer bone metastases by blocking the PAF/PTAFR signaling pathway. Oncol Lett 2018;16:2255–62. https://doi.org/10.3892/ol.2018.8935.

    • PubMed
    • Export Citation
  • 60.

    Kulis, M, Esteller, M. DNA methylation and cancer. Adv Genet 2010;70:27–56. https://doi.org/10.1016/b978-0-12-380866-0.60002-2.

    • Crossref
    • PubMed
    • Export Citation
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    Manhattan plot of methylation level (β-value) of detected CpG sites among the studied groups of PC. (a) Differentially methylated CpG sites associated with the high-risk group of PC. (b) Differentially methylated CpG sites associated with the unfavorable prognosis within the high-risk group of PC. (c) Differentially methylated CpG sites associated the TMPRSS2-ERG molecular subtype within the high-risk group of PC.