Epigenome-wide DNA methylation profiling of periprostatic adipose tissue in prostate cancer patients with excess adiposity—a pilot study
Cheng et al. Clinical Epigenetics
Epigenome-wide DNA methylation profiling of periprostatic adipose tissue in prostate cancer patients with excess adiposity-a pilot study
Yan Cheng 2 3
Cátia Monteiro 0 8
Andreia Matos 6 7
Jiaying You 2
Avelino Fraga 5 6
Carina Pereira 0 10
Victoria Catalán 4 9
Amaia Rodríguez 4 9
Javier Gómez-Ambrosi 4 9
Gema Frühbeck 4 9 12
0 Molecular Oncology Group, Portuguese Institute of Oncology , Porto , Portugal
1 Equal contributors
2 Department of Biochemistry and Medical Genetics & Department of Electrical and Computer Engineering, University of Manitoba , Winnipeg , Canada
3 Experimental Center, Northwest University for Nationalities , Lanzhou , People's Republic of China
4 CIBER Fisiopatología de la Obesidad y Nutricion, Instituto de Salud Carlos III , Madrid , Spain
5 Department of Urology, Centro Hospitalar Universitário do Porto , Porto , Portugal
6 Tumor & Microenvironment Interactions, i3S/ INEB, Institute for Research and Innovation in Health, and Institute of Biomedical Engineering, University of Porto , Porto , Portugal
7 Laboratory of Genetics and Environmental Health Institute, Faculty of Medicine, University of Lisboa , Lisbon , Portugal
8 Research Department, Portuguese League Against Cancer-North , Porto , Portugal
9 Metabolic Research Laboratory, Universidad de Navarra , Pamplona , Spain
10 CINTESIS, Center for Health Technology and Services Research, Faculty of Medicine, e, University of Porto , Porto , Portugal
11 i3S/INEB, Instituto de Investigação e Inovação em Saúde/Instituto Nacional de Engenharia Biomédica, Universidade do Porto, Tumor & Microenvironment Interactions , Rua Alfredo Allen, 208 4200-135 Porto , Portugal
12 Department of Endocrinology, Clínica Universidad de Navarra , Pamplona , Spain
13 Department of Clinical Pathology, Centro Hospitalar e Universitário de Coimbra , Coimbra , Portugal
Background: Periprostatic adipose tissue (PPAT) has been recognized to associate with prostate cancer (PCa) aggressiveness and progression. Here, we sought to investigate whether excess adiposity modulates the methylome of PPAT in PCa patients. DNA methylation profiling was performed in PPAT from obese/overweight (OB/OW, BMI > 25 kg m−2) and normal weight (NW, BMI < 25 kg m−2) PCa patients. Significant differences in methylated CpGs between OB/OW and NW groups were inferred by statistical modeling. Results: Five thousand five hundred twenty-six differentially methylated CpGs were identified between OB/OW and NW PCa patients with 90.2% hypermethylated. Four hundred eighty-three of these CpGs were found to be located at both promoters and CpG islands, whereas the representing 412 genes were found to be involved in pluripotency of stem cells, fatty acid metabolism, and many other biological processes; 14 of these genes, particularly FADS1, MOGAT1, and PCYT2, with promoter hypermethylation presented with significantly decreased gene expression in matched samples. Additionally, 38 genes were correlated with antigen processing and presentation of endogenous antigen via MHC class I, which might result in fatty acid accumulation in PPAT and tumor immune evasion. Conclusions: Results showed that the whole epigenome methylation profiles of PPAT were significantly different in OB/OW compared to normal weight PCa patients. The epigenetic variation associated with excess adiposity likely resulted in altered lipid metabolism and immune dysregulation, contributing towards unfavorable PCa microenvironment, thus warranting further validation studies in larger samples.
DNA methylation; Periprostatic adipose tissue; Obesity; Prostate cancer; Microenvironment
Prostate cancer (PCa) is one of the most frequent
malignancies in men and the second leading cause of
cancerrelated death in the North America and most western
European countries [
]. Epidemiological studies support
obesity or excess adiposity as an important environmental
risk factor for PCa, being primarily associated with
advanced disease and death . Periprostatic adipose tissue
(PPAT), a white fat depot surrounding the prostate
capsular-like structure, has been recognized to have the
potential to exert pro-tumoral endocrine and paracrine
influences on prostate cancer cell’s biological phenotypes
]. There is now evidence that obesity and overweight
result in excess fat deposit at PPAT [
], altered fatty acid
], migration of tumor cells [
], secretion of a
variety of adipokines, such as interleukin-1 beta (IL-1b),
osteopontin, leptin, tumor necrosis factor alpha (TNF-a),
and decreased adiponectin, thus contributing to a tumor
microenvironment that ultimately facilitates PCa
DNA methylation is a well-known epigenetic
mechanism resulting from the interaction between
environmental factors and the genome [
]. DNA methylation with
variation of CpG sites is associated with tissue-specific
gene modulation and involved in phenotype
transmission and in the development of diseases [
adiposity, as a consequence of environmental factors
such as excessive food consumption or inactive lifestyle,
has been identified as a regulator of epigenetic
modification in adipose tissue. Recent findings from experimental
studies suggested that modification of DNA methylation
pattern in adipose tissue and adipocytes was related with
development of cancer, type 2 diabetes, and
cardiovascular diseases through influencing metabolism and
]. Additionally, several studies reported
altered DNA methylation in PCa cells as compared with
adjacent benign tissue, and some significantly
methylated CpG sites and genes were found to be responsible
for the occurrence and progression of PCa [
Nevertheless, the epigenome-wide DNA methylation
profile of PPAT from excess adiposity PCa patients is
currently unknown despite its potential mechanistic
involvement in obesity association with PCa.
The aim of this study was to perform a
epigeneticwide association study (EWAS) in order to evaluate
DNA methylation profile of PPAT obtained from obese/
overweight (OB/OW) in comparison with normal weight
(NW) PCa patients and identify differentially methylated
sites. We also explored the consequential potential
biological functions that account for the effect of PPAT
from OB/OW subjects in PCa molecular mechanisms.
This study included ten prostate cancer patients from
the Portuguese Institute of Oncology, Porto Centre.
Inclusion criteria and conditions of this study have been
previously reported, including the procedures for PPAT
collection, handling, and storage [
]. Briefly, PPAT was
collected and immediately processed in the operating
room and transported to the laboratory within 2 h in
appropriate culture media and temperature conditions,
in order to minimize pre-analytical errors. Patients’
signed informed consent and research procedures were
approved by the institute’s ethics committee.
The clinical and pathological characteristics of
participants are presented in Table 1. The ten subjects were
selected from a larger group of patients undergoing
prostate surgery (n = 51) [
] that fitted the strict
inclusion and exclusion criteria, in order to control for
variables that might influence adipose tissue gene
expression or methylation (e.g., anti-diabetic or
antidyslipidemia drugs, stage of disease and PSA, concomitant
diseases such as diabetes, other neoplasia or metabolic
syndrome). Subjects were matched for age at diagnosis,
PSA value, Gleason grade, and stage of disease, which
differed in body mass index (BMI). BMI was calculated by
dividing weight in kilograms by the squared height in
meters and categorized using the WHO (World Health
Organization) criteria: normal weight, BMI < 25 kg m−2,
overweight, 25 ≤ BMI < 30 kg m−2, and obese, BMI ≥
30 kg m−2. Obese and overweight were combined into one
excess adiposity group (n = 5, BMI≥25 kg m−2) versus
normal weight group (n = 5, BMI < 25 kg m−2). Therefore,
the two groups were selected to differ only by BMI, in
order to reflect our objective of assessing whether excess
adiposity (BMI) influences PPAT methylation profile.
Epigenome-wide DNA methylation analysis
DNA was isolated from PPAT using Puregene hisalt
extraction method (Qiagen/Gentra). Briefly, the tissue
was minced with scalpels in a sterile petri dish on ice
and then transferred to Puregene Cell Kit for overnight
Proteinase K digest at 55 °C. A second Proteinase K
digest was done the next morning for 5 h. DNA from
the digested tissue was purified using Puregene
extraction protocol (Qiagen/Gentra). Purified DNA was
washed 2× with 70% ethanol and DNA pellet air dried
and rehydrated in TE (10 mM Tris-Cl, 1 mM EDTA
pH 7.5). Epigenome-wide DNA methylation was
analyzed using the Infinium Human Methylation450
(HM450) BeadChip (Illumina, San Diego, CA, USA) in
the Center for Applied Genomics (Toronto). This array
contains 485,577 probes, which cover 21,231 (99%)
RefSeq genes. Briefly, DNA was bisulfite-converted using
the EZ DNA methylation kit (Zymo Research, Orange,
CA, USA) and then used on the Infinium Assay®
followed by the Infinium HD Assay Methylation Protocol
(Illumina). The imaging data on the BeadChips was
captured by Illumina iScan system.
Functions, pathway, and network enrichment analysis
Gene ontology (GO) and KEGG pathway enrichment
analyses were performed to explore the biological functions
of significantly methylated genes using the online
bioinformatic tool Enrichr [
]. Protein-protein interaction (PPI)
analysis of all DMC-related genes was performed using
NetworkAnalyst according to STRING database [
Data filtering and normalization
Raw methylation level for each probe was represented
by methylation β value, which was calculated based
on β = intensity of the methylated allele/(intensity of
the unmethylated allele + intensity of the methylated
allele + 100). M values were the logit transformation
of β values based on M = log2 (β/(1 − β)), which makes
the data more homoscedastic and appropriate for
further bioinformatic and statistical analysis.
Methylation values were normalized using the
functional normalization algorithm implemented in Minfi R
]. Quality control was performed by
excluding CpG probes, which are found by Chen et al. to be
cross-reactive with areas of the genome not at the site of
], as well as control probes and probes on sex
chromosomes. We analyzed a total of 438,458 CpG sites
from the PPAT of 5 OB/OW PCa patients and 5 NW
Differential methylation analysis
A statistical linear modeling approach was applied to the
detected differentially methylated CpG sites (DMCs)
associated with obesity in PPAT using the Bioconductor
“limma” package [
]. Hyper- or hypomethylation was
determined when methylation levels of CpGs increased or
decreased between the OB/OW PCa group and the NW
PCa group based on mean different β > 0 or < 0. False
discovery rate (FDR)-corrected P values were determined
according to the method of Benjamin and Hochberg’s
(BH method) multiple testing procedure [
Differentially methylated regions (DMRs) were identified
using the “Bumphunter” method implemented in the
“chAMP” R package with the parameters (B = 1000,
useWeights = TRUE, minProbes = 10, pickCutoff = TRUE,
and other settings with default values) [
The proportions of significant hyper- or
hypomethylated CpGs were calculated and visualized according to
their relation to the nearest genes or to the CpG islands,
separately. Gene promoter region was defined as 1500
base pairs (bp) and 200 bp upstream of the transcription
start site (TSS) (TSS1500 and TSS200) [
genes were selected when more than two significantly
hypermethylated CpGs were simultaneously located in
the promoter region.
Association analysis between DNA methylation and gene expression
We have previously performed gene expression
experiment of the PPAT of the 5 OB/OW PCa patients and
the 5 NW PCa patients using the HG-U133 Plus 2.0
Affymetrix GeneChip Array (Affymetrix, Santa Clara,
CA, USA) [
]. Differential gene expression (DGE)
analysis between the OB/OW PCa patients and the NW
PCa patients was re-performed using the Bioconductor
“limma” package as previously described [
rank correlation analysis was performed between the
methylation profiles of the hypermethylated CpGs and
the gene expression profiles of the genes in PPAT.
Clinical characteristics of PCa patients in this study were
stratified according to obesity classification groups and
are presented in Table 1. Mean age, PSA level, Gleason
sum score, and cancer stage in subjects with PCa were
similar (P value > 0.05) between OB/OW and NW
groups. As expected, the mean BMI of the OB/OW
group was significantly higher than that of the NW
subjects (P value < 0.01). All the patients in the OB/OW
group are ex-smokers or active smokers, while only one
patient in the NW group is a smoker (P value = 0.05).
Epigenome-wide DNA methylation profiling of PPAT
To study the impact of obesity status on DNA
methylation profiles and to identify differentially methylated CpG
sites in PPAT from OB/OW and NW prostate cancer
patients, we conducted epigenome-wide DNA
methylation analyses. A flowchart of the data analysis is depicted
in Additional file 1: Figure S1. After quality control and
filtering, the Infinium array generated methylation data
for 438,458 CpG sites, from which 5526 were differentially
methylated after FDR control in the PPAT of OB/OW
PCa patients compared to NW (adjusted P value < 0.25;
Additional file 2: Table S1 and Table 2). The unsupervised
hierarchical clustering of DMCs showed differential DNA
methylation patterns in PPAT between OB/OW and NW
samples (Additional file 3: Figure S2). The majority of
DMCs were hypermethylated (n = 4985, 90.2%), with 9.8%
hypomethylated CpG sites (n = 541) in OB/OW versus
NW prostate cancer patients (Fig. 1a, b, c).
Chromosomal distribution of the DMCs
To further explore the methylation profile, we
investigated the chromosome distribution of DMCs. Results
showed that hypermethylated CpG sites were located at
chromosomes 1, 6, 11, and 17 (proportion > 6%, Fig. 1d)
and hypomethylated CpG sites were located at
chromosomes 1, 2, 6, 7, and 11 (proportion > 6%, Fig. 1e).
Methylation variations of hypermethylated DMCs and
hypomethylated DMCs were found mainly distributed
on chromosomes 1, 6, and 11, suggesting that the DNA
methylation alterations in these chromosomes were
correlated with the body weight changes in prostate
patients. Furthermore, we compared the distribution of
the DMCs (hyper- and hypomethylated, separately) with
the distribution of all evaluated CpG sites based on their
relation to nearest gene regions (Fig. 2f, Additional file 4:
Table S2) or their relation to CpG islands (Fig. 2g,
Additional file 5: Table S3) using χ2 test. The results
showed that hypermethylated CpGs are mainly located at
TSS1500 (transcription start sites 1500), IGR (intergenic
region), N-shore, and S-shore, and hypomethylated CpGs
are mostly located at the gene body and open sea.
Functional enrichment analysis of significantly obesity-associated DMCs
To investigate the potential biological relevance of the
significant DMCs, we further filtered 483 DMCs (distributed
within 413 genes) from a total of 5526 DMCs according
to their locations at both the gene promoter and CpG
island (Additional file 6: Table S4). Four hundred
seventyfive of the 483 DMCs (representing 404 genes) were
hypermethylated. Functional enrichment analysis of the
hypermethylated genes showed that these genes were
enriched for biological processes, such as pattern
specification process, neuron differentiation, neuron fate
specification, and negative regulation of phosphate
metabolic process (adjusted P value < 0.05, Additional file 7:
Table S5), as well as molecular functions, such as
neuropeptide receptor activity and sequence-specific
DNA-binding RNA polymerase II transcription factor
activity (adjusted P value < 0.1, Additional file 8: Table S6).
KEGG pathway enrichment analysis showed that
hypermethylated genes were involved in signaling pathways
regulating pluripotency of stem cells, fatty acid
metabolism, basal cell carcinoma, non-alcoholic fatty liver disease
(NAFLD), and AMPK signaling pathway (P value < 0.05,
Additional file 9: Table S7).
We mapped the 404 hypermethylated genes to the
STRING database and generated a protein-protein
interaction (PPI) network by the NetworkAnalyst. The largest
subnetwork was identified to include 247 nodes (genes)
and 403 edges (Fig. 2a). In the network, the size of the
nodes was based on their degree values and the color of
nodes was based on their P values. This network
contained 118 seed genes from the DMCs, and the
enrichment pathway analysis showed that the genes of the
subnetwork were mostly involved in the pathways of
prostate cancer and other cancers (Fig. 2b, Additional file 10:
Table S8, adjusted P value < 0.05). Particularly, the gene
UBC (ubiquitin C) was found to be a hub connecting with
many other nodes in the network, suggesting that the
gene may play important biological roles in the PPAT of
obese PCa patients.
Selected genes with multiple methylated CpG sites
In order to explore repression of genes by DNA
methylation modifications, we selected genes which had
multiple hypermethylated CpG sites (the number of
methylated CpG sites ≥ 2, in at least one of the sites with
a mean difference of β > 3% and an adjusted P value < 0.25)
(Additional file 1: Figure S1 and Additional file 11: Table S9).
A total of 38 genes with 100 differentially methylated CpG
sites were selected, which included TAPBP, RUNX3, CPT1B,
CPT1C, MOGAT3, WNT2, and AIRE (Additional file 11:
Table S9). Notably, the promoter region of TAPBP
(TAPbinding protein) had eight hypermethylated CpG sites in the
promoter (Fig. 3a), which were significantly more methylated
in the OB/OW than those in the NW groups (Fig. 3b), with
a mean difference of β value greater than 5%
(Additional file 10: Table S8). Spearman’s rank correlation
showed strong association (r2 = 0.73–0.97) of the eight
hypermethylated CpGs in the TAPBP promoter with their
methylation levels (Fig. 3c). Pathway analysis of these genes
revealed enrichment for fatty acid metabolism, PPAR
signaling pathway, glucagon signaling pathway, AMPK
signaling pathway, glycerolipid metabolism, basal cell
carcinoma, antigen processing and presentation, ECM
receptor interaction, and insulin resistance (adjusted
P value < 0.25) (Additional file 12: Table S10).
Differential methylated regions analysis
Ten DMRs were identified (P < 0.01) in obesity PPAT
samples compared to normal weight controls (Table 3).
The size of the DMRs varied from 161 to 1287 bp.
Noteworthy, four out of the ten DMRs were discovered on
chromosome 6. Eight regions were located in genes, and
two were in the intergenic region. Four regions were in
the gene promoter of FAM104A, C17orf80, HOXA4A,
Association analysis between DNA methylation and mRNA expression
Increased DNA methylation of promoter in CpG islands
was obviously linked to gene transcriptional silencing [
Therefore, we related hypermethylated CpG sites in PPAT
with genes showed decreased gene expression level from
our previously generated mRNA expression data [
methylation of 16 CpG sites, corresponding to 14 genes,
was associated with significantly decreased transcripts in
OB/OW group (P value < 0.05) (Table 4). The Spearman’s
rank correlation analysis showed that eight of the 14 genes
have significantly negative association (P value < 0.05)
between the methylation profiles and the gene expression
profiles of these genes (Table 4). The repression genes were
mainly involved in metabolic pathways (Additional file 13:
Table S11, adjusted P value < 0.25), such as MOGAT1
(glycerolipid metabolism), FADS1 (fatty acid metabolism
and biosynthesis of unsaturated fatty acids), and PCYT2
(glycerophospholipid metabolism). The mRNA expression
level of FADS1 was significantly decreased in the PPAT of
obese with prostate cancers in our previous study using
]. Besides these, GO enrichment analysis
showed that these genes are functionally related to receptor
binding (neuropeptide receptor binding, dopamine receptor
binding, and insulin receptor binding) and enzyme activity
(acid phosphatase activity, metallocarboxypeptidase
activity, and acylglycerol O-acyltransferase activity)
(Additional file 14: Table S12, adjusted P value < 0.25).
This pilot study revealed significant differences of DNA
methylation profiles between the PPATs from OB/OW
versus NW PCa patients. Variations in global DNA
methylation demonstrated that excess adiposity played
an important role in DNA methylation level of PPAT
tissues in prostate cancer patients, which provide an
opportunity to explore the effect of obesity on PPAT
epigenetic modification and subsequently on prostate
cancer. These findings reported for the first time in
PPAT depot are in concordance with previous works
reporting that excess adiposity and BMI activate DNA
methylation in adipose tissue [
]. Thus, considering
the present understanding of the potential causal
relationship between excess adiposity and cancer [
], and cardiovascular disease [
], our results
provide methylated candidate genes, which might foster
research on the potential biological mechanisms
underlying epigenetic regulation of PPAT by excess adiposity
and prostate cancer.
Given that DNA methylation of CpGs located at
promoters and islands are associated with gene
transcription silencing, we performed a strict filtering of DMCs
and explored the biological functions of all promoter
hypermethylated genes, aiming to find the critical
methylated CpGs in the PPAT between the obese and normal
weight PCa patients. Bioinformatic analysis showed that
the enriched pathways were mostly involved in
metabolic disorders, particularly fatty acid degradation and
glycerolipid and choline metabolism. These pathways are
known to mediate the pro-tumoral effect of white
adipose tissue in tumors, thus contributing to
tumorigenesis and metastasis [
], particularly in prostate
cancer . Findings from other oncological models
highlight excess adiposity-associated impact in methylation
#The DMR is located at the intergenic region
*The number in the bracket is the quantities of DMCs located at the promoter (TSS200 and TSS1500) regions
FC fold change, DNAm β diff. DNAm β difference
Relation to CpG island
markers known to associate with potential effect in the
cancer microenvironment (e.g., aromatase, prostaglandin
E2 receptor in breast cancer) [
]. Obesity has also
been shown to associate with methylation of
cancerrelated genes (E-cadherin, p16, and RAR-β(2)) directly in
malignant breast cells [
Pathway enrichment analysis showed a strong
association between promoter hypermethylation of CPT1B,
CPT1C, ACADM, and FADS1, with fatty acid
metabolism. CPT1B (carnitine palmitoyltransferase 1B) and
CPT1C (carnitine palmitoyltransferase 1C) genes encode
rate-limiting enzymes in fatty acid degradation and play
critical roles in long-chain fatty acid (LCFA) β-oxidation
by controlling transportation of long-chain fatty
acylCoAs from the cytoplasm across the outer mitochondria
]. Maple et al. reported that increased
methylation of specific CpGs in the CPT1B promoter
was correlated with decreased CPT1B transcripts in the
skeletal muscle after lipid oversupply in severe obesity,
which resulted in obese individual’s incapacity to
increase fat oxidation, contributing to metabolic
]. Although the biochemical function of CPT1C
has been verified to be necessary for the regulation of
energy homeostasis in CPT1C knockout mouse brain
], the study of CPT1C methylation was absent.
CPT1B and CPT1C were previously reported to be
highly expressed in the muscle, brain, and many other
normal tissues including adipocytes [
]. Taken together,
these findings suggest that methylation of specific CpG sites
in the CPT1B and CPT1C promoters likely result in gene
expression silencing, thus consequently contributing to
fatty acid accumulation in adipocytes by decreasing
longchain fatty acid β-oxidation in the mitochondria (Fig. 4).
LCFA and ACADM genes (aliases MCAD,
mediumchain acyl-CoA dehydrogenase) coding for metabolic
enzymes presented increased methylation in the PPAT
of the OB/OW group. ACADM is the critical enzyme of
the initial step of β-oxidation and controls the
mediumchain fatty acid (MCFA) metabolism by catalyzing the
dehydrogenation of medium-chain Acyl-CoA, which is
the common middle product of MCFA and LCFA, in the
mitochondria. Mutations in ACADM cause MCAD
deficiency, which resulted in fatty acid oxidation disorder
leading to disease or infantile death [
]. Greco et al.
] reported inverse association between ACADM
transcript abundance with fat content in the human liver.
Our findings suggest that the hypermethylated ACADM
found in the PPAT of OB/OW PCa patients might fail to
generate medium-chain acyl-CoA β-oxidation and result
in MCFA and LCFA accumulation in adipose tissue,
providing a favorable tumor microenvironment for PCa cell
aggressiveness (Fig. 4). Additional functional studies are
required to confirm this assumption.
The hypermethylation of the FADS1 (fatty acid
desaturase 1) promoter, whose transcriptional activity was
significantly decreased in OB/OW PCa patients in agreement
with our previous study [
], has been described as
correlated with polyunsaturated fatty acid (PUFA) metabolism
by catalyzing the biosynthesis of highly unsaturated fatty
acids (HUFA) from the catalysis of dihomo-gamma-linoleic
acid (DGLA, 20:3 n-6) and eicosatetraenoic acid (ETA, 20:4
n-3) desaturation, in order to generate arachidonic acid
(AA, 20:4 n-6) and eicosapentaenoic acid (EPA, 20:5 n-3)
]. Genetic variants in the FADS1 and FADS2 gene
clusters have been associated with altered (n-6) and (n-3)
PUFA metabolism [
], whereas metabolic disorder in
PUFA exerted effects on PCa by mediating the formation
of eicosanoid inflammatory mediators (prostaglandins,
leukotrienes, thromboxanes, and lipoxins), angiogenesis,
immune cell regulation, and membrane structure and
]. These results illustrated that the
epigenetic modifications of FADS1 may play important roles in
the regulation of fatty acid metabolic genes on PPAT in
response to excess adiposity (Fig. 4).
Besides abnormal fatty acid metabolism, DMC-related
genes identified in our study were also correlated with
glycerolipid metabolism. MOGAT1 and MOGAT3 encode the
monoacylglycerol O-acyltransferase (MOGAT) and catalyze
the formation of diacylglycerol (DAG) from
monoacylglycerol (MAG), which is the precursor of phosphatidylcholine,
phosphatidylethanolamine, and triacylglycerol (TAG), by
transferring fatty acyl-CoA to 2-monoacylglycerol [
While human MGAT1 (aliases for MOGAT1) is involved in
intestinal dietary fat absorption and TAG synthesis in the
liver, its function in adipose tissue has yet to be elucidated.
The expression of MGAT1 was increased in the liver of
dietinduced obese mice with nonalcoholic fatty liver disease
(NAFLD), but, interestingly, there was increased DAG
accumulation and no inflammatory injury reduction in
hepatocytes after MGAT1 knockdown. Similarly, MOGAT3 was
mostly expressed in the human intestine and liver and
maintained a significant DGAT (diacylglycerol O-acyltransferase)
activity. Although results indicate that the metabolic
mechanism of lipid regulation by MGAT1 and MOGAT3 was
altered, evidence of association between lipid metabolic
disorders caused by aberrant expression of MGAT1/MOGAT3
and PCa are lacking. Our data indicate the methylation of
MOGAT1 and 3 genes in PPAT may play important roles in
response to excess adiposity by modulating glycerolipid
metabolism (Fig. 4).
Choline metabolic disorder might be caused by
epigenetic regulation of SLC44A2 (solute carrier family
44 member 2), which encodes choline transporter-like
protein 2 (CTL2) and is mainly expressed on blood
plasma and mitochondrial membrane of different
organisms and cell types. This transporter is a rate-limiting step
in choline metabolism by transporting extracellular
choline into cell and mitochondria. Choline is essential for
synthesizing membrane phospholipid and
neurotransmitter acetylcholine and used as a donor of methyl groups via
choline oxidized in mitochondria [
]. The choline
transporter has been associated with choline metabolic
disorders, thus playing an important role in regulating
immune response, inflammation, and oxidation [
Concordantly, abnormal choline metabolism emerged as a
metabolic hallmark, associated with oncogenesis and
tumor progression in prostate cancer and other
]. The increased uptake of choline by the
cancer cell was important to meet the needs of
phosphatidylcholine synthesis [
]. We hypothesize that
hypermethylated SLC44A2 in adipocytes might be associated
with lower uptake and oxidation of extracellular choline,
resulting in choline accumulation in PPAT extracellular
media (Fig. 4) and increasing the availability of choline for
PCa cell metabolism.
Besides metabolic modifications, altered immune
regulation pathways were also enriched in DMC-related
genes. TAPBP (alias tapasin) encodes a transmembrane
glycoprotein, which mediates the interaction between
MHC class I molecules and a transport protein TAP
(transporter associated with antigen processing), being
responsible for antigen processing and presentation. This
mechanism occurs via mediating TAP to translocate
endo/exogenous antigen peptides from the cytoplasm
into the endoplasmic reticulum and deliver the antigen
peptides to MHC class I molecules. The cancer cell’s
survival depends on successful escape to immune
surveillance. Loss of MHC class I has been described as
a major immune evasion strategy for cancer cells.
Downregulation of antigen-presenting MHC class I pathway in
tumor cells was a common mechanism for tumor cells
escaped from specific immune responses, which can be
associated with coordinated silencing of
antigenpresenting machinery genes, such as TAPBP [
Crosspresentation is the ability of certain antigen-presenting
cells to take up, process, and present extracellular
antigens with MHC class I molecules to CD8+ T cells. This
process is necessary for immunity against most tumors.
Recent studies revealed that TAPBP is a major target for
cancer immune evasion mechanisms and decreased
TAPBP expression in cancer was associated with
reduced CD8+ T cell-mediated killing of the tumor cells,
lowered immune responses, and enhanced tumor
metastases via downregulation of antigen presentation the
MHC class I pathway [
]. Our results showed that
TAPBP promoter hypermethylation in the PPAT of
obese PCa subjects likely reduced the expression or
activity of TAPBP, downregulating tumor cell’s antigen
presentation of immune cells in PPAT, leading to
impaired CD8+ T cell activation (Fig. 4). This indicates that
methylation of TAPBP might be a mechanism by which
prostate cancer cells escape the immune surveillance and
provide an appropriate microenvironment for tumor
aggressiveness, allowing prostatic cancer cells’ transfer,
spread, and growth. The significant DMR identified with
eight DMCs located in the TAPBP promoter further
supported its role in prostate cancer.
From the PPI analysis, the network which was
connected through ubiquitin C is characterized, suggesting
UBC played a significant biological function with the
methylated genes in PPAT between OB/OW and NW
patients and somehow was correlated with the
methylation. Ubiquitin is much known with the functions
including roles in protein degradation, DNA repair, cell
cycle regulation, kinase modification, and cell signaling
]. Recent reports expressed that the
ubiquitin-proteasome system was associated with the
progression and metastasis of prostate cancers [
And long-term silencing of the UBC was found to be
correlated with DNA methylation at the promoters [
Additional studies are needed to clarify whether the
protein network for methylated genes impacts prostate
cancer and if this difference is associated with ubiquitin C.
Although we present the first report on periprostatic
adipose tissue methylation profile in association with
excess adiposity measured by BMI, our results should be
interpreted in the context of several potential
limitations. This study is limited by small sample size, even
though representative groups of OB/OW and NW are
likely to be selected following the strict inclusion/exclusion
criteria and between-group match by clinicopathological
and demographic variables. Although we matched patients
by clinicopathological characteristics between adiposity
groups, tobacco smoking was more frequent among OB/
OW compared with NW patients. Actually, albeit we
cannot exclude an effect of smoking status on the presumably
adiposity-associated findings presented herein, due to a
known effect of tobacco on overall DNA methylation, data
from previous reports indicate that methylation profiles are
] and that adiposity-associated DNA
methylation occurs independently of tobacco smoking
]. Future studies will benefit from the confirmation
of these results in larger sample sizes, determination of
correspondence to matched prostate tumor methylation
patterns, investigation of interactome at the interface
between tumor and PPAT, and prospective investigations
on the value of PPAT epigenetic modifications on cancer
recurrence and survival. Future validation and replication
are important to establish the accuracy and generalizability
of the reported associations.
In summary, we observed differences in PPAT
methylation between NW and OB individuals at several loci known
to be involved in the metabolism of choline (SLC44A2),
fatty acids (CPT1B, CPT1C, ACADM, FADS1), and
glycerolipid (MOGAT1, MOGAT3) and in the regulation of
exogenous tumor antigen presentation (TAPBP). These
findings suggest a relationship of adiposity status with the
methylation profile, which ultimately modulates tumor
microenvironment and may influence PCa behavior.
In this preliminary study, we report DNA methylation
changes in PPAT underlying the association between excess
adiposity and PCa. Whole epigenome methylation profiling
of PPAT of PCa patients revealed significant differences in
OB/OW versus normal weight subjects. Epigenetic
imprinting in association with excess adiposity expressed the
methylated modifications in genes functionally related with lipid
metabolism and immune function, which could ultimately
contribute to an unfavorable tumor microenvironment and
decreased immune surveillance for prostate tumors. This
association analyses provided us novel insights into how
prostate cancer patients with excess adiposity differ from
those of patients with normal weight in epigenome.
Findings from this study warrant confirmation in PPAT
samples from larger number of patients.
Additional file 1: Figure S1. Research flowchart. Whole research
flowchart. NW normal weight, OB/OW obese/overweight, BMI body mass
index, PPAT periprostatic adipose tissue, QC quality control, DMCs
differentially methylated CpG sites, DMRs differentially methylated
regions, Limma linear models for microarray and RNA-seq analysis data
using R, GO gene ontology, KEGG Kyoto Encyclopedia of Genes and
Genomes, PPI protein-protein interaction network. (JPEG 128 kb)
Additional file 2: Table S1. Differentially methylated CpG sites in PPAT
between obese/overweight PCa patients and normal weight controls.
The table shows 5526 DMCs in PPAT between obese/overweight PCa
patients and normal weight patients, which were identified by using the
“Limma” method. (XLSX 663 kb)
Additional file 3: Figure S2. Heatmap of differentially methylated CpG
sites between the PPAT of OB/OW PCa and NW PCa patients. The
graphical display of hierarchical clustering for DMCs. The selected CpGs
are those with FDR < 0.25 and beta difference between obesity and
normal weight group larger than 10%. (JPEG 1797 kb)
Additional file 4: Table S2. Distribution of differentially methylated
CpG sites in relation to the nearest gene regions. The table shows the
distribution of DMCs according to the relation to the nearest gene
regions. (XLSX 11 kb)
Additional file 5: Table S3. Distribution of differentially methylated
CpG sites in relation to CpG islands. The table shows the distribution of
DMCs according to the relation to CpG islands. (XLSX 13 kb)
Additional file 6: Table S4. Differentially methylated CpG sites located
at both gene promoters and CpG islands. This table shows the 483 DMCs
which locate at both gene promoters and CpG islands. (XLSX 76 kb)
Additional file 7: Table S5. GO biological process analysis of promoter
hypermethylated genes. GO biological process analysis for 404 promoter
hypermethylated genes. (XLSX 18 kb)
Additional file 8: Table S6. GO molecular function analysis of promoter
hypermethylated genes. GO molecular function analysis for 404 promoter
hypermethylated genes. (XLSX 13 kb)
Additional file 9: Table S7. Pathway enrichment analysis of promoter
hypermethylated genes. Pathway enrichment analysis for 404 promoter
hypermethylated genes. (XLSX 11 kb)
Additional file 10: Table S8. Pathway enrichment analysis of the genes
included in PPI networks. Pathway enrichment analysis for methylated
genes and related genes included in PPI networks. (XLSX 13 kb)
Additional file 11: Table S9. Selected genes with multiple
hypermethylated CpG sites in PPAT with obese/overweight. The table
shows the 38 selected genes which have multiple hypermethylated CpG
sites. (XLSX 24 kb)
Additional file 12: Table S10. Pathway enrichment analysis of the
selected genes with multiple hypermethylated CpG sites. Pathway
enrichment analysis for the 38 selected genes which have multiple
hypermethylated CpG sites. (XLSX 13 kb)
Additional file 13: Table S11. Pathway enrichment analysis of the
overlapping genes. Pathway enrichment analysis for the 14 overlapping
genes. (XLSX 13 kb)
Additional file 14: Table S12. GO molecular function analysis of the
overlapping genes. GO molecular function analyses for the 14 overlapping
genes. (XLSX 13 kb)
AA: Arachidonic acid; ACADM: Aliases MCAD, medium-chain acyl-CoA
dehydrogenase; BH: Benjamin and Hochberg; BMI: Body mass index; CPT1B: Carnitine
palmitoyltransferase 1B; CPT1C: Carnitine palmitoyltransferase 1C; CTL2: Choline
transporter-like protein 2; DAG: Diacylglycerol; DGAT: Diacylglycerol
Oacyltransferase; DGAT2: Diacylglycerol O-acyltransferase 2; DGEs: Differential
gene expressions; DGLA: Dihomo-gamma-linoleic acid; DMCs: Differentially
methylated CpG sites; DMRs: Differentially methylated regions;
EPA: Eicosapentaenoic acid; ER: Endoplasmic reticulum; EWAS: Epigenetic-wide
Association Studies; FADS1: Fatty acid desaturase 1; FDR: False discovery rate;
GO: Gene ontology; GWAS: Genome-wide Association Studies; HUFA: Highly
unsaturated fatty acid; KEGG: Kyoto Encyclopedia of Genes and Genomes;
LCFA: Long-chain fatty acids; limma: Linear models for microarray and RNA-seq
data; MAG: Monoacylglycerol; MCFA: Medium-chain fatty acid; MGAT1: Aliases
for MOGAT1; MHC: Major histocompatibility complex;
MOGAT: Monoacylglycerol O-acyltransferase; MOGAT1: Monoacylglycerol
Oacyltransferase 1; MOGAT3: Monoacylglycerol O-acyltransferase 3;
NAFLD: Nonalcoholic fatty liver disease; PCa: Prostate cancer; PPAT: Periprostatic
adipose tissue; PPI: Protein-protein interaction analysis; PSA: Prostate-specific
antigen; PUFA: Polyunsaturated fatty acid; QC: Quality control; SLC44A2: Solute
carrier family 44 member 2; TAG: Triacylglycerol; TAP: Transporter associated
with antigen processing; TAPBP: TAP binding protein; TSS: Transcription start
site; TSS1500: 1500 bp upstream of the transcription start site; TSS200: 200 bp
upstream of the transcription start site; UBC: Ubiquitin C
This work was supported in part by the Natural Sciences and Engineering
Research Council of Canada, Manitoba Research Health Council, University of
Manitoba, and China Scholarship Council.
Availability of data and materials
The datasets used and/or analyzed during the current study are available
from the corresponding author on reasonable request.
YC designed and implemented the experiments and drafted the
manuscripts. JY helped generate the figures. PH supervised and monitored
the whole project. RR, CM, AM, CP, VC, JGA, GF, and AR performed the tissue
handling and processing, isolated the RNA, and conducted the gene
expression experiment. RR, AF, AM, and CM collected the adipose tissue and
clinicopathological patient information and edited the manuscript. RR, GF,
and PH designed the study and edited the manuscript. All authors read,
edited, and approved the final manuscript.
Ethics approval and consent to participate
Informed consent has been obtained from all the participants, and the study
has received ethical approval from the Ethics Committee of Portuguese
Institute of Oncology, Porto Centre.
Consent for publication
The participants/patients have given their consent for their data to be
published in the report.
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
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