Basic Study Open Access
Copyright ©The Author(s) 2016. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Dec 21, 2016; 22(47): 10325-10340
Published online Dec 21, 2016. doi: 10.3748/wjg.v22.i47.10325
Aging related methylation influences the gene expression of key control genes in colorectal cancer and adenoma
Orsolya Galamb, Barnabás Wichmann, Gábor Valcz, Zsolt Tulassay, Béla Molnár, Molecular Medicine Research Group, Hungarian Academy of Sciences, H-1088 Budapest, Hungary
Orsolya Galamb, Alexandra Kalmár, Barbara Kinga Barták, Árpád V Patai, Katalin Leiszter, Bálint Péterfia, 2nd Department of Internal Medicine, Semmelweis University, H-1088 Budapest, Hungary
Gábor Veres, 1st Department of Paediatrics, Semmelweis University, H-1083 Budapest, Hungary
Author contributions: Galamb O, Kalmár A, Péterfia B and Molnár B designed the study; Patai ÁV, Veres G and Molnár B collected the samples; Galamb O, Patai ÁV, Leiszter K, Valcz G and Veres G contributed to the collection of clinical data and histological analysis of the samples; Galamb O, Kalmár A, Barták BK and Patai ÁV performed the experiments; Galamb O, Kalmár A, Wichmann B and Valcz G analyzed the experimental data; Tulassay Z and Molnár B contributed to the design and critical review of the manuscript, obtained fundings; all authors were involved in writing the paper, made a critical revision of the manuscript for important intellectual content and had final approval of the submitted and published versions.
Supported by the National Research, Development and Innovation Office, No. KMR-12-1-2012-0216; and the Hungarian Scientific Research Fund, No. OTKA-K111743.
Institutional review board statement: All routine colonic biopsy samples from the patients were taken after informed consent and ethical permission was obtained for participation in the study.
Conflict-of-interest statement: The authors declare that no conflict of interest exists.
Data sharing statement: Additional data are available in a supplementary file.
Open-Access: This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Correspondence to: Orsolya Galamb, PhD, 2nd Department of Internal Medicine, Semmelweis University, Szentkirályi str 46, H-1088 Budapest, Hungary. orsg1@yahoo.com
Telephone: +36-1-2660926 Fax: +36-1-2660816
Received: June 22, 2016
Peer-review started: June 24, 2016
First decision: August 22, 2016
Revised: September 20, 2016
Accepted: November 13, 2016
Article in press: November 13, 2016
Published online: December 21, 2016
Processing time: 180 Days and 3 Hours

Abstract
AIM

To analyze colorectal carcinogenesis and age-related DNA methylation alterations of gene sequences associated with epigenetic clock CpG sites.

METHODS

In silico DNA methylation analysis of 353 epigenetic clock CpG sites published by Steve Horvath was performed using methylation array data for a set of 123 colonic tissue samples [64 colorectal cancer (CRC), 42 adenoma, 17 normal; GEO accession number: GSE48684]. Among the differentially methylated age-related genes, secreted frizzled related protein 1 (SFRP1) promoter methylation was further investigated in colonic tissue from 8 healthy adults, 19 normal children, 20 adenoma and 8 CRC patients using bisulfite-specific PCR followed by methylation-specific high resolution melting (MS-HRM) analysis. mRNA expression of age-related “epigenetic clock” genes was studied using Affymetrix HGU133 Plus2.0 whole transcriptome data of 153 colonic biopsy samples (49 healthy adult, 49 adenoma, 49 CRC, 6 healthy children) (GEO accession numbers: GSE37364, GSE10714, GSE4183, GSE37267). Whole promoter methylation analysis of genes showing inverse DNA methylation-gene expression data was performed on 30 colonic samples using methyl capture sequencing.

RESULTS

Fifty-seven age-related CpG sites including hypermethylated PPP1R16B, SFRP1, SYNE1 and hypomethylated MGP, PIPOX were differentially methylated between CRC and normal tissues (P < 0.05, Δβ≥ 10%). In the adenoma vs normal comparison, 70 CpG sites differed significantly, including hypermethylated DKK3, SDC2, SFRP1, SYNE1 and hypomethylated CEMIP, SPATA18 (P < 0.05, Δβ≥ 10%). In MS-HRM analysis, the SFRP1 promoter region was significantly hypermethylated in CRC (55.0% ± 8.4 %) and adenoma tissue samples (49.9% ± 18.1%) compared to normal adult (5.2% ± 2.7%) and young (2.2% ± 0.7%) colonic tissue (P < 0.0001). DNA methylation of SFRP1 promoter was slightly, but significantly increased in healthy adults compared to normal young samples (P < 0.02). This correlated with significantly increased SFRP1 mRNA levels in children compared to normal adult samples (P < 0.05). In CRC tissue the mRNA expression of 117 age-related genes were changed, while in adenoma samples 102 genes showed differential expression compared with normal colonic tissue (P < 0.05, logFC > 0.5). The change of expression for several genes including SYNE1, CLEC3B, LTBP3 and SFRP1, followed the same pattern in aging and carcinogenesis, though not for all genes (e.g., MGP).

CONCLUSION

Several age-related DNA methylation alterations can be observed during CRC development and progression affecting the mRNA expression of certain CRC- and adenoma-related key control genes.

Key Words: DNA methylation; Aging; Colorectal cancer; Adenoma; Epigenetic drift; Epigenetic clock; Secreted frizzled related protein 1

Core tip: Several age-related DNA methylation alterations could be observed during colorectal cancer (CRC) formation and progression affecting the mRNA expression of certain CRC- and adenoma-related key control genes such as hypermethylated secreted frizzled related protein 1 (SFRP1), spectrin repeat containing nuclear envelope protein 1 and hypomethylated cell migration-inducing protein. For the first time significantly lower SFRP1 methylation levels were demonstrated in colonic tissue from children (under 18 years) compared to healthy adults. The main CRC-associated signal transduction pathways, such as WNT signaling and PI3K/Akt pathways are also influenced during aging.



INTRODUCTION

DNA methylation alterations in connection with aging include epigenetic drift and epigenetic clock phenomena. Epigenetic drift is defined as the global DNA methylation changes caused by random and environmental individual-specific factors, while the epigenetic clock is defined as a group of progressive age-related epigenetic alterations at specific genomic sites which are common across individuals and occassionally across various tissue types[1,2]. The epigenetic clock concept is an approach to biological age prediction of different tissues based on the DNA methylation status of 353 CpG sites measured using the Illumina Beadchip450K methylation array platform[2].

Although age-related (A type) and cancer-related (C type) DNA methylation are often distinguished, the main age-related disease is cancer and the age of patients is one of the risk factor for carcinogenesis[3]. In human development, following a transient increase in average DNA methylation in early childhood (during the first year of life)[4,5], global hypomethylation is characteristic during aging[6,7]. Similarly global hypomethylation is observed in various types of cancers including colorectal cancer (CRC)[8]. With aging, besides global hypomethylation, local hypermethylation can occur on promoters of certain genes, including tumor suppressor gene promoters in various types of cancers, and many tumor suppressor genes were reported among the age-dependently hypermethylated genes[6]. Among others, promoter hypermethylation of APC[7,9-12], CDKN2A[7,9], ESR1[7,13,14], GATA5[15,16], HPP1[7,15,17], SFRP1[7,12,15,18-23] and SFRP2[7,18-21] genes was reported for colonic tissues during both aging and colorectal carcinogenesis. Although DNA methylation data from adult colonic tissue samples has been expansively published, data for children/young patients are limited.

In this study, we analyzed DNA methylation and/or gene expression changes of genes covered by the 353 epigenetic clock CpG sites[2] for patients of different ages as well as stages in the progression through to CRC in order to study the possible relationship between age-related and cancer-associated epigenetic alterations. Gene expression analysis was performed using colonic tissue samples from healthy children, healthy adults, and patients with adenomas and CRC. Among the differentially methylated/expressed age-related genes, secreted frizzled related protein 1 (SFRP1) promoter methylation was further analyzed in healthy, premalignant and cancerous colonic tissue samples, and to our knowledge this is the first study to also include colonic biopsy specimens from children.

MATERIALS AND METHODS
In silico DNA methylation analysis

The DNA methylation status of 353 age-related CpG sites[2] was analyzed in silico using methylation array data from the Illumina BeadChip450K. Analysis was performed on 123 CRC, adenoma and normal tissue samples available in the NCBI Gene Expression Database database (GEO accession number: GSE48684[24]). Differences between average methylation values of the compared diagnostic groups (Δβ-values) and P values were determined for each CpG site (cg IDs). For statistical evaluation, normal distribution was checked using Kolmogorov-Smirnov test. Hence normal distribution was observed in any cases, Student’s t-test with Benjamini and Hochberg correction was applied for paired group comparisons. Significance criteries were P < 0.05 in all cases.

In silico gene expression analysis

The expression of age-related “epigenetic clock” genes was analyzed using whole transcriptome data from Affymetrix HGU133 Plus2.0. Data was obtained from 153 colonic biopsy samples (49 healthy, 49 adenoma, 49 CRC and 6 healthy children) previously hybridized by our research group (GEO serial accession numbers: GSE37364[25], GSE10714[26], GSE4183[27], GSE37267[28]). Gene expression levels were compared using unpaired Student’s t-test with Benjamini and Hochberg correction (P value of < 0.05 was considered as significant). For gene expression analysis, normal distribution was found using Kolmogorov-Smirnov test, therefore Student’s t-test (in case of differentiation of two groups with equal variances) or Welch-test (in case of differentiation of two groups with unequal variances) and ANOVA (when more than two groups were compared) were applied. For paired comparisons Benjamini and Hochberg correction was applied. In case of ANOVA, Tukey HSD post-test was used in order to find out which group refers to the differentiation if any. Significance criteries were P < 0.05 in any cases. For the logFC calculation, the differences between the averages of groups were considered (abs logFC ≥ 0.5 criteria).

Methyl capture sequencing - in silico data analysis

Whole methylome data from 6 normal adjacent tissue (NAT), 15 adenoma and 9 CRC tissue samples were determined in a previous study using methyl capture sequencing[12]. Using this dataset, the whole promoter methylation status of genes showing an inverse relation between gene expression and DNA methylation was evaluated. Differentially methylated genes were determined as described earlier[12]. For statistical evaluation normal distribution was determined and the applied tests were chosen according to the above-mentioned criteria. Differences with P < 0.05 were considered as significant. Methylation alterations between diagnostic groups were characterized by Δβ-values (the differences of the average β-values of sample groups).

Clinical samples

All patients provided informed consent. Colorectal biopsy samples were obtained during routine endoscopic intervention at the 2nd Department of Internal Medicine and 1st Department of Paediatrics, Semmelweis University, Budapest, Hungary. In total 55 colonic tissue samples (from 19 healthy children (under age of 18 years), 8 healthy adults, 20 patients with adenomas and 8 CRC samples) were tested in SFRP1 methylation-specific high resolution melting (MS-HRM) study (Table 1). Biopsy samples from all adults and 5 of children were stored in RNALater Stabilization Solution (Ambion, ThermoFisher Scientific) at -80 °C until use. Biopsy samples from the same site were immediately fixed in buffered formalin for histological evaluation. For 14 children, only FFPE blocks were available. Histological diagnoses were established by experienced pathologists. Altogether 27 tissue samples (19 from children and 8 from adults) with normal histology (so called healthy normal colonic tissue samples) were involved in SFRP1 MS-HRM study. Children and adults in the study had been referred to the outpatient clinic with rectal bleeding, constipation or chronic abdominal pain. Ileocolonoscopy was part of their diagnostic procedure (exclude organic disease) and the biopsy specimens showed normal macroscopic appearance and histology[28]. The study was conducted according to the Helsinki declaration and approved by the local ethics committee and government authorities (Regional and Institutional Committee of Science and Research Ethics (TUKEB) Nr.: 69/2008, 202/2009 and 23970/2011 Semmelweis University, Budapest, Hungary).

Table 1 Clinical data of samples involved in the high resolution melting study.
Sample IDAgeGenderLocalizationHistologyTNMGradeDukes’ stage (MAC)DysplasiaAdenoma sizeSample type
Ch14MColonNormalFFPE
Ch27FCecumNormalFFPE
Ch311MColonNormalFFPE
Ch414MTransverseNormalFFPE
Ch55FSigmoidNormalFFPE
Ch67FDescendentNormalFFPE
Ch71MDescendentNormalFFPE
Ch81MSigmoidNormalFFPE
Ch910MCecumNormalFFPE
Ch103MSigmoidNormalFFPE
Ch1117FCecumNormalFFPE
Ch1217FSigmoidNormalFFPE
Ch1316FSigmoidNormalFFPE
Ch1416FCecumNormalFFPE
Ch151MLeft colonNormalFF
Ch163FSigmoidNormalFF
Ch176MSigmoidNormalFF
Ch189MSigmoidNormalFF
Ch1917MColonNormalFF
N144FSigmoidNormalFF
N231FSigmoidNormalFF
N359FSigmoidNormalFF
N454MColonNormalFF
N568FSigmoidNormalFF
N671FSigmoidNormalFF
N769FSigmoidNormalFF
N857FSigmoidNormalFF
AD178MAsc, sigmoid, rectumTubulovillous adenomaLow-grade30 mm, 3 mm, 15 mmFF
AD260MSigmoidTubular adenomaLow-grade6 mmFF
AD388MAsc, transv, sigmoidTubular adenomaLow-grade4 mm, 3 mm, 7-8 mmFF
AD472FRectumTubular adenomaLow-grade10 mmFF
AD545FDescendentTubular adenomaHigh-grade5-6 mmFF
AD668FRectumTubular adenomaLow-grade5 mmFF
AD763FSigmoidTubular adenomaLow-grade8 mmFF
AD865FAsc, transv, rectumTubular adenomaLow-grade2-3 mm, 2-3 mm, 2-3 mmFF
AD960FSigmoidTubular adenomaLow-grade5 mm, 4 mmFF
AD1077FRectosigmoidTubular adenomaLow-grade5 mmFF
AD1155FAsc colonTubular adenomaLow-grade10 mmFF
AD1276MCecum, sigmoidTubular adenomaLow-grade5 mm, 8-10 mmFF
AD1362FSigmoidTubular adenomaHigh-grade30 mmFF
AD1483MAsc colonTubulovillous adenomaHigh-grade50-60 mmFF
AD1573MCecum, asc, descTubular adenomaLow-grade12 mm, 10 mm, 6-8 mmFF
AD1664MTransv, sigmoid, rectumTubular adenomaLow-grade5 mm, 25 mm, 15 mmFF
AD1763MAsc, transv, rectumTubular adenomaLow-grade2-3 mm, 5 mm, 5-6 mm, 15 mmFF
AD1863FSigmoidTubulovillous adenomaLow-grade25 mmFF
AD1963MRectumTubulovillous and tubular adenomaLow-grade25 mm, 30 mmFF
AD2087MSigmoid, rectumTubulovillous adenomaLow-grade20 mm, 15 mmFF
AD2163FSigmoidTubulovillous adenomaLow-grade25 mmFF
CRC167FSigmoidAdenocarcinomaUnknownUnknownUnknownFF
CRC276FHepatic flexureAdenocarcinomaT2N0M02B1FF
CRC373FSigmoidAdenocarcinomaT3N2M12DFF
CRC465MSigmoidAdenocarcinomaT2N0M01B1FF
CRC585FCecumAdenocarcinomaT2N0M02B1FF
CRC660MHepatic flexureAdenocarcinomaunknownDFF
CRC768MSigmoid, rectumAdenocarcinomaT3N0M01B2FF
CRC887FSigmoidAdenocarcinomaT3N0M02B2FF
DNA isolation

Tissue samples were homogenized in 2% sodium dodecyl sulphate, and digested with 4 mg/mL proteinase K for 16 h at 56 °C. Genomic DNA was isolated using the High Pure PCR Template Preparation Kit (Roche Applied Science) according to the manufacturer’s instructions[18]. DNA was eluted in 2 × 100 μL RNase- and DNase-free water and stored at -20 °C. The quantity of the isolated DNA samples was measured by Qubit fluorometer using the Qubit dsDNA HS Assay (Invitrogen, ThermoFisher Scientific).

Bisulfite-specific PCR and MS-HRM experiments for SFRP1 promoter methylation analysis

Bisulfite conversion was performed using the EZ DNA Methylation Direct™ Kit (Zymo Research, Irvine, CA, United States) according to the manufacturer’s instructions. For fresh frozen samples, 500 ng isolated DNA was converted, while for FFPE samples the total recovered DNA after deparaffinization and digestion was bisulfite converted. Bisulfite-specific PCR (BS-PCR) reactions were performed in 15 μL volume using LightCycler 480 Probes Master (Roche Applied Science), LightCycler® 480 ResoLight Dye (Roche), SFRP1 primers at 0.2 μmol/L final concentrations and bisulfite converted DNA (bcDNA) samples (approx, 5 ng bcDNA/well). The sequences of the applied SFRP1 BS-PCR primers were previously described[19]. Real-time PCR amplification was carried out on LightCycler 480 System with the following thermocycling conditions: 95 °C for 10 min, then 95 °C for 30 s, 62 °C with 0.4 °C decreasement/cycle for 30 s, 72 °C for 30 s for 10 touchdown cycles, followed by the amplification at 95 °C for 30 s, 58 °C for 30 s, and 72 °C for 30 s in 50 cycles. For HRM calibration, unmethylated and methylated bisulfite converted control DNA (EpiTect PCR Control DNA Set, Qiagen) were used in different ratios (0%, 10%, 25%, 50%, 75% and 100% methylated controls). HRM analyis was performed according to the following thermal conditions: after denaturation at 95 °C for 1 min, and cooling at 40 °C for 1 min, the reactions were continuously warmed up to 95 °C with a 25 acquisition/°C rate. Raw HRM data were exported and the HRM peak heights of the negative derivative of fluorescence over temperature curves (-(d/dT) Fluorescence) of the biological samples were retrieved at the melting temperatures of the methylated and unmethylated standards. The methylation percent was calculated by the ratio of values at the methylated and unmethylated melting temperatures. For statistical evaluation normal distribution was determined and the applied tests were chosen according to the above-mentioned criteria.

SFRP1 immunohistochemistry

Parallel with our epigenetic examinations, 4 nm thick FFPE samples from healthy children (n = 6) and healthy adults (n = 7) were examined. SFRP1 immunohistochemistry was performed on colonic tissue samples of healthy adults (n = 7; mean age at histology examination: 48 ± 17 years; 5 f/2 m) and of children (mean age: 12 ± 6 years); 3 f/3 m). Histology was diagnosed by an expert pathologist on routinely stained HE sections. Following deparaffinization and rehydration, microwave based antigen retrieval was performed in TRIS EDTA buffer (pH 9.0) (900 W/10 min, then 340 W/40 min). Samples were immunostained with SFRP1 polyclonal antibody (ab4193, Abcam, Cambridge, United Kingdom, 1:80 dilution) with diaminobenzidine - hydrogen peroxidase - chromogen-substrate system (cat#30014.K, HISTOLS-DAB, Histopathology Ltd., Hungary) and were digitalized by Pannoramic 250 Flash II scanner (with Zeiss Plan-Apochromat 20 × objective; 3DHISTECH Ltd, Hungary). Digital slides were semi-quantitatively analyzed with Pannoramic Viewer (ver.:1.15.3; 3DHISTECH) based on Q-score method (scored by multiplying the percentage of positive cells (P) by the intensity (I: +3, +2, +1, 0). Formula: Q = P × I; Maximum = 300). Epithelial and stromal compartments were examined separately, then these scores were summarized (Σ) (Σ Q-score maximum: 600) in order to have comparable results with our whole biopsy methylation analyses.

Statistical analysis

The applied statistical methods are outlined above after the descriptions of molecular and in silico analyses. The statistical review of the study was performed by a biomedical statistician.

RESULTS
Gene ontology of 353 CpG sites of the "epigenetic clock"

The “epigenetic clock” signature includes 353 CpG sites[2] including different genes, gene promoters and other genomic regions such as enhancers, insulators, Polycomb-repressed regions. From the above 353 CpG sites, DNA methylation levels of 193 were positively and of 160 were negatively correlated with age[2]. First we updated the annotation of the CpG sites and assigned official gene symbols according to the newest version of NCBI Gene Database. Approximately 80% of the genes belonging to 353 CpG sites could be classified into functional groups including highly represented transcriptional regulation, translation (15.93%), metabolism (12.36%), development and ontogenesis (8.24%) and transport (8.24%). Approximately 20% of the genes had unknown function. According to the Encode ChromHMM results of nine human cell lines, the majority of the 353 CpG sites (76.2 %) were located in the promoter regions of genes. Also, 57.2% of them were categorized as active promoters (declared if it was found “active promoter” in at least one of the nine analyzed cell lines), while 19% were found to be “weak promoter” (“weak promoter” in at least one of the nine analyzed cell lines). One fourth (23.8%) of the CpG sites was located in non-promoter regions such as enhancers, insulators, transcribed and repressed regions (Supplementary Table 1).

In silico DNA methylation analysis

Analysis of the Illumnina Beadchip450K methylation array data set of Luo et al[24] showed 137 (38.8%) of the epigenetic clock CpG sites were found to be significantly differentially methylated between CRC and normal tissue samples (P < 0.05). Approximately two third of these CpG sites had similar methylation changes in CRC samples as during aging, while one third of these CpG sites showed opposite alterations in CRC tissue as during aging (Supplementary Table 1). Among these, 57 CpG sites showed at least a 10% methylation difference: from the 57 CpG sites 45 were hypermethylated (including ADHFE1/cg08090772, MCAM/cg21096399, DKK3/cg13216057, SFRP1/cg02388150, SYNE1/cg26620959), while 12 CpG sites showed hypomethylation (such as MGP/cg00431549, PIPOX/cg06144905, STRA6/cg00075967, ERG/cg17274064) in CRCs (P < 0.05, Δβ≥ 10%) (Table 2, Supplementary Table 1). In the adenoma vs normal comparison, DNA methylation of 165 CpG sites (46.7%) were significantly altered from which 70 CpG sites showed a ≥ 10% methylation difference: 36 CpG sites were hypermethylated (e.g., SDC2/cg25070637, SFRP1/cg02388150, SYNE1/cg26620959) and 34 showed decreased methylation levels (including CEMIP/cg20828084, SPATA18/cg03103192, STRA6/cg00075967) in adenoma samples (P < 0.05, Δβ≥ 10%) (Table 3). In CRC samples 33 CpG sites were found to be hypermethylated (such as KRT20/cg00091693, STRA6/cg00075967, UROS/cg19346193) and only one (LTBP3/cg08965235) was hypomethylated compared to adenomas (P < 0.05, Δβ≥ 10%) (Table 4). A heatmap for the differentially methylated epigenetic clock CpG sites (P < 0.05, Δβ≥ 10%), with hierarchical cluster analysis results of normal, adenoma and CRC samples is shown in Figure 1.

Figure 1
Figure 1 DNA methylation heatmap of normal, adenoma and colorectal carcinoma samples according to the methylation status of age-related CpG sites. From the 353 epigenetic clock CpG sites (cg IDs)[2] significantly differentially methylated in CRC vs normal, adenoma vs normal and CRC vs adenoma comparisons were selected and colonic tissue samples (GSE48684[24]) were classified according to their methylation levels. The analyzed samples are illustrated on X axis, significantly altered CpG sites (cg IDs) are represented on Y axis. DNA methylation intensities (β values) are visualized, red shows hypermethylation, while hypomethylation was marked with green color. CRC: Colorectal carcinoma (light green); Ad: Adenoma (dark blue); N: Normal (light blue).
Table 2 Significant DNA methylation alterations of age-related CpG sites in CRC samples compared to normal tissue.
cgIDGene symbolP valueΔβ (CRC - N)
cg06462291NT5DC33.81 × 10-6-0.27
cg06144905PIPOX1.81 × 10-7-0.22
cg10345936SLC36A22.49 × 10-5-0.18
cg13828047MPI1.78 × 10-5-0.17
cg11314684AKT34.55 × 10-5-0.16
cg00431549MGP0.020-0.12
cg14409958ENPP20.021-0.11
cg00091693KRT200.010-0.11
cg17274064ERG1.48 × 10-4-0.11
cg22809047RPL311.91 × 10-3-0.10
cg00075967STRA60.010-0.10
cg06952310NCAN7.13 × 10-3-0.10
cg02388150SFRP13.33 × 10-30.10
cg03588357GPR687.72 × 10-30.10
cg26297688TMEM2636.21 × 10-30.10
cg04528819KLF140.0360.10
cg26372517TFAP2E9.23 × 10-30.11
cg08030082POMC1.93 × 10-30.11
cg05675373KCNC47.80 × 10-30.11
cg10281002TBX50.0120.11
cg06117855CLEC3B1.87 × 10-30.11
cg09509673CCR101.91 × 10-40.11
cg14597908GNAS1.48 × 10-40.11
cg27494383LTK9.32 × 10-30.12
cg21870884GPR255.77 × 10-30.13
cg25657834NTSR24.43 × 10-30.13
cg22449114TCF153.48 × 10-50.13
cg04126866C10orf994.52 × 10-40.13
cg25552492LGI39.44 × 10-30.14
cg08965235LTBP33.12 × 10-30.14
cg06836772PRKAA20.0130.14
cg02364642ARHGEF251.20 × 10-30.16
cg18573383KCNC21.38 × 10-30.16
cg12616277ESYT30.0150.16
cg17729667NINL3.46 × 10-30.16
cg04999691ZBED6CL3.32 × 10-110.16
cg12373771CECR63.70 × 10-30.17
cg02489552CCDC1058.91 × 10-80.17
cg25148589GRIA27.77 × 10-60.19
cg21096399MCAM5.47 × 10-60.19
cg20914508GAP434.24 × 10-40.20
cg12768605LYPD51.85 × 10-40.21
cg13216057DKK32.10 × 10-40.21
cg12351433LHCGR4.97 × 10-70.22
cg08434234DGKI5.16 × 10-50.23
cg10920957JPH33.00 × 10-40.26
cg27092035ARL103.73 × 10-50.27
cg06557358TMEM132E3.17 × 10-60.27
cg26620959SYNE11.32 × 10-60.30
cg07663789NPR38.73 × 10-60.31
cg09191327PRDM121.79 × 10-60.31
cg25070637SDC21.64 × 10-60.34
cg10486998GALR11.81 × 10-80.35
cg24834740PPP1R16B3.56 × 10-120.37
cg27319898ZNF804B5.41 × 10-80.38
cg08090772ADHFE17.18 × 10-140.39
cg02217159KHDRBS26.67 × 10-130.43
Table 3 Significant DNA methylation changes of age-related CpG sites in adenoma samples compared to normal tissue.
cgIDGene symbolP valueΔβ (AD - N)
cg10345936SLC36A23.49 × 10-10-0.34
cg00091693KRT202.49 × 10-12-0.30
cg00075967STRA62.58 × 10-9-0.26
cg11314684AKT31.03 × 10-9-0.26
cg06462291NT5DC37.45 × 10-5-0.25
cg17099569GLI21.25 × 10-10-0.23
cg00168942GJD42.48 × 10-7-0.20
cg20828084CEMIP1.33 × 10-9-0.19
cg17274064ERG1.82 × 10-10-0.19
cg07408456PGLYRP22.41 × 10-12-0.19
cg03019000TEX2642.25 × 10-7-0.19
cg17589341SLC14A11.42 × 10-9-0.19
cg02580606KRT33B9.08 × 10-5-0.17
cg00436603CYP2E11.37 × 10-8-0.16
cg19346193UROS6.59 × 10-7-0.15
cg03103192SPATA181.39 × 10-7-0.15
cg25564800KPNA12.18 × 10-8-0.14
cg06144905PIPOX3.63 × 10-7-0.14
cg06952310NCAN2.72 × 10-4-0.14
cg13038560C2orf472.13 × 10-5-0.13
cg22190114NLRP81.67 × 10-4-0.13
cg13302154MGP7.27 × 10-6-0.13
cg01262913DSCR92.67 × 10-6-0.13
cg14258236OR5V13.71 × 10-4-0.12
cg13828047MPI1.01 × 10-3-0.12
cg01459453SELP1.65 × 10-4-0.12
cg14894144LAMA32.29 × 10-4-0.12
cg07337598ANXA92.02 × 10-5-0.12
cg03270204DDR17.47 × 10-5-0.12
cg12946225HMG20B1.37 × 10-6-0.11
cg09646392TNFSF13B2.66 × 10-5-0.10
cg19305227SLC28A20.033-0.10
cg03578041LARP62.22 × 10-5-0.10
cg07455279NDUFA30.013-0.10
cg13899108PDE4C5.00 × 10-60.10
cg04999691ZBED6CL4.92 × 10-50.10
cg14597908GNAS9.38 × 10-50.10
cg21870884GPR259.49 × 10-30.11
cg12616277ESYT30.0220.12
cg12373771CECR63.55 × 10-30.12
cg17729667NINL4.43 × 10-30.13
cg02364642ARHGEF252.33 × 10-30.14
cg22449114TCF152.39 × 10-50.14
cg03565323ZNF2874.52 × 10-30.14
cg02388150SFRP11.20 × 10-70.15
cg12768605LO×L28.01 × 10-30.15
cg25657834NTSR21.46 × 10-40.16
cg20914508GAP439.41 × 10-40.16
cg10281002TBX51.26 × 10-50.17
cg02489552CCDC1057.16 × 10-80.18
cg05675373KCNC41.82 × 10-60.18
cg25148589GRIA26.11 × 10-70.18
cg13216057DKK39.64 × 10-40.18
cg27092035ARL108.81 × 10-40.19
cg12351433LHCGR2.20 × 10-60.20
cg18573383KCNC25.43 × 10-70.21
cg08434234DGKI6.60 × 10-50.22
cg21096399MCAM5.20 × 10-80.22
cg10920957JPH31.03 × 10-40.25
cg08965235LTBP36.03 × 10-100.25
cg27319898ZNF804B5.48 × 10-50.26
cg26620959SYNE13.89 × 10-60.29
cg25070637SDC21.82 × 10-50.30
cg07663789NPR36.41 × 10-70.31
cg09191327PRDM124.08 × 10-70.31
cg06557358TMEM132E2.04 × 10-80.33
cg10486998GALR12.61 × 10-90.36
cg24834740PPP1R16B2.23 × 10-90.36
cg08090772ADHFE15.19 × 10-110.39
cg02217159KHDRBS21.87 × 10-110.41
Table 4 Significant DNA methylation alterations of age-related CpG sites in colorectal cancer samples compared to adenoma tissue.
cgIDGene symbolP valueΔβ (CRC - AD)
cg08965235LTBP39.09 × 10-4-0.11
cg00945507SEC61G1.64 × 10-50.10
cg15974053HSD17B142.27 × 10-30.10
cg24262469TIPARP5.33 × 10-80.10
cg07158339FXN1.84 × 10-70.10
cg11314684AKT34.52 × 10-40.10
cg03578041LARP62.57 × 10-90.10
cg17853587NDST35.94 × 10-40.10
cg07408456PGLYRP21.51 × 10-70.11
cg02580606KRT33B1.22 × 10-40.11
cg03019000TEX2645.39 × 10-50.11
cg27319898ZNF804B0.0230.12
cg00436603CYP2E11.97 × 10-60.12
cg15804973MAP3K56.27 × 10-120.12
cg06117855CLEC3B1.73 × 10-70.13
cg24126851DCHS13.58 × 10-80.13
cg16034652UNC794.23 × 10-110.13
cg01262913DSCR91.64 × 10-80.13
cg26372517TFAP2E9.80 × 10-60.13
cg03270204DDR14.24 × 10-70.13
cg17589341SLC14A17.35 × 10-100.13
cg22679120SNX88.16 × 10-90.14
cg20828084CEMIP2.00 × 10-80.14
cg19305227SLC28A21.11 × 10-60.14
cg00168942GJD41.18 × 10-70.14
cg13038560C2orf478.96 × 10-80.15
cg26614073SCAP1.33 × 10-90.15
cg19346193UROS2.09 × 10-80.16
cg00075967STRA64.87 × 10-70.16
cg03103192SPATA186.07 × 10-120.16
cg10345936SLC36A24.09 × 10-60.16
cg17099569GLI29.04 × 10-70.16
cg04126866C10orf994.23 × 10-100.18
cg00091693KRT201.17 × 10-80.19
In silico gene expression analysis

Genes belonging to 353 age-related CpG sites were mapped to 768 Affymerix transcript IDs. In the CRC vs N comparison, 215 “epigenetic clock” genes were found to be significantly differentially expressed (P < 0.05), of which 117 were altered with absolute logFC > 0.5 (70 upregulated such as ERG, MGP, MCAM, CEMIP and 47 downregulated like SFRP1, KRT20, CLEC3B, SYNE1) (Supplementary Table 2A). Expression of 196 “epigenetic clock” genes changed significantly (P < 0.05) in adenoma samples compared to healthy normal controls, 102 with absolute logFC higher than 0.5 (47 overexpressed such as CEMIP, PLK1, CCNF and 55 underexpressed like SFRP1, SDC2, SYNE1) (Supplementary Table 2B). Forty-three genes including MCAM, MGP and AKT3 showed increased expression in CRC compared to adenoma samples, while 17 genes including CEMIP, SPATA18 were downregulated (P < 0.05, absolute logFC > 0.5) (Supplementary Table 2C).

For genes with an inverse relation between gene expression and promoter methylation, the genes with both significant mRNA expression changes with absolute logFC > 0.5 and significant DNA methylation alterations with at least 10% difference were taken into consideration. Based on these criteria, eleven genes, including ERG, MGP, PIPOX, CLEC3B, LTK, SFRP1 and SYNE1 were found to be inversely expressed with the promoter methylation status in CRC compared to normal tissue. Compared to the promoter methylation alterations, the expression of 8 genes, including CEMIP, SPATA18, SDC2, SFRP1 and SYNE1 changed oppositely in AD vs N comparison, while in CRC vs AD tissues 3 genes, namely CEMIP, SPATA18 and SLC28A2 showed this expression pattern. The genes showing an inverse relation between gene expression and DNA methylation in CRC vs normal, AD vs normal and CRC vs AD comparisons are represented in Table 5.

Table 5 Genes showing inverse DNA methylation and gene expression data.
Gene symbolGene nameDNA methylation
Gene expression
cg IDP valueΔβAffymetrix IDP valueLogFC
CRC vs N
AKT3AKT serine/threonine kinase 3cg113146844.55 × 10-5-0.16224229_s_at0.0150.07
222880_at2.26 × 10-30.61
212609_s_at4.72 × 10-40.62
212607_at9.76 × 10-40.64
ERGv-ets avian erythroblastosis virus E26 oncogene homologcg172740641.48 × 10-4-0.11211626_x_at5.84 × 10-40.20
241926_s_at2.05 × 10-50.64
213541_s_at4.69 × 10-91.08
MGPMatrix Gla proteincg004315490.020-0.12202291_s_at2.34 × 10-111.97
PIPOXPipecolic acid and sarcosine oxidasecg061449051.81 × 10-7-0.22221605_s_at0.0190.50
CCR10C-C motif chemokine receptor 10cg095096731.91 × 10-40.11220565_at0.010-0.51
CLEC3BC-type lectin domain family 3 member Bcg061178551.88 × 10-30.11205200_at1.45 × 10-13-1.79
LTKLeukocyte receptor tyrosine kinasecg274943839.32 × 10-30.12217184_s_at2.14 × 10-11-1.32
207106_s_at2.00 × 10-5-0.75
PPP1R16BProtein phosphatase 1 regulatory subunit 16Bcg248347403.56 × 10-120.3741577_at1.76 × 10-4-0.79
212750_at7.68 × 10-3-0.48
PRKAA2Protein kinase AMP-activated catalytic subunit alpha 2cg068367720.0130.14227892_at4.15 × 10-3-0.87
238489_at4.67 × 10-3-0.22
SFRP1Secreted frizzled related protein 1cg023881503.33 × 10-30.10202036_s_at8.66 × 10-8-1.47
202037_s_at3.98 × 10-5-1.11
202035_s_at0.039-0.28
SYNE1Spectrin repeat containing nuclear envelope protein 1cg266209591.32 × 10-60.30209447_at4.27 × 10-5-0.63
AD vs N
CEMIPCell migration inducing hyaluronan binding proteincg208280841.33 × 10-9-0.171554685_a_at1.71 × 10-111.12
212942_s_at2.41 × 10-436.88
NT5DC35'-nucleotidase domain containing 3cg064622917.45 × 10-5-0.25218786_at6.83 × 10-90.76
SPATA18Spermatogenesis associated 18cg031031921.39 × 10-7-0.15230723_at4.41 × 10-50.45
229331_at1.20 × 10-101.50
DKK3Dickkopf WNT signaling pathway inhibitor 3cg132160579.64 × 10-40.18202196_s_at9.82 × 10-5-0.65
214247_s_at1.52 × 10-4-0.50
PPP1R16BProtein phosphatase 1 regulatory subunit 16Bcg248347402.23 × 10-90.3641577_at1.20 × 10-10-1.44
212750_at3.28 × 10-7-0.81
SDC2Syndecan 2cg250706371.82 × 10-50.30212158_at1.74 × 10-10-1.17
212157_at2.14 × 10-8-0.66
212154_at9.19 × 10-5-0.64
SFRP1Secreted frizzled related protein 1cg023881501.20 × 10-70.15202036_s_at3.62 × 10-13-1.79
202037_s_at4.43 × 10-11-1.48
202035_s_at0.016-0.32
SYNE1Spectrin repeat containing nuclear envelope protein 1cg266209593.89 × 10-60.29209447_at5.76 × 10-15-1.19
CRC vs AD
CEMIPCell migration inducing hyaluronan binding proteincg208280842.00 × 10-80.14212942_s_at9.88 × 10-3-1.17
SLC28A2Solute carrier family 28 member 2cg193052271.11 × 10-60.14207249_s_at0.027-1.24
SPATA18Spermatogenesis associated 18cg031031926.07 × 10-120.16229331_at3.52 × 10-9-1.73
230723_at7.35 × 10-4-0.41

In the comparison of healthy young colonic samples and normal adult tissues, 150 genes showed significantly altered expression from which 94 genes with absolute logFC > 0.5 including overexpressed LTBP3, REEP1, MGP, PLK1, SFRP1, SYNE1 and downregulated PRKG2, PDCD6IP and TMEM56 (P < 0.05) (Supplementary Table 2D). The pattern of expression of several genes including SYNE1, CLEC3B, LTBP3 (Figure 2) and SFRP1 (Figure 3A-C) was similar for increased age as that observed for cancer progression. However, there were some genes such as MGP (Figure 2) which showed similar expression pattern in young people and in cancer patients compared to healthy adult tissue.

Figure 2
Figure 2 Genes showing both age- and carcinogenesis-related expression alterations. SYNE1 (spectrin repeat containing nuclear envelope protein 1), LTBP3 (latent transforming growth factor beta binding protein 3) and CLEC3B (C-type lectin domain family 3 member B) genes were downregulated during the colorectal carcinogenesis, similar decreasing expression was found during aging (significantly higher mRNA levels were detected in young colonic samples than in healthy adult biopsy specimens). MGP (matrix Gla protein) was overexpressed in children and in CRC samples compared to adenoma and healthy adults (P < 0.035), hence its opposite expression was found during aging and colorectal carcinogenesis. X axis shows the analyzed sample groups, the normalized mRNA expression can be seen on Y axis. Red dots indicate the normalized mRNA expression values, boxplots represent the medians and standard deviations. Ch: Children; N: Normal; Ad: Adenoma; CRC: Colorectal cancer.
Figure 3
Figure 3 SFRP1 mRNA expression and promoter DNA methylation alterations during aging and in different stages of colorectal carcinogenesis. SFRP1 mRNA expression was significantly downregulated in adenoma and CRC samples compared to normal controls in case of all three Affymetrix probeset IDs representing SFRP1 [202035_s_at: P < 0.05 (A); 202036_s_at: P < 0.0003 (B); 202037_s_at: P < 0.005 (C)]. In colonic biopsy samples of healthy young patients, higher SFRP1 mRNA levels could be measured than in normal adults samples, this overexpression was proven to be significant in two of three transcript IDs [202035_s_at: P < 0.05 (A); 202037_s_at: P < 0.005 (C)]. SFRP1 promoter region was significantly hypermethylated in CRC and adenoma tissue samples compared to normal adult and young colonic tissue (P < 0.0001) (D). In pairwise comparison, DNA methylation of SFRP1 promoter was slightly, but significantly increased in healthy adults compared to normal young samples (P < 0.02) (E). The analyzed sample groups are illustrated on X axis, the normalized mRNA expression (A, B, C) and percentage of SFRP1 promoter methylation (D, E) are represented on Y axis. Red dots indicate the normalized mRNA expression values (A, B, C) and the normalized DNA methylation percentage values (D, E), respectively. Boxplots represent the medians and standard deviations. Ch: Children; N: Normal; Ad: Adenoma; CRC: Colorectal cancer.
Whole promoter methylation status of genes showing inverse relationship between gene expression and DNA methylation

The DNA methylation status of whole promoter regions of genes showing an inverse relation between gene expression and DNA methylation (Table 5) was determined using methyl capture sequencing data of 6 NAT, 15 adenoma and 9 CRC tissue samples[12]. In the CRC vs N/NAT comparison, similar DNA methylation alterations (such as hypomethylated AKT3, MGP promoters and hypermethylated PPP1R16B, SFRP1, SYNE1 promoters) were observed in the promoter regions of 7 of the 11 inversely expressed genes (Table 6). Between adenoma and normal samples, promoter regions of 7 of the 8 inversely expressed genes showed DNA methylation differences (e.g., hypomethylated CEMIP/KIAA1199, SPATA18 promoters and hypermethylated DKK3, SDC2, SFRP1, SYNE1 promoters) (Table 6) as detected in case of age-related CpG sites. In CRC samples compared to adenoma tissue, significant hypermethylation of CEMIP/KIAA199 promoter could be demonstrated in “epigenetic clock” CpG sites and whole promoter methylation analyses (Table 6).

Table 6 Whole promoter methylation status of genes with inverse age-related CpG site DNA methylation and gene expression data.
Gene symbolDNA methylation on age-related CpG site
Whole promoter DNA methylation status
cg IDP valueΔβStart-Stop positionP valueΔβ
CRC vs N
AKT3cg113146844.55 × 10-5-0.16chr1:244005801-2440059006.56 × 10-3-0.33
ERGcg172740641.48 × 10-4-0.11chr21:39871501-398716000.044-0.21
MGPcg004315490.020-0.12chr12:15038501-150386000.030-0.27
CCR10cg095096731.91 × 10-40.11chr17:40835101-408352000.0450.18
chr17:40835201-408353000.0220.26
PPP1R16Bcg248347403.56 × 10-120.37chr20:37433201-374333003.76 × 10-30.35
chr20:37434701-374348000.0480.40
chr20:37435301-374354000.0420.37
SFRP1cg023881503.33 × 10-30.10chr8:41166001-411661000.0170.44
chr8:41166101-411662001.31 × 10-40.56
chr8:41166201-411663003.40 × 10-30.49
chr8:41166301-411664000.0140.27
chr8:41166401-411665001.28 × 10-30.53
chr8:41166501-411666000.0270.44
chr8:41166601-411667000.0340.39
chr8:41166901-411670000.0420.41
SYNE1cg266209591.32 × 10-60.30chr6:152622201-1526223000.0270.35
chr6:152957601-1529577000.0320.39
chr6:152957701-1529578000.0430.40
chr6:152958101-1529582002.86 × 10-40.65
AD vs N
CEMIPcg208280841.33 × 10-9-0.17chr15: 81070701-810708000.028-0.25
SPATA18cg031031921.39 × 10-7-0.15chr4:52915901-529160000.042-0.23
DKK3cg132160579.64 × 10-40.18chr11:12029701-120298003.11 × 10-30.26
chr11:12029801-120299001.67 × 10-30.33
chr11:12029901-120300008.08 × 10-50.56
chr11:12030001-120301000.0470.15
chr11:12030401-120305007.38 × 10-30.28
chr11: 12030501-120306000.0120.28
PPP1R16Bcg248347402.23 × 10-90.36chr20:37434201-374343000.0450.26
chr20:37434601-374347000.0220.39
chr20:37434701-374348000.0130.39
chr20:37434801-374349006.87 × 10-30.30
chr20:37434901-374350001.70 × 10-30.44
chr20:37435001-374351001.93 × 10-30.45
chr20:37435101-374352006.49 × 10-30.35
chr20:37435201-374353008.93 × 10-40.51
chr20:37435301-374354000.0140.42
SDC2cg250706371.82 × 10-50.30chr8:97505901-975060002.30 × 10-30.59
chr8:97506101-975062001.96 × 10-40.65
chr8:97506201-975063008.43 × 10-40.63
chr8:97506301-975064000.0120.49
chr8:97506401-975065000.0190.47
chr8:97506601-975067003.99 × 10-30.51
chr8:97506701-975068000.0210.45
chr8:97506801-975069000.0140.52
chr8:97507101-975072000.0150.44
chr8:97507201-975073000.0100.52
chr8:97507301-975074003.47 × 10-30.59
SFRP1cg023881501.20 × 10-70.15chr8:41165901-411660002.30 × 10-30.59
chr8:41166001-411661001.57 × 10-30.59
chr8:41166101-411662003.54 × 10-40.56
chr8:41166201-411663001.49 × 10-40.60
chr8:41166301-411664006.54 × 10-30.27
chr8:41166401-411665001.32 × 10-50.55
chr8: 41166501-411666002.30 × 10-30.60
chr8:41166601-411667005.45 × 10-30.57
chr8:41166701-411668008.15 × 10-30.51
chr8:41166801-411669000.0320.39
chr8:41166901-411670000.0190.45
chr8:41167001-411671000.0460.34
chr8:41167101-411672000.0250.41
chr8:41167201-411673000.0240.40
SYNE1cg266209593.89 × 10-60.29chr6:152702701-1527028000.0120.20
chr6:152702801-1527029000.0350.21
chr6:152957501-1529576006.05 × 10-40.37
chr6:152957601-1529577002.34 × 10-40.60
chr6:152957701-1529578000.0170.51
chr6:152957801-1529579001.09 × 10-30.42
chr6:152957901-1529580001.03 × 10-40.57
chr6:152958001-1529581007.13 × 10-40.43
chr6:152958101-1529582001.72 × 10-50.72
CRC vs AD
CEMIPcg208280842.00 × 10-80.14chr15:81070701-810708007.59 × 10-40.31
chr15:81070801-810709000.0110.36
SFRP1 promoter methylation analysis in healthy children, healthy adult, adenoma and CRC tissues

Based on the gene expression analysis results, SFRP1 was found to be overexpressed in normal adult samples compared to adenoma and CRC biopsy specimens, and in healthy young patients even higher SFRP1 mRNA levels could be detected than in normal adult samples (Figure 3A-C). As SFRP1 is proven to be a methylation-regulated gene with literature data regarding its age-related DNA methylation alterations, hence it was chosen for detailed methylation analysis of normal, premalignant and cancerous colonic specimens including tissue samples from healthy children.

SFRP1 promoter sequences were highly methylated in CRC samples (average methylation% = 55.0% ± 8.4%) and in adenoma tissue (49.9% ± 18.1 %), while low methylation levels could be measured in colonic tissues of both healthy adults (5.2% ± 2.7%) and children (2.2% ± 0.7%). Significant considerable hypermethylation was found in SFRP1 promoter both between CRC and adult normal and between CRC and healthy children colonic tissue samples (P < 0.0001) (Figure 3D). In the healthy adult vs healthy children comparison, significant, but moderate DNA methylation alterations were detected: in adults higher DNA methylation levels were found in the analyzed region of SFRP1 promoter (P = 0.017) (Figure 3E).

SFRP1 protein expression in colonic tissue samples of healthy normal children and adults

In healthy children samples the epithelial layer showed strong (representative scoring values: +3 and +2), diffuse SFRP1 expression both in cytoplasmic and nuclear region (Q-score: 226.67 ± 17.51), whereas the stromal cells showed heterogeneous protein expression (scoring values: form +3 to 0; Q-score: 176.66 ± 18.61; ΣQ-score: 403.33 ± 22.51; Figure 4A). Among stromal cells subepithelial fibroblast and several immune cells showed strong cytoplasmic and/or nuclear SFRP1 expression. Not significantly, but remarkably lower (representative scoring value: +2) SFRP1 protein expression was detectable both in epithelial (Q-score: 202.14 ± 24.12) and stromal component (140.71 ± 41.47; Σ Q-score: 351.42 ± 68.66; Figure 4B) of adult persons (P values: 0.063, 0.073 and 0.105 respectively).

Figure 4
Figure 4 SFRP1 protein expression in colonic tissue samples of healthy normal children and adults. Strong/moderate cytoplasmic and nuclear SFRP1 expression both in epithelial and stromal compartments of healthy children (A) and healthy adult (B) samples. Digital microscopic images; magnification × 40; scale: 50 μm.
DISCUSSION

Cancer is considered a primary age-related disease[3], and therefore age-related molecular changes including epigenetic alterations such as epigenetic drift and epigenetic clock[1,2] necessarily show relationship with carcinogenesis-associated differences. Besides global hypomethylation, local, genomic site specific hypermethylation principally in the promoter regions of tumor suppressor genes can occur during both processes[6-8].

In this study, the potential correspondence between age-related and CRC-associated DNA methylation changes was studied using the 353 epigenetic clock CpG sites published by Horvath[2] as a model for age-related DNA methylation changes. With the analysis of methyl capture sequencing and Illumina BeadChip450K methylation array data, the methylation status of age-related CpG sites and genes was determined during CRC development and progression, and the relevant mRNA expression changes were also evaluated. Among the differentially methylated/expressed age-related genes, SFRP1 promoter methylation was further analyzed in healthy, premalignant and cancerous colonic tissue samples, including biopsy specimens from young children.

Similarly to previous findings[29], DNA methylation alterations in a considerable proportion of age-related CpG sites/gene promoters (approximately 40%) were observed in samples representing different stages of CRC formation and progression. Approximately two third of these CpG sites had similar DNA methylation alterations in CRC compared to normal tissue samples as during aging. When the effect of DNA methylation of epigenetic clock genes was studied, whole promoter methylation was also observed in addition to the analysis of DNA methylation status of representing age-related CpG sites.

In accordance with our results, hypermethylation of several genes belonging to aging-associated CpG sites such as SFRP1[7,12,15,18-23], TFAP2E[30], TBX5[31], GNAS[32], DKK3[18,33], DGKI[34], SYNE1[35-37], SDC2[38,39], ADHFE1[40-42] was observed in tissue and/or blood samples of CRC patients. SFRP1 tumor suppressor protein with a putative Wnt-binding site impedes the frizzled ligand - Wnt receptor interaction. Its reduced expression caused by promoter hypermethylation can lead to constitutive activation of Wnt pathway which is best characterized signaling pathway in CRC pathogenesis[7,12,15,18-23,43]. Worthley et al[15] showed strong positive correlation between SFRP1 methylation and age (Spearmen’s rank P = 0.72, P < 0.0001) on a set of 166 CRC tissue samples from adults [median age: 61.2 years (22.8-89.2 years)]. In this study, the increase of SFRP1 methylation during the aging was also observed, moreover to our best knowledge, we provide the first evidence of significantly lower SFRP1 methylation levels in children (under 18 years) compared to healthy adult colonic tissues. Preliminary results of methylation-sensitive restriction enzyme methylation array analysis of our reseach groups suggested SFRP1 hypomethylation in young colonic tissue samples, though the high standard deviations of methylation percentages and the low samples size limited our conclusions[18]. In accordance with the DNA methylation data, elevated mRNA and protein levels could be detected in colonic tissues of normal children compared to adults.

Dickkopf Wnt signaling pathway inhibitors including DKK3 are also frequent targets of epigenetic silencing in gastrointestinal tumors promoting carcinogenesis by loss of/reduction their expression[33]. Hypermethylated syndecan 2 (SDC2) is a potential biomarker for early CRC detection both in serum and tissue, although the gene silencing effect of elevated promoter methylation is not unambiguous according to the literature data[44], moreover some observations support its tumorigenic activity in CRC[45]. Hypermethylation of spectrin repeat containing nuclear envelope protein 1 (SYNE1) suggests its tumor suppressor function in CRC[37], which was detected not only in CRC tissue samples[35,36], but it appears to be a promising marker for blood-based CRC detection[37]. Alcohol dehydrogenase, iron containing 1 (ADHFE1) promoter hypermethylation was found to be associated with CRC differentiation[41], furthermore it is involved in cell proliferation induction by alcohol in colon carcinoma cells[46].

In the case of some genes like AKT3, CEMIP and DDR1, promoter hypomethylation was observed in different types of cancers such as breast cancer, lung cancer and CRC[47-49]. PI3K/Akt pathway is thought to be the most commonly activated intracellular signaling pathway in human malignancies[50]. AKT kinases including AKT3 are remarkable contributors to malignant diseases as they are involved in the regulation of cell proliferation, growth and survival[50,51]. Hypomethylation and overexpression of the cell migration-inducing protein (CEMIP/KIAA1199) was previously described in CRC[49,52]. In our study, hypomethylation of this gene could be detected mainly in adenoma samples, however a slight but significant decrease in methylation level was observed in CRC samples compared to normal controls. In accordance with the methylation data, strong upregulation of CEMIP mRNAs was shown both in adenoma and CRC samples with higher expression values in adenoma tissue[53,54]. Due to its robust overexpression at mRNA and also at protein levels, CEMIP is considered as a candidate prognostic marker for CRC and a potential therapeutic target[55]. CEMIP facilitates colon cancer cell proliferation via enhancing Wnt signaling[49] and promotes tumor growth[55] and cancer dissemination under hypoxic conditions[56].

In conclusion, our results regarding DNA methylation alterations of age-related, epigenetic clock genes during colorectal carcinogenesis supports the concept that aging is one of the main factors predisposing cancer including CRC. Several age-related DNA methylation alterations could be observed during development and progression of CRC affecting the mRNA expression of certain CRC- and adenoma-related key control genes. The main CRC-associated signal transduction pathways, such as WNT signaling and PI3K/Akt pathways are also influenced during aging.

ACKNOWLEDGMENTS

We thank hereby the help of Gabriella Kónyáné Farkas with the immunohistochemical analysis. Furthermore we thank Theo deVos PhD for his careful language assistance.

COMMENTS
Background

Cancer is considered a primary age-related disease, and therefore age-related molecular changes including epigenetic alterations such as epigenetic drift and epigenetic clock necessarily show relationship with carcinogenesis-associated differences. Besides global hypomethylation, local, genomic site specific hypermethylation principally in the promoter regions of tumor suppressor genes can occur during both processes.

Research frontiers

Several age-related DNA methylation alterations could be observed during colorectal cancer (CRC) formation and progression affecting the mRNA expression of certain CRC- and adenoma-related key control genes such as hypermethylated secreted frizzled related protein 1 (SFRP1), spectrin repeat containing nuclear envelope protein 1 and hypomethylated cell migration-inducing protein.

Innovations and breakthroughs

For the first time significantly lower SFRP1 methylation levels were demonstrated in colonic tissue from children (under 18 years) compared to healthy adults. The main CRC-associated signal transduction pathways, such as WNT signaling and PI3K/Akt pathways are also influenced during aging.

Peer-review

In this paper, the authors analyzed the methylation and expression levels of 353 age-related “epigenetic clock” genes in colonic tissue samples. They identified many differentially methylated and/or differentially expressed genes. Among these genes, the DNA methylation and mRNA levels of SFRP1 was further analyzed. This is an interesting work using a large number of data and the results may be useful in related field.

Footnotes

Manuscript source: Invited manuscript

Specialty type: Gastroenterology and hepatology

Country of origin: Hungary

Peer-review report classification

Grade A (Excellent): 0

Grade B (Very good): B

Grade C (Good): C, C, C, C

Grade D (Fair): 0

Grade E (Poor): 0

P- Reviewer: Essani K, Lakatos PL, M’Koma A, Xiao Y, Lakatos PL S- Editor: Yu J L- Editor: A E- Editor: Wang CH

References
1.  Jones MJ, Goodman SJ, Kobor MS. DNA methylation and healthy human aging. Aging Cell. 2015;14:924-932.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 481]  [Cited by in F6Publishing: 548]  [Article Influence: 60.9]  [Reference Citation Analysis (0)]
2.  Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013;14:R115.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 3263]  [Cited by in F6Publishing: 3915]  [Article Influence: 391.5]  [Reference Citation Analysis (0)]
3.  Finkel T, Serrano M, Blasco MA. The common biology of cancer and ageing. Nature. 2007;448:767-774.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 717]  [Cited by in F6Publishing: 691]  [Article Influence: 40.6]  [Reference Citation Analysis (0)]
4.  Grönniger E, Weber B, Heil O, Peters N, Stäb F, Wenck H, Korn B, Winnefeld M, Lyko F. Aging and chronic sun exposure cause distinct epigenetic changes in human skin. PLoS Genet. 2010;6:e1000971.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 190]  [Cited by in F6Publishing: 169]  [Article Influence: 12.1]  [Reference Citation Analysis (0)]
5.  Ong ML, Holbrook JD. Novel region discovery method for Infinium 450K DNA methylation data reveals changes associated with aging in muscle and neuronal pathways. Aging Cell. 2014;13:142-155.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 59]  [Cited by in F6Publishing: 59]  [Article Influence: 5.9]  [Reference Citation Analysis (0)]
6.  Jung M, Pfeifer GP. Aging and DNA methylation. BMC Biol. 2015;13:7.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 283]  [Cited by in F6Publishing: 315]  [Article Influence: 35.0]  [Reference Citation Analysis (0)]
7.  Belshaw NJ, Elliott GO, Foxall RJ, Dainty JR, Pal N, Coupe A, Garg D, Bradburn DM, Mathers JC, Johnson IT. Profiling CpG island field methylation in both morphologically normal and neoplastic human colonic mucosa. Br J Cancer. 2008;99:136-142.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 67]  [Cited by in F6Publishing: 75]  [Article Influence: 4.7]  [Reference Citation Analysis (0)]
8.  Sheaffer KL, Elliott EN, Kaestner KH. DNA Hypomethylation Contributes to Genomic Instability and Intestinal Cancer Initiation. Cancer Prev Res (Phila). 2016;9:534-546.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 66]  [Cited by in F6Publishing: 88]  [Article Influence: 11.0]  [Reference Citation Analysis (0)]
9.  Silva TD, Vidigal VM, Felipe AV, DE Lima JM, Neto RA, Saad SS, Forones NM. DNA methylation as an epigenetic biomarker in colorectal cancer. Oncol Lett. 2013;6:1687-1692.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 45]  [Cited by in F6Publishing: 48]  [Article Influence: 4.4]  [Reference Citation Analysis (0)]
10.  Syed Sameer A, Shah ZA, Abdullah S, Chowdri NA, Siddiqi MA. Analysis of molecular aberrations of Wnt pathway gladiators in colorectal cancer in the Kashmiri population. Hum Genomics. 2011;5:441-452.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 19]  [Cited by in F6Publishing: 21]  [Article Influence: 1.8]  [Reference Citation Analysis (0)]
11.  Molinari C, Casadio V, Foca F, Zingaretti C, Giannini M, Avanzolini A, Lucci E, Saragoni L, Passardi A, Amadori D. Gene methylation in rectal cancer: predictive marker of response to chemoradiotherapy? J Cell Physiol. 2013;228:2343-2349.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 27]  [Cited by in F6Publishing: 28]  [Article Influence: 2.8]  [Reference Citation Analysis (0)]
12.  Galamb O, Kalmár A, Péterfia B, Csabai I, Bodor A, Ribli D, Krenács T, Patai ÁV, Wichmann B, Barták BK, Tóth K, Valcz G, Spisák S, Tulassay Z, Molnár B. Aberrant DNA methylation of WNT pathway genes in the development and progression of CIMP-negative colorectal cancer. Epigenetics. 2016;11:588-602.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 48]  [Cited by in F6Publishing: 55]  [Article Influence: 6.9]  [Reference Citation Analysis (0)]
13.  Kaz AM, Wong CJ, Dzieciatkowski S, Luo Y, Schoen RE, Grady WM. Patterns of DNA methylation in the normal colon vary by anatomical location, gender, and age. Epigenetics. 2014;9:492-502.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 45]  [Cited by in F6Publishing: 51]  [Article Influence: 5.1]  [Reference Citation Analysis (0)]
14.  Horii J, Hiraoka S, Kato J, Harada K, Kuwaki K, Fujita H, Toyooka S, Yamamoto K. Age-related methylation in normal colon mucosa differs between the proximal and distal colon in patients who underwent colonoscopy. Clin Biochem. 2008;41:1440-1448.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 21]  [Cited by in F6Publishing: 22]  [Article Influence: 1.4]  [Reference Citation Analysis (0)]
15.  Worthley DL, Whitehall VL, Buttenshaw RL, Irahara N, Greco SA, Ramsnes I, Mallitt KA, Le Leu RK, Winter J, Hu Y. DNA methylation within the normal colorectal mucosa is associated with pathway-specific predisposition to cancer. Oncogene. 2010;29:1653-1662.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 82]  [Cited by in F6Publishing: 91]  [Article Influence: 6.1]  [Reference Citation Analysis (0)]
16.  Hellebrekers DM, Lentjes MH, van den Bosch SM, Melotte V, Wouters KA, Daenen KL, Smits KM, Akiyama Y, Yuasa Y, Sanduleanu S. GATA4 and GATA5 are potential tumor suppressors and biomarkers in colorectal cancer. Clin Cancer Res. 2009;15:3990-3997.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 135]  [Cited by in F6Publishing: 148]  [Article Influence: 9.9]  [Reference Citation Analysis (0)]
17.  Ebert MP, Mooney SH, Tonnes-Priddy L, Lograsso J, Hoffmann J, Chen J, Röcken C, Schulz HU, Malfertheiner P, Lofton-Day C. Hypermethylation of the TPEF/HPP1 gene in primary and metastatic colorectal cancers. Neoplasia. 2005;7:771-778.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 46]  [Cited by in F6Publishing: 50]  [Article Influence: 2.6]  [Reference Citation Analysis (0)]
18.  Patai ÁV, Valcz G, Hollósi P, Kalmár A, Péterfia B, Patai Á, Wichmann B, Spisák S, Barták BK, Leiszter K, Tóth K, Sipos F, Kovalszky I, Péter Z, Miheller P, Tulassay Z, Molnár B. Comprehensive DNA Methylation Analysis Reveals a Common Ten-Gene Methylation Signature in Colorectal Adenomas and Carcinomas. PLoS One. 2015;10:e0133836.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 36]  [Cited by in F6Publishing: 39]  [Article Influence: 4.3]  [Reference Citation Analysis (0)]
19.  Kalmár A, Péterfia B, Hollósi P, Wichmann B, Bodor A, Patai ÁV, Schöller A, Krenács T, Tulassay Z, Molnár B. Bisulfite-Based DNA Methylation Analysis from Recent and Archived Formalin-Fixed, Paraffin Embedded Colorectal Tissue Samples. Pathol Oncol Res. 2015;21:1149-1156.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 9]  [Cited by in F6Publishing: 9]  [Article Influence: 1.0]  [Reference Citation Analysis (0)]
20.  Kalmár A, Péterfia B, Hollósi P, Galamb O, Spisák S, Wichmann B, Bodor A, Tóth K, Patai ÁV, Valcz G, Nagy ZB, Kubák V, Tulassay Z, Kovalszky I, Molnár B. DNA hypermethylation and decreased mRNA expression of MAL, PRIMA1, PTGDR and SFRP1 in colorectal adenoma and cancer. BMC Cancer. 2015;15:736.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 35]  [Cited by in F6Publishing: 45]  [Article Influence: 5.0]  [Reference Citation Analysis (0)]
21.  Silva AL, Dawson SN, Arends MJ, Guttula K, Hall N, Cameron EA, Huang TH, Brenton JD, Tavaré S, Bienz M. Boosting Wnt activity during colorectal cancer progression through selective hypermethylation of Wnt signaling antagonists. BMC Cancer. 2014;14:891.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 52]  [Cited by in F6Publishing: 63]  [Article Influence: 6.3]  [Reference Citation Analysis (0)]
22.  Dallol A, Al-Maghrabi J, Buhmeida A, Gari MA, Chaudhary AG, Schulten HJ, Abuzenadah AM, Al-Ahwal MS, Sibiany A, Al-Qahtani MH. Methylation of the polycomb group target genes is a possible biomarker for favorable prognosis in colorectal cancer. Cancer Epidemiol Biomarkers Prev. 2012;21:2069-2075.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 20]  [Cited by in F6Publishing: 22]  [Article Influence: 1.8]  [Reference Citation Analysis (0)]
23.  Kim J, Kim S. In silico Identification of SFRP1 as a Hypermethylated Gene in Colorectal Cancers. Genomics Inform. 2014;12:171-180.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 12]  [Cited by in F6Publishing: 12]  [Article Influence: 1.2]  [Reference Citation Analysis (0)]
24.  Luo Y, Wong CJ, Kaz AM, Dzieciatkowski S, Carter KT, Morris SM, Wang J, Willis JE, Makar KW, Ulrich CM. Differences in DNA methylation signatures reveal multiple pathways of progression from adenoma to colorectal cancer. Gastroenterology. 2014;147:418-29.e8.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 161]  [Cited by in F6Publishing: 145]  [Article Influence: 14.5]  [Reference Citation Analysis (0)]
25.  Galamb O, Wichmann B, Sipos F, Spisák S, Krenács T, Tóth K, Leiszter K, Kalmár A, Tulassay Z, Molnár B. Dysplasia-carcinoma transition specific transcripts in colonic biopsy samples. PLoS One. 2012;7:e48547.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 36]  [Cited by in F6Publishing: 34]  [Article Influence: 2.8]  [Reference Citation Analysis (0)]
26.  Galamb O, Sipos F, Solymosi N, Spisák S, Krenács T, Tóth K, Tulassay Z, Molnár B. Diagnostic mRNA expression patterns of inflamed, benign, and malignant colorectal biopsy specimen and their correlation with peripheral blood results. Cancer Epidemiol Biomarkers Prev. 2008;17:2835-2845.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 58]  [Cited by in F6Publishing: 55]  [Article Influence: 3.4]  [Reference Citation Analysis (0)]
27.  Galamb O, Györffy B, Sipos F, Spisák S, Németh AM, Miheller P, Tulassay Z, Dinya E, Molnár B. Inflammation, adenoma and cancer: objective classification of colon biopsy specimens with gene expression signature. Dis Markers. 2008;25:1-16.  [PubMed]  [DOI]  [Cited in This Article: ]
28.  Leiszter K, Galamb O, Sipos F, Krenács T, Veres G, Wichmann B, Kalmár A, Patai ÁV, Tóth K, Valcz G, Molnár B, Tulassay Z. Sporadic colorectal cancer development shows rejuvenescence regarding epithelial proliferation and apoptosis. PLoS One. 2013;8:e74140.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 10]  [Cited by in F6Publishing: 10]  [Article Influence: 0.9]  [Reference Citation Analysis (0)]
29.  Toyota M, Ahuja N, Ohe-Toyota M, Herman JG, Baylin SB, Issa JP. CpG island methylator phenotype in colorectal cancer. Proc Natl Acad Sci USA. 1999;96:8681-8686.  [PubMed]  [DOI]  [Cited in This Article: ]
30.  Beggs AD, Dilworth MP, Domingo E, Midgley R, Kerr D, Tomlinson IP, Middleton GW. Methylation changes in the TFAP2E promoter region are associated with BRAF mutation and poorer overall & amp; disease free survival in colorectal cancer. Oncoscience. 2015;2:508-516.  [PubMed]  [DOI]  [Cited in This Article: ]
31.  Yu J, Ma X, Cheung KF, Li X, Tian L, Wang S, Wu CW, Wu WK, He M, Wang M. Epigenetic inactivation of T-box transcription factor 5, a novel tumor suppressor gene, is associated with colon cancer. Oncogene. 2010;29:6464-6474.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 66]  [Cited by in F6Publishing: 73]  [Article Influence: 5.2]  [Reference Citation Analysis (0)]
32.  Kim J, Bretz CL, Lee S. Epigenetic instability of imprinted genes in human cancers. Nucleic Acids Res. 2015;43:10689-10699.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 46]  [Cited by in F6Publishing: 46]  [Article Influence: 5.1]  [Reference Citation Analysis (0)]
33.  Sato H, Suzuki H, Toyota M, Nojima M, Maruyama R, Sasaki S, Takagi H, Sogabe Y, Sasaki Y, Idogawa M. Frequent epigenetic inactivation of DICKKOPF family genes in human gastrointestinal tumors. Carcinogenesis. 2007;28:2459-2466.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 154]  [Cited by in F6Publishing: 166]  [Article Influence: 9.8]  [Reference Citation Analysis (0)]
34.  Abdel-Rahman WM, Lotsari-Salomaa JE, Kaur S, Niskakoski A, Knuutila S, Järvinen H, Mecklin JP, Peltomäki P. The Role of Chromosomal Instability and Epigenetics in Colorectal Cancers Lacking β-Catenin/TCF Regulated Transcription. Gastroenterol Res Pract. 2016;2016:6089658.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 12]  [Cited by in F6Publishing: 16]  [Article Influence: 2.0]  [Reference Citation Analysis (0)]
35.  Mokarram P, Kumar K, Brim H, Naghibalhossaini F, Saberi-firoozi M, Nouraie M, Green R, Lee E, Smoot DT, Ashktorab H. Distinct high-profile methylated genes in colorectal cancer. PLoS One. 2009;4:e7012.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 100]  [Cited by in F6Publishing: 114]  [Article Influence: 7.6]  [Reference Citation Analysis (0)]
36.  Papadia C, Louwagie J, Del Rio P, Grooteclaes M, Coruzzi A, Montana C, Novelli M, Bordi C, de’ Angelis GL, Bassett P. FOXE1 and SYNE1 genes hypermethylation panel as promising biomarker in colitis-associated colorectal neoplasia. Inflamm Bowel Dis. 2014;20:271-277.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 25]  [Cited by in F6Publishing: 20]  [Article Influence: 2.0]  [Reference Citation Analysis (0)]
37.  Melotte V, Yi JM, Lentjes MH, Smits KM, Van Neste L, Niessen HE, Wouters KA, Louwagie J, Schuebel KE, Herman JG. Spectrin repeat containing nuclear envelope 1 and forkhead box protein E1 are promising markers for the detection of colorectal cancer in blood. Cancer Prev Res (Phila). 2015;8:157-164.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 25]  [Cited by in F6Publishing: 26]  [Article Influence: 2.6]  [Reference Citation Analysis (0)]
38.  Mitchell SM, Ross JP, Drew HR, Ho T, Brown GS, Saunders NF, Duesing KR, Buckley MJ, Dunne R, Beetson I. A panel of genes methylated with high frequency in colorectal cancer. BMC Cancer. 2014;14:54.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 114]  [Cited by in F6Publishing: 135]  [Article Influence: 13.5]  [Reference Citation Analysis (0)]
39.  Oh T, Kim N, Moon Y, Kim MS, Hoehn BD, Park CH, Kim TS, Kim NK, Chung HC, An S. Genome-wide identification and validation of a novel methylation biomarker, SDC2, for blood-based detection of colorectal cancer. J Mol Diagn. 2013;15:498-507.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 103]  [Cited by in F6Publishing: 121]  [Article Influence: 11.0]  [Reference Citation Analysis (0)]
40.  Naumov VA, Generozov EV, Zaharjevskaya NB, Matushkina DS, Larin AK, Chernyshov SV, Alekseev MV, Shelygin YA, Govorun VM. Genome-scale analysis of DNA methylation in colorectal cancer using Infinium HumanMethylation450 BeadChips. Epigenetics. 2013;8:921-934.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 93]  [Cited by in F6Publishing: 106]  [Article Influence: 9.6]  [Reference Citation Analysis (0)]
41.  Tae CH, Ryu KJ, Kim SH, Kim HC, Chun HK, Min BH, Chang DK, Rhee PL, Kim JJ, Rhee JC. Alcohol dehydrogenase, iron containing, 1 promoter hypermethylation associated with colorectal cancer differentiation. BMC Cancer. 2013;13:142.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 16]  [Cited by in F6Publishing: 17]  [Article Influence: 1.5]  [Reference Citation Analysis (0)]
42.  Oster B, Thorsen K, Lamy P, Wojdacz TK, Hansen LL, Birkenkamp-Demtröder K, Sørensen KD, Laurberg S, Orntoft TF, Andersen CL. Identification and validation of highly frequent CpG island hypermethylation in colorectal adenomas and carcinomas. Int J Cancer. 2011;129:2855-2866.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 117]  [Cited by in F6Publishing: 125]  [Article Influence: 9.6]  [Reference Citation Analysis (0)]
43.  Valcz G, Patai AV, Kalmár A, Péterfia B, Fűri I, Wichmann B, Műzes G, Sipos F, Krenács T, Mihály E. Myofibroblast-derived SFRP1 as potential inhibitor of colorectal carcinoma field effect. PLoS One. 2014;9:e106143.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 27]  [Cited by in F6Publishing: 31]  [Article Influence: 3.1]  [Reference Citation Analysis (0)]
44.  Kwon MJ, Kim Y, Choi Y, Kim SH, Park S, Han I, Kang DH, Oh ES. The extracellular domain of syndecan-2 regulates the interaction of HCT116 human colon carcinoma cells with fibronectin. Biochem Biophys Res Commun. 2013;431:415-420.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 15]  [Cited by in F6Publishing: 16]  [Article Influence: 1.5]  [Reference Citation Analysis (0)]
45.  Choi S, Kim Y, Park H, Han IO, Chung E, Lee SY, Kim YB, Lee JW, Oh ES, Yi JY. Syndecan-2 overexpression regulates adhesion and migration through cooperation with integrin alpha2. Biochem Biophys Res Commun. 2009;384:231-235.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 42]  [Cited by in F6Publishing: 45]  [Article Influence: 3.0]  [Reference Citation Analysis (0)]
46.  Moon JW, Lee SK, Lee YW, Lee JO, Kim N, Lee HJ, Seo JS, Kim J, Kim HS, Park SH. Alcohol induces cell proliferation via hypermethylation of ADHFE1 in colorectal cancer cells. BMC Cancer. 2014;14:377.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 17]  [Cited by in F6Publishing: 21]  [Article Influence: 2.1]  [Reference Citation Analysis (0)]
47.  Lindqvist BM, Wingren S, Motlagh PB, Nilsson TK. Whole genome DNA methylation signature of HER2-positive breast cancer. Epigenetics. 2014;9:1149-1162.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 38]  [Cited by in F6Publishing: 41]  [Article Influence: 4.1]  [Reference Citation Analysis (0)]
48.  Nelson HH, Marsit CJ, Christensen BC, Houseman EA, Kontic M, Wiemels JL, Karagas MR, Wrensch MR, Zheng S, Wiencke JK. Key epigenetic changes associated with lung cancer development: results from dense methylation array profiling. Epigenetics. 2012;7:559-566.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 35]  [Cited by in F6Publishing: 36]  [Article Influence: 3.0]  [Reference Citation Analysis (0)]
49.  Birkenkamp-Demtroder K, Maghnouj A, Mansilla F, Thorsen K, Andersen CL, Øster B, Hahn S, Ørntoft TF. Repression of KIAA1199 attenuates Wnt-signalling and decreases the proliferation of colon cancer cells. Br J Cancer. 2011;105:552-561.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 72]  [Cited by in F6Publishing: 88]  [Article Influence: 6.8]  [Reference Citation Analysis (0)]
50.  Agarwal E, Brattain MG, Chowdhury S. Cell survival and metastasis regulation by Akt signaling in colorectal cancer. Cell Signal. 2013;25:1711-1719.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 129]  [Cited by in F6Publishing: 122]  [Article Influence: 11.1]  [Reference Citation Analysis (0)]
51.  Clark AR, Toker A. Signalling specificity in the Akt pathway in breast cancer. Biochem Soc Trans. 2014;42:1349-1355.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 56]  [Cited by in F6Publishing: 59]  [Article Influence: 6.6]  [Reference Citation Analysis (0)]
52.  Tiwari A, Schneider M, Fiorino A, Haider R, Okoniewski MJ, Roschitzki B, Uzozie A, Menigatti M, Jiricny J, Marra G. Early insights into the function of KIAA1199, a markedly overexpressed protein in human colorectal tumors. PLoS One. 2013;8:e69473.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 42]  [Cited by in F6Publishing: 50]  [Article Influence: 4.5]  [Reference Citation Analysis (0)]
53.  Galamb O, Spisák S, Sipos F, Tóth K, Solymosi N, Wichmann B, Krenács T, Valcz G, Tulassay Z, Molnár B. Reversal of gene expression changes in the colorectal normal-adenoma pathway by NS398 selective COX2 inhibitor. Br J Cancer. 2010;102:765-773.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 56]  [Cited by in F6Publishing: 56]  [Article Influence: 4.0]  [Reference Citation Analysis (0)]
54.  Sabates-Bellver J, Van der Flier LG, de Palo M, Cattaneo E, Maake C, Rehrauer H, Laczko E, Kurowski MA, Bujnicki JM, Menigatti M. Transcriptome profile of human colorectal adenomas. Mol Cancer Res. 2007;5:1263-1275.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 348]  [Cited by in F6Publishing: 376]  [Article Influence: 23.5]  [Reference Citation Analysis (0)]
55.  Fink SP, Myeroff LL, Kariv R, Platzer P, Xin B, Mikkola D, Lawrence E, Morris N, Nosrati A, Willson JK. Induction of KIAA1199/CEMIP is associated with colon cancer phenotype and poor patient survival. Oncotarget. 2015;6:30500-30515.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 49]  [Cited by in F6Publishing: 52]  [Article Influence: 6.5]  [Reference Citation Analysis (0)]
56.  Evensen NA, Li Y, Kuscu C, Liu J, Cathcart J, Banach A, Zhang Q, Li E, Joshi S, Yang J. Hypoxia promotes colon cancer dissemination through up-regulation of cell migration-inducing protein (CEMIP). Oncotarget. 2015;6:20723-20739.  [PubMed]  [DOI]  [Cited in This Article: ]  [Cited by in Crossref: 42]  [Cited by in F6Publishing: 51]  [Article Influence: 6.4]  [Reference Citation Analysis (0)]