Printer Friendly

GLAD-PCR assay of DNA methylation markers associated with colorectal cancer.

Abstract

Hypermethylation of the gene regulatory regions is documented for many cancer diseases. Such an aberrant DNA methylation in cancer cells is catalyzed by DNA methyltransferases Dnmt3a and Dnmt3b, which predominantly recognize and methylate RCGY sequences with formation of R(5mC)GY sites. Recently, based on a new methyl-directed DNA endonuclease Glal, we developed a GLAD-PCR assay, which allows determining R(5mC)GY site in a defined position of the genomic DNA. In this work we applied GLAD-PCR assay for identification of the methylated RCGY sites in the regulatory regions of some downregulated genes associated with colorectal cancer (CRC). This list includes ADHFE1, ALX4, CNRIP1, EID3, ELMO1, ESR1, FBN1, HLTF, LAMM, NEUROG1, NGFR, RARB, RXRG, RYR2, SDC2, SEPT9, SFRP2, SOCS3, SOX17, THBD, TMEFF2, UCHL1, and VIM genes. GLAD-PCR analysis of selected RCGY sites within the regulatory regions of some of these genes demonstrates a good prognostic potential with relatively high sensitivity and specificity of CRC detection in tumor DNA.

Keywords: Colorectal cancer; DNA methylation; Epigenetics; GLAD-PCR assay; Methyl-directed endonuclease GlaI

Introduction

Colorectal cancer (CRC) is one of the major malignancies leading to a high incidence of cancer death worldwide [1,2]. However, early disease detection significantly decreases CRC-related mortality [3]. Nowadays, the detection of epigenetic biomarkers is one of the most promising diagnostic and prognostic tools [4]. It is well known that hypermethylation of CpG-islands in regulatory regions of promoter and/or first exon in a variety of genes often occurs at early stages of sporadic carcinogenesis. This leads to downregulation of the genes expression in tumor cells, whereas in a healthy tissue the corresponding genes remain to be active [5]. In particular, such an aberrant methylation had been reported for about fifty genes in tumor tissues, blood and stool samples from CRC patients [4,6-11].

In mammals, de novo DNA methylation, including abnormal hypermethylation in cancer cells, is performed by DNA methyltransferases Dnmt3a and Dnmt3b, which predominantly recognize RCGY site and modify internal CG-dinucleotide to form 5'-R(5mC)GY-373'-YG(5mC) R-5' sequence [12]. Subsequently, such a de novo methylation events are maintained during DNA replication by DNA methyltransferase Dnmt1 [13]. Meanwhile, recently discovered and characterized methyl-directed site-specific DNA endonuclease GlaI recognizes and cleaves DNA sequence R(5mC) [down arrow]GY as indicated by the arrow with formation of blunt ends [14]. Due to this unique substrate specificity, GlaI is a convenient tool for identification of de novo methylated sites in the human and mammalian DNA. On the basis of this enzyme, we developed a GLAD-PCR assay (GlaI hydrolysis and Ligation Adapter Dependent PCR) allowing quick and inexpensive estimation of 5'-R(5mC)GY-3' sequence in a definite position of human genome without bisulfite DNA conversion [15]. Briefly, GLAD-PCR assay is carried out in three steps. At the first step, GlaI cleaves R(5mC)GY sites in genomic DNA. Then the obtained DNA fragments are ligated with the unique oligonucleotide adapter. The last step is a PCR with TaqMan probe and a genomic primer, which are complementary to target DNA fragment, and a so-called hybrid primer, which is complementary to the adapter and partially to the genomic sequence at the cleavage point of the site of interest. As a result, despite the presence of a huge number of different DNA fragments obtained after GlaI hydrolysis and a ligation step, PCR takes place specifically from the target region of DNA.

Recently we applied this new assay to determine the methylated RCGY sites in the promoter region of ESR1 gene and the first exon region of ELMO1 gene in tumor tissues from CRC patients [16]. In the present study we expand our research on a larger number of DNA samples and CRC-associated, epigenetically downregulated genes in order to identify in human genome RCGY sites with the most prognostic potential.

Materials and Methods

Patients and samples

The study group included twenty-one CRC patients (8 male and 13 female) who had undergone surgery between September 2014 and August 2015. The age of patients was from 46 to 82 years. Fifteen patients had CRC without distant metastases (stage I to III), while the other six had metastatic stage IV disease. A total of thirty fresh-frozen surgical resection samples were studied, including colorectal adenocarcinomas of varying degree of differentiation (n = 21) and several paired normal colon mucosa controls (n = 9). The samples were collected at the Seversk Biophysical Research Centre (Seversk, Russia). All participating patients voluntarily joined this study with the written informed consent to have their biologic specimens to be analyzed, and the work had been approved by the Ethical Committee of the Seversk Biophysical Research Centre, consistent with the WMA Declaration of Helsinki. A colorectal adenocarcinoma cell line SW837 was obtained from SRC VB "Vector," Novosibirsk, Russia.

DNA isolation

Samples of SW837 cells, fresh tumor and normal mucosa tissues were stored at -20[degrees]C until processing. The samples were ground in liquid nitrogen and DNA preparations were isolated by standard phenol-chloroform method [17]. Nucleic acid concentrations were estimated by UV spectrophotometry using Nano Vue Plus (GE Healthcare, UK).

Selection of genes and RCGY sites for GLAD-PCR assay

We carried out a literature search of epigenetically downregulated genes involved in colorectal carcinogenesis. The selection criterion was a different methylation of the gene regulation regions in CRC tissues or cell lines and noncancerous controls. In addition the findings of ENCODE/HudsonAlpha project (https://www.encodeproject.org) [18] rendered by UCSC Genome Browser (http://genome-euro.ucsc.edu) were taken into consideration. The main criterion of RCGY sites choice was a rather long distance (50 bases or more) between the two RCGY sites to place a genomic primer and TagMan probe herein.

Primers, probes, and oligonucleotide adapter

To design the primer and probe, we used the GenBank database, Vector NTI 11.5 software (Invitrogen, USA), andNCBI BLAST resource (http://blast.ncbi.nlm.nih.gov). The sequences were calculated to allow uniform PCR conditions with an annealing temperature of 61[degrees]C A list of primers and probes is provided in Table 1. The primers are indicated "g" (direct genomic primer) and "r" (reversed genomic primer) and serve to monitor a successful PCR amplification of target regulatory region and for normalization of tissue DNA concentration. In GLAD-PCR assay experiments, a reversed genomic primer was replaced by a "hybrid" primer, which corresponds to the methylated RCGY site in the studied DNA region. In detail, the determination of hybrid primers for GLAD-PCR assay is described in the Results section. As an adapter we used oligonucleotide duplex 5'-CCTGCTCTTTCATCG-373'-pGGACGAGAAAGTAGCp-5', where "p" means phosphate.

GLAD-PCR assay protocol

Methyl-directed DNA endonuclease GlaI, T4 DNA ligase, Hot Start Taq DNA polymerase, dNTPs, 10 x GlaI reaction buffer (0.1 M Tris-HCl, pH 8.5, 0.1 M NaCl, 50 mM [MgCl.sub.2], 10 mM [beta]-mercaptoethanol), 10 x GLAD-PCR buffer (0.5 M Tris-[SO.sub.4], pH 9.0, 0.1 M [[[NH.sub.4]].sub.2][SO.sub.4], 0.3 M KCl, 0.1% Tween 20, 30 mM [MgCl.sub.2]), and 5x Stabilizer for GC-rich DNA amplification (2.7 M betaine, 6.7 mM DTT, 6.7% DMSO) were supplied by SibEnzyme (Novosibirsk, Russia). The normalization of the tissue DNA concentration for each studied region of genome was performed by real-time PCR using primers and probes listed in Table 1. The PCR conditions were the same as described below for the PCR step of GLAD-PCR assay.

We performed GlaI hydrolysis and adapter ligation in one step. The reaction mixture contained 1 x GlaI buffer, 100 ng/[micro]L BSA, 6 mM [beta]-mercaptoethanol, 0.5 mM ATP, 0.5 [micro]M adapter oligonucleotide duplex, 0.04 U/[micro]L GlaI endonuclease, and 33 U/[micro]l T4 DNA ligase. Three nanograms of the tissue DNA or fifteen nanograms of SW837 DNA were used as the template. The reaction was conducted in 30 [micro]l at 25[degrees]C for 1 h followed by inactivation of enzymes at 65[degrees]C for 20 min. Further the following components were added to the final concentration: 1 x GLAD-PCR buffer,1 x Stabilizer, 0.2 mM of each of dNTPs, the mixture of genomic primer, hybrid primer and probe at 0.4 [micro]M concentration of each, and 0.05 U/[micro]L Hot Start Taq DNA polymerase. The hybrid primers, selected experimentally for each gene, are listed in Table 2. Real-time PCR was performed using the CXF-96 detection system (Bio-Rad Laboratories, Hercules, USA) in a final volume of 20 [micro]l under the following conditions: preheating at 95[degrees]C for 3 min, then 5 cycles at 95[degrees]C for 10 s, 61[degrees]C for 15 s, 72[degrees]C for 20 s without detection followed by the another 50 cycles with detection of FAM fluorescence during the annealing step at 61[degrees]C. All the GLAD-PCR reactions were performed in triplicates.

Statistical analysis

The statistical analysis of results of GLAD-PCR assay was performed with the MedCalc 15.11 software (MedCalc Software, Ostend, Belgium). According to the quantification cycle (Cq) values obtained for each studied R(5mC)GY site, receiver operating characteristic (ROC) curves with 95% confidence interval (CI) were determined to assess the assay sensitivity and specificity with an area under the ROC curve (AUC) being estimated nonparametrically [19]. The optimal cutoff value was used to establish the marker methylation status (positive or negative).

Results

Selection of RCGY sites and corresponding hybrid primers for GLAD-PCR assay

In the first part of the work, we carried out a literature search of epigenetically downregulated genes involved in colorectal carcinogenesis. For the present study, we chose well-known genes, regulation regions of which were often hypermethylated at colorectal cancer: SEPT9, FBN1, VIM, SDC2, THBD, SFRP2, ESR1, TMEFF2, NGFR, ALX4, HLTF, and NEUROG1 [20,21]. Less studied putative CRC biomarkers, such as CNRIP1 [22], ADHFE1 [23], UCHL1, ELM01 [24], LAMA1 [25], and SOX17 [26], were also examined. Three genes were selected on the basis of the recent genome-wide studies (SOCS3, RYR2 [11], and EID3 [27]). Finally we also analyzed the genes for retinoid receptors RARB and RXRG, which were found to be methylated in many types of cancer [28]. A list of the names and function of the studied genes, their chromosomal location and structure of probe, direct and reverse primers for PCR study of regulation regions is provided in Table 1.

At the second step we conducted screening of RCGY sites within the studied regulatory regions of genes in order to select the methylated sites for further GLAD-PCR analysis of clinical samples. To find these sites, we used the DNA preparation from colorectal adenocarcinoma cell line SW837. The screening was performed by the GLAD-PCR assay with hybrid primers corresponding to the RCGY sites located within ~200 bp distance from the probe position. A general DNA structure of hybrid primer is 5'-CCTGCTCTTTCATCGGYNN-3', where first 15 bases are complementary to the oligonucleotide adapter, whereas the underlined tetranucleotide is complementary to the genome sequence at GlaI cleavage point. This sequence corresponds to 32 possible variants of the hybrid primer depending on the structure of the blunt ends obtained after GlaI hydrolysis of sequence 5'-NNR(5mC)[down arrow]GY-3'. An example of determination of the methylated site in the regulation region of FBN1 gene is given in Figure 1. In case of FBN1 gene, we studied four parts of its regulation region presented in Figure 1a, and they are indicated as FBN1(1), FBN1(2), FBN1(3.3), and FBN1(3.1). In the first three parts, a genomic primer is located before TaqMan probe, whereas in case of FBN1(3.1), a genomic primer and TaqMan probe lie in opposite direction. In the FBN1(3.1) region we have studied three RCGY sites closest to the probe annealing sequence: two GCGC sites and one ACGC site (Figure 1a). Figure 1b shows the result of GLAD-PCR assay of FBN 1(3.1) regulation region and demonstrates that site GCGC in position 48645483 is methylated whereas sites GCGC and ACGC in positions 48645488 and 48645494, respectively, are not methylated. The results of the methylated site determination in all 26 studied regulation regions are presented in Table 2. As it follows from Table 2, FBN1(3.3) and FBN1(3.1) regulation regions show the same methylated site in position 48645483. So, we identified 25 methylated sites in regulation regions of 23 genes. In all cases we observed a presence of methylated and unmethylated sites, which were similar to a methylation scheme of FBN1 (3.1) regulation region: there is one methylated site and other sites are not methylated or significantly less methylated. Earlier we observed a similar situation with ELMO1 and ESR1 genes where only one from three RCGY sites was significantly methylated in both cases [16]. As follows from Table 2, most of the target sites are GCGC (18 from 25), and other 7 target sites are GCGT or its complement ACGC. Surprisingly, there is no ACGT sequence among all target sites.

[FIGURE 1 OMITTED]

GLAD-PCR assay of selected RCGY sites in the clinical samples

DNAs isolated from the tissue samples (n = 21) of colorectal adenocarcinomas of varying degree of differentiation and nine paired normal colon mucosa samples were studied by GLAD-PCR assay using primers and TaqMan probes listed in Tables 1 and 2. Figure 2 presents the diagrams of the obtained Cq values for selected RCGY sites in all 26 studied DNA regions. According to Figure 2, the methylated selected sites (displaying a small Cq) in DNA preparations from tumor samples are presented in all regulation regions. We see the methylated target sites in CNRIP1, ESR1, and SOX17 regulation regions in all DNA preparations from tumor samples. Target sites in EID3 and TMEFF2 regulation regions are methylated in all tumor DNA samples except one (136T and 75T, respectively). A maximal number of tumor DNA samples with unmethylated target RCGY sites are in HLTF and SDC2 regulation regions (9 and 6, respectively). In case of ELMO1, THBD, and VIM regulation regions, we see five tumor DNA samples with unmethylated target sites. At the same time, GLAD-PCR assay of DNA preparations from normal colon samples shows an absence of the selected RCGY sites methylation in case of SDC2, FBN1(3.3), FBN1(3.1), SEPT9, THBD, and VIM genes. Surprisingly, we observed methylation of selected RCGY sites in all normal samples in case of TMEFF2 and SOX17 regulation regions and in most of the normal samples in EID3 and ESR1 genes. Based on Cq values obtained for each selected RCGY site, the receiver operating characteristic (ROC) curves were determined (Figure 3) to assess the assay sensitivity and specificity under the optimal cutoff values as well as an area under the ROC curve (AUC) with 95% CI. The results of ROC analysis of data are summarized in Table 3, where AUC presents the overall accuracy of the individual markers for distinguishing colorectal cancer from normal mucosa. The order of diagnostic performance for better markers (AUC > 0.8) looks as follows: FBN1(3.3) > FBN1(3.1) = CNRIP1 > ADHFE1 > FBN1(2) > FBN1(1) > SDC2 > UCHL1 > SEPT9 = VIM > THBD > SOCS3.

Discussion

Earlier we used GLAD-PCR assay for DNA methylation study of regulation regions of ELMO1 and ESR1 genes in tumor tissues from CRC patients [16]. GLAD-PCR assay provides several advantages in comparison to the existing methods of locus-specific methylation analysis. GLAD-PCR assay is not based on DNA bisulfite treatment, which causes serious DNA degradation (up to 90% [29]). And this DNA damage may be especially important for currently developing diagnostic techniques, which use small amounts of free-circulating blood DNA for analysis. The unique degenerate recognition site of GlaI R(5mC)GY is more abundant in the human genome in comparison to the recognition sites of restriction enzymes used in epigenetic

studies, such as HpaII, MspI, SmaI, and XmaI. Thus, GlaI usage allows to cover the putative methylation positions and to analyze sites that are not tested with other DNA endonucleases. A specificity of GLAD-PCR assay is rather high because it is based on DNA structure of genomic primer, TaqMan probe, and four complementary nucleotides of "hybrid" primer. A procedure of the sample DNA treatment in GLAD-PCR assay is simple and includes four steps in one tube: DNA cleavage with GlaI followed by heat inactivation, adapter ligation, and real-time PCR.

In this work we applied GLAD-PCR assay for determination of R(5mC)GY sites in regulation regions of 23 CRC-associated genes in order to select sites with the most prognostic potential. GLAD-PCR assay of DNA from a colorectal adenocarcinoma cell line SW837 was used to reveal R(5mC)GY sites in a studied part of regulation region of 23 genes. In case of FBN1 regulation region, we studied four parts of regulation region indicated as FBN1(1), FBN1(2), FBN1(3.3), and FBN1(3.1). In each studied DNA region, we revealed one methylated site, whereas other RCGY sites were not methylated or significantly less methylated. Methylated site in FBN1(3.3) and FBN1(3.1) regulation regions is the same and located in position 48645483 (Table 2). So, we studied 26 DNA regions and identified 25 R(5mC)GY sites in regulation regions of 23 genes. Results of GLAD-PCR assay of FBN1(3.3) regulation region (data are not shown) indicate that sites GCGC and ACGT, located in positions 48645433 and 48655447, respectively, are not methylated. Thus, a GLAD-PCR assay showed that in FBN1(3.1) and FBN1(3.3) regulation regions (positions 48645378-48655638, see Figure la), there is one methylated site G(5mC)GC in position 48645483, whereas other four RCGY sites (two RCGY sites upstream and two RCGY sites downstream this location) are not methylated.

At the next step, we performed GLAD-PCR assay of DNA preparations from 21 tumor tissues samples and 9 paired tissue samples from normal colon mucosa studying the abovementioned 26 DNA regions. The Cq data obtained for all studied DNA samples are shown in Figure 2. In all studied regulation regions, we see the methylated target sites in most of the tumor DNA samples. Comparing the obtained Cq data, we can see that FBN1(3.3) and FBN1(3.1) regulation regions give a similar pattern of selected RCGY site methylation in all 30 studied DNA samples. This coincidence corresponds to GLAD-PCR assay of two DNA regions but the same RCGY site located in position 48645483 of 15th chromosome. ROC curves and calculated AUC values are also identical for both regions (Figure 3 and Table 3).

We compared the data obtained in our study and published earlier. In most cases the results of our analysis confirm the high levels of methylation for many well-studied genes in CRC. All investigated genes with the highest AUC values also have shown sensitivity higher than 60% and specificity higher than 80% in the published literature. The sensitivity/specificity values for FBN1 and CNRIP1 biomarkers were determined as 79/99 and 94/95, correspondingly, in the study of DNA methylation in tumor and adjacent tissue samples [22]. Other tissue-based studies of ADHFE1, SDC2, and UCHL1 methylation showed values of 82/92 [23], 75/83 [8], and 60/100 [24]. VIM gene methylation frequency was determined using DNA from stool and blood samples of CRC patients and healthy donors. In this case the sensitivity/specificity values were 73/87 [30] and 59/93 [31], correspondingly. SEPT9 methylation is considered to be one of the most promising CRC biomarkers, so it was investigated in a number of works. For example, the cancer tissue DNA study resulted in 87/100 sensitivity/specificity values [8]. The changes in SEPT9 methylation in blood samples of CRC patients compared to healthy donors were also significant and sensitivity/specificity values of 72/90 [32], 90/89 [33], 69/86 [34], and 73/91 [35] were reported earlier. THBD is also frequently methylated in the blood of CRC patients showing 71/80 sensitivity/specificity values [36].

It should be noted that the difference in sensitivity/specificity values for the same gene in different studies is not a rare case. For example, we can observe such results in literature for SEPT9, ALX4, RASSF1, and some other genes [20,21]. Probably the variability of the methylation data may be explained by the difference in the methodologies used in the experiments or by samples' selection. So, the experimental data mainly show the tendency and probability of the gene methylation in malignant cells.

Thus, the GLAD-PCR analysis allows identifying aberrantly methylated RCGY sites of gene regulatory regions in tumor tissues and cell line SW837. We are planning to continue a work with tissue samples to identify aberrantly methylated sites suitable for GLAD-PCR analysis in regulatory regions of other genes. A second group of scientists will analyze a methylation of established target sites in DNA preparations of peripheral blood of CRC patients.

Conclusion

In this study we applied a new GLAD-PCR assay to identify aberrantly methylated RCGY sites in a number of downregulated genes in the tissue DNA samples of CRC patients. The analysis of the methylation status of RCGY sites demonstrated good prognostic potential with relatively high sensitivity and specificity of CRC detection in the tissue DNAs. We believe that the selected RCGY sites may be used in GLAD-PCR assay of CRC determination in case of noninvasive blood and stool DNA analysis.

Conflict of Interests

The authors have not declared any conflict of interests.

Acknowledgment

The present study was supported by the Ministry of Science and Education of the Russian Federation under the Agreement No.14.604.21.0102 from 05.08.2014 (unique identifier RFMEFI60414X0102), concluded in the framework of the Federal Targeted Program "Research and development on priority directions of scientific-technological complex of Russia for 2014-2020 years." According to the results of this study, the application for patent "Method for determining the methylation sites PuCGPy in regulatory regions of genes-markers of colorectal cancer by a method of GLAD-PCR assay and a set of oligonucleotide primers and fluorescently labeled probes for the implementation of said method" was made, and the priority date is 21.07.16, number 2016129968.

References

[1.] Weng W, Feng J, Qin H, Ma Y (2015) Molecular therapy of colorectal cancer: progress and future directions. Int J Cancer 136(3): 493-502.

[2.] Jemal A, Bray F, Center MM, Ferlay J, Ward E, et al. (2011) Global cancer statistics. CA CancerJ Clin 61(2): 69-90.

[3.] Brenner H, Bouvier AM, Foschi R, Hackl M, Larsen IK, et al. (2012) Progress in colorectal cancer survival in Europe from the late 1980s to the early 21st century: the EUROCARE study. Int J Cancer 131(7): 1649-1658.

[4.] Gyparaki MT, Basdra EK, Papavassiliou AG (2013) DNA methylation biomarkers as diagnostic and prognostic tools in colorectal cancer. J Mol Med (Berl) 91(11): 1249-1256.

[5.] de Caceres II, Cairns P (2007) Methylated DNA sequences for early cancer detection, molecular classification and chemotherapy response prediction. Clin Trans Oncol 9(7): 429-437.

[6.] Caiazza F, Ryan EJ, Doherty G, Winter DC, Sheahan K (2015) Estrogen receptors and their implications in colorectal carcinogenesis. Front Oncol 5:19.

[7.] Li WH, Zhang H, Guo Q, Wu XD, Xu ZS, et al. (2015) Detection of SNCA and FBN1 methylation in the stool as a biomarker for colorectal cancer. Dis Markers 2015:657570.

[8.] Mitchell SM, Ross JP, Drew HR, Ho T, Brown GS, et al. (2014) A panel of genes methylated with high frequency in colorectal cancer. BMC Cancer 14: 54.

[9.] Naumov VA, Generozov EV, Zaharjevskaya NB, Matushkina DS, Larin AK, et al. (2013) Genome-scale analysis of DNA methylation in colorectal cancer using Infinium HumanMethylation450 BeadChips. Epigenetics 8(9): 921-934.

[10.] LaPointe LC, Pedersen SK, Dunne R, Brown GS, Pimlott L, et al. (2012) Discovery and validation of molecular biomarkers for colorectal adenomas and cancer with application to blood testing. PLoS One 7: e29059.

[11.] Kibriya MG, Raza M, Jasmine F, Roy S, Paul-Brutus R, et al. (2011) A genomewide DNA methylation study in colorectal carcinoma. BMC Med Genomics 4:50.

[12.] Handa V, Jeltsch A (2005) Profound flanking sequence preference of Dnmt3a and Dnmt3b mammalian DNA methyltransferases shape the human epigenome. J Mol Biol 348(5): 1103-1112.

[13.]Jurkowska RZ, Jurkowski TP, Jeltsch A (2011) Structure and function of mammalian DNA methyltransferases. Chembiochem 12(2): 206-226.

[14.] Tarasova GV, Nayakshina TN, Degtyarev SK (2008) Substrate specificity of new methyl-directed DNA endonuclease Glal. BMC Mol Biol 9: 7.

[15.] Kuznetsov VV, Akishev AG, Abdurashitov MA, Degtyarev SK (2014) Method of detecting nucleotide sequence Pu(5mC)GPy at predetermined position of longdistance DNA. The Bulletin "Inventions. Utility Models" (in Russian) 23: patent RU 2525710.

[16.] Evdokimov AA, Netesova NA, Smetannikova NA, Abdurashitov MA, Akishev AG, et al. (2016) Application of GLAD-PCR analysis for the methylation sites detection in the regulatory areas of tumor-suppressor genes ELM01 and ESR1 in colorectal cancer. Prob Oncol (Voprosy Onkologii, in Russian) 62(1): 117-121.

[17.] Green MR, Sambrook J (2012) Molecular Cloning: A Laboratory Manual (4th edn). New York: Cold Spring Harbor Laboratory Press.

[18.] ENCODE Project Consortium (2011) Auser's guide to the encyclopedia of DNA elements (ENCODE). PLoS Biol 9(4): e1001046.

[19.] DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44(3): 837-845.

[20.] Xue M, Lai SC, Xu ZP, Wang LJ (2015) Noninvasive DNA methylation biomarkers in colorectal cancer: a systematic review. J Dig Dis 16(12): 699-712.

[21.] Rasmussen SL, Krarup HB, Sunesen KG, Pedersen IS, Madsen PH, et al. (2016) Hypermethylated DNA as a biomarker for colorectal cancer: a systematic review. Colorectal Dis 18(6): 549-561.

[22.] Lind GE, Danielsen SA, Ahlquist T, Merok MA, Andresen K, et al. (2011) Identification of an epigenetic biomarker panel with high sensitivity and specificity for colorectal cancer and adenomas. Mol Cancer 10: 85.

[23.]Tae CH, Ryu KJ, Kim SH, Kim HC, Chun HK, et al. (2013) Alcohol dehydrogenase, iron containing, 1 promoter hypermethylation associated with colorectal cancer differentiation. BMC Cancer 13: 142.

[24.] Yagi K, Akagi K, Hayashi H, Nagae G, Tsuji S, et al. (2010) Three DNA methylation epigenotypes in human colorectal cancer. Clin Cancer Res 16: 21-33.

[25.]Varley KE, Mitra RD (2010) Bisulfite Patch PCR enables multiplexed sequencing of promoter methylation across cancer samples. Genome Res 20(9): 1279-1287.

[26.] Zhang W, Glockner SC, Guo M, Machida EO, Wang DH, et al. (2008) Epigenetic inactivation of the canonical Wnt antagonist SRY-box containing gene 17 in colorectal cancer. Cancer Res 68(8): 2764-2772.

[27.] Ashktorab H, Daremipouran M, Goel A, Varma S, Leavitt R, et al. (2014) DNA methylome profiling identifies novel methylated genes in African American patients with colorectal neoplasia. Epigenetics 9(4): 503-512.

[28.] Tang XH, Gudas LJ (2011) Retinoids, retinoic acid receptors, and cancer. Annu Rev Pathol 6: 345-364.

[29.] Grunau C, Clark SJ, Rosenthal A (2001) Bisulfite genomic sequencing: systematic investigation of critical experimental parameters. Nucleic Acids Res 29 (13):E65-5.

[30.] Itzkowitz SH, Jandorf L, Brand R, Rabeneck L, Schroy PC 3rd, et al. (2007) Improved fecal DNA test for colorectal cancer screening. Clin Gastroenterol Hepatol 5(1): 111-117.

[31.] Li M, Chen WD, Papadopoulos N, Goodman SN, Bjerregaard NC, et al. (2009) Sensitive digital quantification of DNA methylation in clinical samples. Nat Biotechnol 27: 858-863.

[32.] Grutzmann R, Molnar B, Pilarsky C, Habermann JK, Schlag PM, et al. (2008) Sensitive detection of colorectal cancer in peripheral blood by septin 9 DNA methylation assay. PLoS One 3: e3759.

[33.] Warren JD, Xiong W, Bunker AM, Vaughn CR Furtado LV, et al. (2011) Septin 9 methylated DNA is a sensitive and specific blood test for colorectal cancer. BMC Med 9: 133.

[34.] Lofton-Day C, Model F, Devos T, Tetzner R, Distler J, et al. (2008) DNA methylation biomarkers for blood-based colorectal cancer screening. Clin Chem 54(2): 414-423.

[35.] Tanzer M, Balluff B, Distler J, Hale K, Leodolter A, et al. (2010) Performance of epigenetic markers SEPT9 and ALX4 in plasma for detection of colorectal precancerous lesions. PLoS One 5(2): e9061.

[36.] Lange CP, Campan M, Hinoue T, Schmitz RF, van der Meulen-de Jong AE, et al. (2012) Genome-scale discovery of DNA-Methylation biomarkers for blood-based detection of colorectal cancer. Plos One 7(11): e50266.

Alexey A. Evdokimov (1), Nina A. Netesova (1), Natalia A. Smetannikova (1), Murat A. Abdurashitov (1*), Alexandr G. Akishev (1), Boris S. Malyshev (1), Evgeniya S. Davidovich (1), Vladimir V. Fedotov (1), Vitaliy V. Kuznetsov (1), Yuriy D. Ermolaev (2), Andrey B. Karpov (2), Alexey E. Sazonov (2), Ravil M. Tahauov (2), Sergey Kh. Degtyarev (1)

(1) State Research Center of Virology and Biotechnology "Vector, " Novosibirsk 630559, Russia

(2) Seversk Biophysical Research Centre of the Federal Medico-Biological Agency, Seversk 636000, Russia

(*) Corresponding author: Abdurashitov MA, SibEnzyme, 2/12 Ak.Timakov Str., Novosibirsk 630117, Russia; Tel: +7 383 3336854; Fax: +7 383 3336853; E-mail: abdurashitov_ma@vector.nsc.ru

Received: Aug 4, 2016; Accepted: Sep 1, 2016; Published: Oct 7, 2016
Gene (a)
(region)   Gene name (a)

ADHFE1     Alcohol dehydrogenase,
           iron containing 1

ALX4       ALX homeobox 4


CNRIP1     Cannabinoid receptor
           interacting protein 1

EID3       EP300 interacting inhibitor of
           differentiation 3

ELM01      Engulfment and cell motility 1


ESR1       Estrogen receptor 1


FBAM(1)    Fibrillin 1


FBN1(2)    --


FBN1(3A)   --


FBN1(3.3)  --


HLTF       Helicase like transcription
           factor

LAMA1      Laminin subunit alpha 1


NEUROG1    NEUROG1 Neurogenin 1


NGFR       NGFR Nerve growth factor receptor


RARB       RARB Retinoic acid receptor beta


RXRG       RXRG Retinoid X receptor gamma


RYR2       RYR2 Ryanodine receptor 2


SDC2       SDC2 Syndecan 2


SEPT9      SEPT9 Septin 9


SFRP2      SFRP2 Secreted frizzled-related


SOCS3      SOCS3 Suppressor of cytokine
            signaling 3

SOX17      SOX17 SRY-box 17


THBD       THBD Thrombomodulin

            Transmembrane protein with
TMEFF2     TMEFF2 EGF-like and two follista-
            tin-like domains 2

UCHL1      UCHL1 Ubiquitin C-torminal
            hydro-lase L1

VIM        VIM Vimentin


Gene (a)   Chromosomal
(region)   location (a)

ADHFE1     8q12.3


ALX4       11p11.2


CNRIP1     2p13


EID3       12q23.3


ELM01      7p14.1


ESR1       6q24-q27


FBAM(1)    15q21.1


FBN1(2)    --


FBN1(3A)   --


FBN1(3.3)  --


HLTF       3q25.1-q26.1


LAMA1      18p11.3


NEUROG1    Neurogenin 1 5q23-q31


NGFR       Nerve growth factor receptor 17q21-q22


RARB       Retinoic acid receptor beta 3p24


RXRG       Retinoid X receptor gamma 1q22-q23


RYR2       Ryanodine receptor 2 1q43


SDC2       Syndecan 2 8q22-q23


SEPT9      Septin 9 17q25.3


SFRP2      Secreted frizzled-related 4q31.3


SOCS3      Suppressor of cytokine 17q25.3
           signaling 3

SOX17      SRY-box 17 8q11.23


THBD       Thrombomodulin 20p11.21

           Transmembrane protein with
TMEFF2     EGF-like and two follista- 2q32.3
           tin-like domains 2

UCHL1      Ubiquitin C-torminal 4p13
           hydro-lase L1

VIM        Vimentin 10p13


Gene (a)
(region)    Primer/probe sequence (b)
            g: GTGGGCACCCTGCGGTCC
ADHFE1      p: FAM-CCCGCCGGCCCCGCACTC-BHQ1
            r: GGTCGCGTACTTGCTGAGGCA
            g: CGTCAACAACCTCTCATCC
ALX4        p: FAM-TCCATTTCTTATTTCAGTTTGCCACCA-BHQ1
            r: GGACTCTGGTTTCTAAGATCAG
            g:GCCAGACCCTCGCCCAGACA
CNRIP1      p: FAM-CGAGGCCCGGCAGGTCCCCC-BHQ1
            r: CGTCATTAGGCTGGATGCGCA
            g: GCTCCGCGGGAAGACAGCC
EID3        p: FAM-CCCGGCCCAGCCACAAGC-BHQ1
            r: TTTGAAAAAGAATAGCTGTCCCCTGA
            g: GGGTCGCCGGAGCTCTGA
ELM01       p:FAM-AACCCTTGCCGCTGCTGTCCTGC-BHQ1
            r: CGCCAGCCCAGGAAACTTTAC
            g: CGCAGGGCAGAAGGCTCAGAA
ESR1        p:FAM-TGCTCTTTTTCCAGGTGGCCCGCC-BHQ1
            r: CGGGACATGCGCTGCGTC
            g: GCGGGGAGACTTTCAGGGCA
FBAM(1)     p:FAM-ATGCTGAAGCCTCGCGGTCCCC-BHQ1
            r: CACGGGTTGGGCTTGGGA
            g: CAGCAGCCCCGGCCGATC
FBN1(2)     p: FAM-CCTCCCGGGCCCCGCCAGA-BHQ1
            r: GGGTACTTTGCGCCGCGCTC
            g: GTAGCGGCCACGACTGGGA
FBN1(3A)    p: FAM-CAGCCGCCGCCGCCTCCTC-BHQ1
            r: CCGGCTGCACCCACTGGA
            g: GAGCCCGGCACCAAGAGC
FBN1(3.3)   p:FAM-CCCTCCCCTGCCTGACAGCTTCC-BHQ1
            r: CACCGGGGCTGGAGCTGC
            g: AACAAAACACCGGCACCGCA
HLTF        p:FAM-CAGTCGCACTCCTGGGGCCTCGT-BHQ1
            r: GTTCTTCCCCGCCCCCAA
            g: CCACCTTCTCTGCCCACCTCCTA
LAMA1       p: FAM-CTGACCGCGGCCGCCTCCC -BHQ1
            r: CCGCCACCCAGACCCCTC
            g: GTGCCTCGGCCGCTAATCG
NEUROG1    5q23-q31 p:FAM-CCGACCCCGCCTCTGTTTCACTGC-BHQ1
            r: CCGTAATTACCGCCGGCCAATC
            g: TGGCTTCACCCAGCCTCTC
NGFR       17q21-q22 p: FAM-CAGCCAGAGCGAGCCGAGCC-BHQ1
            r: TCCAGCTCGGTCCGCTTTG
            g: TTCAGAGGCAGGAGGGTCTATTC
RARB         3p24 p:FAM-TCCCAGTCCTCAAACAGCTCGCATGG-BHQ1
            r: GGTTCCCAGAAAGATCCCAAGTTC
            g: GCCGCCGTCACCGCTACT
RXRG         1q22-q23 p: FAM-CCACCGCCGTCGCTGCTGC-BHQ1
            r: GTGCCACCCGGTAGGGACC
            g: GGGGACCACGGAGGCGACT
RYR2       1q43 p:FAM-TTTCCCCCAAGTCAAGGTGCTGCGAAA-BHQ1
            r: CGGGGGTGATGGTGCAGGA
            g: GCGATTGCGGCTCAGGCT
SDC2       8q22-q23 p: FAM-CCCCGAGCCCGAGTCCCCG-BHQ1
            r: GGGAGTGCAGAAACCAACAAGTGA
            g: GCAGGAGGCTGTATTTGGG
SEPT9      17q25.3 p:FAM-AGCCAAACAAAGTTCTCTGTCACCGCC-BHQ1
            r: CGCTGCCGTTTAACCCTTG
            g: GCACAGCCAGAGTTTTCTTG
SFRP2        4q31.3 p: FAM-TACCCTTCATTGGCTCCTCCCTTGCT-BHQ1
            r: AGGCTTCTCTGTTTGTTGTTTAAAG
            g: CAGTCCCGGGGGCCCTTCT
SOCS3      17q25.3 p: FAM-TGCTCCCCACCCGGCCACACTCC-BHQ1
              r: CAAGGGCGCAGCGTGGGA
            g: CGCCCTCCGACCCTCCAA
SOX17      8q11.23 p: FAM-TCCCGGATTCCCCAGGTGGCC-BHQ1
            r: CAGTTCAGGGCCAAGGGTGCT
            g: GTTCGGGAAAAGGAAGGAAGTGC
THBD       20p11.21 p:FAM-ATTGCTGGGTTCTCTGGCCGCC-BHQ1
            r: TTACTCATCCCGGCGAGGTGA
            g: GGTGGGCTACCCGCACACTCATA
TMEFF2     2q32.3 p:FAM-CCATTCGCCTCACTCTCCGCTCCA-BHQ1
            r: CGCCGACTCGCCCCTCTC
            g: GCAGAACCAAGCGAGGGGGAA
UCHL1      4p13 p: FAM-CGTACCCATCTGGCCGCGACCGTC-BHQ1
              r: GGGGCCCGGCCGTACCAC
            g: TCCGCAGCCATGTCCACCA
VIM        10p13 p:FAM-CCGTGTCCTCGTCCTCCTACCGCAG-BHQ1
            r: GCTGCCCAGGCTGTAGGTGC


(a) Gene symbol, gene name, and chromosomal location are in
accordance with the approved guidelines from the HUGO Gene
Nomenclature Committee (http://www.genenames.org);
(b) g--direct genomic primer, p--probe, r--reverse genomic primer,
FAM--6-carboxyfluorescein, BHQ1--Black Hole Quencher 1.
Table 1 : The genes selected for the study and a list
of specific primers and probes used

Gene (region)  Target site  Site location (a)
ADHFE1         GCGT         chr8: 66432544-66432547
ALX4           GCGC         chr11: 44304711-4304714
CNRIP1         GCGC         chr2: 68319361-68319364
EID3           GCGT         chr12: 104303610-104303613
ELM01          GCGC         chr7: 37448622-37448625
ESR1           GCGT         chr6: 151807784-151807787
FBN1(1)        GCGT         chr15: 48645054-18645057
FBN1(2)        GCGC         chr15: 48645174-18645177
FBN1(3A)       GCGC         chr15: 48645489-18645492
FBN1(3.3)      GCGC         chr15: 48645489-18645492
HLTF           GCGC         chr3: 149086718-149086721
LAMA1          ACGC         chr18: 7117965-7117968
NEUROG1        GCGC         chr5: 135536188-135536191
NGFR           GCGC         chr17: 49495342-19495345
RARB           ACGC         chr3: 25428374-25428377
RXRG           GCGC         chr1: 165445276-165445279
RYR2           GCGC         chr1 : 237043053-237043056
SDC2           GCGT         chr8: 96494057-96494060
SEPT9          GCGC         chr17: 77372894-77372897
SFRP2          GCGC         chr4: 153789220-153789223

SOCS3          GCGC         chr17: 78359427-78359430
SOX17          GCGC         chr8: 54458724-54458727
THBD           GCGC         chr20: 23049816-23049819
TMEFF2         GCGC         chr2: 192195257-192195260
UCHL1          GCGC         chr4: 41256788-11256791
VIM            GCGC         chr10: 17229549-17229552

Gene (region)  Hybrid primer (b)
ADHFE1         CCTGCTCTTTCATCGGTAC
ALX4           CCTGCTCTTTCATCGGCGC
CNRIP1         CCTGCTCTTTCATCGGCGA
EID3           CCTGCTCTTTCATCGGTGT
ELM01          CCTGCTCTTTCATCGGCCG
ESR1           CCTGCTCTTTCATCGGTGT
FBN1(1)        CCTGCTCTTTCATCGGCGC
FBN1(2)        CCTGCTCTTTCATCGGCTC
FBN1(3A)       CCTGCTCTTTCATCGGCGG
FBN1(3.3)      CCTGCTCTTTCATCGGCCG
HLTF           CCTGCTCTTTCATCGGCGG
LAMA1          CCTGCTCTTTCATCGGCGG
NEUROG1        CCTGCTCTTTCATCGGCGG
NGFR           CCTGCTCTTTCATCGGCTC
RARB           CCTGCTCTTTCATCGGTTC
RXRG           CCTGCTCTTTCATCGGCCG
RYR2           CCTGCTCTTTCATCGGCGT
SDC2           CCTGCTCTTTCATCGGTTC
SEPT9          CCTGCTCTTTCATCGGCAG
SFRP2          CCTGCTCTTTCATCGGCGT

SOCS3          CCTGCTCTTTCATCGGCGG
SOX17          CCTGCTCTTTCATCGGCCG
THBD           CCTGCTCTTTCATCGGCGA
TMEFF2         CCTGCTCTTTCATCGGCGG
UCHL1          CCTGCTCTTTCATCGGCTG
VIM            CCTGCTCTTTCATCGGCGC

(a) Site locations are given in accordance with the recent
human genome assembly GRCh38/hg38;
(b) 3'-terminal tetranucleotide sequence of hybrid primer,
which is complemented to the genomic sequence at the point
of Glal hydrolysis, is underlined.

Table 2: Studied DNA regions with indication of the target
RCGY sites, their locations, and a structure of
corresponding hybrid primers selected for GLAD-PCR assay

               Number of                    Number of
               detected CRC                 negative controls/total
               samples/total                number of
               number of CRC  Sensitivity,  normal mucosa
Gene (region)  samples        %             controls
FBN1(3.3)      20/21          95.2          9/9
FBN1(3A)       20/21          95.2          9/9
CNRIP1         16/21          76.2          9/9
ADHFE1         18/21          85.7          9/9
FBN1(2)        19/21          90.5          8/9
FBN1(1)        17/21          81.0          9/9
SDC2           15/21          71.4          9/9
UCHL1          13/21          61.9          9/9
SEPT9          16/21          76.2          9/9
VIM            15/21          71.4          9/9
THBD           15/21          71.4          9/9
SOCS3          13/21          61.9          9/9
RXRG           12/21          57.1          9/9
EID3           18/21          85.7          7/9
SFRP2          15/21          71.4          8/9
ELM01          12/21          57.1          9/9
ESR1           12/21          57.1          8/9
TMEFF2         15/21          71.4          8/9
RYR2           17/21          81.0          6/9
NGFR           11/21          52.4          8/9
LAMA1           7/21          33.3          9/9
ALX4           12/21          57.1          8/9
HLTF            9/21          42.9          9/9
RARB           11/21          52.4          6/9
SOX17          14/21          66.7          5/9
NEUROG1        10/21          47.6          8/9

                Specificity,  AUC
Gene (region)   %             (standard error)  95% CI
FBN1(3.3)       100           0.976 (0.024)     0.843-1.000
FBN1(3A)        100           0.971 (0.029)     0.834-1.000
CNRIP1          100           0.952 (0.036)     0.806-0.997
ADHFE1          100           0.915(0.055)      0.755-0.985
FBN1(2)          88.9         0.902 (0.065)     0.737-0.980
FBN1(1)         100           0.884 (0.062)     0.714-0.971
SDC2            100           0.873 (0.058)     0.701-0.966
UCHL1           100           0.870 (0.068)     0.697-0.964
SEPT9           100           0.862 (0.067)     0.688-0.960
VIM             100           0.862 (0.056)     0.688-0.960
THBD            100           0.841 (0.058)     0.662-0.948
SOCS3           100           0.825 (0.074)     0.644-0.939
RXRG            100           0.778 (0.085)     0.589-0.908
EID3             77.8         0.778 (0.103)     0.589-0.908
SFRP2            88.9         0.772 (0.099)     0.584-0.905
ELM01           100           0.757 (0.089)     0.566-0.894
ESR1             88.9         0.730 (0.097)     0.538-0.875
TMEFF2           88.9         0.714 (0.114)     0.521-0.863
RYR2             66.7         0.704(0.115)      0.510-0.855
NGFR             88.9         0.667 (0.108)     0.472-0.827
LAMA1           100           0.651 (0.106)     0.456-0.815
ALX4             88.9         0.646(0.112)      0.451-0.810
HLTF            100           0.550(0.106)      0.359-0.731
RARB             66.7         0.550(0.114)      0.359-0.731
SOX17            55.6         0.548(0.127)      0.356-0.729
NEUROG1          88.9         0.540(0.110)      0.349-0.722

Table 3: Receiver operating characteristics for diagnosis of
CRC versus normal mucosa determined by means of GLAD-PCR assay
of selected RCGY sites (sorted by AUC values)
COPYRIGHT 2016 HATASO Enterprises, LLC
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2016 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Title Annotation:Research Article
Author:Evdokimov, Alexey A.; Netesova, Nina A.; Smetannikova, Natalia A.; Abdurashitov, Murat A.; Akishev,
Publication:Biology and Medicine
Article Type:Report
Date:Dec 1, 2016
Words:6064
Previous Article:Comparative assessment of the feasibility of some probiotic cultures as a means for sanitization of cows' udders.
Next Article:Evaluation of esthetic rehabilitation of teeth with severe fluorosis using direct and indirect laminate veneer: A case report.
Topics:

Terms of use | Privacy policy | Copyright © 2018 Farlex, Inc. | Feedback | For webmasters