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Year : 2019  |  Volume : 15  |  Issue : 1  |  Page : 192-203

Epigenetic deregulations of Wnt/β-catenin and transforming growth factor beta-Smad pathways in esophageal cancer: Outcome of DNA methylation

1 Tumor Biology Group, National Institute of Pathology (ICMR); Cancer Biology Laboratory, School of Life Sciences, Jawaharlal Nehru University, New Delhi, India
2 Tumor Biology Group, National Institute of Pathology (ICMR), New Delhi, India
3 Department of Pathology, Dr. B. Borooah Cancer Institute, Guwahati, Assam, India
4 Department of Surgical Oncology, Dr. B. Borooah Cancer Institute, Guwahati, Assam, India
5 DBT Centre for Molecular Biology and Cancer Research, Dr. B. Borooah Cancer Institute, Guwahati, Assam, India
6 Gynecologic Oncology, Dr. B. Borooah Cancer Institute, Guwahati, Assam, India

Date of Web Publication13-Mar-2019

Correspondence Address:
Dr. Sunita Saxena
National Institute of Pathology (ICMR), Safdarjung Hospital Campus, New Delhi - 110 029
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/jcrt.JCRT_634_17

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 > Abstract 

Background: Promoter methylation of tumor suppressor genes (TSGs) is a well-reported portent in carcinogenesis; hence, it is worthy to investigate this in high-risk Northeast population of India. The study was designed to investigate methylation status of 94 TSGs in esophageal squamous cell carcinoma (ESCC). Further, the effect of OPCML promoter methylation on gene expression was analyzed by immunohistochemistry. Moreover, in silico protein–protein interactions were examined among 8 TSGs identified in the present study and 23 epigenetically regulated genes reported previously by our group in ESCC.
Materials and Methods: Methylation profiling was carried out by polymerase chain reaction array and OPCML protein expression was examined by tissue microarray-based immunohistochemistry.
Results: OPCML, NEUROG1, TERT, and WT1 genes were found hypermethylated and SCGB3A1, CDH1, THBS1, and VEGFA were hypomethylated in Grade 2 tumor. No significant change in OPCML expression was observed among control, Grade 1, and Grade 2 tumor. Conclusively, hypermethylation of the studied OPCML promoter in Grade 2 tumor produced no effect on expression. Unexpectedly, OPCML expression was downregulated in Grade 3 tumor in comparison to other groups signifying that downregulation of OPCML expression may lead to higher grade of tumor formation at the time of diagnosis of ESCC in patients. Significant interactions at protein level were found as VEGFA:PTK2, CTNNB1:CDH1, CTNNB1:VEGFA, CTNNB1:NEUROG1, CTNND2:CDH1, and CTNNB1:TERT. These interactions are pertinent to Wnt/β-catenin and TGF-β-Smad pathways.
Conclusions: Deranged OPCML expression may lead to high-grade ESCC as well as epigenetically regulated genes, that is, CDH1, CTNNB1, CTNND2, THBS1, PTK2, WT1, OPCML, TGFB1, and SMAD4 may alter the Wnt/β-catenin and TGF-β-Smad pathways in ESCC. Further study of these genes could be useful to understand the molecular pathology of ESCC with respect to epithelial-mesenchymal transition (EMT) mediated by Wnt/β-catenin and TGF-β signaling pathways.

Keywords: Esophageal cancer, methylation, OPCML, tissue microarray, Wnt/β-catenin pathway

How to cite this article:
Singh V, Singh AP, Sharma I, Singh LC, Sharma J, Borthakar BB, Rai AK, Kataki AC, Kapur S, Saxena S. Epigenetic deregulations of Wnt/β-catenin and transforming growth factor beta-Smad pathways in esophageal cancer: Outcome of DNA methylation. J Can Res Ther 2019;15:192-203

How to cite this URL:
Singh V, Singh AP, Sharma I, Singh LC, Sharma J, Borthakar BB, Rai AK, Kataki AC, Kapur S, Saxena S. Epigenetic deregulations of Wnt/β-catenin and transforming growth factor beta-Smad pathways in esophageal cancer: Outcome of DNA methylation. J Can Res Ther [serial online] 2019 [cited 2022 Dec 6];15:192-203. Available from: https://www.cancerjournal.net/text.asp?2019/15/1/192/241087

 > Introduction Top

The etiological distinctions in terms of dietary habits or nutritional factors, tobacco/betel quid chewing and alcohol habits allied with the Northeast Indian population have elevated the incidence rates of esophageal cancer in this population.[1] Geographically, the region extending from Northern Iran through the Central Asian republics to Northcentral China corresponds to the highest risk zone for esophageal squamous cell carcinomas (ESCC).[2] Our group has earlier reported microarray expression, genome-wide methylation and their integration, as well as differential expression of chromatin modification enzymes in ESCC.[3],[4],[5] We have previously identified 23 genes in an integration study of genome-wide methylation and microarray expression data for ESCC in Northeast Indian population.[4] The results displayed genes relevant for tumor progression and associated with the processes involved in metastasis such as cell adhesion, integrin signaling, cytoskeleton, and extracellular matrix organizations.[4] Methylation of promoter CpGs islands is a well-recognized epigenetic portent for silencing tumor suppressor genes (TSGs).[6] SPINT2, CACNA2D3, DIRAS1, and Rab25 TSGs are reported to be hypermethylated (HM) and downregulated in esophageal cancer in Chinese population.[7],[8],[9],[10] Previously, methylation of large set of genes has been studied by targeted GoldenGate methylation assay (Illumina) and genome-wide 450k Infinium assay (Illumina) by other investigators as well as our group.[4],[11],[12] Genome-wide approaches such as 450k Infinium assay gives the picture of all genes with selected CpG sites within different genomic locations including promoters. However, targeted TSGs promoter methylation profiling in Northeast population gives information about the status of all TSGs reported previously in different cancers.

Our group previously reported consistency in microarray gene expression profiles of esophageal cancer in Northeast Indian and Chinese populations.[3] Similar promoter methylation profiles of TSGs could play role in ESCC in high-risk Northeast Indian population. Formerly, only two studies have been reported from this population, which deal with promoter methylation of TSGs p16 and MGMT suggesting the synergistic effect of their promoter methylation and betel quid and tobacco chewing habit on enhancement of the risk of ESCC.[13],[14] The abovementioned studies were limited to only two TSGs and done in blood samples of patients and controls using semi-quantitative methylation-specific polymerase chain reaction (PCR). Thus, there was an unmet need for comprehensively profiling promoter methylation status of various TSGs in tissue samples of ESCC patients from Northeast population of India by a quantitative method. This impelled us to carry out the present investigation dealing with quantitative differential methylation profiling of targeted promoter of 94 TSGs in tumor and adjacent normal tissue samples of esophageal cancer patients. Tumor suppressor proteins regulate expression level and functionality of various cancer pathway-specific proteins. Furthermore, to investigate the possible interactions among 8 TSGs identified in the present study and 23 epigenetically regulated genes reported previously by our group in ESCC, an in silico protein–protein interaction analysis was also done.

 > Materials and Methods Top

Sample collection

The study has been approved by the Institutional Ethics Committee at Bhubaneshwar Borooah Cancer Institute (BBCI), Guwahati, Assam. Tumor and normal tissue samples were collected from newly diagnosed esophageal cancer patients at BBCI. A well-written consent was obtained before collection of the endoscopic biopsies. A part of each collected tissue was preserved in formalin for histopathologic examination at BBCI. The remaining tissue was immediately stored in RNA later solution (Ambion, Austin, USA) and stored at −80°C until processed. A questionnaire regarding patient's dietary habits, socioeconomic status, as well as family history of esophageal cancer was given to the patients at the time of sample collection. The patients having positive family history of esophageal cancer were excluded so as to cover only sporadic cases. All the patients considered for the study had a habit of tobacco and betel quid chewing. Paired tumor and normal tissue samples were collected from newly diagnosed 104 esophageal cancer patients. Among them, 90 patients were of ESCC and rest 14 were of adenocarcinoma. The ESCC patients were further classified based on tumor grade as well differentiated (Grade 1), moderately differentiated (Grade 2), and poorly differentiated (Grade 3), which was found in 15, 62, and 13 number of patients, respectively.

DNA isolation and differential methylation profiling of tumor suppressor genes

Genomic DNA was isolated from tissue samples by Qiagen QIAamp DNA Mini kit (Qiagen, Hilden, Germany) as per manufacturer's protocol. Integrity of the isolated DNA was analyzed by 0.8% agarose gel electrophoresis. The ratio of absorbance at 260 nm and 280 nm was taken for purity of DNA, and samples having ratio of 1.7–1.9 were considered for methylation profiling. Promoter methylation status of 94 TSGs in 6 paired Grade 2 tumor and adjacent normal tissue samples were checked by Human TSGs EpiTect Methyl Complete PCR Arrays (Qiagen, Hilden, Germany). Tissue samples used for methylation profiling were collected from age, sex (all male), and grade of tumor (all moderately differentiated Grade 2) matched patients. The real-time PCR array system relies on the differential cleavage of target sequences by two different restriction endonucleases whose activities require either the presence or absence of methylated cytosines in their respective recognition sequences. After digestion, the remaining DNA was measured by quantitative PCR in each individual enzyme reaction using primers that flank a promoter region of the gene of interest. The relative fractions of methylated and unmethylated DNA were subsequently determined by comparing the amount in each digest with that of a mock (no enzymes added) digest using the ΔCt method. Results were expressed as percentage of hypermethylation for each gene.

Tissue microarray-based immunohistochemistry

To check the effect of promoter hypermethylation of OPCML, tissue microarray (TMA)-based immunohistochemistry was done in 75 tumor and 20 nonneoplastic control tissue samples collected from the initial cohort of 90 ESCC patients. Cohort comprised samples of patients having different grade of ESCC, that is, well differentiated (Grade 1), moderately differentiated (Grade 2), and poorly differentiated (Grade 3). A TMA was constructed from the formalin-fixed paraffin-embedded blocks of these tissue samples. Sampling sites were marked on the donor blocks and the tissue cylinders were precisely arrayed into two recipient blocks, each with a core size of 1.5 mm using semi-automatic TMA (Alphelys, SAS, France). TMA block had 20 nonneoplastic esophageal epithelial tissues taken from distant sites (control) and 75 samples from ESCC. Immunohistochemistry experiment was done as per the protocol described earlier.[5] OPCML primary antibody (Abcam) and HRP tagged secondary antibody (polymer, Dako) were used for the experiment. Images of the cores were taken by scanning the processed TMA slides by Digital Scanning Microscope (MetaSystems) at ×10 magnification. Staining was scored semi-quantitatively as <10% or no staining = 0, 10%–40% = 1, and 41%–100% = 2 and termed as nonreactive, weak immunoexpression, and strong immunoexpression, respectively.

Individual tissue-based immunohistochemistry

A total of 12 samples comprising 6 controls and 6 ESCC tumor (Grade 2) tissues were evaluated for WT1 and CDH1 immunoexpression as per the protocol described earlier.[5] WT1 primary antibody (Dako) and CDH1 primary antibody (Biogenex) with HRP-tagged secondary antibody (Polymer, Dako) were used for experiments. Staining was scored semi-quantitatively as described in the above section.

Protein–protein Interaction analysis by STRING version 10.0

Protein–protein interaction analysis with two sets of genes was done by STRING 10.0 online software (). The interactions included direct (physical) and indirect (functional) associations; derived from four sources (genomic context, high-throughput experiments, coexpression, and previous literature). Gene enrichment analysis identified significant biological processes in ESCC. The two sets of genes comprised 8 TSGs (Set 1) from the present study and 23 genes (Set 2) from genome-wide methylation and microarray expression data integration network reported previously by our group in the same population for ESCC.[4]

Statistical methods

The percentage of hypermethylation for each gene was calculated by the Microsoft Excel template-based calculations provided by QIAGEN. The Excel template determine the relative fractions of methylated and unmethylated DNA by comparing the amount in each digest with that of a mock (no enzymes added) digest using the ΔCt method. Due to the inversely proportional relationship between threshold cycle and the amount of input DNA, and due to the doubling of PCR product with every cycle in the exponential phase of the reaction, the initial DNA amount in each digest before PCR is expressed as CMo= 2−Ct(Mo); CMs= 2−Ct(Ms); CMd= 2−Ct(Md); and CMsd= 2−Ct(Msd).

The fraction of DNA in each digest was calculated by normalizing the DNA amount to the amount of digestible DNA. The amount of digestible DNA is equal to the total amount of DNA (determined from the mock digest) minus the amount of DNA resistant to DNA digestion (determined from the double digest). HM DNA fraction was calculated as FHM= CMs/CMo– CMsd= 2−Ct(Ms)/2−Ct(Mo) − 2−Ct(Msd). Unmethylated (UM) DNA fraction was calculated as FUM= CMd/CMo− CMSd= 2−Ct(Md)/2−Ct(Mo) −2−Ct(Msd). Intermediately methylated (IM) DNA fraction was calculated as FIM= 1 − FHM– FUM.

Results were expressed as percentage of hypermethylation for each gene. The values of percentage of hypermethylation of tumor and normal tissue for each gene were used for Student's t-test analysis to calculate significant differentially methylated genes. P ≤ 0.05 was considered statistically significant. Frequency of different immunostaining intensity scores (0, 1, and 2) were calculated in control and different grades of the ESCC for TMA experiment. The data were analyzed by Chi-squared test, however, Fisher exact test was applied in case the cell frequencies were found <5. P ≤ 0.05 was considered statistically significant.

 > Results Top

Differential methylation of tumor suppressor genes

Hierarchical clustering method with a Pearson correlation was used for similarity measurement, and genes with similar methylation levels were grouped together [Figure 1]. Among the genes taken for methylation profiling, about half of them displayed differential promoter methylation pattern between paired tumor and normal tissue [Figure 2]. Only eight genes, i.e., CDH1 (cadherin 1, type 1, E-cadherin [epithelial]), NEUROG1 (neurogenin 1), OPCML (opioid-binding protein/cell adhesion molecule-like), SCGB3A1 (Secretoglobin, family 3A, member 1), TERT (telomerase reverse transcriptase), WT1 (Wilms tumor 1), THBS1 (Thrombospondin 1), and VEGFA (vascular endothelial growth factor A) showed statistically significant differential methylation between Grade 2 tumor and normal tissue [Figure 3]a. OPCML, NEUROG1, TERT, and WT1 were found HM and SCGB3A1, CDH1, THBS1 and VEGFA were hypomethylated in Grade 2 tumor [Figure 3]a. The mean percentage of methylation was significantly highest for OPCML (P = 0.03) as compared to the normal samples. Although marginally significant (P = 0.05), NEUROG1 had highest fold hypermethylation followed by OPCML [Figure 3]b. Likewise, maximum-fold hypomethylation was found for VEGFA followed by THBS1 [Figure 3]b. In view of the above outcomes, OPCML gene was selected for evaluation of the effect of promoter methylation on gene expression along with WT1 and CDH1 genes.
Figure 1: Hierarchical clustering with a Pearson correlation used for similarity measurement of differential methylation data of 94 tumor suppressor genes. Genes with similar methylation levels are grouped together (clusters). Green, Black, and Red color represent 0%, 50%, and 100% methylation of promoters of genes

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Figure 2: Bar diagram showing differential hypermethylation of all tumor suppressor genes present on the polymerase chain reaction-array. The X-axis represents the mean of percentage of hypermethylation and Y-axis represents genes. Blue bars denote normal and orange color bars represent tumor tissues

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Figure 3: Statistically significant differential promoter methylation of tumor suppressor genes. (a) Bar diagram showing statistically significant (P ≤ 0.05) differentially methylated genes, that is, WT1 (Wilms tumor 1), TERT (telomerase reverse transcriptase), NEUROG1 (neurogenin 1), OPCML (opioid binding protein/cell adhesion molecule-like), SCGB3A1 (Secretoglobin, family 3A, member 1), CDH1 (Cadherin 1, type 1, E-cadherin [epithelial]), THBS1 (thrombospondin 1), and VEGFA (vascular endothelial growth factor a) between tumor and adjacent normal tissue. Blue bars represent normal and orange bars signify tumor tissues. The X-axis denotes the name of genes and Y-axis represents the mean of percentage of hypermethylation (error bars showing standard deviation of mean). (b) Bar diagram showing fold change in methylation. Orange-colored bars are showing fold upregulation in methylation and blue bars are displaying fold down-regulation

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Expression of OPCML in control and esophageal squamous cell carcinoma

Differential protein expression profiling of OPCML was done by TMA-based immunohistochemistry in different grades of ESCC and nonneoplastic control tissue samples. A total of 20 controls and 75 squamous carcinoma tissue samples were used. However, some cores were not found suitable for interpretation of the results after staining. Weak immunoexpression score was found in 13, whereas 41 tumor samples displayed strong immunoexpression score [Figure 4]a and [Figure 4]b. In case of controls, a total of 6 and 14 samples exhibited weak and strong expression, respectively. None of the tumor and control tissue samples showed nonreactivity to OPCML antibody. Frequencies of strong and weak expression score were not found statistically different between tumor and control samples [Figure 5]a.
Figure 4: Tissue microarray-based immunohistochemistry for OPCML. (a) A representative section of tissue microarray slide is showing array of esophageal squamous cell carcinoma tissues with cytoplasmic expression of OPCML. (b) Selected cores of tissue microarray are showing weak and strong cytoplasmic expression in esophageal squamous cell carcinoma. Green color arrow indicates weak expression in Grade 3 tumor, whereas red arrow designates Grade 2 tumor with strong expression. Zoom image of selected region from the core (inset)

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Figure 5: Immunoexpression intensity scores of OPCML in different grades of esophageal squamous cell carcinoma. (a) Line diagram shows frequencies of weak and strong immunoexpression scores of OPCML in nonneoplastic control and esophageal squamous cell carcinoma tissues. (b) Line diagram displays frequencies of weak and strong expression scores of OPCML in nonneoplastic control and different grades of esophageal squamous cell carcinoma tissues. (c) Bar diagram is displaying differential frequencies of weak and strong immunoexpression scores of OPCML in nonneoplastic control and different grades of esophageal squamous cell carcinoma tissues with respective statistical significance. Well-differentiated esophageal squamous cell carcinoma (Grade 1), moderately differentiated esophageal squamous cell carcinoma (Grade 2), and poorly differentiated esophageal squamous cell carcinoma (Grade 3). Blue line/bar represents weak immunoexpression (score 1) and orange line/bar denotes strong immunoexpression (score 2)

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Expression of OPCML in different grades of esophageal squamous cell carcinoma

Frequency of nonreactive, weak, and strong immunoexpression scores was also calculated in different grades of ESCC. In Grade 2 tumor, the frequency of strong immunoexpression score (0.89; 31/35) was significantly (P = 0.003) higher than Grade 3 (0.40; 4/10) [Figure 5]b and [Figure 5]c. Likewise, Grade 1 tumor also had higher frequency of strong expression score (0.67; 6/9) compared to Grade 3 (0.40; 4/10) nevertheless the difference was not statistically significant (P value; 0.36) [Figure 5]b and [Figure 5]c. Nonetheless, control and Grade 1 tumor did not unveil any alteration in frequencies of expression score [Figure 5]b and [Figure 5]c. Statistically significant OPCML promoter hypermethylation found in Grade 2 tumor could not direct its effect on gene expression as the frequency of strong immunoexpression score was not altered between the two. Although protein expression was marginally increased as we moved from Grade 1 to Grade 2 tumor, it further sharply decreased in Grade 3 ESCC.

Expression of WT1 and CDH1

Weak expression score of WT1 was found in 2 out of 6 Grade 2 tumor tissues and rest 4 showed nonreactivity [Figure 6]a. While all control tissues showed strong immunoexpression for WT1. In case of CDH1, all 6 Grade 2 ESCC samples unveiled strong immunoreactivity with all control tissues displaying weak expression [Figure 6]b.
Figure 6: Immunoexpression of WT1 and CDH1 (E-cadherin) in esophageal squamous cell carcinoma. (a) Esophageal squamous cell carcinoma with no WT1 expression marked by green arrows. Red color arrows indicate WT1 expression in the endothelial cells of blood vessels. (b) Esophageal squamous cell carcinoma tissue is showing CDH1 expression marked by red arrows, whereas epithelial cells having no immunoreactivity against CDH1 antibody as indicated by green arrows

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Protein–protein interaction analysis

Network generated revealed suggestively more interactions than expected, which meant that proteins had more interactions among themselves than expected from a random set of proteins of similar size, drawn from the genome. Analysis indicates that proteins are at least partially biologically connected as a group (PPI enrichment P = 5.93e-07). Among Set 1 genes (8 TSGs), the significant interactions were VEGFA:THBS1 (Score; 0.974) and WT1:TERT (Score; 0.944) [Figure 7]. However, more number of interactions were found between Set 1 genes (8 TSGs identified in the present investigation) and Set 2 genes (23 genes identified previously) as VEGFA:PTK2 (Score; 0.954), CTNNB1:CDH1 (Score; 0.999), CTNNB1:VEGFA (Score; 0.912), CTNNB1:NEUROG1 (Score; 0.931), CTNND2:CDH1 (Score; 0.937), and CTNNB1:TERT (Score; 0.975) [Figure 7]. Conversely, no significant protein–protein interaction was found among the Set 2 genes reported previously in a study of integration of epigenomic and transcriptomic data.[4]
Figure 7: Protein–protein interaction network generated by STRING version 10.0 online software. Two gene sets were used, that is, 8 tumor suppressor genes from the present study and 23 genes identified in a previous study of integration of epigenomic and transcriptomic data for esophageal squamous cell carcinoma. Figure shows the known, predicted, and other type of interactions. Network nodes represent proteins where splice isoforms or posttranslational modifications are collapsed, that is, each node represents all the proteins produced by a single, protein-coding gene locus. Edges represent protein–protein associations which are meant to be specific and meaningful, that is, proteins jointly contribute to a shared function. However, this does not necessarily mean they are physically binding each other. Small nodes are representing proteins of unknown three-dimensional structure while large nodes denote proteins with some three-dimensional structure is known or predicted

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Pathway analysis

Further gene enrichment analysis by STRING revealed significant biological processes (GO), that is, cellular response to nitrogen compound (GO: 1901699), cellular responses to organic cyclic compound (GO: 0071407), and regulation of epithelial cell proliferation (GO: 0050678) [Table 1] other than those reported earlier by our group with 23 integrome network enriched genes.[4] Statistically significant KEGG pathways identified were Focal adhesion (ID; 04510), Bacterial invasion of epithelial cells (ID; 05100), Rap1 signaling pathway (ID; 04015), Proteoglycans in cancer (ID; 05205), Hippo signaling pathway (ID; 04390), and Pathways in cancer (ID; 05200) [Table 1]. Nonetheless, previously, no KEGG pathway was found statistically significant with 23 genes. Each of the biological processes and KEGG pathways mentioned above had participant genes from both the groups, that is, 8 TSGs from the present study and 23 network enriched genes reported previously.
Table 1: Gene enrichment analysis done by STRING version 10.0 software with two gene sets, that is, 8 tumor suppressor genes from the present study (Set 1) and 23 network enriched genes (Set 2) identified in a previous study of integration of epigenomic and transcriptomic data for esophageal squamous cell carcinoma in the same Northeast population. Table represents significant biological processes and KEGG pathways

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 > Discussion Top

An investigation of methylation status of 94 TSGs was done by PCR-array for ESCC. OPCML, NEUROG1, TERT, and WT1 genes were HM and SCGB3A1, CDH1, THBS1, and VEGFA were hypomethylated in Grade 2 tumor tissue. Among the genes analyzed, highest magnitude of promoter methylation was found in OPCML, which is a member of the IgLON family (OPCML, LSAMP, NEGR1, and HNT) of immunoglobulin (Ig) domain-containing glycosylphosphatidylinositol-anchored cell adhesion molecules.[15],[16] Ample reports in epithelial ovarian cancers propose that the gene is inactivated by loss of heterozygosity and epigenetic silencing. Its expression inhibits ovarian cancer cell growth, enhances intercellular attachment, and abrogates both subcutaneous and intraperitoneal tumorigenicity.[16] The negative regulation of a specific spectrum of receptor tyrosine kinases (EPHA2, FGFR1, FGFR3, HER2, and HER4 receptors) is also reported to be mediated by OPCML through binding to their extracellular domains in ovarian cancer cells. In addition, it also alters trafficking through nonclathrin-dependent endocytosis and promotes their degradation through a polyubiquitination-associated proteasomal mechanism leading to signaling and growth inhibition.[16] Likewise, exogenous recombinant OPCML protein is found to inhibit the growth of ovarian cancer cells in murine ovarian cancer intraperitoneal models.[16] Frequent CpG island methylation of OPCML is also reported in epithelial ovarian cancer.[17] A Chinese population study has revealed reduced OPCML expression due to promoter methylation in 66% of esophageal samples.[15] Similarly, Anglim et al. have also reported a highly significant hypermethylation of OPCML in tumor tissue compared to adjacent nontumor tissue in squamous cell lung cancer.[18]

In the present study, hypermethylation of OPCML promoter in Grade 2 tumor had no effect on the gene expression as no significant differential OPCML protein expression was found between tumor and control tissues. The reason could be usage of multiple promoter DNA sequences in OPCML transcription, which have also been reported hitherto.[15] Possibly, promoter DNA sequence targeted in the present methylation study is not involved in active transcription of gene in esophageal tissue as also reported by Cui et al.[15] Furthermore, alternative mRNA splicing is being reported as a feature of OPCML and other IgLONs.[19],[20] Reed et al. have identified two alternative splice transcripts of OPCML, variant 1 (v1) (NM_002545) and variant 2 (v2) (NM_001012393) differing only in their 5' exons but encoding an identical mature protein, thus advocating that transcription of OPCML may take place from an alternative promoter in brain tumor.[19] However, Cui et al. have identified another alternatively spliced variant v3, which is found widely expressed in adult tissues including esophageal tissue.[15] The authors have reported OPCML-v1 promoter methylation in 66% of esophageal tissue compared to corresponding normal tissue. Although the same variant 1 (v1) (NM_002545) region of OPCML was found HM in Grade 2 tumor, a slight upregulation in protein expression was detected in the present investigation. The reason could probably be the novel isoforms of OPCML (v3, v4, v5, v6), derived from alternative splicing or promoter usages as reported earlier.[15] Cui et al. also found the expression of OPCML in several tumor cell lines (Hep3B, H292, SW480, L1236), where the OPCML-v1 and v2 were found totally silenced.[15] The authors finally concluded that transcription of OPCML generally occurs from alternative unknown promoters. The aforesaid conclusions are in concordance to our findings. Nevertheless, unstable OPCML immunoexpression level has been found among different grades of ESCC, thus paying impetus to the fact that OPCML plays an important role in esophageal carcinogenesis. The involvement of other epigenetic processes, that is, histone modifications and miRNAs-mediated gene regulation resulting in differential expression of OPCML during esophageal carcinogenesis cannot be ignored and warrants further analysis.

Recent evidence from in vitro experiments and clinical observations suggest that CDH1 acts as an invasion-suppressor gene for esophageal cancer.[21],[22] Likewise, Chinese studies recommend CDH1 gene silencing by promoter hypermethylation and the subsequent reduction of CDH1 protein expression, which is involved in the progression of ESCC posing it to be a significant predictor of survival in ESCC patients after surgery.[21],[23] CDH1 and integrin alpha4 CpG island hypermethylation are testified to be related with a high threat of recurrence and a poor recurrence-free survival after surgery in Stage I and Stage II ESCC, respectively.[24] CDH1 methylation status has been studied in invasive squamous carcinomas, low-to-high-grade dysplasia and in normal esophagus, which suggests its role in progression in ESCC.[25] CDH1 methylation has also reported to be rarely found in Barrett's esophagus and associated adenocarcinoma, although it has been found most commonly methylated in normal stomach.[26] Contrarily, another report states that CDH1 is inactivated by CpG island hypermethylation in esophageal adenocarcinoma.[27] CDH1 promoter hypomethylation succeeded by its upregulated gene expression in ESCC was the key annotations of the current study. Contradictory reports of CDH1 methylation in ESCC implies to the need of further exploration in a large number of samples.

WT1 is another protein involved in the regulation of human cell growth and differentiation, and it also acts as a modulator of oncogenic KRas signaling in lung cancer.[28] Methylation status of WT1 has been examined by researchers in ductal carcinoma in situ, invasive breast cancer, and nonsmall cell lung cancer.[28],[29] Combined array-comparative genomic hybridization data with matched gene expression microarray data exposes WT1 gene with high copy number-expression correlations along with validation by immunohistochemistry in a study of esophageal adenocarcinoma.[30] In the literature, there is no report available for WT1 promoter methylation in ESCC, although it is reported as overexpressed in esophageal squamous dysplasia and ESCC.[31] Contrarily, the present study reports WT1 promoter hypermethylation and downregulation in protein expression in ESCC, which warrants further investigation with large cohort of samples.

Although methylation analysis was done in less number of samples, the effect of OPCML hypermethylation was further validated by TMA-based immunohistochemistry in a large cohort of samples. OPCML expression was slightly increased in Grade 2 and significantly decreased in Grade 3 tumor compared to Grade 1 and control groups. Thus, it was concluded that downregulation of OPCML expression may lead to higher grade of tumor formation at time of diagnosis in the studied ESCC patients. Evaluation of the effect of methylation on gene expression for WT1 and CDH1 was done only in limited number of samples and needs to be further explored to reach a definite conclusion. Nevertheless, it shows the effect of promoter methylation on gene expression.

Combined gene enrichment analysis revealed significant biological processes comprising of participant genes from both gene sets. Gene sets comprised 8 TSGs from the present study (Set 1) and 23 genes (Set 2) from genome-wide methylation and expression data integration network reported previously by our group in the same high-risk Northeast Indian population for ESCC.[4] Gene enrichment analysis exposed different biological processes (GO), such as cellular response to nitrogen compound (GO:1901699) and cellular responses to organic cyclic compound (GO:0071407). The processes are authoritative to the betel quid chewing habit (with or without tobacco) in Northeast Indian population, which comprise of compounds with nitrogen atoms as well as organic cyclic compounds. Principally, betel quid contains betel leaf (Piper betel), areca nut; the main psychoactive ingredient and slaked lime (calcium hydroxide).[32] In India, tobacco and Catechu (called Kattha in local Hindi language) are also added to the quid. Areca nut has Arecoline, a nicotinic acid-based alkaloid that contains basic nitrogen atoms. Catechu is an extract of acacia trees used as a food additive, astringent and dye, and chemically contains tannins and flavonoids. Tannins are polyphenolic biomolecule and flavonoids having two phenyl rings and a heterocyclic ring. Hence, betel quid containing these nitrogenous and organic cyclic compounds on consumption may evoke the above mentioned biological processes in esophageal tissue. CDH1, WT1, TERT, THBS1, PTK2, COL1A1, CTNNB1, and NPC1 genes participating in abovementioned biological processes are vital for the management of ESCC.

Genes showing noteworthy protein–protein interactions, that is, VEGFA, CDH1 (E-cadherin), WT1, NEUROG1, TERT, PTK2, CTNNB1 (β-Catenin), and CTNND2 (δ-Catenin) along with OPCML are essential for better understanding of molecular pathology of ESCC. E-cadherin is involved in the maintenance and homeostasis of epithelial tissue structure in normal adult epithelial tissue by primarily forming adherens junctions. E-cadherin-mediated adhesion is a dynamic process regulated by several signal transduction pathways. Cadherins are targets for signaling pathways that regulate adhesion; however, cadherins are also suggested to send signals for the regulation of basic cellular processes, such as migration, proliferation, apoptosis, and cell differentiation.[33] Epithelial–mesenchymal transition (EMT) witnessed in malignant tumors of epithelial origin is a process similar to developmental events but is uncontrolled in nature. The deprived intercellular adhesions, loss of the differentiated epithelial morphology, and augmented cellular motility are the characteristics of malignant carcinoma cells having downregulation or a complete shutdown of E-cadherin expression, mutation in CDH1 gene, or other mechanisms that interfere with the integrity of the adherens junctions.[33] In clinical biopsies, loss of E-cadherin-mediated cell adhesion connects with the deprivation of the epithelial morphology and with the gain of metastatic potential by the carcinoma cells.[34] In our immunohistochemistry experiments of ESCC tissues, E-cadherin expression has not been detected in epithelial cells, which is in agreement with the above statements regarding carcinoma cells. WT1 gene encodes a zinc-finger transcription factor, which functions as a tumor suppressor for nephroblastoma, but in some perspectives, it has also functioned as an oncogene.[35] Genome-wide screening for direct WT1 targets propose that it directly binds to and regulate nine genes involved in the Wnt signaling pathway.[35] SLUG and SNAIL transcription factors regulate EMT by repressing the intercellular adhesion molecule CDH1 (E-cadherin).[36] Whereas, WT1 is proposed to directly regulate these transcription factors and E-cadherin in epicardial cells. Consequently, WT1 loss of function putatively causes upregulation of E-cadherin, thereby inhibiting epicardial EMT.[37] WT1 promotes EMT in epicardium epithelial cells, whereas it promotes mesenchymal to epithelial transition in mesenchymal cells, such as kidney metanephric tissue and Wilm's tumor cells.[38] Epicardium, a mesothelial sheet of cells, covers the surface of the heart and participates in heart development by forming mesenchymal cells through EMT.[35],[38] Collectively, WT1 is required for EMT, acting upstream of canonical Wnt, noncanonical Wnt, and retinoic acid signaling pathways.[38]

Wnt signaling leads to alternative cell fates such as proliferation or differentiation by β-catenin-mediated recruitment of several histone remodeling complexes such as BRG1, CBP/p300, TRAAP, Mll1/Mll2, and SWR1, as well as proteins associated with initiation and elongation by RNA polymerase II, such as MED12, hyrax, Pygopus, and CDK8.[39] Some of these cofactors may be context-specific to explain how Wnt signaling leads to alternative cell fates, such as proliferation or differentiation. Telomerase has been represented as one such cofactor that possibly acts in a progenitor cell context to facilitate a Wnt-regulated program of self-renewal, proliferation, or survival. Telomerase modulates Wnt/β-catenin signaling by serving as a cofactor in a β-catenin transcriptional complex. Telomerase protein component TERT interacts with BRG1, a SWI/SNF-related chromatin remodeling protein and activates Wnt/β-catenin target genes by physically occupying their promoters.[39] δ-Catenin (CTNND2) is reported to be commonly overexpressed in prostate, esophageal, breast, lung, and ovarian cancers and promotes canonical Wnt/β-catenin/LEF-1-mediated transcription.[40] Gao et al. reported that FAK (PTK2) and PYK2 (PTK2B) elevated in adenomas in APCmin/+ mice and in human colorectal cancer tissues, function redundantly to promote the Wnt/β-catenin pathway by phosphorylating GSK3 βY216 to underpin pathway output-β-catenin accumulation and intestinal carcinogenesis.[41] Contrastingly, PTK2 was found downregulated in our previous report in ESCC. Instead, OPCML promoter methylation is reported by Li et al. in human colorectal cancer.[42] Moreover, the increased OPCML expression overturns EMT transition and inhibits cell growth, migration, and invasiveness. The authors suggest that these activities are mediated through the inactivation of TGF-β-Smad signaling pathway.[42] OPCML expression has been found decreased in high-grade tumor and TGFB1 and SMAD4 genes have shown hypomethylation in tumor tissue in the present investigation [Figure 2] that probably enhances EMT transition and permits added cellular growth, migration, and invasive properties in high-grade ESCC.

Decisively, the above reports advocate that WT1 promotes EMT by acting upstream of Wnt/β-catenin pathway and TERT regulates target genes of Wnt pathway by participating at the endpoint of pathway. In the present investigation, WT1 and E-cadherin are found deregulated by promoter methylation in ESCC, with β-Catenin and δ-Catenin (CTNND2) upregulated as previously reported. Equally, TERT too is HM in ESCC in the present investigation. In addition, deranged OPCML expression found in the present investigation is in accordance with the previous report stating it to regulate EMT through TGF-β signaling pathway.[42] Suppression of TGF-β signaling by the aberrant methylation of THBS1 in colorectal cancer is also exposed by Rojas et al.[43] Moreover, Cheruku et al. also reviewed the interaction between Wnt and TGF-β signaling pathways, which together regulate genes that are involved in the EMT in colorectal cancer.[44] Therefore, investigation of deregulated status of OPCML, THBS1, WT1, E-cadherin, TERT, β-Catenin, and δ-Catenin proteins in cell culture and xenograft mouse model-based systems with respect to EMT mediated by Wnt/β-catenin and TGF-β signaling pathways may be useful for understanding the molecular pathology and for finding new drug targets for ESCC. The graphical summary of the investigation is represented in [Figure 8]. Concisely, deregulation of Wnt/β-catenin and TGF-β signaling pathways mediated by epigenetic alterations in CDH1, WT1, THBS1, TERT, PTK2, CTNNB1, and CTNND2 genes and distorted OPCML protein expression status are the major outcomes of the present investigation.
Figure 8: Summary and workflow of the study: Out of 104 histopathologically confirmed cases of esophageal cancer, 90 esophageal squamous cell carcinoma patients were selected for investigation. Tumor suppressor genes methylation profiling was done in 6 paired Grade 2 tumor and their adjacent normal tissues. Peak hypermethylation was found in OPCML, hence its expression was examined by tissue microarray-based immunohistochemistry. No significant change in OPCML expression among control, Grade 1, and Grade 2 tumor was found. Therefore, no effect on gene expression is manifested by hypermethylated promoter in question. However, protein expression of OPCML was found sharply decreased in Grade 3 compared to Grade 2, Grade 1 tumor and control tissues suggesting its association with early carcinogenesis episode (Differential OPCML expression represented in the figure is not scale based). Twenty three genes identified in Integrome network analysis in a previous genome-wide methylation study from the same population along with eight tumor suppressor genes from the present investigation were subjected to protein–protein interaction analysis by STRING. Gene enrichment revealed significant Biological Processes (GO) as cellular response to nitrogen compound (GO: 1901699) and cellular responses to organic cyclic compound (GO: 0071407). Deregulation of Wnt/β-catenin and TGF-β signaling pathways is revealed in the present study, which mediates by epigenetic alterations in CDH1, WT1, THBS1, TERT, PTK2, CTNNB1, and CTNND2 genes and also by distorted OPCML protein expression

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Financial support and sponsorship

The authors sincerely thank Department of Biotechnology (Ministry of Science and Technology) New Delhi, India, for providing financial support. Virendra Singh acknowledges University Grants Commission (UGC), New Delhi, India, for providing Dr. D. S. Kothari postdoctoral fellowship.

Conflicts of interest

There are no conflicts of interest.

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  [Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6], [Figure 7], [Figure 8]

  [Table 1]

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