A DNA Methylation-based Test for Esophageal Cancer Detection

A DNA Methylation-based Test for Esophageal Cancer Detection | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research A DNA Methylation-based Test for Esophageal Cancer Detection Sofia Salta, Catarina Macedo-Silva, Vera Miranda-Gonçalves, Nair Lopes, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-44848/v3 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Nov, 2020 Read the published version in Biomarker Research → Version 3 posted 7 You are reading this latest preprint version Show more versions Abstract Background: Esophageal cancer (ECa) is the 7 th most incident cancer and the 6 th leading cause of cancer-related death. Most patients are diagnosed with locally advanced or metastatic disease, enduring poor survival. Biomarkers enabling early cancer detection may improve patient management, treatment effectiveness, and survival, are urgently needed. In this context, epigenetic-based biomarkers such as DNA methylation are potential candidates. Methods: Herein, we sought to identify and validate DNA methylation-based biomarkers for early detection and prediction of response to therapy in ECa patients. Promoter methylation levels were assessed in a series of treatment-naïve ECa, post-neoadjuvant treatment ECa, and normal esophagus tissues, using quantitative methylation-specific PCR for COL14A1 , GPX3, and ZNF569 . Results: ZNF569 methylation ( ZNF569me) levels significantly differed between ECa and normal samples ( p <0.001). Moreover, COL14A1 methylation ( COL14A1me) and GPX3 methylation ( GPX3me) levels discriminated adenocarcinomas and squamous cell carcinomas, respectively, from normal samples ( p =0.002 and p =0.009, respectively). COL14A1me & ZNF569me accurately identified adenocarcinomas (82.29%) whereas GPX3me & ZNF569me identified squamous cell carcinomas with 81.73% accuracy. Furthermore, ZNF569me and GPX3me levels significantly differed between normal and pre-treated ECa. Conclusion: The biomarker potential of a specific panel of methylated genes for ECa was confirmed. These might prove useful for early detection and might allow for the identification of minimal residual disease after adjuvant therapy. Cancer Biology Oncology Esophageal Cancer DNA methylation early detection treatment response Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Esophageal cancer (ECa) is the 7 th most common malignancy and the 6 th cause of cancer-related mortality worldwide (1). ECa comprises two main histological subtypes: adenocarcinoma and squamous cell carcinoma, the most prevalent type (2). Although therapeutic improvements have increased ECa survival rates (3), curative-intent treatment options remain limited to surgery, chemotherapy (ChT), and radiotherapy (RT), either alone or in trimodal therapy (4). Furthermore, most tumors are diagnosed at an advanced stage, entailing very low 5-year survival rates, ranging from 5-20% (5, 6). Intensive research was, thus, carried out to find alternative treatment strategies to prolong life expectancy and quality of life (QoL) for ECa patients. Currently, neoadjuvant treatment with ChT and/or RT is the standard of care for patients with locally advanced disease (2). Nevertheless, surgery is often associated with decreased QoL due to complications and comorbidities (7, 8). Epigenetic alterations, including their key players, have emerged as promising biomarkers for ECa. Among those, gene methylation is the most extensively studied epigenetic modification. Although esophageal adenocarcinomas (EA) and esophageal squamous cell carcinomas (ESCC) display different morphological and molecular features, several studies have shown that methylation is an early event in both histotypes, already present in premalignant lesions (9-12). Thus, promoter methylation of specific genes might be used to discriminate normal from cancerous esophageal cells, enabling the detection of disease at early stages, increasing the likelihood of curative treatment. Except for CDKN2A and APC , which are commonly aberrant methylated genes in both EA and ESCC, a specific methylome has been reported for each histotype (13). The importance of these alterations is further highlighted by the numerous reports correlating aberrant DNA methylation with ECa patient prognosis (14, 15). Methods Based on the literature evidence, we aimed to identify and validate methylome alterations that might constitute biomarkers for early detection, as well as identification of minimal residual disease after neoadjuvant treatment, eventually precluding the need for esophagectomy. Hence, the studies selected from the literature clearly reported sensitivity and specificity for the detection of ECa or Barrett’s Esophagus using DNA methylation-based biomarkers (Additional Table 1). Studies with a small cohort of patients [n≤40 for each group (cases and controls)] were also excluded. Furthermore, only genes with a specificity higher than 98% were selected for testing. Of the remaining 5 genes, no specific primers were obtained to test ZNF345 and EPB41L3 . Hence, promoter methylation levels of three genes selected (16, 17) - GPX3 ( glutathione peroxidase 3 ), COL14A1 ( collagen type XIV alpha 1 chain ), and ZNF569 ( zinc finger protein 569 ) - were tested in a series of ECa and normal esophageal tissues. Patients and samples collection A total of 124 formalin-fixed paraffin-embedded (FFPE) tissues samples from patients diagnosed with ECa between 2007 and 2017 at the Portuguese Oncology Institute of Porto (IPO-Porto) were included in this study (Table 1). Among the tumor samples, 88 were collected before any treatment previous surgery, and 36 were collected after neoadjuvant treatment (ChT and/or RT). Additionally, 56 FFPE tissues samples of normal esophagus from patients diagnosed with gastric carcinoma and without evidence of esophageal cancer were used as control. All samples were archived at the Department of Pathology of IPO-Porto. All the cases were revised by an experienced pathologist and classified according to the World Health Organization (WHO) classification of Tumors of the Digestive System (4 th edition) and staged according to the 7 th edition American Joint Committee on Cancer (AJCC) system (18, 19). Relevant clinical data were collected from medical charts. For DNA extraction, a 4 μm section was cut from a representative tissue block and stained with hematoxylin-eosin. Tumor areas were delimited, enabling macrodissection in eight consecutive 8μm sections. This study was approved by the institutional ethics committee of IPO Porto (CES 202/017). Promoter methylation evaluation DNA extraction from FFPE sections was performed using FFPE RNA/DNA Purification Plus Kit (Norgen Biotek, Thorold, Canada) following the manufacturer’s instructions. DNA concentrations and purity ratios were determined using the NanoDrop Lite spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA) and modified with sodium bisulfite, using the EZ DNA Methylation-Gold™ Kit (Zymo Research, Orange, CA, USA) according to manufacturer’s instructions. For quantitative methylation-specific PCR (QMSP), modified DNA was used as template. Primers to specifically amplify methylated bisulfite converted complementary sequences were used and are listed in Additional Table 2. QMSP reactions were carried out in LightCyler 480 II (Roche, Germany) using 2µL of modified DNA and 5 µL Xpert Fast SYBR (2X) (GRiSP, Porto, Portugal). All samples were run in triplicate and melting curves were obtained for each case by gene. β- actin ( ACTβ ) was used to normalize for DNA input in each sample (20). To ascertain PCR efficiency, and samples’ quantification, modified CpGenome™ Universal Methylated DNA (Merck Millipore, France) was used in each plate to generate a standard curve. The relative methylation for each gene was calculated by the ratio of mean quantity for the target gene and the mean quantity of ACTβ , multiplied by 1000 for easier tabulation. Statistical analysis All the comparisons were performed using non-parametric tests. Specifically, the Kruskal-Wallis test was used for comparisons among three or more groups, whereas the Mann-Whitney U test was used in comparisons between two groups. To assess biomarker performance, Receiver Operator Characteristic (ROC) curves were constructed for each gene and the Area Under the Curve (AUC) was calculated. The highest value obtained by the ROC curve analysis [sensitivity + (1-specificity)] was established as cut-off to categorize samples as methylated or unmethylated, according to Youden’s J index method (21, 22). Furthermore, specificity, sensitivity, accuracy, positive likelihood ratio (+LH), and negative likelihood ratio (-LH) were determined. For combination of biomarkers, cases were considered positive if at least one of the individual biomarkers was positive. Spearman non-parametric correlation was used to assed the correlation methylation levels and age. Disease-specific and disease-free survival curves (Kaplan-Meier with log-rank test) were built for standard clinicopathological variables and categorized methylation status. Disease-specific survival curves and disease-free survival curves (Kaplan–Meier with log-rank test) were computed for standard clinicopathological variables and categorized methylation status. Two-tailed P-values were derived from statistical tests, using a computer-assisted program (SPSS Version 25.0, Chicago, IL), and results were considered statistically significant at p < 0.05, with Bonferroni´s correction for multiple tests, when applicable (* p<0.05; ** p<0.01; *** p<0.001; **** p<0.0001; ns – non significant). Graphics were assembled using GraphPad 6 Prism (GraphPad Software, USA). Results Clinical and pathological data The most relevant clinical and pathological data are depicted in Table 1. In addition to normal esophageal tissues, ECa cases were segregated into treatment-naïve (samples collected before any treatment) and post-neoadjuvant treatment groups. No significant differences were disclosed concerning age among the three groups of samples ( p =0.06). Gene promoter methylation levels in naïve ECa tumors vs. normal esophagus samples . To assess performance for ECa detection, COL14A1 methylation ( COL14A1me), GPX3 methylation ( GPX3me), and ZNF569 methylation ( ZNF569me ) levels in naïve tumors (n=88) were compared to normal esophagus samples (n=56). ZNF569 me levels significantly differed between cancerous and normal samples ( p <0.001, Figure 1c), whereas no significant differences were found for COL14A1 me and GPX3 me levels ( p =0.382 and p =0.094, respectively, Figure 1a, b). Using ROC curve analysis (Figure 2), a cut-off of 55.17 was set for ZNF569 me to assess biomarker performance. Thus, over 90% specificity and 69.3% sensitivity (Table 2) was disclosed, corresponding to an AUC of 0.8467 (Figure 2). Association between promoter methylation levels and standard clinicopathologic features COL14A1 me, GPX3 me, and ZNF569 me levels were tested for associations with standard clinicopathological features in naïve ECa patients. All genes disclosed significant differences in promoter methylation levels according to histological subtype (Figure 3). ESCC displayed higher COL14A1 me and GPX3 me levels than EA ( p =0.001 and p =0.024, respectively), whereas EA displayed higher ZNF569 me levels ( p =0.020). Additionally, COL14A1 me levels were significantly higher in pT1 tumors compared to pT3 ( p =0.006) (Additional Figure 1). However, no significant differences on methylation levels were found among different N stages (Additional Figure 2). Biomarker performance according to histological subtype Because methylation levels differed between histological subtypes, samples were stratified according to this parameter for assessing histotype-specific biomarker performance. Concerning EA, COL14A1 me and ZNF569 me levels significantly differed from normal samples ( p= 0.002 and p <0.001, respectively). COL14A1 me and ZNF569 me levels identified EA with an AUC of 0.68 and 0.91, respectively (Figure 4a), whereas the combination of both genes disclosed a sensitivity above 97% and 82.29 accuracy (Table 3). Furthermore, GPX3 me and ZNF569 me levels differed significantly between ESCC and normal samples ( p= 0.009 and p <0.001, respectively), individually discriminating this tumor type from controls with AUC of 0.65 and 0.79 (Figure 4b), respectively. Accuracy of detection improved to 81.73 when the two genes were combined in a single panel (Table 3). Assessment of promoter methylation in post-treatment samples GPX3 me and ZNF569 me levels were significantly higher in residual EA samples after neoadjuvant treatment compared to normal samples ( p <0.001, for both). Nonetheless, COL14A1 me levels in tumors after neoadjuvant treatment did not differ significantly from normal samples ( p= 0.493) (Figure 5). Concerning ESCC, only GPX3 me levels remained significantly different between post-treatment tumor and normal samples ( p= 0.001). Survival analysis Survival analysis was carried out in the treatment-naïve ECa cohort. For analysis, a maximum of 5 years follow-up was considered. During this period, 39 patients died from the disease (44.3%), three patients died with the disease (3.4%) and six patients died without evidence of cancer (6.8%). Among the remainder patients, 39 were alive without evidence of disease (44.3%) and one patient was alive with disease (1.2%). No associations were depicted between COL14A1 me , GPX3 me, and ZNF569 me levels and disease-specific or disease-free survival, whereas the pT stage, pN stage, and stage associated with both disease-specific and disease-free survival ( p =0.010, p =0.002, p = 0.002 for disease-specific survival and p =0.012, p =0.001, p =0.025 for disease-free survival). Discussion ECa remains a leading cause of cancer-related mortality globally (1) and in Portugal (23). Most patients are diagnosed with locally advanced or metastatic disease, entailing poor 5-years survival rate (about 25% and 5%, respectively) (24). Thus, new strategies for early detection of this malignancy are urgently needed. In this context, epigenetic alterations such as DNA methylation have emerged as promising biomarkers in several cancers, including ECa (25, 26). Herein, we tested three gene promoters’ methylation as ECa DNA methylation-based biomarkers, following a literature review. We selected the genes ZNF569 , GPX3, and COL14A1 , all previously reported to harbor promoter methylation and suggested to have an oncossuppressive function . ZNF569 protein has been reported as a potential transcriptional repressor implicated in MAPK signaling pathway (27). COL14A1 encodes for the alpha chain of type XIV collagen which interacts with decorin associated with cell growth and survival (28). GPX3 is a glutathione peroxidase found to catalyze glutathione’s reduction of organic hydroperoxides and hydrogen peroxide and thereby protecting cells against oxidative damage (29, 30). COL14A1 aberrant methylation has been reported in ESCC (16), as well as in renal cell carcinoma, sarcomas, and endometrial carcinoma (28, 31, 32), whereas hypomethylation has been shown in coronary artery disease (33). GPX3 promoter methylation has been shown in ESCC and esophageal glandular lesions, including Barrett’s esophagus and EA (30, 34, 35). In the same vein, ZNF569 promoter hypermethylation has been associated with glandular lesions, like Barrett’s esophagus (17) in comparison with the normal esophagus. Interestingly, we showed that ZNF569me levels could discriminate between ECa and normal esophagus with high specificity, regardless of the histotype, extending those previous reports. Furthermore, ZNF569 me was shown to play a tumor-suppressive role in head and neck squamous cell carcinoma (36) and a DNA-methylation based panel, which included ZNF569me , discriminated gastric adenocarcinoma from normal mucosa (37). ESCC and EA displayed different cancer-specific methylation patterns. Accordingly, differential methylation patterns have been previously reported between ESCC and EA (38, 39). Indeed, in treatment-naïve tumors, the three selected genes disclosed different methylation levels among ESCC and EA, variably comparing to normal esophageal tissue samples. In particular, available data support the value of identification specific ESCC methylation panels to enable early detection (16, 40, 41). We found ZNF569 hypermethylated in both histological subtypes and, thus, this gene constitutes a promising biomarker for ECa detection, regardless of histological subtype. Moreover, we found COL14A1 promoter methylation levels slightly higher in ESCC when compared with normal esophagus samples, although not statistically significant. This can be partially explained by variations in the population (Asian vs. Caucasian), as previously attested for some genes (42), and the different nature of samples tested (plasma vs. FFPE). Notwithstanding, COL14A1 promoter methylation levels were significantly lower in EA compared to normal. To our knowledge, this is the first reported association between COL14A1 methylation levels and EA. Conversely, GPX3 promoter methylation levels did not differ between EA from normal tissues, although a few cases disclosed higher methylation levels (data not shown). Notwithstanding, different sample processing (fresh frozen tissues vs. FFPE) (30, 35), different methodologies to assess GPX3 methylation levels among studies (qMSP vs. MSP vs. methylation ligation-dependent macroarray vs. pyrosequencing), and the smaller size of some cohorts (30, 34) may explain some disparate results. Overall, we propose two different methylation-based panels, both with high accuracy to early detect ECa according to histological subtype. The ESCC-panel displayed higher specificity (87.5%), whereas the EA-panel disclosed higher sensitivity (97.5%). In fact, the performance of both methylation-based panels compares well with that of other studies (39, 43, 44). For EA detection, Moinova et al reported a two-gene methylation panel, comprising CCNA1 and VIM , with higher specificity (91.7%), similar to TAC1 hypermethylation reported by Jin et al (45, 46). Additionally, for ESCC Li et al . and Wang et al. reported panels with higher performance in Asian populations (16, 47). Currently, most ECa patients are treated with neoadjuvant therapy followed by surgery, if diagnosed with locally advanced disease (2). The randomized CROSS trial showed that surgery has a major impact on ECa patients’ QoL. Features such as fatigue and physical performance are decreased even in long-term survivors. Those effects are similar in patients undergoing neoadjuvant treatment or surgery only, emphasizing the impact of surgery in QoL (7, 8). Hence, biomarkers enabling the identification of patients complete response to neoadjuvant treatment (who might be spared surgery) and to early detect disease recurrence are needed (48) to improve QoL without risking the likelihood of cure. Thus, we evaluated methylation levels of candidate genes in 36 samples from non-complete responders after neoadjuvant treatment. In our series, ZNF569 me levels only significantly differed in EA comparatively to the normal esophagus, whereas GPX3 promoter methylation levels were significantly higher both in ESCC and EA than in the normal esophagus. Because GPX3me has been associated with ChT resistance (29), GPX3 me levels observed in EA after neoadjuvant treatment might be explained by selective pressure caused upon neoplastic cells, entailing adaptative alterations induced by treatment (49). Several studies have associated DNA methylation with ChT or RT resistance (50-53). However, most used samples before any treatment or in vitro studies with immortalized cell lines (52). Thus, a direct comparison between our results and previously reported data should be made with caution. Nonetheless, the lack of information on methylation status before treatment, the small size of the pre-treated patient cohort along with the retrospective nature of our series, and the limited access to normal esophagus samples are major limitations of this study. Importantly, our series comprised ESCC and EA samples in similar proportions, contrarily to most of the previous studies which evaluated methylation status. Moreover, this study reported the potential of DNA methylation-based biomarkers for patients’ monitoring after neoadjuvant treatment. Conclusions In conclusion, we identified two gene panels that might detect ESCC and EA with good accuracy, which might prove useful for early disease detection among high-risk populations, as well as to detect residual disease after neoadjuvant treatment. As future perspectives, we intend to validate these panels in liquid biopsies, using plasma samples as a minimally invasive approach, not only for ECa early detection and diagnosis but also to identify patients with residual disease after neoadjuvant treatment, which are the most likely to benefit from surgery. Abbreviations -LH – Negative likelihood ratio +LH – Positive likelihood ratio ACTβ – β-actin AJCC – American Joint Committee on Cancer AUC – Area Under the Curve ChT – Chemotherapy COL14A1 – Collagen Type XIV alpha 1 chain COL14A1me – COL14A1 methylation EA - Esophageal Adenocarcinomas ECa – Esophageal cancer ESCC – Esophageal Squamous Cell Carcinomas FFPE – Formalin-fixed paraffin-embedded GPX3 – Glutathione Peroxidase 3 GPX3me – GPX3 methylation IPO-Porto – Portuguese Oncology Institute of Porto QMSP – Quantitative methylation-specific PCR QoL – Quality of Life ROC – Receiver Operator Characteristic RT – Radiotherapy WHO – World Health Organization ZNF569 – Zinc finger protein 569 ZNF569me – ZMF569 methylation Declarations Ethics approval and consent to participate This study was approved by the institutional ethics committee of IPO Porto (CES 202/017). Consent for publication Not applicable Availability of data and materials All data generated or analyzed during this study are included in this published article and its supplementary information files. Competing interests The authors declare that they have no competing interests. Funding This work was supported by a grant from ESTIMA-NORTE-01-0145-740 FEDER-000027. SS was supported by a PhD fellowship IPO/ESTIMA-1 NORTE-01-0145- 740 FEDER-000027. VMG and NL received a post-doc fellowship from ESTIMA-NORTE-01-0145-740 FEDER-000027. CM-S was supported by a fellowship NORTE-01-0247-FEDER-033399. Authors’ Contributions SS prepared samples for molecular analyses, including DNA extraction, bisulfite modification, performed qMSP assays, analyzed data and drafted the manuscript. CM-S and VMG assisted in samples preparation (DNA extraction, bisulfite modification, and qMSP). NL contributed in draft manuscript. 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Clinical cancer research : an official journal of the American Association for Cancer Research. 2007;13(21):6293-300. Wang HQ, Yang CY, Wang SY, Wang T, Han JL, Wei K, et al. Cell-free plasma hypermethylated CASZ1, CDH13 and ING2 are promising biomarkers of esophageal cancer. J Biomed Res. 2018;32(5):424-33. Noordman BJ, Spaander MCW, Valkema R, Wijnhoven BPL, van Berge Henegouwen MI, Shapiro J, et al. Detection of residual disease after neoadjuvant chemoradiotherapy for oesophageal cancer (preSANO): a prospective multicentre, diagnostic cohort study. The Lancet Oncology. 2018;19(7):965-74. Ramon YCS, Sese M, Capdevila C, Aasen T, De Mattos-Arruda L, Diaz-Cano SJ, et al. Clinical implications of intratumor heterogeneity: challenges and opportunities. J Mol Med (Berl). 2020;98(2):161-77. Takahashi T, Yamahsita S, Matsuda Y, Kishino T, Nakajima T, Kushima R, et al. ZNF695 methylation predicts a response of esophageal squamous cell carcinoma to definitive chemoradiotherapy. J Cancer Res Clin Oncol. 2015;141(3):453-63. Hamilton JP, Sato F, Greenwald BD, Suntharalingam M, Krasna MJ, Edelman MJ, et al. Promoter methylation and response to chemotherapy and radiation in esophageal cancer. Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association. 2006;4(6):701-8. Kuo IY, Huang YL, Lin CY, Lin CH, Chang WL, Lai WW, et al. SOX17 overexpression sensitizes chemoradiation response in esophageal cancer by transcriptional down-regulation of DNA repair and damage response genes. J Biomed Sci. 2019;26(1):20. Kurimoto K, Hayashi M, Guerrero-Preston R, Koike M, Kanda M, Hirabayashi S, et al. PAX5 gene as a novel methylation marker that predicts both clinical outcome and cisplatin sensitivity in esophageal squamous cell carcinoma. Epigenetics : official journal of the DNA Methylation Society. 2017;12(10):865-74. Tables Table 1 Clinicopathological data of Normal Esophagus and Esophageal Tumor patient’s Clinicopathological Features Esophageal Tumor Samples Normal Esophagus Naïve Tumors Post ChT/RT a Treatment Tumors Patients (no.) 88 36 56 Age median (range) 63 (37-83) 60 (44-73) 66 (36-84) Sex (no.) Man 74 31 37 Woman 14 5 19 Histological subtype (no.) n.a. Adenocarcinoma 40 16 Squamous Cell Carcinoma 48 20 Localization (no.) n.a. Upper 3 - Middle 27 5 Lower 30 18 GEJ b 28 13 pT Stage/ypT Stage n.a. pT1/ypT1 16 5 pT2/ypT2 14 7 pT3/ypT3 56 23 pT4/ypT4 2 1 pN Stage/ ypN Stage n.a. pN0/ypN0 39 17 pN1/ypN1 17 7 pN2/ypN2 21 6 pN3/ypN3 11 6 Stage n.a. I 13 4 II 32 6 III 28 19 IV 15 7 a ChT/RT – Chemotherapy and/or Radiotherapy; b GEJ - Gastroesophageal Junction; n.a – non- applicable Table 2 Performance of promoter gene methylation as biomarkers for detection of Esophageal Cancer Gene Sensitivity % Specificity % Accuracy % LH + LH- ZNF569 69.3 96.4 79.7 19.06 0.32 Table 3 Performance of promoter gene methylation as biomarkers for detection of Esophageal Cancer according histological subtype Gene Sensitivity % Specificity % Accuracy % LH + LH- EA a COL14A1 80.00 71.43 75.00 2.80 0.28 ZNF569 82.5 96.4 90.5 22.69 0.18 Panel-EA 97.50 71.43 82.29 3.41 0.04 ESCC b GPX3 52.1 91.132 73.1 5.83 0.53 ZNF569 58.3 96.4 78.6 16.04 0.43 Panel-ESCC 75.00 87.50 81.73 6.00 0.29 a EA – Esophageal Adenocarcinoma; b ESCC – Esophageal Squamous Cell Carcinoma Supplementary Files Saltaetal.Additionalfile1.docx Cite Share Download PDF Status: Published Journal Publication published 25 Nov, 2020 Read the published version in Biomarker Research → Version 3 posted Review # 1 received at journal 11 Nov, 2020 Editorial decision: Accept 11 Nov, 2020 Reviewer # 1 agreed at journal 10 Nov, 2020 Reviewers invited by journal 08 Nov, 2020 Editor assigned by journal 03 Nov, 2020 Submission checks completed at journal 03 Nov, 2020 Editor invited by journal 03 Nov, 2020 You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-44848","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research","associatedPublications":[],"authors":[{"id":4402462,"identity":"df2e987b-81b7-471c-85a0-2fdf6ade04f4","order_by":0,"name":"Sofia Salta","email":"","orcid":"","institution":"Instituto Portugues de Oncologia do Porto Francisco Gentil EPE","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sofia","middleName":"","lastName":"Salta","suffix":""},{"id":4402463,"identity":"5f57b56c-e797-4187-a245-728d07b3fae3","order_by":1,"name":"Catarina Macedo-Silva","email":"","orcid":"","institution":"Instituto Portugues de Oncologia do Porto Francisco Gentil EPE","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Catarina","middleName":"","lastName":"Macedo-Silva","suffix":""},{"id":4402464,"identity":"bde6c17c-0862-401a-b116-6c93a1a3c2b3","order_by":2,"name":"Vera Miranda-Gonçalves","email":"","orcid":"","institution":"Instituto Portugues de Oncologia do Porto Francisco Gentil EPE","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Vera","middleName":"","lastName":"Miranda-Gonçalves","suffix":""},{"id":4402465,"identity":"f7f0b39e-2346-426c-a54c-7302a0cac066","order_by":3,"name":"Nair Lopes","email":"","orcid":"","institution":"Instituto Portugues de Oncologia do Porto Francisco Gentil EPE","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Nair","middleName":"","lastName":"Lopes","suffix":""},{"id":4402466,"identity":"4828bb43-3cba-4ac8-bf7f-82d527a35dad","order_by":4,"name":"Davide 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10:58:22","currentVersionCode":3,"declarations":"","doi":"10.21203/rs.3.rs-44848/v3","doiUrl":"https://doi.org/10.21203/rs.3.rs-44848/v3","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s40364-020-00248-7","type":"published","date":"2020-11-25T15:02:19+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":3509219,"identity":"c84ec415-d83e-4981-b05c-964f54934020","added_by":"auto","created_at":"2020-11-11 12:46:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":24229,"visible":true,"origin":"","legend":"Boxplots with all points of COL14A1 (a), GPX3 (b) and ZNF569 (c) relative methylation levels in the normal esophagus (n=56) and esophageal tumor tissues (n=88). *** p\u003c0.001; n.s- non-significant. ","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-44848/v3/fbb16250a1424cd8d7cc1e12.png"},{"id":3509220,"identity":"2950d432-c54d-4e43-a4cd-6638e769fab6","added_by":"auto","created_at":"2020-11-11 12:46:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":19182,"visible":true,"origin":"","legend":"Receiver Operating Characteristic Curve of the ZNF569 in Esophageal naïve tumors tissues.","description":"","filename":"2.PNG","url":"https://assets-eu.researchsquare.com/files/rs-44848/v3/3c08f62861762d4aebdf5c6b.PNG"},{"id":3509221,"identity":"a17199cc-7a53-4e8f-addc-d810e450926d","added_by":"auto","created_at":"2020-11-11 12:46:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":20153,"visible":true,"origin":"","legend":"Boxplots with all points of COL14A1 (a), GPX3 (b) and ZNF569 (c) relative methylation levels in Esophageal Squamous Cell Carcinoma (ESCC), n=48 and Esophageal Adenocarcinoma (EA), n=40 tissues. * p\u003c0.05; ** p\u003c0.01. ","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-44848/v3/c8b706bdf5a731ff32d3fbdf.png"},{"id":3509222,"identity":"165a8d8e-96c2-4171-83cf-8e732cccc2b5","added_by":"auto","created_at":"2020-11-11 12:46:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":60316,"visible":true,"origin":"","legend":"Receiver Operating Characteristic Curve of the two-gene panel (a) COL14A1 and ZNF569 in Esophageal Adenocarcinoma; (b) GPX3 and ZNF569 in Esophageal Squamous Cell Carcinoma ","description":"","filename":"4.PNG","url":"https://assets-eu.researchsquare.com/files/rs-44848/v3/9578e8d6bf97baa1df5218f2.PNG"},{"id":3509223,"identity":"5a759bda-a9ca-4fb7-a179-cff67f12cce8","added_by":"auto","created_at":"2020-11-11 12:46:24","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":32495,"visible":true,"origin":"","legend":"Boxplots with all points of COL14A1 (a), GPX3 (b) and ZNF569 (c) relative methylation levels in the normal esophagus, n=56, Esophageal Adenocarcinoma (EA), n=16 and Esophageal Squamous Cell Carcinoma (ESCC), n=20 Post-Chemotherapy and/or Radiotherapy patient’s samples. * p\u003c0.05; *** p\u003c0.001. ","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-44848/v3/b97c9cdd6237ce40d4cb5dd6.png"},{"id":13612997,"identity":"89b61ed3-aeb3-4f4a-8f5f-20a19bc7493e","added_by":"auto","created_at":"2021-09-17 06:35:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":588744,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-44848/v3/c28fb920-cd97-4fbc-9341-ba6258387a25.pdf"},{"id":3509218,"identity":"e29bfb5e-8152-40d5-9afc-176011145cfb","added_by":"auto","created_at":"2020-11-11 12:46:23","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":142360,"visible":true,"origin":"","legend":"","description":"","filename":"Saltaetal.Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-44848/v3/3e45e678ffe49acce2146da4.docx"}],"financialInterests":"","formattedTitle":"A DNA Methylation-based Test for Esophageal Cancer Detection","fulltext":[{"header":"Background","content":"\u003cp\u003eEsophageal cancer (ECa)\u0026nbsp;is the 7\u003csup\u003eth\u003c/sup\u003e most common malignancy and the 6\u003csup\u003eth\u003c/sup\u003e cause of cancer-related mortality worldwide\u0026nbsp;(1). ECa comprises two main histological subtypes: adenocarcinoma and squamous cell carcinoma, the most prevalent type\u0026nbsp;(2). Although therapeutic improvements have increased ECa survival rates\u0026nbsp;(3), curative-intent treatment options remain limited to surgery, chemotherapy (ChT), and radiotherapy (RT), either alone or in trimodal therapy\u0026nbsp;(4). Furthermore, most tumors are diagnosed at an advanced stage, entailing very low 5-year survival rates, ranging from 5-20%\u0026nbsp;(5, 6). Intensive research was, thus, carried out to find alternative treatment strategies to prolong life expectancy and quality of life (QoL) for ECa patients. Currently, neoadjuvant treatment with ChT and/or RT is the standard of care for patients with locally advanced disease\u0026nbsp;(2). Nevertheless, surgery is often associated with decreased QoL due to complications and comorbidities\u0026nbsp;(7, 8).\u003c/p\u003e\n\u003cp\u003eEpigenetic alterations, including their key players, have emerged as promising biomarkers for ECa. Among those, gene methylation is the most extensively studied epigenetic modification. Although esophageal adenocarcinomas (EA) and esophageal squamous cell carcinomas (ESCC) display different morphological and molecular features, several studies have shown that methylation is an early event in both histotypes, already present in premalignant lesions\u0026nbsp;(9-12). Thus, promoter methylation of specific genes might be used to discriminate normal from cancerous esophageal cells, enabling the detection of disease at early stages, increasing the likelihood of curative treatment. Except for \u003cem\u003eCDKN2A\u003c/em\u003e and \u003cem\u003eAPC\u003c/em\u003e, which are commonly aberrant methylated genes in both EA and ESCC, a specific methylome has been reported for each histotype (13). The importance of these alterations is further highlighted by the numerous reports correlating aberrant DNA methylation with ECa patient prognosis (14, 15).\u0026nbsp;\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eBased on the literature evidence, we aimed to identify and validate methylome alterations that might constitute biomarkers for early detection, as well as identification of minimal residual disease after neoadjuvant treatment, eventually precluding the need for esophagectomy. Hence, the studies selected from the literature clearly reported sensitivity and specificity for the detection of ECa or Barrett\u0026rsquo;s Esophagus using DNA methylation-based biomarkers (Additional Table 1). Studies with a small cohort of patients [n\u0026le;40 for each group (cases and controls)] were also excluded. Furthermore, only genes with a specificity higher than 98% were selected for testing. Of the remaining 5 genes, no specific primers were obtained to test \u003cem\u003eZNF345\u003c/em\u003e and \u003cem\u003eEPB41L3\u003c/em\u003e. Hence, promoter methylation levels of three genes selected (16, 17) - \u003cem\u003eGPX3\u003c/em\u003e (\u003cem\u003eglutathione peroxidase 3\u003c/em\u003e), \u003cem\u003eCOL14A1\u003c/em\u003e (\u003cem\u003ecollagen type XIV alpha 1 chain\u003c/em\u003e), and \u003cem\u003eZNF569\u003c/em\u003e (\u003cem\u003ezinc finger protein 569\u003c/em\u003e) - were tested in a series of ECa and normal esophageal tissues.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003ePatients and samples collection\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eA total of 124 formalin-fixed paraffin-embedded (FFPE) tissues samples from patients diagnosed\u0026nbsp;with\u0026nbsp;ECa\u0026nbsp;between 2007 and 2017 at the Portuguese Oncology Institute of Porto (IPO-Porto) were included in this study (Table 1). Among the tumor samples, 88 were collected before any treatment previous surgery, and 36 were collected after neoadjuvant treatment (ChT and/or RT). Additionally, 56 FFPE tissues samples of normal esophagus from patients diagnosed with gastric carcinoma and without evidence of esophageal cancer were used as control. All samples were archived at the Department of Pathology of IPO-Porto. All the cases were revised by an experienced pathologist and classified according to the World Health Organization (WHO) classification of Tumors of the Digestive System (4\u003csup\u003eth\u003c/sup\u003e edition) and staged according to the 7\u003csup\u003eth\u003c/sup\u003e edition American Joint Committee on Cancer (AJCC) system (18, 19). Relevant clinical data were collected from medical charts. For DNA extraction, a 4 \u0026mu;m section was cut from a representative tissue block and stained with hematoxylin-eosin. Tumor areas were delimited, enabling macrodissection in eight consecutive 8\u0026mu;m sections. This study was approved by the institutional ethics committee of IPO Porto (CES 202/017).\u003c/p\u003e\n\u003cp\u003e\u003cu\u003ePromoter methylation evaluation\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eDNA extraction from FFPE sections was performed using FFPE RNA/DNA Purification Plus Kit (Norgen Biotek, Thorold, Canada) following the manufacturer\u0026rsquo;s instructions. DNA concentrations and purity ratios were determined using the NanoDrop Lite spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA) and modified with sodium bisulfite, using the EZ DNA Methylation-Gold\u0026trade; Kit (Zymo Research, Orange, CA, USA) according to manufacturer\u0026rsquo;s instructions. For quantitative methylation-specific PCR (QMSP), modified DNA was used as template. Primers to\u0026nbsp;specifically amplify methylated bisulfite converted complementary sequences were used and are listed in Additional Table 2. QMSP reactions were carried out in LightCyler 480 II (Roche, Germany) using 2\u0026micro;L of modified DNA and 5 \u0026micro;L Xpert Fast SYBR (2X) (GRiSP, Porto, Portugal). All samples were run in triplicate and melting curves were obtained for each case by gene. \u0026beta;-\u003cem\u003eactin\u003c/em\u003e (\u003cem\u003eACT\u0026beta;\u003c/em\u003e) was used to normalize for DNA input in each sample (20). To ascertain PCR efficiency, and samples\u0026rsquo; quantification, modified CpGenome\u0026trade; Universal Methylated DNA (Merck Millipore, France) was used in each plate to generate a standard curve. The relative methylation for each gene was calculated by the ratio of mean quantity for the target gene and the mean quantity of \u003cem\u003eACT\u0026beta;\u003c/em\u003e, multiplied by 1000 for easier tabulation.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eStatistical analysis\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eAll the comparisons were performed using non-parametric tests. Specifically, the Kruskal-Wallis test was used for comparisons among three or more groups, whereas the Mann-Whitney U test was used in comparisons between two groups.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo assess biomarker performance, Receiver Operator Characteristic (ROC) curves were constructed for each gene and the Area Under the Curve (AUC) was calculated. The highest value obtained by the ROC curve analysis\u0026nbsp;[sensitivity + (1-specificity)] was established as cut-off to categorize samples as methylated or unmethylated, according to Youden\u0026rsquo;s J index method\u0026nbsp;(21, 22).\u0026nbsp;Furthermore, specificity, sensitivity,\u0026nbsp;accuracy, positive likelihood ratio (+LH), and negative likelihood ratio (-LH) were determined. For combination of\u0026nbsp;biomarkers, cases were considered positive if at least one of the individual\u0026nbsp;biomarkers\u0026nbsp;was positive.\u003c/p\u003e\n\u003cp\u003eSpearman non-parametric correlation was used to assed the correlation methylation levels and age. Disease-specific and disease-free survival curves (Kaplan-Meier with log-rank test) were built for standard clinicopathological variables and categorized methylation status. Disease-specific survival curves and disease-free survival curves (Kaplan\u0026ndash;Meier with log-rank test) were computed for standard clinicopathological variables and categorized methylation status.\u003c/p\u003e\n\u003cp\u003eTwo-tailed P-values were derived from statistical tests, using a computer-assisted program (SPSS Version 25.0, Chicago, IL), and results were considered statistically significant at p \u0026lt; 0.05, with Bonferroni\u0026acute;s correction for multiple tests, when applicable (* p\u0026lt;0.05; ** p\u0026lt;0.01; *** p\u0026lt;0.001; **** p\u0026lt;0.0001; ns \u0026ndash; non significant). Graphics were assembled using GraphPad 6 Prism (GraphPad Software, USA).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cu\u003eClinical and pathological data\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe most relevant clinical and pathological data are depicted in Table 1. In addition to normal esophageal tissues, ECa cases were segregated into treatment-na\u0026iuml;ve (samples collected before any treatment) and post-neoadjuvant treatment groups. No significant differences were disclosed concerning age among the three groups of samples (\u003cem\u003ep\u003c/em\u003e=0.06).\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eGene promoter methylation levels in na\u0026iuml;ve ECa tumors \u003cem\u003evs.\u003c/em\u003e normal esophagus samples\u003c/u\u003e.\u003c/p\u003e\n\u003cp\u003eTo assess performance for ECa detection, \u003cem\u003eCOL14A1\u003c/em\u003e methylation (\u003cem\u003eCOL14A1me), GPX3\u003c/em\u003e methylation (\u003cem\u003eGPX3me),\u0026nbsp;\u003c/em\u003eand\u0026nbsp;\u003cem\u003eZNF569\u0026nbsp;\u003c/em\u003emethylation (\u003cem\u003eZNF569me\u003c/em\u003e)\u003cem\u003e\u0026nbsp;\u003c/em\u003elevels in na\u0026iuml;ve tumors (n=88) were compared to normal esophagus samples (n=56). \u003cem\u003eZNF569\u003c/em\u003eme levels significantly differed between cancerous and normal samples (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001, Figure 1c), whereas no significant differences were found for \u003cem\u003eCOL14A1\u003c/em\u003eme\u003cem\u003e\u0026nbsp;\u003c/em\u003eand \u003cem\u003eGPX3\u003c/em\u003eme\u003cem\u003e\u0026nbsp;\u003c/em\u003elevels (\u003cem\u003ep\u003c/em\u003e=0.382 and \u003cem\u003ep\u003c/em\u003e=0.094, respectively, Figure 1a, b).\u003c/p\u003e\n\u003cp\u003eUsing ROC curve analysis (Figure 2), a cut-off of 55.17 was set for \u003cem\u003eZNF569\u003c/em\u003eme to assess biomarker performance. Thus, over 90% specificity and 69.3% sensitivity (Table 2) was disclosed, corresponding to an AUC of 0.8467 (Figure 2).\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAssociation between promoter methylation levels and standard clinicopathologic features\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCOL14A1\u003c/em\u003eme,\u003cem\u003e\u0026nbsp;GPX3\u003c/em\u003eme, and \u003cem\u003eZNF569\u003c/em\u003eme levels were tested for associations with standard clinicopathological features in na\u0026iuml;ve ECa patients. All genes disclosed significant differences in promoter methylation levels according to histological subtype (Figure 3). ESCC displayed higher \u003cem\u003eCOL14A1\u003c/em\u003eme\u003cem\u003e\u0026nbsp;\u003c/em\u003eand \u003cem\u003eGPX3\u003c/em\u003eme levels than EA (\u003cem\u003ep\u003c/em\u003e=0.001 and \u003cem\u003ep\u003c/em\u003e=0.024, respectively), whereas EA displayed higher \u003cem\u003eZNF569\u003c/em\u003eme levels (\u003cem\u003ep\u003c/em\u003e=0.020). Additionally, \u003cem\u003eCOL14A1\u003c/em\u003eme levels were significantly higher in pT1 tumors compared to pT3 (\u003cem\u003ep\u003c/em\u003e=0.006) (Additional Figure 1). However, no significant differences on methylation levels were found among different N stages (Additional Figure 2).\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eBiomarker performance according to histological subtype\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eBecause methylation levels differed between histological subtypes, samples were stratified according to this parameter for assessing histotype-specific biomarker performance. Concerning EA, \u003cem\u003eCOL14A1\u003c/em\u003eme\u003cem\u003e\u0026nbsp;\u003c/em\u003eand \u003cem\u003eZNF569\u003c/em\u003eme levels significantly differed from normal samples (\u003cem\u003ep=\u003c/em\u003e0.002 and \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001, respectively). \u003cem\u003eCOL14A1\u003c/em\u003eme and \u003cem\u003eZNF569\u003c/em\u003eme levels identified EA with an AUC of 0.68 and 0.91, respectively (Figure 4a), whereas the combination of both genes disclosed a sensitivity above 97% and 82.29 accuracy (Table 3). Furthermore, \u003cem\u003eGPX3\u003c/em\u003eme\u003cem\u003e\u0026nbsp;\u003c/em\u003eand \u003cem\u003eZNF569\u003c/em\u003eme levels differed significantly between ESCC and normal samples (\u003cem\u003ep=\u003c/em\u003e0.009 and \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001, respectively), individually discriminating this tumor type from controls with AUC of 0.65 and 0.79 (Figure 4b), respectively. Accuracy of detection improved to 81.73 when the two genes were combined in a single panel\u0026nbsp;(Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAssessment of promoter methylation in post-treatment samples\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGPX3\u003c/em\u003eme\u003cem\u003e\u0026nbsp;\u003c/em\u003eand \u003cem\u003eZNF569\u003c/em\u003eme levels were significantly higher in residual EA samples after neoadjuvant treatment compared to normal samples (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001, for both). Nonetheless, \u003cem\u003eCOL14A1\u003c/em\u003eme levels in tumors after neoadjuvant treatment did not differ significantly from normal samples (\u003cem\u003ep=\u003c/em\u003e0.493) (Figure 5). Concerning ESCC, only \u003cem\u003eGPX3\u003c/em\u003eme levels remained significantly different between post-treatment tumor and normal samples (\u003cem\u003ep=\u003c/em\u003e0.001).\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eSurvival analysis\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eSurvival analysis was carried out in the treatment-na\u0026iuml;ve ECa cohort. For analysis, a maximum of 5 years follow-up was considered. During this period, 39 patients died from the disease (44.3%), three patients died with the disease (3.4%) and six patients died without evidence of cancer (6.8%). Among the remainder patients, 39 were alive without evidence of disease (44.3%) and one patient was alive with disease (1.2%).\u003c/p\u003e\n\u003cp\u003eNo associations were depicted between\u003cem\u003e\u0026nbsp;COL14A1\u003c/em\u003eme\u003cem\u003e, GPX3\u003c/em\u003eme, and \u003cem\u003eZNF569\u003c/em\u003eme levels and disease-specific or disease-free survival, whereas the pT stage, pN stage, and stage associated with both disease-specific and disease-free survival (\u003cem\u003ep\u003c/em\u003e=0.010, \u003cem\u003ep\u003c/em\u003e=0.002, \u003cem\u003ep\u003c/em\u003e= 0.002 for disease-specific survival and \u003cem\u003ep\u003c/em\u003e=0.012, \u003cem\u003ep\u003c/em\u003e=0.001, \u003cem\u003ep\u003c/em\u003e=0.025 for disease-free survival).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eECa remains a leading cause of cancer-related mortality globally\u0026nbsp;(1)\u0026nbsp;and in Portugal\u0026nbsp;(23). Most patients are diagnosed with locally advanced or metastatic disease, entailing poor 5-years survival rate (about 25% and 5%, respectively)\u0026nbsp;(24). Thus, new strategies for early detection of this malignancy are urgently needed. In this context, epigenetic alterations such as DNA methylation have emerged as promising biomarkers in several cancers, including ECa\u0026nbsp;(25, 26).\u003c/p\u003e\n\u003cp\u003eHerein, we tested three gene promoters\u0026rsquo; methylation as ECa DNA methylation-based biomarkers, following a literature review. We selected the genes \u003cem\u003eZNF569\u003c/em\u003e, \u003cem\u003eGPX3,\u003c/em\u003e and \u003cem\u003eCOL14A1\u003c/em\u003e, all previously reported to harbor promoter methylation and suggested to have an oncossuppressive function\u003cem\u003e.\u003c/em\u003e ZNF569 protein has been reported as a potential transcriptional repressor implicated in MAPK signaling pathway\u0026nbsp;(27). \u003cem\u003eCOL14A1\u003c/em\u003e encodes for\u0026nbsp;the alpha chain of type XIV collagen which interacts with decorin associated with cell growth and survival\u0026nbsp;(28).\u0026nbsp;GPX3 is a glutathione peroxidase found to catalyze glutathione\u0026rsquo;s reduction of organic hydroperoxides and hydrogen peroxide and thereby protecting cells against oxidative damage\u0026nbsp;(29, 30).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCOL14A1\u003c/em\u003e aberrant methylation has been reported in ESCC\u0026nbsp;(16), as well as in renal cell carcinoma, sarcomas, and endometrial carcinoma\u0026nbsp;(28, 31, 32), whereas hypomethylation has been shown in coronary artery disease\u0026nbsp;(33). \u003cem\u003eGPX3\u0026nbsp;\u003c/em\u003epromoter methylation has been shown in ESCC and esophageal glandular lesions, including Barrett\u0026rsquo;s esophagus and EA\u0026nbsp;(30, 34, 35). In the same vein, \u003cem\u003eZNF569\u0026nbsp;\u003c/em\u003epromoter hypermethylation has been associated with glandular lesions, like Barrett\u0026rsquo;s esophagus\u0026nbsp;(17)\u0026nbsp;in comparison with the normal esophagus.\u003c/p\u003e\n\u003cp\u003eInterestingly, we showed that \u003cem\u003eZNF569me\u0026nbsp;\u003c/em\u003elevels could discriminate between ECa and normal esophagus with high specificity, regardless of the histotype, extending those previous reports. Furthermore, \u003cem\u003eZNF569\u003c/em\u003eme was shown to play a tumor-suppressive role in head and neck squamous cell carcinoma\u0026nbsp;(36)\u0026nbsp;and a DNA-methylation based panel, which included \u003cem\u003eZNF569me\u003c/em\u003e, discriminated gastric adenocarcinoma from normal mucosa\u0026nbsp;(37).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eESCC and EA displayed different cancer-specific methylation patterns. Accordingly, differential methylation patterns have been previously reported between ESCC and EA\u0026nbsp;(38, 39). Indeed, in treatment-na\u0026iuml;ve tumors, the three selected genes disclosed different methylation levels among ESCC and EA, variably comparing to normal esophageal tissue samples. In particular, available data support the value of identification specific ESCC methylation panels to enable early detection\u0026nbsp;(16, 40, 41). We found \u003cem\u003eZNF569\u0026nbsp;\u003c/em\u003ehypermethylated in both histological subtypes and, thus, this gene constitutes a promising biomarker for ECa detection, regardless of histological subtype. Moreover, we found \u003cem\u003eCOL14A1\u003c/em\u003e promoter methylation levels slightly higher in ESCC when compared with normal esophagus samples, although not statistically significant. This can be partially explained by variations in the population (Asian vs. Caucasian), as previously attested for some genes\u0026nbsp;(42), and the different nature of samples tested (plasma vs. FFPE). Notwithstanding, \u003cem\u003eCOL14A1\u003c/em\u003e promoter methylation levels were significantly lower in EA compared to normal. To our knowledge, this is the first reported association between \u003cem\u003eCOL14A1\u003c/em\u003e methylation levels and EA. Conversely, \u003cem\u003eGPX3\u003c/em\u003e promoter methylation levels did not differ between EA from normal tissues, although a few cases disclosed higher methylation levels (data not shown). Notwithstanding, different sample processing (fresh frozen tissues vs. FFPE)\u0026nbsp;(30, 35), different methodologies to assess \u003cem\u003eGPX3\u003c/em\u003e methylation levels among studies (qMSP vs. MSP vs. methylation ligation-dependent macroarray vs. pyrosequencing), and the smaller size of some cohorts\u0026nbsp;(30, 34)\u0026nbsp;may explain some disparate results.\u003c/p\u003e\n\u003cp\u003eOverall, we propose two different methylation-based panels, both with high accuracy to early detect ECa according to histological subtype. The ESCC-panel displayed higher specificity (87.5%), whereas the EA-panel disclosed higher sensitivity (97.5%). In fact, the performance of both methylation-based panels compares well with that of other studies\u0026nbsp;(39, 43, 44). For EA detection, Moinova et al reported a two-gene methylation panel, comprising \u003cem\u003eCCNA1\u003c/em\u003e and \u003cem\u003eVIM\u003c/em\u003e, with higher specificity (91.7%), similar to \u003cem\u003eTAC1\u003c/em\u003e hypermethylation reported by Jin et al\u0026nbsp;(45, 46). Additionally, for ESCC Li et al\u003cem\u003e.\u003c/em\u003e and Wang et al. reported panels with higher performance in Asian populations\u0026nbsp;(16, 47).\u003c/p\u003e\n\u003cp\u003eCurrently, most ECa patients are treated with neoadjuvant therapy followed by surgery, if diagnosed with locally advanced disease\u0026nbsp;(2). The randomized CROSS trial showed that surgery has a major impact on ECa patients\u0026rsquo; QoL. Features such as fatigue and physical performance are decreased even in long-term survivors. Those effects are similar in patients undergoing neoadjuvant treatment or surgery only, emphasizing the impact of surgery in QoL\u0026nbsp;(7, 8). Hence, biomarkers enabling the identification of patients complete response to neoadjuvant treatment (who might be spared surgery) and to early detect disease recurrence are needed\u0026nbsp;(48)\u0026nbsp;to improve QoL without risking the likelihood of cure. Thus, we evaluated methylation levels of candidate genes in 36 samples from non-complete responders after neoadjuvant treatment. In our series, \u003cem\u003eZNF569\u003c/em\u003eme levels only\u003cem\u003e\u0026nbsp;\u003c/em\u003esignificantly differed in EA comparatively to the normal esophagus, whereas \u003cem\u003eGPX3\u003c/em\u003e promoter methylation levels were significantly higher both in ESCC and EA than in the normal esophagus. Because \u003cem\u003eGPX3me\u0026nbsp;\u003c/em\u003ehas been associated with ChT resistance\u0026nbsp;(29), \u003cem\u003eGPX3\u003c/em\u003eme levels observed in EA after neoadjuvant treatment might be explained by selective pressure caused upon neoplastic cells, entailing adaptative alterations induced by treatment\u0026nbsp;(49). Several studies have associated DNA methylation with ChT or RT resistance\u0026nbsp;(50-53). However, most used samples before any treatment or \u003cem\u003ein vitro\u003c/em\u003e studies with immortalized cell lines (52). Thus, a direct comparison between our results and previously reported data should be made with caution. Nonetheless, the lack of information on methylation status before treatment, the small size of the pre-treated patient cohort along with the retrospective nature of our series, and the limited access to normal esophagus samples are major limitations of this study. Importantly, our series comprised ESCC and EA samples in similar proportions, contrarily to most of the previous studies which evaluated methylation status. Moreover, this study reported the potential of DNA methylation-based biomarkers for patients\u0026rsquo; monitoring after neoadjuvant treatment.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, we identified two gene panels that might detect ESCC and EA with good accuracy, which might prove useful for early disease detection among high-risk populations, as well as to detect residual disease after neoadjuvant treatment. As future perspectives, we intend to validate these panels in liquid biopsies, using plasma samples as a minimally invasive approach, not only for ECa early detection and diagnosis but also to identify patients with residual disease after neoadjuvant treatment, which are the most likely to benefit from surgery.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e-LH \u0026ndash; Negative likelihood ratio\u003c/p\u003e\n\u003cp\u003e+LH \u0026ndash; Positive likelihood ratio\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eACT\u0026beta;\u003c/em\u003e \u0026ndash; \u003cem\u003e\u0026beta;-actin\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAJCC \u0026ndash; American Joint Committee on Cancer\u003c/p\u003e\n\u003cp\u003eAUC \u0026ndash; Area Under the Curve\u003c/p\u003e\n\u003cp\u003eChT \u0026ndash; Chemotherapy\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCOL14A1\u003c/em\u003e \u0026ndash; \u003cem\u003eCollagen Type XIV alpha 1 chain\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCOL14A1me\u003c/em\u003e \u0026ndash; \u003cem\u003eCOL14A1 \u003c/em\u003emethylation\u003c/p\u003e\n\u003cp\u003eEA - Esophageal Adenocarcinomas\u003c/p\u003e\n\u003cp\u003eECa \u0026ndash; Esophageal cancer\u003c/p\u003e\n\u003cp\u003eESCC \u0026ndash; Esophageal Squamous Cell Carcinomas\u003c/p\u003e\n\u003cp\u003eFFPE \u0026ndash; Formalin-fixed paraffin-embedded\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGPX3\u003c/em\u003e \u0026ndash; \u003cem\u003eGlutathione Peroxidase 3\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGPX3me \u0026ndash; GPX3\u003c/em\u003e methylation\u003c/p\u003e\n\u003cp\u003eIPO-Porto \u0026ndash; Portuguese Oncology Institute of Porto\u003c/p\u003e\n\u003cp\u003eQMSP \u0026ndash; Quantitative methylation-specific PCR\u003c/p\u003e\n\u003cp\u003eQoL \u0026ndash; Quality of Life\u003c/p\u003e\n\u003cp\u003eROC \u0026ndash; Receiver Operator Characteristic\u003c/p\u003e\n\u003cp\u003eRT \u0026ndash; Radiotherapy\u003c/p\u003e\n\u003cp\u003eWHO \u0026ndash; World Health Organization\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eZNF569\u003c/em\u003e \u0026ndash; \u003cem\u003eZinc finger protein 569\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eZNF569me \u0026ndash; ZMF569 \u003c/em\u003emethylation\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the institutional ethics committee of IPO Porto (CES 202/017).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article and its supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by a grant from ESTIMA-NORTE-01-0145-740 FEDER-000027. SS was supported by a PhD fellowship IPO/ESTIMA-1 NORTE-01-0145- 740 FEDER-000027. VMG and NL received a post-doc fellowship from ESTIMA-NORTE-01-0145-740 FEDER-000027. CM-S was supported by a fellowship NORTE-01-0247-FEDER-033399.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSS prepared samples for molecular analyses, including DNA extraction, bisulfite modification, performed qMSP assays, analyzed data and drafted the manuscript. CM-S and VMG assisted in samples preparation (DNA extraction, bisulfite modification, and qMSP). NL contributed in draft manuscript. DG collected clinical follow-up data and performed histopathological evaluation of FFPE stained by H\u0026amp;E. RG performed the FFPE sections of all cases. MF performed histopathological evaluation of FFPE stained by H\u0026amp;E. OS collected clinical follow-up data. RH and CJ designed and supervised the study and revised the manuscript. All the authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFerlay J, Colombet M, Soerjomataram I, Mathers C, Parkin DM, Pineros M, et al. Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods. International journal of cancer Journal international du cancer. 2019;144(8):1941-53.\u003c/li\u003e\n\u003cli\u003eLordick F, Mariette C, Haustermans K, Obermannova R, Arnold D, Committee EG. Oesophageal cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Annals of oncology : official journal of the European Society for Medical Oncology / ESMO. 2016;27(suppl 5):v50-v7.\u003c/li\u003e\n\u003cli\u003eAbbas G, Krasna M. Overview of esophageal cancer. 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Oncogene. 2010;29(14):2104-17.\u003c/li\u003e\n\u003cli\u003eChen B, Rao X, House MG, Nephew KP, Cullen KJ, Guo Z. GPx3 promoter hypermethylation is a frequent event in human cancer and is associated with tumorigenesis and chemotherapy response. Cancer letters. 2011;309(1):37-45.\u003c/li\u003e\n\u003cli\u003eLee OJ, Schneider-Stock R, McChesney PA, Kuester D, Roessner A, Vieth M, et al. Hypermethylation and loss of expression of glutathione peroxidase-3 in Barrett's tumorigenesis. Neoplasia. 2005;7(9):854-61.\u003c/li\u003e\n\u003cli\u003eChen YC, Tsao CM, Kuo CC, Yu MH, Lin YW, Yang CY, et al. Quantitative DNA methylation analysis of selected genes in endometrial carcinogenesis. Taiwan J Obstet Gynecol. 2015;54(5):572-9.\u003c/li\u003e\n\u003cli\u003eRenner M, Wolf T, Meyer H, Hartmann W, Penzel R, Ulrich A, et al. Integrative DNA methylation and gene expression analysis in high-grade soft tissue sarcomas. Genome Biol. 2013;14(12):r137.\u003c/li\u003e\n\u003cli\u003eGuay SP, Brisson D, Mathieu P, Bosse Y, Gaudet D, Bouchard L. A study in familial hypercholesterolemia suggests reduced methylomic plasticity in men with coronary artery disease. Epigenomics. 2015;7(1):17-34.\u003c/li\u003e\n\u003cli\u003eGuilleret I, Losi L, Chelbi ST, Fonda S, Bougel S, Saponaro S, et al. DNA methylation profiling of esophageal adenocarcinoma using Methylation Ligation-dependent Macroarray (MLM). Biochem Biophys Res Commun. 2016;479(2):231-7.\u003c/li\u003e\n\u003cli\u003ePeng DF, Razvi M, Chen H, Washington K, Roessner A, Schneider-Stock R, et al. DNA hypermethylation regulates the expression of members of the Mu-class glutathione S-transferases and glutathione peroxidases in Barrett's adenocarcinoma. Gut. 2009;58(1):5-15.\u003c/li\u003e\n\u003cli\u003eLiu X, Zhao X, Gou C. Identification of novel methylated DNA marker ZNF569 for head and neck squamous cell carcinoma. J Cancer. 2019;10(10):2250-60.\u003c/li\u003e\n\u003cli\u003eAnderson BW, Suh YS, Choi B, Lee HJ, Yab TC, Taylor WR, et al. Detection of Gastric Cancer with Novel Methylated DNA Markers: Discovery, Tissue Validation, and Pilot Testing in Plasma. Clinical cancer research : an official journal of the American Association for Cancer Research. 2018;24(22):5724-34.\u003c/li\u003e\n\u003cli\u003eLi D, Zhang L, Liu Y, Sun H, Onwuka JU, Zhao Z, et al. Specific DNA methylation markers in the diagnosis and prognosis of esophageal cancer. Aging (Albany NY). 2019;11(23):11640-58.\u003c/li\u003e\n\u003cli\u003eGonzaga IM, Soares Lima SC, Nicolau MC, Nicolau-Neto P, da Costa NM, de Almeida Simao T, et al. TFF1 hypermethylation and decreased expression in esophageal squamous cell carcinoma and histologically normal tumor surrounding esophageal cells. Clinical epigenetics. 2017;9:130.\u003c/li\u003e\n\u003cli\u003eTungekar A, Mandarthi S, Mandaviya PR, Gadekar VP, Tantry A, Kotian S, et al. ESCC ATLAS: A population wide compendium of biomarkers for Esophageal Squamous Cell Carcinoma. Sci Rep. 2018;8(1):12715.\u003c/li\u003e\n\u003cli\u003eHe Y, Wang Y, Li P, Zhu S, Wang J, Zhang S. Identification of GPX3 epigenetically silenced by CpG methylation in human esophageal squamous cell carcinoma. Digestive diseases and sciences. 2011;56(3):681-8.\u003c/li\u003e\n\u003cli\u003eChen S, Zhou K, Yang L, Ding G, Li H. Racial Differences in Esophageal Squamous Cell Carcinoma: Incidence and Molecular Features. Biomed Res Int. 2017;2017:1204082.\u003c/li\u003e\n\u003cli\u003ePu W, Wang C, Chen S, Zhao D, Zhou Y, Ma Y, et al. Targeted bisulfite sequencing identified a panel of DNA methylation-based biomarkers for esophageal squamous cell carcinoma (ESCC). Clinical epigenetics. 2017;9:129.\u003c/li\u003e\n\u003cli\u003eRoss-Innes CS, Chettouh H, Achilleos A, Galeano-Dalmau N, Debiram-Beecham I, MacRae S, et al. Risk stratification of Barrett's oesophagus using a non-endoscopic sampling method coupled with a biomarker panel: a cohort study. The lancet Gastroenterology \u0026amp; hepatology. 2017;2(1):23-31.\u003c/li\u003e\n\u003cli\u003eMoinova HR, LaFramboise T, Lutterbaugh JD, Chandar AK, Dumot J, Faulx A, et al. Identifying DNA methylation biomarkers for non-endoscopic detection of Barrett's esophagus. Science translational medicine. 2018;10(424):eaao5848.\u003c/li\u003e\n\u003cli\u003eJin Z, Olaru A, Yang J, Sato F, Cheng Y, Kan T, et al. Hypermethylation of tachykinin-1 is a potential biomarker in human esophageal cancer. Clinical cancer research : an official journal of the American Association for Cancer Research. 2007;13(21):6293-300.\u003c/li\u003e\n\u003cli\u003eWang HQ, Yang CY, Wang SY, Wang T, Han JL, Wei K, et al. Cell-free plasma hypermethylated CASZ1, CDH13 and ING2 are promising biomarkers of esophageal cancer. J Biomed Res. 2018;32(5):424-33.\u003c/li\u003e\n\u003cli\u003eNoordman BJ, Spaander MCW, Valkema R, Wijnhoven BPL, van Berge Henegouwen MI, Shapiro J, et al. Detection of residual disease after neoadjuvant chemoradiotherapy for oesophageal cancer (preSANO): a prospective multicentre, diagnostic cohort study. The Lancet Oncology. 2018;19(7):965-74.\u003c/li\u003e\n\u003cli\u003eRamon YCS, Sese M, Capdevila C, Aasen T, De Mattos-Arruda L, Diaz-Cano SJ, et al. Clinical implications of intratumor heterogeneity: challenges and opportunities. J Mol Med (Berl). 2020;98(2):161-77.\u003c/li\u003e\n\u003cli\u003eTakahashi T, Yamahsita S, Matsuda Y, Kishino T, Nakajima T, Kushima R, et al. ZNF695 methylation predicts a response of esophageal squamous cell carcinoma to definitive chemoradiotherapy. J Cancer Res Clin Oncol. 2015;141(3):453-63.\u003c/li\u003e\n\u003cli\u003eHamilton JP, Sato F, Greenwald BD, Suntharalingam M, Krasna MJ, Edelman MJ, et al. Promoter methylation and response to chemotherapy and radiation in esophageal cancer. Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association. 2006;4(6):701-8.\u003c/li\u003e\n\u003cli\u003eKuo IY, Huang YL, Lin CY, Lin CH, Chang WL, Lai WW, et al. SOX17 overexpression sensitizes chemoradiation response in esophageal cancer by transcriptional down-regulation of DNA repair and damage response genes. J Biomed Sci. 2019;26(1):20.\u003c/li\u003e\n\u003cli\u003eKurimoto K, Hayashi M, Guerrero-Preston R, Koike M, Kanda M, Hirabayashi S, et al. PAX5 gene as a novel methylation marker that predicts both clinical outcome and cisplatin sensitivity in esophageal squamous cell carcinoma. Epigenetics : official journal of the DNA Methylation Society. 2017;12(10):865-74.\u003c/li\u003e\n\u003c/ol\u003e\n"},{"header":"Tables","content":"\u003cp style=\"page-break-after: avoid;\"\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 12pt;\"\u003eTable \u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 12pt;\"\u003e1\u003c/span\u003e\u003c/strong\u003e \u003cstrong\u003e\u003cspan style=\"font-size: 12pt;\"\u003eClinicopathological data of Normal Esophagus and Esophageal Tumor patient\u0026rsquo;s\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable style=\"border-collapse: collapse; border: none;\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 141.5pt; border: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" rowspan=\"2\" width=\"189\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e\u003cstrong\u003eClinicopathological Features\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 196.35pt; border: solid windowtext 1.0pt; border-left: none; padding: 0in 5.4pt 0in 5.4pt;\" colspan=\"2\" width=\"262\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e\u003cstrong\u003eEsophageal Tumor Samples\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86.85pt; border: solid windowtext 1.0pt; border-left: none; padding: 0in 5.4pt 0in 5.4pt;\" rowspan=\"2\" width=\"116\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e\u003cstrong\u003eNormal Esophagus\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 98.15pt; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e\u003cstrong\u003eNa\u0026iuml;ve Tumors\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.2pt; border-top: none; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e\u003cstrong\u003ePost ChT/RT\u003csup\u003ea\u003c/sup\u003e Treatment Tumors\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 141.5pt; border: solid windowtext 1.0pt; border-top: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"189\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt;\"\u003ePatients (no.)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.15pt; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.2pt; border-top: none; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86.85pt; border-top: none; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"116\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e56\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 141.5pt; border: solid windowtext 1.0pt; border-top: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"189\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt;\"\u003eAge median (range)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.15pt; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e63 (37-83)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.2pt; border-top: none; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e60 (44-73)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86.85pt; border-top: none; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"116\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e66 (36-84)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 141.5pt; border-top: none; border-left: solid windowtext 1.0pt; border-bottom: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"189\"\u003e\n\u003cp style=\"margin-bottom: 4.25pt;\"\u003eSex (no.)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.15pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.2pt; border: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86.85pt; border: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"116\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 141.5pt; border-top: none; border-left: solid windowtext 1.0pt; border-bottom: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"189\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt;\"\u003eMan\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.15pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.2pt; border: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86.85pt; border: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"116\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e37\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 141.5pt; border: solid windowtext 1.0pt; border-top: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"189\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt;\"\u003eWoman\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.15pt; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.2pt; border-top: none; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86.85pt; border-top: none; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"116\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e19\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 141.5pt; border-top: none; border-left: solid windowtext 1.0pt; border-bottom: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"189\"\u003e\n\u003cp style=\"margin-bottom: 4.25pt;\"\u003eHistological subtype (no.)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.15pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.2pt; border: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86.85pt; border: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"116\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003en.a.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 141.5pt; border-top: none; border-left: solid windowtext 1.0pt; border-bottom: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"189\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt;\"\u003eAdenocarcinoma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.15pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e40\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.2pt; border: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86.85pt; border: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"116\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 141.5pt; border: solid windowtext 1.0pt; border-top: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"189\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt;\"\u003eSquamous Cell Carcinoma\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.15pt; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.2pt; border-top: none; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86.85pt; border-top: none; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"116\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 141.5pt; border-top: none; border-left: solid windowtext 1.0pt; border-bottom: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"189\"\u003e\n\u003cp style=\"margin-bottom: 4.25pt;\"\u003eLocalization (no.)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.15pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.2pt; border: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86.85pt; border: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"116\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003en.a.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 141.5pt; border-top: none; border-left: solid windowtext 1.0pt; border-bottom: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"189\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt;\"\u003eUpper\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.15pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.2pt; border: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e-\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86.85pt; border: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"116\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 141.5pt; border-top: none; border-left: solid windowtext 1.0pt; border-bottom: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"189\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt;\"\u003eMiddle\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.15pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.2pt; border: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86.85pt; border: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"116\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 141.5pt; border-top: none; border-left: solid windowtext 1.0pt; border-bottom: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"189\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt;\"\u003eLower\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.15pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.2pt; border: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86.85pt; border: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"116\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 141.5pt; border: solid windowtext 1.0pt; border-top: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"189\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt;\"\u003eGEJ\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.15pt; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.2pt; border-top: none; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86.85pt; border-top: none; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"116\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 141.5pt; border-top: none; border-left: solid windowtext 1.0pt; border-bottom: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"189\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt;\"\u003epT Stage/ypT Stage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.15pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.2pt; border: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86.85pt; border: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"116\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003en.a.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 141.5pt; border-top: none; border-left: solid windowtext 1.0pt; border-bottom: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"189\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt;\"\u003epT1/ypT1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.15pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.2pt; border: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86.85pt; border: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"116\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 141.5pt; border-top: none; border-left: solid windowtext 1.0pt; border-bottom: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"189\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt;\"\u003epT2/ypT2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.15pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.2pt; border: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86.85pt; border: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"116\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 141.5pt; border-top: none; border-left: solid windowtext 1.0pt; border-bottom: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"189\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt;\"\u003epT3/ypT3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.15pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.2pt; border: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86.85pt; border: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"116\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 141.5pt; border: solid windowtext 1.0pt; border-top: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"189\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt;\"\u003epT4/ypT4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.15pt; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.2pt; border-top: none; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86.85pt; border-top: none; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"116\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 141.5pt; border-top: none; border-left: solid windowtext 1.0pt; border-bottom: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"189\"\u003e\n\u003cp style=\"margin-bottom: 4.25pt;\"\u003epN Stage/ ypN Stage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.15pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.2pt; border: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86.85pt; border: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"116\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003en.a.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 141.5pt; border-top: none; border-left: solid windowtext 1.0pt; border-bottom: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"189\"\u003e\n\u003cp style=\"margin-bottom: 4.25pt;\"\u003epN0/ypN0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.15pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.2pt; border: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86.85pt; border: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"116\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 141.5pt; border-top: none; border-left: solid windowtext 1.0pt; border-bottom: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"189\"\u003e\n\u003cp style=\"margin-bottom: 4.25pt;\"\u003epN1/ypN1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.15pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.2pt; border: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86.85pt; border: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"116\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 141.5pt; border-top: none; border-left: solid windowtext 1.0pt; border-bottom: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"189\"\u003e\n\u003cp style=\"margin-bottom: 4.25pt;\"\u003epN2/ypN2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.15pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e21\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.2pt; border: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86.85pt; border: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"116\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 141.5pt; border: solid windowtext 1.0pt; border-top: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"189\"\u003e\n\u003cp style=\"margin-bottom: 4.25pt;\"\u003epN3/ypN3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.15pt; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.2pt; border-top: none; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86.85pt; border-top: none; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"116\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 141.5pt; border-top: none; border-left: solid windowtext 1.0pt; border-bottom: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"189\"\u003e\n\u003cp style=\"margin-bottom: 4.25pt;\"\u003eStage\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.15pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.2pt; border: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86.85pt; border: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"116\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003en.a.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 141.5pt; border-top: none; border-left: solid windowtext 1.0pt; border-bottom: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"189\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt;\"\u003eI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.15pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.2pt; border: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86.85pt; border: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"116\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 141.5pt; border-top: none; border-left: solid windowtext 1.0pt; border-bottom: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"189\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt;\"\u003eII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.15pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.2pt; border: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86.85pt; border: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"116\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 141.5pt; border-top: none; border-left: solid windowtext 1.0pt; border-bottom: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"189\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt;\"\u003eIII\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.15pt; border: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.2pt; border: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86.85pt; border: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"116\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 141.5pt; border: solid windowtext 1.0pt; border-top: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"189\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt;\"\u003eIV\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.15pt; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 98.2pt; border-top: none; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"131\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 86.85pt; border-top: none; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"116\"\u003e\n\u003cp style=\"margin-bottom: 2.4pt; text-align: center;\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eChT/RT \u0026ndash; Chemotherapy and/or Radiotherapy; \u003csup\u003eb \u003c/sup\u003eGEJ - Gastroesophageal Junction; n.a \u0026ndash; non- applicable\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp style=\"page-break-after: avoid;\"\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 12pt;\"\u003eTable \u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 12pt;\"\u003e2\u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 12pt;\"\u003e Performance of promoter gene methylation as biomarkers for detection of Esophageal Cancer \u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable style=\"border-collapse: collapse; border: none;\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 69.65pt; border: solid windowtext 1.0pt; border-right: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"93\"\u003e\n\u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003eGene\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 69.65pt; border-top: solid windowtext 1.0pt; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"93\"\u003e\n\u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003eSensitivity %\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 69.65pt; border-top: solid windowtext 1.0pt; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"93\"\u003e\n\u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003eSpecificity %\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 69.65pt; border-top: solid windowtext 1.0pt; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"93\"\u003e\n\u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003eAccuracy %\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 69.65pt; border-top: solid windowtext 1.0pt; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"93\"\u003e\n\u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003eLH +\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 69.7pt; border: solid windowtext 1.0pt; border-left: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"93\"\u003e\n\u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003eLH-\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 69.65pt; border-top: none; border-left: solid windowtext 1.0pt; border-bottom: solid windowtext 1.0pt; border-right: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"93\"\u003e\n\u003cp style=\"text-align: center;\"\u003e\u003cem\u003eZNF569\u003c/em\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 69.65pt; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"93\"\u003e\n\u003cp style=\"text-align: center;\"\u003e69.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 69.65pt; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"93\"\u003e\n\u003cp style=\"text-align: center;\"\u003e96.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 69.65pt; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"93\"\u003e\n\u003cp style=\"text-align: center;\"\u003e79.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 69.65pt; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"93\"\u003e\n\u003cp style=\"text-align: center;\"\u003e19.06\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 69.7pt; border-top: none; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"93\"\u003e\n\u003cp style=\"text-align: center;\"\u003e0.32\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp style=\"page-break-after: avoid;\"\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 12pt;\"\u003eTable \u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 12pt;\"\u003e3\u003c/span\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cspan style=\"font-size: 12pt;\"\u003e Performance of promoter gene methylation as biomarkers for detection of Esophageal Cancer according histological subtype\u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003ctable style=\"border-collapse: collapse; border: none;\" width=\"614\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 42.3pt; border: solid windowtext 1.0pt; border-right: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"56\"\u003e\n\u003cp style=\"text-align: center;\"\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77.95pt; border-top: solid windowtext 1.0pt; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"104\"\u003e\n\u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003eGene\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70.9pt; border-top: solid windowtext 1.0pt; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"95\"\u003e\n\u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003eSensitivity %\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70.85pt; border-top: solid windowtext 1.0pt; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"94\"\u003e\n\u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003eSpecificity %\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 63.8pt; border-top: solid windowtext 1.0pt; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"85\"\u003e\n\u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003eAccuracy %\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 56.7pt; border-top: solid windowtext 1.0pt; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"76\"\u003e\n\u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003eLH +\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77.95pt; border: solid windowtext 1.0pt; border-left: none; padding: 0in 5.4pt 0in 5.4pt;\" width=\"104\"\u003e\n\u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003eLH-\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 42.3pt; border: solid windowtext 1.0pt; border-top: none; padding: 0in 5.4pt 0in 5.4pt;\" rowspan=\"3\" width=\"56\"\u003e\n\u003cp style=\"text-align: center;\"\u003eEA\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77.95pt; 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padding: 0in 5.4pt 0in 5.4pt;\" width=\"76\"\u003e\n\u003cp style=\"text-align: center;\"\u003e16.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77.95pt; border: none; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"104\"\u003e\n\u003cp style=\"text-align: center;\"\u003e0.43\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"width: 77.95pt; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"104\"\u003e\n\u003cp style=\"text-align: center;\"\u003ePanel-ESCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70.9pt; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"95\"\u003e\n\u003cp style=\"text-align: center;\"\u003e75.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 70.85pt; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"94\"\u003e\n\u003cp style=\"text-align: center;\"\u003e87.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 63.8pt; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"85\"\u003e\n\u003cp style=\"text-align: center;\"\u003e81.73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 56.7pt; border: none; border-bottom: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"76\"\u003e\n\u003cp style=\"text-align: center;\"\u003e6.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"width: 77.95pt; border-top: none; border-left: none; border-bottom: solid windowtext 1.0pt; border-right: solid windowtext 1.0pt; padding: 0in 5.4pt 0in 5.4pt;\" width=\"104\"\u003e\n\u003cp style=\"text-align: center;\"\u003e0.29\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eEA \u0026ndash; Esophageal Adenocarcinoma; \u003csup\u003eb \u003c/sup\u003eESCC \u0026ndash; Esophageal Squamous Cell Carcinoma\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"biomarker-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmre","sideBox":"Learn more about [Biomarker Research](http://biomarkerres.biomedcentral.com)","snPcode":"40364","submissionUrl":"https://submission.nature.com/new-submission/40364/3","title":"Biomarker Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Esophageal Cancer, DNA methylation, early detection, treatment response","lastPublishedDoi":"10.21203/rs.3.rs-44848/v3","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-44848/v3","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\t\u003cstrong\u003eBackground: \u003c/strong\u003eEsophageal cancer (ECa) is the 7\u003csup\u003eth\u003c/sup\u003e most incident cancer and the 6\u003csup\u003eth\u003c/sup\u003e leading cause of cancer-related death. Most patients are diagnosed with locally advanced or metastatic disease, enduring poor survival. Biomarkers enabling early cancer detection may improve patient management, treatment effectiveness, and survival, are urgently needed. In this context, epigenetic-based biomarkers such as DNA methylation are potential candidates. \u003c/p\u003e\u003cp\u003e\t\u003cstrong\u003eMethods: \u003c/strong\u003eHerein, we sought to identify and validate DNA methylation-based biomarkers for early detection and prediction of response to therapy in ECa patients. Promoter methylation levels were assessed in a series of treatment-naïve ECa, post-neoadjuvant treatment ECa, and normal esophagus tissues, using quantitative methylation-specific PCR for \u003cem\u003eCOL14A1\u003c/em\u003e, \u003cem\u003eGPX3,\u003c/em\u003e and \u003cem\u003eZNF569\u003c/em\u003e.\u003c/p\u003e\u003cp\u003e\t\u003cstrong\u003eResults: \u003c/strong\u003e\u003cem\u003eZNF569\u003c/em\u003e methylation (\u003cem\u003eZNF569me) \u003c/em\u003elevels significantly differed between ECa and normal samples (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001). Moreover, \u003cem\u003eCOL14A1\u003c/em\u003e methylation (\u003cem\u003eCOL14A1me) \u003c/em\u003eand \u003cem\u003eGPX3\u003c/em\u003e methylation (\u003cem\u003eGPX3me) \u003c/em\u003elevels discriminated adenocarcinomas and squamous cell carcinomas, respectively, from normal samples (\u003cem\u003ep\u003c/em\u003e=0.002 and \u003cem\u003ep\u003c/em\u003e=0.009, respectively). \u003cem\u003eCOL14A1me\u003c/em\u003e \u0026amp; \u003cem\u003eZNF569me\u003c/em\u003e accurately identified adenocarcinomas (82.29%) whereas \u003cem\u003eGPX3me \u0026amp; ZNF569me\u003c/em\u003e identified squamous cell carcinomas with 81.73% accuracy. Furthermore, \u003cem\u003eZNF569me\u003c/em\u003e and \u003cem\u003eGPX3me\u003c/em\u003e levels significantly differed between normal and pre-treated ECa.\u003c/p\u003e\u003cp\u003e\t\u003cstrong\u003eConclusion: \u003c/strong\u003eThe biomarker potential of a specific panel of methylated genes for ECa was confirmed. These might prove useful for early detection and might allow for the identification of minimal residual disease after adjuvant therapy.\u003c/p\u003e","manuscriptTitle":"A DNA Methylation-based Test for Esophageal Cancer Detection","msid":"","msnumber":"","nonDraftVersions":[{"code":3,"date":"2020-11-11 12:46:22","doi":"10.21203/rs.3.rs-44848/v3","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2020-11-12T00:00:00+00:00","index":1,"fulltext":"Recommendation: Reviewer's comments unavailable due to the journal's 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