Biophysical Properties of CTCFL and AHR Condensate Regulate Glutathione S-Transferase Mediated Lymph Node Density in Esophageal Squamous Cell Carcinoma

Biophysical Properties of CTCFL and AHR Condensate Regulate Glutathione S-Transferase Mediated Lymph Node Density in Esophageal Squamous Cell Carcinoma | 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 Article Biophysical Properties of CTCFL and AHR Condensate Regulate Glutathione S-Transferase Mediated Lymph Node Density in Esophageal Squamous Cell Carcinoma Huanrong Zhang, Rong Wang, Haihui Zhong, Peigui Gu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4675218/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Esophageal squamous cell carcinoma (ESCC) prognosis is closely associated with lymph node density (LND). The identification of biomarkers and regulatory mechanisms influencing LND could enhance our understanding of ESCC progression and inform therapeutic strategies. Methods This study analyzed 8,716 esophageal cancer patients to determine the prognostic significance of LND. Univariate and multivariate Cox regression analyses were performed to assess clinical factors. Gene expression data from The Cancer Genome Atlas (TCGA) were used to identify differentially expressed genes (DEGs) between LND < 0.12 and LND ≥ 0.12 groups. Functional enrichment, protein-protein interactions, and transcriptional regulation were investigated using advanced computational tools, immunoprecipitation, immunofluorescence, CUT&Tag sequencing, and phase separation assays. Results Higher LND (≥ 0.12) was associated with poorer survival outcomes. DEGs analysis revealed significant enrichment in glutathione metabolic pathways. CTCFL and AHR transcription factors were identified as key regulators of glutathione S-Transferase (GSTs) genes. These transcription factors exhibited phase separation properties, enhancing GSTs transcription. Knockdown experiments confirmed that CTCFL and AHR collaboratively regulate GSTs, affecting reactive oxygen species (ROS) levels and LND. In vivo, ESCC models demonstrated upregulation of CTCFL, AHR, and GSTs in high-LND mice, corroborating the regulatory role of these factors in tumor progression. Conclusion The transcription factors CTCFL and AHR regulate GST-mediated glutathione metabolism, influencing LND and ESCC progression. Targeting these regulatory pathways may offer novel therapeutic approaches for managing ESCC. esophageal squamous cell carcinoma lymph node density optimal cutoff cox regression glutathione metabolism phase separation Glutathione S-Transferase Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Esophageal cancer is ranking as the sixth leading cause of cancer-related deaths worldwide. 1 , 2 In the last 20 years, the incidence of esophageal cancer in the United States has been arising. 3 It was estimated that there were approximately 455,800 new cases and 400,200 deaths for esophageal cancer in 2012 worldwide. 4 In 2019, it was reported 17,650 new cases and 16,080 deaths esophageal cancer in US. 5 The advances in esophageal cancer treatment, such as the application of minimally invasive esophagectomy and endoscopic treatment, can significantly reduce the incidence of complications, decrease the length of hospital stay after surgery, and improve quality of life, however, most of esophageal cancer cases are diagnosed at the late stage, thus the 5-year survival rate of esophageal cancer patients in the United States is relatively poor. 6 – 10 Therefore, developing a more comprehensive system of prognostic factors is important for improving the prognosis of esophageal cancer patients. Recently, increasing evidence had identified lymph node density as an important prognostic factor for various cancer patients. For instance, it was reported that the LNR was a key prognostic factor which should be applied in stratification schemes for those clinical trials investigating adjuvant treatments in colorectal cancer patients who received curative resection. 111 Furthermore, LNR was the most important prognostic factor for overall as well as disease-free survival in rectal cancer patients even in those patients with less than lymph nodes examined. 12 Similarly, LNR was also recognized as an independent prognostic factor for oral squamous cell carcinoma patients. 13 Here, we intend to use the SEER database, which is generated by the National Cancer Institute including 18 registries that cover 30% of the US population, 14 , 15 to explore the prognostic potential of lymph node density in esophageal cancer patients. Glutathione(GSH)plays an important component of the intracellular antioxidant system. 16 In cancer cells, high levels of glutathione are necessary for clearing excess reactive oxygen species (ROS) and detoxifying exogenous substances, including synthetize its product by GCLC and a conjugation reaction mediated by glutathione S-transferases (GST S ). 17 GST S are mainly expressed in the liver. However, some of GSTs are also expression in a high level in ESCC. 18 Glutathione metabolism have long been associated with prognosis and process in ESCC. For example, GSTO1, GSTP1 and GST-pi have been perceived as tumor-associated antigens and act as predictor in ESCC. 19 Previous research demonstrated that deletion of GSTM1 polymorphism was recognized as a risk factor in ESCC. 18 In skin SCC, the glutathione metabolism pathway is specifically enriched in the TGF-β-activating cancer stem cell population and is responsible for the chemo-resistant property of these cell, 20 thus suggesting that glutathione metabolism might be critical for ESCC tumor progression. 2. Materials and Methods 2.1. Patients selection For ESCC patients from 2004 to 2019 using SEER*Stat, version 8.3.6, Only patients over 18 years old were enrolled. All cases were stratified according to the original tumor sites: C15.0 cervical esophagus, C15.1 thoracic esophagus, C15.2 abdominal esophagus, C15.3 upper third of esophagus, C15.4 middle third of esophagus, C15.5 lower third of esophagus, C15.8 overlapping lesion of esophagus, and C15.9 esophagus, NOS. All patients were microscopically confirmed. The exclusion criteria include: patients without surgery, diagnosis, or microscopic examination; patients only have autopsy examinations result; patients with missing or inappropriate follow-up information. These exclusion criteria finally recruited 8716 eligible patients for the current study. The following information was obtained for each patient: age at diagnosis, marital status, race, sex, grade, SEER stage, surgery, radiation, chemotherapy, number of lymph nodes examined, staging according to American Joint Committee on Cancer (AJCC), year of diagnosis, survival months and vital status. For ESCC samples with corresponding clinical data from The Cancer Genome Atlas database (TCGA, https://portal.gdc.cancer.gov/ ), Prior to inclusion, patients must possess comprehensive clinical data encompassing age, survival status, survival duration, lymph node density, among other pertinent factors. 2.2. Data processing If not specifically addressed, continuous data were shown as the mean ± standard deviations, and categorical data were presented as frequencies or proportions. Cox regression was used to search independent risk factors first, only those variables with statistical significance by univariate analysis were then analyzed in multivariate cox regression. The informed consent from each patient was not required as the patients included in the SEER database were anonymized and de-identified before release. 21 , 22 All statistical tests were two sided, P value less than 0.05 was accepted statistical significant. All statistical analyses were performed using SPSS and R software. To identify the differently expressed genes between LND < 0.12 and LND ≥ 0.12 in ESCC samples, We employed the Limma package ( http://www.bioconductor.org/packages/release/bioc/html/limma.html ) to conduct analyses utilizing the negative binomial distribution. All raw data underwent preprocessing via the Limma package, wherein genes with an average count value below 1 were excluded. Furthermore, the Limma package were utilized to ascertain differentially expressed genes based on |log2 fold change (FC)|≥1 and false discovery rate (FDR). Unless otherwise specified, continuous data were depicted as mean ± standard deviations, while categorical data were represented as frequencies or proportions. 2.3. KEGG pathway and GO enrichment analysis The functional roles of the differentially expressed genes were thoroughly assessed through GO enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The GO analysis encompassed terms related to cellular component (CC), molecular function (MF), and biological process (BP). Enrichment analyses were conducted using the R package (Cluster Profiler) and the DAVID online tool. Both p-values and false discovery rate (FDR) values were found to be less than 0.05, indicating statistical significance. 2.4. PPI network construction and module screening The differentially expressed genes identified between LND < 0.12 and LND ≥ 0.12 were queried against the STRING database ( http://www.string-db.org/ ) to elucidate protein-protein interaction details. Subsequently, Cytoscape 3.7.0 software was employed to build and visualize the protein-protein interaction (PPI) network. Key modules and genes within this network were identified using the Molecular Complex Detection (MCODE) plug-in, with selection criteria of MCODE score and node counts exceeding 5. A significance threshold of P ≤ 0.05 was applied to denote statistically significant differences. 2.5. Plasmids, antibodies and siRNA Plasmids containing CTCFL-Flag, AHR-Myc, CTCFL-EGFP, AHR-mCherry were constructed by PCR. These sequences were amplified by PCR(P520-01, Vazyme) using human cDNA made by priming KYSE150 total RNA, and assembled into the expression vectors (pCS2) with tags (2×Myc, 3×Flag, EGFP, mCherry). Plasmid were generated with a one-step cloning kit (Vazyme, C115-02), and the sequences were verified by colony genetic identification and Sanger sequencing. For immunoblotting and immunoprecipitation detection of endogenous CTCFL (ThermoFisher, MA5-49545), AHR (ThermoFisher, 14-9854-82), GSTA1(abcam, ab259727), GSTA1(abcam, ab211449), GSTM3(abcam, ab229858) and GSTM4(absin, abs117890) against specific to homo sapiens. The siRNA were designed from GenePharma (Suzhou, China). 2.6. CUT&Tag library preparation and data analysis In order to find out which genes were transcriptionally activated by CTCFL or AHR, we used the CUT&Tag Assay Kit (N259, Novoprotein, China) to capture the CTCFL or AHR binding site. The experimental procedure was performed according to the manufacturer's instructions. Briefly, 1×10 5 ESCC cells were prepared and immobilized on ConA Binding magnetic beads. The beads were incubated with anti primary antibody and then with anti-rabbit IgG secondary antibody. The cell-fixed magnetic beads were washed twice, and the pA/G-Tnp Pro transposon was added, followed by fragmentation of the target DNA. DNA was extracted and a CUT&Tag library was constructed according to the manufacturer's instructions. The analysis of CUT&Tag data followed a rigorous methodology. Initially, we conducted a comprehensive quality control assessment to ensure the integrity of the data. Subsequently, the data were aligned to the reference genome employing Bowtie2, followed by peak calling using MACS2 to pinpoint DNA binding sites accurately. Functional annotation was then performed utilizing GO and KEGG databases, followed by a differential analysis facilitated by DESeq2. The results were visually represented using IGV for clarity, aiding in the interpretation of biologically significant peaks. 2.7. In vitro droplet assay The recombinant fusion proteins were appropriately diluted using a buffer composed of 25mM Tris-HCl and 0.5mM DTT, pH 7.4, supplemented with 5% PEG 3000. Specifically, AHR-mCherry protein at a concentration of 20µM was diluted in the buffer and applied onto a coverslip containing CTCFL-EGFP protein also at 20µM. The mixture was then incubated at 37°C for 10 minutes, and droplets were visualized using either phase contrast microscopy or confocal microscopy (LSM880, Zeiss). Quantification of droplets was performed using ImageJ software. 2.8. Flourescence recovery after photobleacing (FRAP) For fluorescence recovery after photobleaching (FRAP) analysis of droplets within living cells, the entire droplets were designated as the region of interest (ROI) to ensure precise quantification of CTCFL-EGFP and AHR-mCherry's propensity for phase separation into droplets. CTCFL-EGFP granules were photobleached using a 488-nm laser, while AHR-mCherry granules were photobleached with a 561-nm laser, each for 1 µm at a frame rate. Subsequent frames captured after photobleaching corresponded to 3 seconds of recovery time. Each data point presented reflects the average and standard deviation of fluorescence intensities across ten unbleached (control) or ten photobleached (experimental) droplets, ensuring statistical robustness. 2.9. Immunofluorescence 4% paraformaldehyde was used to fix cells for 5 min and followed by three PBS washes. Permeabilize using 0.2% Triton X-100 for 10 minutes and rinse three times with PBS. Block with serum from the same host as the secondary antibody for 30 minutes, followed by three PBS washes. Incubate with the primary antibody overnight at 4 degrees Celsius, with subsequent three PBS washes for optimal efficacy. Apply the secondary antibody at room temperature for 2 hours or at 37 degrees Celsius for 1.5 hours, followed by three PBS washes. Next, stain the nuclei with DAPI and visualize the fluorescent signal directly. Remove PBS, seal the slide with glycerol, and protect the edges with nail polish for preservation. 2.10. Co-immunoprecipitation (co-IP) and Western blotting Co-IP and Western blotting were performed as previously described. 23 2.11. Animals and Carcinogen Treatment. Five-week-old female C57BL/6 mice were purchased from Gempharmatech Co., Ltd. (Jiangsu, China). All mice were fed a basal diet (Shenzhen Topbiotech Co., Ltd., Shenzhen, China) (NO. SYXK, 2020 − 0230) and allowed free access to deionized water. All mice were reared in a specific pathogen-free (SPF) laboratory, and all the animal procedures and experiments conducted in this study were approved by the Animal Ethics Committee of Shenzhen Topbiotech Co., Ltd. (Permit NO. TOP-IACUC-2022-0205). We used 4-nitroquinoline 1-oxide (4NQO) from Sigma Aldrich (St. Louis, MO, USA) to induce esophageal cancer in a working concentration of 100 µg/mL, and stored at 4°C. Drinking water containing 4NQO was freshly prepared every week in total 12 weeks. Mice were euthanatized with a 4% paraformaldehyde solution at the 18th weeks or obtain tumor tissue for organoids culture, respectively. 2.12. glutathione (GSH) and reactive oxygen species (ROS) assays The intracellular GSH and ROS levels were assessed using a GSH colorimetric assay kit. All of these reagents were purchased from Solarbio (Beijing, China) and used according to the manufacturer’s instructions. 3. Results 3.1. LND is a critical factor in determining the prognosis of ESCC In total 8716 eligible esophageal cancer patients were analyzed. The value of consecutive lymph nodes was determined and stratified using R code. As list in Table 1 , the average ages at the patients alive or dead were 62.21 ± 9.46 and 64.15 ± 10.02, respectively. Most of the patients in the present cohort were married, white, male, received surgery, radiation therapy and chemotherapy. In the LND (Stratified) < 0.12 (69.0%) subgroup, nearly half of patients were alive (2701, 45.2%), whereas in the LND (Stratified) ≥ 0.12 (31.0%) subgroup, majority of patients were dead (2279, 83.2%). The selection strategy for eligible patients was shown in Fig. 1A. Table 1 Patient characteristics Variables Alive Dead p-value Total 3162(36.3%) 5554(63.7%) Age 62.21 ± 9.46 64.15 ± 10.02 Sex Male 2597(35.9%) 4631(64.1%) 0.136 Female 565(38.0%) 923(62%) Marital status Married 2150(37.4%) 3606(62.6%) < 0.001 Unmarried 455(37.6%) 756(62.4%) SWD 444(30.3%) 1020(69.7%) Unknown 113(39.6%) 172(60.4%) Race White 2854(36.6%) 4951(63.4%) < 0.001 Black 134(28.4%) 338(71.6%) Other 160(37.9%) 262(62.1%) Unknown 14(82.4%) 3(17.6%) First malignant primary indicator Yes 2718(37.0%) 4622(63.0%) 0.001 No 444(32.3%) 93267.7%) LND < 0.12 2701(45.2%) 3275(54.8%) < 0.001 ≥ 0.12 461(16.8%) 2279(83.2%) Surgery Yes 3084(37.9%) 5045(62.1%) < 0.001 No/Unknown 78(13.3%) 509(86.7%) Radiation Yes 1809(34.8%) 3388(65.2%) 0.001 No/Unknown 1353(38.4%) 2166(61.6%) Chemotherapy Yes 1990(34.9%) 3719(65.1%) < 0.001 No/Unknown 1172(39.0%) 1835(61.0%) AJCC I 1203(56.8%) 914(43.2%) < 0.001 II 1166(37.6%) 1935(62.4%) III 653(23.9%) 2078(76.1%) IV 140(18.3%) 627(81.7%) N N0 2204(47,7%) 2415(52.3%) < 0.001 N1 753(20.4) 2940(79.6%) N2 161(55.9%) 127(44.1%) N3 44(37.9%) 72(62.1%) According to the univariate cox analysis, we found that age, marital status, race, sex, surgery, radiation, chemotherapy, LND consequent and AJCC stage had significant impact on the survival of esophageal cancer patients. We further included all of these variables in the multivariate cox regression. We found the age of diagnosis was a significant variable in mutivariate analysis, age at diagnosis (hazard ratio [HR] = 1.020, 95% confidence interval [CI] = 1.017–1.023, p < 0.001). Thus, age might be an important factor affecting the esophageal cancer patients' survival. Indeed, marital status (unmarried vs married: HR = 1.141, 95%CI = 1.053–1.238, p = 0.001; separated, widowed or divorced [SDW] vs married: HR = 1.161, 95%CI = 1.081–1.246, p < 0.001), race (black vs white, HR = 1.278, 95%CI = 1.142–1.430, p < 0.001), first malignant primary indicator (no vs yes: HR = 1.150, 95%CI = 1.069–1.237, p < 0.001), surgery (no/unknown vs yes: HR = 1.696, 95%CI = 1.531–1.879, p < 0.001), chemotherapy (no/unknown vs yes: HR = 1.345, 95%CI = 1.257–1.439, p < 0.001), AJCC stage (II vs I: HR = 2.164, 95%CI = 1.983–2.362, p < 0.001; III vs I: HR = 3.366, 95%CI = 3.039–3.727, p < 0.001; IV vs I: HR = 3.779, 95%CI = 3.332–4.286, p < 0.001) were significantly correlated with the risk of esophageal cancer. R software was used to determine the following optimal cutoffs for the LND stratified: <0.12, ≥ 0.12 (Fig. 1B). Importantly, LND stratified was also related to an increased risk of esophageal cancer (LND ≥ 0.12 vs LND < 0.12: HR = 1.629, 95%CI = 1.521–1.745, p < 0.001)(Table 2 ). Table 2 Univariate and multivariate cox regression Variables Univariate analysis Multivariate analysis HR 95%CI p -value HR 95%CI p -value Age at diagnosis 1.017 1.014–1.020 < 0.001 1.020 1.017–1.023 < 0.001 Marital status Married Reference Reference Unmarried 1.061 0.981–1.148 0.137 1.141 1.053–1.238 0.001 SWD 1.222 1.140–1.310 < 0.001 1.161 1.081–1.246 < 0.001 Unknown 1.033 0.886–1.203 0.680 1.141 0.979–1.330 0.092 Race White Reference Reference Black 1.292 1.157–1.442 < 0.001 1.278 1.142–1.430 < 0.001 Other 1.004 0.887–1.137 0.948 0.948 0.836–1.074 0.378 Unknown 0.216 0.070–0.671 0.008 0.221 0.071–0.686 0.009 Sex Male Reference Reference Female 0.970 0.904–1.041 0.398 ... First malignant primary indicator Yes Reference Reference No 1.223 1.140–1.313 < 0.001 1.150 1.069–1.237 < 0.001 Surgery Yes Reference Reference No/Unknown 2.840 2.590–3.114 < 0.001 1.696 1.531–1.879 < 0.001 Radiation Yes Reference Reference No/Unknown 0.775 0.734–0.819 < 0.001 ... Chemotherapy Yes Reference Reference No/Unknown 0.762 0.720–0.806 < 0.001 1.345 1.257–1.439 < 0.001 LND (Stratified) < 0.12 Reference Reference ≥ 0.12 2.648 2.507–2.797 < 0.001 1.629 1.521–1.745 < 0.001 AJCC Stage I Reference Reference II 1.982 1.831–2.144 < 0.001 2.164 1.983–2.362 < 0.001 III 3.541 3.271–3.833 < 0.001 3.366 3.039–3.727 < 0.001 IV 4.502 4.062–4.989 < 0.001 3.779 3.332–4.286 < 0.001 We further stratified the esophageal cancer patients according to LND stratification. We first analyzed all of the variables both in the univariate cox regression analysis and multivariate cox regression. As listed in (Table 3 ) . Table 3 Multivariate cox regression analysis in the LND Stratified Variables LND (Stratified) < 0.12 LND (Stratified) ≥ 0.12 HR 95%CI p -value HR 95%CI p -value Age at diagnosis 1.025 1.021–1.029 < 0.001 1.012 1.008–1.016 < 0.001 Marital status Married Reference Reference Unmarried 1.098 0.986–1.222 0.087 1.169 1.034–1.322 0.013 SWD 1.126 1.027–1.236 0.012 1.179 1.056–1.317 0.003 Unknown 1.148 0.949–1.389 0.156 1.181 0.912–1.530 0.207 Race White Reference Reference Black 1.423 1.233–1.643 < 0.001 Other 1.084 0.927–1.268 0.312 ... Unknown 0.228 0.057–0.911 0.037 Sex Male Reference Reference Female ... ... First malignant primary indicator Yes Reference Reference No 1.278 1.166–1.401 < 0.001 ... Surgery Yes Reference Reference No/Unknown 1.747 1.412–2.163 < 0.001 1.669 1.484–1.877 < 0.001 Radiation Yes Reference Reference No/Unknown 0.787 0.695–0.892 < 0.001 ... Chemotherapy Yes Reference Reference No/Unknown 1.299 1.141–1.479 < 0.001 1.821 1.599–2.074 < 0.001 AJCC Stage I Reference II 1.931 1.758–2.120 < 0.001 Reference III 3.129 2.789–3.510 < 0.001 1.497 1.332–1.682 < 0.001 IV 2.985 2.471–3.605 < 0.001 1.766 1.533–2.035 < 0.001 In the LND < 0.12 cohort, age at diagnosis (HR = 1.025, 95%CI = 1.021–1.029, p < 0.001), marital status (unmarried vs married was no significant difference whereas widowed or divorced [SDW] vs married: HR = 1.126, 95%CI = 1.027–1.236, p = 0.012), race (black vs white, HR = 1.423, 95%CI = 1.233–1.643, p < 0.001), first malignant primary indicator (no vs yes: HR = 1.278, 95%CI = 1.166–1.401, p < 0.001), surgery (no/unknown vs yes: HR = 1.747, 95%CI = 1.412–2.163, p < 0.001), chemotherapy (no/unknown vs yes: HR = 1.299, 95%CI = 1.141–1.479, p < 0.001) and AJCC stage (II vs I: HR = 1.931, 95%CI = 1.758–2.120, p < 0.001; III vs I: HR = 3.129, 95%CI = 2.789–3.510, p < 0.001; IV vs I: HR = 2.985, 95%CI = 2.471–3.602, p < 0.001) were significantly correlated with the risk of esophageal cancer. In the LND ≥ 0.12 cohort, age at diagnosis (HR = 1.012, 95%CI = 1.008–1.016, p < 0.001), marital status (unmarried vs married: HR = 1.169, 95%CI = 1.034–1.322, p = 0.013; separated, widowed or divorced [SDW] vs married: HR = 1.179, 95%CI = 1.056–1.317, p = 0.003), surgery (no/unknown vs yes: HR = 1.669, 95%CI = 1.484–1.877, p < 0.001), chemotherapy (no/unknown vs yes: HR = 1.821, 95%CI = 1.599–2.074, p < 0.001) and AJCC stage (III vs II: HR = 1.497, 95%CI = 1.322–1.682, p < 0.001; IV vs II: HR = 1.766, 95%CI = 1.533–2.035, p < 0.001) were significantly increased the risk of esophageal cancer. Patients in LND < 0.12 subgroup were in a lower burden of deaths and hazard ratio than in LND ≥ 0.12 subgroup deaths and hazard ratio during the study period (Fig. 1C) . 3.2. Identification of Biomarkers of Lymph Node Metastasis in Esophageal Squamous Cell Carcinoma In this study, we conducted a comprehensive analysis using advanced computational methods to examine the key roles and prognostic significance of genes between patient in N0 or NX (lymph node metastasis). We obtained ESCC datasets from TCGA, comprising 77 ESCC tumor samples. Data processing and identification of differentially expressed genes (DEGs) were performed using R software packages. Out of 16,190 genes analyzed, 382 genes met the screening criteria of this study (P < 0.05, qValue |1.0), including 168 upregulated genes and 214 downregulated genes.. (Fig. 2A) . To investigate the function of the identified genes, we performed functional enrichment analysis on the differentially expressed genes (DEGs). The results indicated that pathways related to glutathione metabolism were enriched in both Gene Ontology Biological Processes (GO BP) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis (Fig. 2B and 2C) . Following that, we developed a genetic risk score model for predicting prognosis in lymph node metastasis samples of esophageal squamous cell carcinoma (Fig. 2D) . This model was constructed using 26 candidate hub genes associated with prognosis, identified through a unique stepwise Cox regression analysis. We performed further analysis using multiple stepwise Cox regression to assess their impact on patient survival status, seven of these hub genes were found to be independent predictors of lymph node metastasis in ESCC (Fig. 2E) . The survival analysis was performed to assess the predictive ability of the prognostic model, which indicated that patients in the high-risk subgroup exhibited significantly poorer overall survival (OS) compared to those in the low-risk subgroup (Fig. 2F) . Subsequently, a total of 77 LUAD patients were divided into train or test groups, each group was stratified into low-risk and high-risk subgroups based on the median risk score. Consistent with previous findings, the high-risk subgroup demonstrated significantly reduced OS relative to the low-risk subgroup (Fig. 2G and 2J). To further validate the prognostic efficacy of the seven biomarkers, a time-dependent ROC analysis was conducted, revealing moderate diagnostic performance (Fig. 2H and 2K) . An expression heatmap and survival status plot of patients, incorporating seven genes, were generated for both subgroups (Fig. 2I and 2L) . These findings underscore that the prognostic model possesses robust sensitivity and specificity. 3.3. The transcription factors CTCFL and AHR collaboratively regulate Glutathione S-Transferase (GSTs)-mediated lymph node density. To investigate the regulatory mechanisms underlying lymph node metastasis in esophageal cancer, we delved into the downstream pathways associated with this process. Among the seven identified hub genes, CTCFL and AHR were notable as transcription factors, implying that lymph node metastasis in esophageal squamous cell carcinoma patients may be driven by transcriptional regulatory mechanisms. (Fig. 3A) . Notably, CTCFL and AHR, known for their elevated expression across various tumor types (Fig. 3B) . Besides, analysis demonstrated a significant positive correlation in patients with esophageal squamous cell carcinoma (ESCC) (Fig. 3C) . Given the consistent expression and functional patterns of CTCFL and AHR, we endeavored to elucidate their intrinsic relationship. To investigate potential interactions among these two transcription factors promoting lymph node density (LND), we employed a protein-protein interaction docking prediction model merging the three-dimensional structures of Alpha Fold2 and ZDOCK docking. Our findings revealed a stable interaction between CTCFL and AHR, with a binding free energy of 32.8 kcal/mol. This stability is facilitated by five hydrogen bond connections and one halogen bond connection between the two proteins (Fig. 3D) . Following that, we conducted Co-IP experiments to validate the interaction between CTCFL and AHR (Fig. 3E) . Subsequently, immunofluorescence staining confirmed the colocalization of CTCFL and AHR, a finding further supported by overexpression using distinct tags (Fig. 3F) . It appears that these two factors interact to execute specific biological functions. To delve into the regulatory role of their interaction in the glutathione metabolism pathway, we considered the close relationship between esophageal squamous cell carcinoma's lymph node density and GSTs expression. Hence, we conducted CUT&Tag sequencing analysis on CTCFL, AHR, and their combined form to investigate their role in activating downstream genes within this pathway. The data revealed that CTCFL or AHR individually can bind to the transcription initiation regions of GSTA1, GSTA2, GSTM3, and GSTM4 (Fig. 3G) . Surprisingly, upon interaction between CTCFL and AHR, there was a notable enhancement in their binding to the promoter regions of GSTs (Fig. 3G) . 3.4. CTCFL and AHR condensates enrich the transcription apparatus to promote GSTs transactivation. It's notable that nuclei in ESCC cells displayed a droplet-like distribution of co-localized CTCFL and AHR staining, suggesting the self-aggregation capability of these proteins. To delve into the structural basis of CTCFL and AHR condensates, we employed IUPred2 and PONDR to analyze their amino acid sequences. The results uncovered a classic intrinsically disordered region (IDR) from amino acids 37–252 within CTCFL protein, but the IDRs of AHR is not obvious (Fig. 4A and 4B) . To validate the formation of CTCFL and AHR condensates, we conducted overexpression and purification of EGFP-tagged CTCFL and mCherry-tagged AHR proteins in HEK293T cells. Fluorescence phase contrast microscopy revealed that at 37°C, CTCFL exhibited protein concentration-dependent condensation-forming ability, whereas AHR did not. However, the fusion proteins of CTCFL and AHR exhibited droplet-forming ability (Fig. 4C) . To assess whether CTCFL or AHR demonstrate liquid-like properties within cells, we conducted in vivo experiments using the fluorescence recovery after photobleaching (FRAP) assay. The results indicated rapid recovery of CTCFL and the fusion proteins of CTCFL and AHR in solution, while AHR protein alone did not exhibit this ability. This suggests the significance of the intrinsically disordered region (IDR) domain for CTCFL's phase separation potential. Moreover, the fusion of CTCFL with AHR enabled the latter to recover after bleaching, emphasizing the collaborative role in this process (Fig. 4D) . The findings above indicate that CTCFL and AHR interact through phase separation, with AHR's phase-separation ability contingent on CTCFL. Additionally, the fusion protein of CTCFL and AHR notably enhances their capacity to transcribe downstream genes. We next employed siRNAs to downregulate CTCFL, AHR, and both of two TFs in KYSE150 esophageal squamous cell carcinoma cells. The combined Wound Healing and Transwell experiments demonstrated that CTCFL along with AHR collectively governs the migration capability of esophageal cancer cells (Fig. 4E and 4F) . 3.5. GSTs impact LND in esophageal squamous cell carcinoma by controlling Reactive Oxygen Species (ROS) levels. The results above indicate that CTCFL undergoes liquid-liquid phase separation, leading to the formation of droplets with AHR and the aggregation into a CTCFL-AHR complex. AHR likely functions as a transcriptional regulator of CTCFL, enhancing its transcriptional activity. For instance, AHR facilitates CTCFL in transcribing GSTs (Fig. 5A) . To validate the regulatory role of CTCFL and AHR in the transcription of GSTs, we employed siRNA to knock down CTCFL, AHR, and the combination of CTCFL with AHR in KYSE150 cells. The results revealed that reducing CTCFL expression led to decreased expression levels of GSTA1, GSTA2, GSTM3, and GSTM4 within the GST family, resulting in varying degrees of transcriptional inhibition. Knocking down AHR individually showed minimal impact. However, when both transcription factors were downregulated, there was a dramatically inhibiting in GST expression levels (Fig. 5B) . The protein expression levels of GSTs corresponded with the observed transcriptional trends (Fig. 5C) . To delve into the mechanism by which GSTA1, GSTA2, GSTM3, and GSTM4 regulate lymph node density (LND), we constructed a protein-protein interaction (PPI) network using Cytoscape software. This network integrated 1065 nodes sourced from the STRING database. Leveraging the MODE tool, we analyzed the co-expression network to identify potential key modules. The primary module obtained genes in pivotal subgroups enriched in processes related to glutathione metabolism and transferase pathways (Fig. 5D) , Suggesting interactions between GSTA1, GSTA2, GSTM3, and GSTM4. To validate their direct interactions, we investigated the relationships between these four transsulfurases in KYSE150 cells. GSTA1-GSTM3 and GSTA2-GSTM4 exhibited co-localization with each other (Fig. 5E and 5F) . Indeed, these proteins demonstrated potential co-localization and played essential roles in the transsulfurization pathway of glutathione synthesis. Based on previous bioinformatics analyses, glutathione metabolism was notably enriched in the high LND group. GSTs serve as pivotal roles in this metabolic pathway. The levels of glutathione (GSH) and its biosynthetic capacity are influenced by the enzymatic activity of GSTs, consequently impacting tumor malignancy through the regulation of ROS levels. 24 – 26 Therefore, we assessed glutathione (GSH) levels (Fig. 5G) and reactive oxygen species (ROS) levels (Fig. 5H) in KYSE 150 cells by overexpressing GSTA1, GSTA2, GSTM3, and GSTM4 in AHR, CTCFL, AHR combined with CTCFL knockdown cell lines, respectively. The findings revealed that knockdown of AHR and CTCFL significantly reduced GSH levels while increasing ROS levels. Supplementing any of these four enzymes individually could partially restore GSH content and reduce ROS levels. However, supplementing a single type of GST alone was insufficient to normalize ROS levels, suggesting a comprehensive regulation of ROS levels by GSTs. These results highlight the role of CTCFL-AHR in regulating GST expression in esophageal cancer cells and mediating ROS changes, which in turn influence the lymph node density of ESCC. 3.6. CTCFL and AHR collaboratively activate GSTA1 and GSTA2, promoting LND of ESCC in mice. To confirm lymph node density (LND) in esophageal cancer relies on CTCFL, AHR and GSTs in vivo, we established a spontaneous ESCC model in p53-/-, ED-L2cre mice and generated organoids from mouse esophageal tumor tissues (Fig. 6A and 6B) . These were then compared with organoids from low-LND mice (Fig. 6C) . Remarkably, the organoids from high-LND mice exhibited clear growth advantages (Fig. 6D) , accompanied by a significant upregulation in protein expression levels of CTCFL, AHR and GSTs (Fig. 6E) . These indicate that the transcriptional factors CTCFL and AHR regulate GSTs expression, thereby influencing the lymph node density in ESCC. 4. Discussion It is well recognized that lymph node metastasis is one of the most important prognostic factors for cancer patients. 27 Increasing studies have shown that lymph node ratio (LNR is the ratio of the number of positive lymph nodes to the number of total lymph nodes examined) or lymph node density (LND) has been correlated with the prognosis 13 . LNR or LND has been found to be more reliable than the AJCC N stage in predicting gastric cancer prognosis. 28 – 31 The study by Han et al. showed that a prognostic scoring system containing lymph node ratio could predict the survival ratio of IIIA-N2 patients after surgery and postoperative chemotherapy, and the lymph node ratio might be a useful complement to TNM staging in IIIA-N2 patients. 32 However, correlation between lymph node density and esophageal cancer patients' survival was still unclear. Thus, our study indicates that lymph node density is an prognostic factor for the survival of esophageal cancer patients for the first time. Therefore, our study elucidates the affect of LND in ESCC patients’ prognosis via clinical analysis and validation of molecule phenotype. We included 8716 cases of esophageal cancer from the SEER database. The age of diagnosis was a risk factor for patients with esophageal cancer (HR = 1.020, 95%CI = 1.017–1.023, p < 0.001). In another study regarding distant organ metastasis in esophageal cancer, age was also proved to be a prognostic factor (HR = 1.016, 95%CI = 1.012–1.020, p < 0.001). 33 Esophageal cancer is a hostile disease with poor prognosis and the surgery is the best treatment option for esophageal cancer patients till now. 34 , 35 After we included LND as a stratified variable in the model, whether received surgery was found to be a risk factor for survival in patients with esophageal cancer (no / unknown vs yes: HR = 1.696, 95%CI = 1.531–1.879, p < 0.001). Radiotherapy plays an important role in the comprehensive treatment of esophageal cancer, and has been recognized as a treatment which can improve the therapeutic outcome. 34 Radiotherapy and chemotherapy can improve overall survival, yet might also enhance the risk of postoperative death in patients with locally advanced resectable esophageal cancer. 24 , 36 Therefore, in our study, whether received radiation therapy was excluded from multivariate analysis. Importantly, chemotherapy was found to significantly affect esophageal cancer patients' survival, failure to receive chemotherapy was dangerous for esophageal cancer patients. Finally, compared with AJCC I, the HR in higher AJCC patients was also constantly increasing, indicating that the higher the AJCC level, the poor prognosis for the patient's survival. Furthermore, we used R software to divide the continuous LND into two groups, with 0.12 as the cutoff point. Compared with the LND < 0.12 group, the group with higher lymph node density was indeed a risk factor for esophageal cancer patients (LND ≥ 0.12 vs LND < 0.12: HR = 1.629, 95% CI = 1.521–1.745, p < 0.001). So far, the prognostic significance of lymph node density in esophageal cancer has not been fully understood. Therefore, assessing the value of LND as a prognostic factor for esophageal cancer is a meaningful attempt. In addition, we further performed subgroup analysis with LND as the stratification. We also performed a subgroup analysis using LND as a stratification, and identified different risk factors for esophageal cancer patients in different LND subgroups. Lymph node metastasis plays a crucial role in the treatment and prognosis of esophageal squamous cell carcinoma (ESCC), and how to prevent which is particularly crucial in the early intervention process of patients. The role implications of glutathione metabolism in relation to ESCC have remained unexplored thus far. In this study, we undertook a comprehensive transcriptome analysis encompassing both high-LND and low-LND samples derived from TCGA ESCC patients. The findings from this analysis have unveiled biophysical properties of CTCFL and AHR condensate as a pivotal orchestrator of lympha node density. One open question in this study pertains to the mechanism by which glutathione metabolic process, as well as the role played the specific enzymes in this process. Our investigation has demonstrated that the interation of GSTA1, GSTA2, GSTM3 and GSTM4 can heighten lymphatic metastasis risk by inhibiting ROS level across diverse ESCC models. In conclusion, our study suggests lymph node density has a significant correlation with poor disease-specific survival for ESCC patients. Importantly, unlike the LND < 0.12 group, the group with higher lymph node density is a prognostic factor for ESCC whose LND ≥ 0.12. The dysregulated expression of CTCFL and AHR leads to decreased ROS levels mediated by GSTs, thereby expediting process of lymph node density (LND) in ESCC. Declarations Authorship contributions: Conceptualization, H.-R.Z.; methodology, H.-R.Z. and R.W.; software, H.-R.Z. and H.-H.Z; resources, H.-R.Z. and R.W.; data curation, P.-G.G. and R.W.; writing—original draft preparation, P.-G.G..; writing-original draft, H.-R.Z. and H.-H.Z.; writing review and editing, H.-R.Z. ; supervision, H.-R.Z.. All authors have read and agreed to the published version of the manuscript. Funding: No. Data availability The clinical data of ESCC patients can gain from the Surveillance, Epidemiology, and End Results (SEER) (https://seer.cancer.gov/) and the Cancer Genome Atlas (TCGA) (https://www.cancer.gov/). The RNA sequencing data of ESCC patients can gain from The Cancer Genome Atlas (TCGA) (https://www.cancer.gov/). The other datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Ethics Declarations Ethics approval and consent to participate This study was performed with full institutional ethical approval via the Meizhou people’s hospital Institutional Review Board. Approval reference number TOP-IACUC-2022-0205. Competing interests. The authors declare no competing interests. Consent for publication statement Not applicable. References Ai D, Chen Y, Liu Q, Deng J, Zhao K. The effect of tumor locations of esophageal cancer on the metastasis to liver or lung. J Thorac Dis. 2019; 11: 4205-10.10.21037/jtd.2019.09.67 Shi Y, Zhang B, Feng X, Qu F, Wang S, Wu L, et al. Apoptosis and autophagy induced by dvdms-pdt on human esophageal cancer eca-109 cells. Photodiagnosis Photodyn Ther. 2018; 24: 198-205.10.1016/j.pdpdt.2018.09.013 Hur C, Miller M, Kong CY, Dowling EC, Nattinger KJ, Dunn M, et al. 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Comparison of endoscopic resection and minimally invasive esophagectomy in patients with early esophageal cancer. J Clin Gastroenterol. 2017; 51: 223-7.10.1097/MCG.0000000000000560 Chan KKW, Saluja R, Delos Santos K, Lien K, Shah K, Cramarossa G, et al. Neoadjuvant treatments for locally advanced, resectable esophageal cancer: a network meta-analysis. Int J Cancer. 2018; 143: 430-7.10.1002/ijc.31312 Additional Declarations No competing interests reported. Supplementary Files GelBlotImages.tif Gelblotimage.rar Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-4675218","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":335596096,"identity":"1cba5f89-dc99-4266-9c49-75d2afed8fab","order_by":0,"name":"Huanrong Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqklEQVRIiWNgGAWjYDACCRBxwIaHn7+BNC1pMpIzDpCm5bCNQUMCkTrkZ/c+k+Y5c57HgOEA44ePOURoYZxz3NiY58ZtHnPmBmbJmduI0MIskcb4OOfDbR7LhgNszLzEaGGTSGM4nPPhHI/BgQQitfCAbblxgAQtEhJpzMZ/ziTzSM442EycX+RnpLFJzjhmZ8/P33zww0ditCABxgbS1I+CUTAKRsEowA0AhQ8zdQlU/s4AAAAASUVORK5CYII=","orcid":"","institution":"Meizhou City People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Huanrong","middleName":"","lastName":"Zhang","suffix":""},{"id":335596097,"identity":"9e96603b-ee33-46b9-badc-4d4be9cf727c","order_by":1,"name":"Rong Wang","email":"","orcid":"","institution":"Meizhou City People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Rong","middleName":"","lastName":"Wang","suffix":""},{"id":335596098,"identity":"7cea6d5e-99f5-486c-b41a-b332f012aa12","order_by":2,"name":"Haihui Zhong","email":"","orcid":"","institution":"Meizhou City People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Haihui","middleName":"","lastName":"Zhong","suffix":""},{"id":335596099,"identity":"c3a03a74-ce88-484a-b234-d59a22cf66a9","order_by":3,"name":"Peigui Gu","email":"","orcid":"","institution":"Meizhou City People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Peigui","middleName":"","lastName":"Gu","suffix":""}],"badges":[],"createdAt":"2024-07-02 15:12:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4675218/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4675218/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62184941,"identity":"e208e1b4-b335-46f0-bfac-6f2dd8911412","added_by":"auto","created_at":"2024-08-10 11:48:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":312532,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLND is a critical factor in determining the prognosis of ESCC.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e, The inclusion and exclusion criteria used to identify patients with esophageal squamous cell carcinoma between 2004–2019 using data from the Surveillance, Epidemiology, and End Results (SEER) database. \u003cstrong\u003eB\u003c/strong\u003e, Patients were stratified according to the LND of 0.12 in the low LND group and high LND group. The stratification cutoff was determined based on the Classification and Regression Tree. \u003cstrong\u003eC\u003c/strong\u003e, Kaplan–Meier curves with univariate analysis of the survival (left) and cumulative hazard (right) of patients with ESCC based on high versus low levels of LND.\u003c/p\u003e","description":"","filename":"FIG1.png","url":"https://assets-eu.researchsquare.com/files/rs-4675218/v1/a0554c4909c14aff86d85f37.png"},{"id":62184942,"identity":"b2cd5857-6e29-43af-9840-eaf7e83f22a8","added_by":"auto","created_at":"2024-08-10 11:48:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1377632,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of Biomarkers of Lymph Node Metastasis in Esophageal Squamous Cell Carcinoma.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e, Different gene expression in low LND (blue) or high LND (pink) esophageal squamous cell carcinoma patients from TCGA database.\u003cstrong\u003e B \u003c/strong\u003eand\u003cstrong\u003e C\u003c/strong\u003e, Gene Ontology Biological Process (GOBP) (\u003cstrong\u003eB\u003c/strong\u003e) and Kyoto Encyclopedia of Genes and Genomes (KEGG) (\u003cstrong\u003eC\u003c/strong\u003e) enrichment analyses, based differential genes from (\u003cstrong\u003eA\u003c/strong\u003e) were enriched in the most significant biological pathways. \u003cstrong\u003eD\u003c/strong\u003e, Univariate Cox regression analysis for identification of LND-related hub genes in the dataset. \u003cstrong\u003eE\u003c/strong\u003e, Multivariate Cox regression analysis for identification of prognosis in LND-related hub genes. \u003cstrong\u003eF, \u003c/strong\u003eSurvival curve for low- and high-risk subgroups, High and low-risk groups identified by multivariate regression analysis from (\u003cstrong\u003eE\u003c/strong\u003e). \u003cstrong\u003eG H \u003c/strong\u003eand\u003cstrong\u003e I,\u003c/strong\u003e risk score analysis of prognostic model in the ESCC train group. Survival curve for low- and high-risk subgroups (\u003cstrong\u003eG\u003c/strong\u003e), ROC curves for predicting OS based on risk score (\u003cstrong\u003eH\u003c/strong\u003e) and expression heat map, risk score distribution along with survival status (\u003cstrong\u003eI\u003c/strong\u003e), respectively. \u003cstrong\u003eJ K\u003c/strong\u003e and \u003cstrong\u003eL\u003c/strong\u003e, risk score analysis of prognostic model in the ESCC test group. Survival curve for low- and high-risk subgroups (\u003cstrong\u003eJ\u003c/strong\u003e), ROC curves for predicting OS based on risk score (\u003cstrong\u003eK\u003c/strong\u003e) and expression heat map, risk score distribution along with survival status (\u003cstrong\u003eL\u003c/strong\u003e), respectively.\u003c/p\u003e","description":"","filename":"FIG2.png","url":"https://assets-eu.researchsquare.com/files/rs-4675218/v1/579951ac4fdf6428c6ed7d00.png"},{"id":62184943,"identity":"4ca592c0-4be1-46d2-871d-0a7f652d9543","added_by":"auto","created_at":"2024-08-10 11:48:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2868959,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe transcription factors CTCFL and AHR collaboratively regulate Glutathione S-Transferase (GSTs)-mediated lymph node density.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e, Different transcriptional factors’ mRNA expression in low LND (blue) or high LND (pink) esophageal squamous cell carcinoma patients from TCGA database.\u003cstrong\u003e B\u003c/strong\u003e, CTCFL expression in variant tumors. Patients were selected from TAGA database. *p\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001, ****p\u0026lt;0.0001. \u003cstrong\u003eC\u003c/strong\u003e, Correlation between CTCFL and AHR based on ESCC patients from TCGA. \u003cstrong\u003eD\u003c/strong\u003e, Modeled complex structure between the AHR protein (green, left) and CTCFL protein (brown, right), AHR and CTCFL interface residues were shown as blue spheres. The hydrogen bonds and salt bridge were shown as above. Enlarged images for the interface residues between AHR and CTCFL were shown in the bottom panels, respectively. \u003cstrong\u003eE\u003c/strong\u003e, Co-IP was performed to examine interaction between CTCFL and AHR in KYSE 150 (upper panels). Western blot was performed on whole cell lysis for input (lower panels) \u003cstrong\u003eF, \u003c/strong\u003eRespective confocal microscopy image of CTCFL and AHR colocalization in KYSE 150. \u003cstrong\u003eG\u003c/strong\u003e, CUT\u0026amp;Tag-seq data of the CTCFL and AHR peaks enriched at the promoter region of GSTA1, GSTA2, GSTM3 and GSTM4, groups are indicated; TSS, transcriptional start site.\u003c/p\u003e","description":"","filename":"FIG3.png","url":"https://assets-eu.researchsquare.com/files/rs-4675218/v1/23295c60249307fa60bc114e.png"},{"id":62184945,"identity":"8b0f54bd-151c-4303-90a2-72aea81cf5c9","added_by":"auto","created_at":"2024-08-10 11:48:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":7137372,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCTCFL and AHR condensates enrich the transcription apparatus to promote GSTs transactivation.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA \u003c/strong\u003eand \u003cstrong\u003eB\u003c/strong\u003e, Disorder plots of human CTCFL and AHR.\u003cstrong\u003e C\u003c/strong\u003e, Liquid droplets were observed with confocal fluorescence microscopy in CTCFL (labeled with EGFP, green) added with AHR (labeled with mCherry, orange) solution at a range of concentration in salt buffer. Scale bars, 10μM. \u003cstrong\u003eD\u003c/strong\u003e, The immunofluoresce images of the colocalization of CTCFL and AHR (left panel), CTCFL (middle panel) and AHR droplets in vivo, Time-lapse images of FRAP on droplets in KYSE150 cells. The curves of fluorescence intensity recorded were shown on the right panel. Data are presented as mean ± sem. \u003cstrong\u003eE\u003c/strong\u003e, Wound healing assay. The upper panel shows the initial scratch created in the cell monolayer, while the lower panel shows the closure of the wound after a specified time period. The degree of wound closure is shown as areas segmented with orange lines. \u003cstrong\u003eF, \u003c/strong\u003eTranswell assay. The upper panel shows cells seeded in the upper chamber, while the lower panel shows cells that have migrated through the membrane and adhered to the underside.\u003c/p\u003e","description":"","filename":"FIG4.png","url":"https://assets-eu.researchsquare.com/files/rs-4675218/v1/00f82a63f6e247500355d6c9.png"},{"id":62184944,"identity":"67940f1d-dc17-456a-aff3-beecae1ea1ae","added_by":"auto","created_at":"2024-08-10 11:48:12","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2508026,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGSTs impact LND in esophageal squamous cell carcinoma by controlling Reactive Oxygen Species (ROS) levels.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e, Schematic illustration of the condensate of CTCFL and AHR transactive GSTs.\u003cstrong\u003eB\u003c/strong\u003e, mRNA expression of the indicated genes in indicated groups. n=3 per group. \u003cstrong\u003eC\u003c/strong\u003e, immunoblot of CTCFL, AHR, GSTA1, GSTA2, GSTM3 and GSTM4 in cells with CTCFL or AHR knockdown.\u003cstrong\u003eD\u003c/strong\u003e, PPI and subnetworks of DEGs from high LND versus low LND. \u003cstrong\u003eE\u003c/strong\u003e, Examination of GSTA1-GSTM3 (upper) and GSTA2-GSTM4 (bottom) colocalization in KYSE150 cells, nucleus was labeled with DNA (4,6-diamidino-2-phenylindole (DAPI), blue). Representative confocal microscopy images are shown. Scale bars, 10 μm (\u003cem\u003en\u003c/em\u003e = 3 independent experiments). \u003cstrong\u003eF\u003c/strong\u003e, Co-IP was performed to examine interaction of the endogenous GSTA1-GSTM3 (left) and GSTA2-GSTM4 (right) in KYSE150. Western blotting was performed on WCEs for input. \u003cstrong\u003eG\u003c/strong\u003e, Relative GSH expression in indicated groups. Experiment was performed 3 times for replicated. \u003cstrong\u003eH\u003c/strong\u003e, Relative ROS level in indicated groups. Experiment was performed 3 times for replicated.\u003c/p\u003e","description":"","filename":"FIG5.png","url":"https://assets-eu.researchsquare.com/files/rs-4675218/v1/c0df64b14cea5f886b8500a4.png"},{"id":62184947,"identity":"55ba8aa1-fe56-4fbb-968b-78b1acf34070","added_by":"auto","created_at":"2024-08-10 11:48:12","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2618707,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCTCFL and AHR collaboratively activate GSTA1 and GSTA2, promoting ESCC LND in mice.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e, Schematic illustration of spontaneous mouse model of ESCC construction. \u003cstrong\u003eB\u003c/strong\u003e, Genotyping of the indicated genes inspontaneous mouse model. \u003cstrong\u003eC\u003c/strong\u003e, Hematoxylin and eosin (H\u0026amp;E) staining of high LND and low LND from spontaneous mouse model of ESCC. \u003cstrong\u003eD\u003c/strong\u003e, Organoids establishment of spontaneous mouse model. \u003cstrong\u003eE\u003c/strong\u003e, Immunoblot of CTCFL, AHR, GSTA1 and GSTA2 of mice tumor, low LND (n=3) and high LND (n=3).\u003c/p\u003e","description":"","filename":"FIG6.png","url":"https://assets-eu.researchsquare.com/files/rs-4675218/v1/c00dc1dfa3169bb071892a82.png"},{"id":65715069,"identity":"e667ac37-08c6-4ca2-a1f6-c75746940e41","added_by":"auto","created_at":"2024-10-01 15:32:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":18973445,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4675218/v1/8e9c6337-09b9-470a-abaa-5f9b6839d56e.pdf"},{"id":62184949,"identity":"53ec6b86-0057-43df-824f-2c7cc604419e","added_by":"auto","created_at":"2024-08-10 11:48:13","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":104475848,"visible":true,"origin":"","legend":"","description":"","filename":"GelBlotImages.tif","url":"https://assets-eu.researchsquare.com/files/rs-4675218/v1/f8d28c0eec6f682d83c8c445.tif"},{"id":62184948,"identity":"9b9a7fae-7a30-4e2c-b6a2-91028b84bdfe","added_by":"auto","created_at":"2024-08-10 11:48:12","extension":"rar","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":35562318,"visible":true,"origin":"","legend":"","description":"","filename":"Gelblotimage.rar","url":"https://assets-eu.researchsquare.com/files/rs-4675218/v1/285ebc39b1a3c75705e15ffd.rar"}],"financialInterests":"No competing interests reported.","formattedTitle":"Biophysical Properties of CTCFL and AHR Condensate Regulate Glutathione S-Transferase Mediated Lymph Node Density in Esophageal Squamous Cell Carcinoma","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eEsophageal cancer is ranking as the sixth leading cause of cancer-related deaths worldwide.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e In the last 20 years, the incidence of esophageal cancer in the United States has been arising.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e It was estimated that there were approximately 455,800 new cases and 400,200 deaths for esophageal cancer in 2012 worldwide.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e In 2019, it was reported 17,650 new cases and 16,080 deaths esophageal cancer in US.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e The advances in esophageal cancer treatment, such as the application of minimally invasive esophagectomy and endoscopic treatment, can significantly reduce the incidence of complications, decrease the length of hospital stay after surgery, and improve quality of life, however, most of esophageal cancer cases are diagnosed at the late stage, thus the 5-year survival rate of esophageal cancer patients in the United States is relatively poor.\u003csup\u003e\u003cspan additionalcitationids=\"CR7 CR8 CR9\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e Therefore, developing a more comprehensive system of prognostic factors is important for improving the prognosis of esophageal cancer patients.\u003c/p\u003e \u003cp\u003eRecently, increasing evidence had identified lymph node density as an important prognostic factor for various cancer patients. For instance, it was reported that the LNR was a key prognostic factor which should be applied in stratification schemes for those clinical trials investigating adjuvant treatments in colorectal cancer patients who received curative resection.\u003csup\u003e111\u003c/sup\u003e Furthermore, LNR was the most important prognostic factor for overall as well as disease-free survival in rectal cancer patients even in those patients with less than lymph nodes examined.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e Similarly, LNR was also recognized as an independent prognostic factor for oral squamous cell carcinoma patients.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e Here, we intend to use the SEER database, which is generated by the National Cancer Institute including 18 registries that cover 30% of the US population,\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e to explore the prognostic potential of lymph node density in esophageal cancer patients.\u003c/p\u003e \u003cp\u003eGlutathione(GSH)plays an important component of the intracellular antioxidant system.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e In cancer cells, high levels of glutathione are necessary for clearing excess reactive oxygen species (ROS) and detoxifying exogenous substances, including synthetize its product by GCLC and a conjugation reaction mediated by glutathione S-transferases (GST\u003csub\u003eS\u003c/sub\u003e).\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e GST\u003csub\u003eS\u003c/sub\u003e are mainly expressed in the liver. However, some of GSTs are also expression in a high level in ESCC.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e Glutathione metabolism have long been associated with prognosis and process in ESCC. For example, GSTO1, GSTP1 and GST-pi have been perceived as tumor-associated antigens and act as predictor in ESCC.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e Previous research demonstrated that deletion of GSTM1 polymorphism was recognized as a risk factor in ESCC.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e In skin SCC, the glutathione metabolism pathway is specifically enriched in the TGF-β-activating cancer stem cell population and is responsible for the chemo-resistant property of these cell,\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e thus suggesting that glutathione metabolism might be critical for ESCC tumor progression.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Patients selection\u003c/h2\u003e \u003cp\u003eFor ESCC patients from 2004 to 2019 using SEER*Stat, version 8.3.6, Only patients over 18 years old were enrolled. All cases were stratified according to the original tumor sites: C15.0 cervical esophagus, C15.1 thoracic esophagus, C15.2 abdominal esophagus, C15.3 upper third of esophagus, C15.4 middle third of esophagus, C15.5 lower third of esophagus, C15.8 overlapping lesion of esophagus, and C15.9 esophagus, NOS. All patients were microscopically confirmed. The exclusion criteria include: patients without surgery, diagnosis, or microscopic examination; patients only have autopsy examinations result; patients with missing or inappropriate follow-up information. These exclusion criteria finally recruited 8716 eligible patients for the current study. The following information was obtained for each patient: age at diagnosis, marital status, race, sex, grade, SEER stage, surgery, radiation, chemotherapy, number of lymph nodes examined, staging according to American Joint Committee on Cancer (AJCC), year of diagnosis, survival months and vital status.\u003c/p\u003e \u003cp\u003eFor ESCC samples with corresponding clinical data from The Cancer Genome Atlas database (TCGA, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), Prior to inclusion, patients must possess comprehensive clinical data encompassing age, survival status, survival duration, lymph node density, among other pertinent factors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data processing\u003c/h2\u003e \u003cp\u003eIf not specifically addressed, continuous data were shown as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations, and categorical data were presented as frequencies or proportions. Cox regression was used to search independent risk factors first, only those variables with statistical significance by univariate analysis were then analyzed in multivariate cox regression. The informed consent from each patient was not required as the patients included in the SEER database were anonymized and de-identified before release.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e All statistical tests were two sided, \u003cem\u003eP\u003c/em\u003e value less than 0.05 was accepted statistical significant. All statistical analyses were performed using SPSS and R software.\u003c/p\u003e \u003cp\u003eTo identify the differently expressed genes between LND\u0026thinsp;\u0026lt;\u0026thinsp;0.12 and LND\u0026thinsp;\u0026ge;\u0026thinsp;0.12 in ESCC samples, We employed the Limma package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.bioconductor.org/packages/release/bioc/html/limma.html\u003c/span\u003e\u003cspan address=\"http://www.bioconductor.org/packages/release/bioc/html/limma.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to conduct analyses utilizing the negative binomial distribution. All raw data underwent preprocessing via the Limma package, wherein genes with an average count value below 1 were excluded. Furthermore, the Limma package were utilized to ascertain differentially expressed genes based on |log2 fold change (FC)|\u0026ge;1 and false discovery rate (FDR). Unless otherwise specified, continuous data were depicted as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations, while categorical data were represented as frequencies or proportions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. KEGG pathway and GO enrichment analysis\u003c/h2\u003e \u003cp\u003eThe functional roles of the differentially expressed genes were thoroughly assessed through GO enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The GO analysis encompassed terms related to cellular component (CC), molecular function (MF), and biological process (BP). Enrichment analyses were conducted using the R package (Cluster Profiler) and the DAVID online tool. Both p-values and false discovery rate (FDR) values were found to be less than 0.05, indicating statistical significance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. PPI network construction and module screening\u003c/h2\u003e \u003cp\u003eThe differentially expressed genes identified between LND\u0026thinsp;\u0026lt;\u0026thinsp;0.12 and LND\u0026thinsp;\u0026ge;\u0026thinsp;0.12 were queried against the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.string-db.org/\u003c/span\u003e\u003cspan address=\"http://www.string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to elucidate protein-protein interaction details. Subsequently, Cytoscape 3.7.0 software was employed to build and visualize the protein-protein interaction (PPI) network. Key modules and genes within this network were identified using the Molecular Complex Detection (MCODE) plug-in, with selection criteria of MCODE score and node counts exceeding 5. A significance threshold of P\u0026thinsp;\u0026le;\u0026thinsp;0.05 was applied to denote statistically significant differences.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Plasmids, antibodies and siRNA\u003c/h2\u003e \u003cp\u003ePlasmids containing CTCFL-Flag, AHR-Myc, CTCFL-EGFP, AHR-mCherry were constructed by PCR. These sequences were amplified by PCR(P520-01, Vazyme) using human cDNA made by priming KYSE150 total RNA, and assembled into the expression vectors (pCS2) with tags (2\u0026times;Myc, 3\u0026times;Flag, EGFP, mCherry). Plasmid were generated with a one-step cloning kit (Vazyme, C115-02), and the sequences were verified by colony genetic identification and Sanger sequencing. For immunoblotting and immunoprecipitation detection of endogenous CTCFL (ThermoFisher, MA5-49545), AHR (ThermoFisher, 14-9854-82), GSTA1(abcam, ab259727), GSTA1(abcam, ab211449), GSTM3(abcam, ab229858) and GSTM4(absin, abs117890) against specific to homo sapiens. The siRNA were designed from GenePharma (Suzhou, China).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. CUT\u0026amp;Tag library preparation and data analysis\u003c/h2\u003e \u003cp\u003eIn order to find out which genes were transcriptionally activated by CTCFL or AHR, we used the CUT\u0026amp;Tag Assay Kit (N259, Novoprotein, China) to capture the CTCFL or AHR binding site. The experimental procedure was performed according to the manufacturer's instructions. Briefly, 1\u0026times;10\u003csup\u003e5\u003c/sup\u003e ESCC cells were prepared and immobilized on ConA Binding magnetic beads. The beads were incubated with anti primary antibody and then with anti-rabbit IgG secondary antibody. The cell-fixed magnetic beads were washed twice, and the pA/G-Tnp Pro transposon was added, followed by fragmentation of the target DNA. DNA was extracted and a CUT\u0026amp;Tag library was constructed according to the manufacturer's instructions. The analysis of CUT\u0026amp;Tag data followed a rigorous methodology. Initially, we conducted a comprehensive quality control assessment to ensure the integrity of the data. Subsequently, the data were aligned to the reference genome employing Bowtie2, followed by peak calling using MACS2 to pinpoint DNA binding sites accurately. Functional annotation was then performed utilizing GO and KEGG databases, followed by a differential analysis facilitated by DESeq2. The results were visually represented using IGV for clarity, aiding in the interpretation of biologically significant peaks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. In vitro droplet assay\u003c/h2\u003e \u003cp\u003eThe recombinant fusion proteins were appropriately diluted using a buffer composed of 25mM Tris-HCl and 0.5mM DTT, pH 7.4, supplemented with 5% PEG 3000. Specifically, AHR-mCherry protein at a concentration of 20\u0026micro;M was diluted in the buffer and applied onto a coverslip containing CTCFL-EGFP protein also at 20\u0026micro;M. The mixture was then incubated at 37\u0026deg;C for 10 minutes, and droplets were visualized using either phase contrast microscopy or confocal microscopy (LSM880, Zeiss). Quantification of droplets was performed using ImageJ software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8. Flourescence recovery after photobleacing (FRAP)\u003c/h2\u003e \u003cp\u003eFor fluorescence recovery after photobleaching (FRAP) analysis of droplets within living cells, the entire droplets were designated as the region of interest (ROI) to ensure precise quantification of CTCFL-EGFP and AHR-mCherry's propensity for phase separation into droplets. CTCFL-EGFP granules were photobleached using a 488-nm laser, while AHR-mCherry granules were photobleached with a 561-nm laser, each for 1 \u0026micro;m at a frame rate. Subsequent frames captured after photobleaching corresponded to 3 seconds of recovery time. Each data point presented reflects the average and standard deviation of fluorescence intensities across ten unbleached (control) or ten photobleached (experimental) droplets, ensuring statistical robustness.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9. Immunofluorescence\u003c/h2\u003e \u003cp\u003e4% paraformaldehyde was used to fix cells for 5 min and followed by three PBS washes. Permeabilize using 0.2% Triton X-100 for 10 minutes and rinse three times with PBS. Block with serum from the same host as the secondary antibody for 30 minutes, followed by three PBS washes. Incubate with the primary antibody overnight at 4 degrees Celsius, with subsequent three PBS washes for optimal efficacy. Apply the secondary antibody at room temperature for 2 hours or at 37 degrees Celsius for 1.5 hours, followed by three PBS washes. Next, stain the nuclei with DAPI and visualize the fluorescent signal directly. Remove PBS, seal the slide with glycerol, and protect the edges with nail polish for preservation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10. Co-immunoprecipitation (co-IP) and Western blotting\u003c/h2\u003e \u003cp\u003eCo-IP and Western blotting were performed as previously described.\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11. Animals and Carcinogen Treatment.\u003c/h2\u003e \u003cp\u003eFive-week-old female C57BL/6 mice were purchased from Gempharmatech Co., Ltd. (Jiangsu, China). All mice were fed a basal diet (Shenzhen Topbiotech Co., Ltd., Shenzhen, China) (NO. SYXK, 2020\u0026thinsp;\u0026minus;\u0026thinsp;0230) and allowed free access to deionized water. All mice were reared in a specific pathogen-free (SPF) laboratory, and all the animal procedures and experiments conducted in this study were approved by the Animal Ethics Committee of Shenzhen Topbiotech Co., Ltd. (Permit NO. TOP-IACUC-2022-0205). We used 4-nitroquinoline 1-oxide (4NQO) from Sigma Aldrich (St. Louis, MO, USA) to induce esophageal cancer in a working concentration of 100 \u0026micro;g/mL, and stored at 4\u0026deg;C. Drinking water containing 4NQO was freshly prepared every week in total 12 weeks. Mice were euthanatized with a 4% paraformaldehyde solution at the 18th weeks or obtain tumor tissue for organoids culture, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.12. glutathione (GSH) and reactive oxygen species (ROS) assays\u003c/h2\u003e \u003cp\u003eThe intracellular GSH and ROS levels were assessed using a GSH colorimetric assay kit. All of these reagents were purchased from Solarbio (Beijing, China) and used according to the manufacturer\u0026rsquo;s instructions.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.1. LND is a critical factor in determining the prognosis of ESCC\u003c/h2\u003e \u003cp\u003eIn total 8716 eligible esophageal cancer patients were analyzed. The value of consecutive lymph nodes was determined and stratified using R code. As list in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the average ages at the patients alive or dead were 62.21\u0026thinsp;\u0026plusmn;\u0026thinsp;9.46 and 64.15\u0026thinsp;\u0026plusmn;\u0026thinsp;10.02, respectively. Most of the patients in the present cohort were married, white, male, received surgery, radiation therapy and chemotherapy. In the LND (Stratified)\u0026thinsp;\u0026lt;\u0026thinsp;0.12 (69.0%) subgroup, nearly half of patients were alive (2701, 45.2%), whereas in the LND (Stratified)\u0026thinsp;\u0026ge;\u0026thinsp;0.12 (31.0%) subgroup, majority of patients were dead (2279, 83.2%). The selection strategy for eligible patients was shown in \u003cb\u003eFig.\u0026nbsp;1A.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlive\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDead\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3162(36.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5554(63.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.21\u0026thinsp;\u0026plusmn;\u0026thinsp;9.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.15\u0026thinsp;\u0026plusmn;\u0026thinsp;10.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2597(35.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4631(64.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.136\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e565(38.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e923(62%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2150(37.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3606(62.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e455(37.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e756(62.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSWD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e444(30.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1020(69.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113(39.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e172(60.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2854(36.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4951(63.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e134(28.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e338(71.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e160(37.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e262(62.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14(82.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(17.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst malignant primary indicator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2718(37.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4622(63.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e444(32.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93267.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2701(45.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3275(54.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e461(16.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2279(83.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3084(37.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5045(62.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo/Unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78(13.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e509(86.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRadiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1809(34.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3388(65.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo/Unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1353(38.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2166(61.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChemotherapy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1990(34.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3719(65.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo/Unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1172(39.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1835(61.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAJCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1203(56.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e914(43.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1166(37.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1935(62.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e653(23.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2078(76.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140(18.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e627(81.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2204(47,7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2415(52.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e753(20.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2940(79.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e161(55.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127(44.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44(37.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72(62.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAccording to the univariate cox analysis, we found that age, marital status, race, sex, surgery, radiation, chemotherapy, LND consequent and AJCC stage had significant impact on the survival of esophageal cancer patients. We further included all of these variables in the multivariate cox regression. We found the age of diagnosis was a significant variable in mutivariate analysis, age at diagnosis (hazard ratio [HR]\u0026thinsp;=\u0026thinsp;1.020, 95% confidence interval [CI]\u0026thinsp;=\u0026thinsp;1.017\u0026ndash;1.023, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Thus, age might be an important factor affecting the esophageal cancer patients' survival. Indeed, marital status (unmarried vs married: HR\u0026thinsp;=\u0026thinsp;1.141, 95%CI\u0026thinsp;=\u0026thinsp;1.053\u0026ndash;1.238, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001; separated, widowed or divorced [SDW] vs married: HR\u0026thinsp;=\u0026thinsp;1.161, 95%CI\u0026thinsp;=\u0026thinsp;1.081\u0026ndash;1.246, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), race (black vs white, HR\u0026thinsp;=\u0026thinsp;1.278, 95%CI\u0026thinsp;=\u0026thinsp;1.142\u0026ndash;1.430, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), first malignant primary indicator (no vs yes: HR\u0026thinsp;=\u0026thinsp;1.150, 95%CI\u0026thinsp;=\u0026thinsp;1.069\u0026ndash;1.237, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), surgery (no/unknown vs yes: HR\u0026thinsp;=\u0026thinsp;1.696, 95%CI\u0026thinsp;=\u0026thinsp;1.531\u0026ndash;1.879, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), chemotherapy (no/unknown vs yes: HR\u0026thinsp;=\u0026thinsp;1.345, 95%CI\u0026thinsp;=\u0026thinsp;1.257\u0026ndash;1.439, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), AJCC stage (II vs I: HR\u0026thinsp;=\u0026thinsp;2.164, 95%CI\u0026thinsp;=\u0026thinsp;1.983\u0026ndash;2.362, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; III vs I: HR\u0026thinsp;=\u0026thinsp;3.366, 95%CI\u0026thinsp;=\u0026thinsp;3.039\u0026ndash;3.727, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; IV vs I: HR\u0026thinsp;=\u0026thinsp;3.779, 95%CI\u0026thinsp;=\u0026thinsp;3.332\u0026ndash;4.286, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were significantly correlated with the risk of esophageal cancer. R software was used to determine the following optimal cutoffs for the LND stratified: \u0026lt;0.12, \u0026ge; 0.12 (Fig.\u0026nbsp;1B). Importantly, LND stratified was also related to an increased risk of esophageal cancer (LND\u0026thinsp;\u0026ge;\u0026thinsp;0.12 vs LND\u0026thinsp;\u0026lt;\u0026thinsp;0.12: HR\u0026thinsp;=\u0026thinsp;1.629, 95%CI\u0026thinsp;=\u0026thinsp;1.521\u0026ndash;1.745, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001)(Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariate cox regression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.014\u0026ndash;1.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.017\u0026ndash;1.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.981\u0026ndash;1.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.053\u0026ndash;1.238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSWD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.140\u0026ndash;1.310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.081\u0026ndash;1.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.886\u0026ndash;1.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.979\u0026ndash;1.330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.092\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.157\u0026ndash;1.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.142\u0026ndash;1.430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.887\u0026ndash;1.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.836\u0026ndash;1.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.378\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.070\u0026ndash;0.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.071\u0026ndash;0.686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.904\u0026ndash;1.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e...\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst malignant primary indicator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.140\u0026ndash;1.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.069\u0026ndash;1.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo/Unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.590\u0026ndash;3.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.531\u0026ndash;1.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eRadiation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo/Unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.734\u0026ndash;0.819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e...\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eChemotherapy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo/Unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.720\u0026ndash;0.806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.257\u0026ndash;1.439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eLND (Stratified)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.507\u0026ndash;2.797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.521\u0026ndash;1.745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eAJCC Stage\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.831\u0026ndash;2.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.983\u0026ndash;2.362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.271\u0026ndash;3.833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.039\u0026ndash;3.727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.062\u0026ndash;4.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.332\u0026ndash;4.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe further stratified the esophageal cancer patients according to LND stratification. We first analyzed all of the variables both in the univariate cox regression analysis and multivariate cox regression. As listed in (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate cox regression analysis in the LND Stratified\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eLND (Stratified)\u0026thinsp;\u0026lt;\u0026thinsp;0.12\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eLND (Stratified)\u0026thinsp;\u0026ge;\u0026thinsp;0.12\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e95%CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.021\u0026ndash;1.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.008\u0026ndash;1.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.986\u0026ndash;1.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.034\u0026ndash;1.322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSWD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.027\u0026ndash;1.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.056\u0026ndash;1.317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.949\u0026ndash;1.389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.912\u0026ndash;1.530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.207\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.233\u0026ndash;1.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.927\u0026ndash;1.268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e...\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.057\u0026ndash;0.911\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e...\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e...\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFirst malignant primary indicator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.166\u0026ndash;1.401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e...\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eSurgery\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo/Unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.412\u0026ndash;2.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.669\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.484\u0026ndash;1.877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eRadiation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo/Unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.695\u0026ndash;0.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e...\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eChemotherapy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo/Unknown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.141\u0026ndash;1.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.599\u0026ndash;2.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eAJCC Stage\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.758\u0026ndash;2.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.789\u0026ndash;3.510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.332\u0026ndash;1.682\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.471\u0026ndash;3.605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.766\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.533\u0026ndash;2.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the LND\u0026thinsp;\u0026lt;\u0026thinsp;0.12 cohort, age at diagnosis (HR\u0026thinsp;=\u0026thinsp;1.025, 95%CI\u0026thinsp;=\u0026thinsp;1.021\u0026ndash;1.029, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), marital status (unmarried vs married was no significant difference whereas widowed or divorced [SDW] vs married: HR\u0026thinsp;=\u0026thinsp;1.126, 95%CI\u0026thinsp;=\u0026thinsp;1.027\u0026ndash;1.236, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.012), race (black vs white, HR\u0026thinsp;=\u0026thinsp;1.423, 95%CI\u0026thinsp;=\u0026thinsp;1.233\u0026ndash;1.643, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), first malignant primary indicator (no vs yes: HR\u0026thinsp;=\u0026thinsp;1.278, 95%CI\u0026thinsp;=\u0026thinsp;1.166\u0026ndash;1.401, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), surgery (no/unknown vs yes: HR\u0026thinsp;=\u0026thinsp;1.747, 95%CI\u0026thinsp;=\u0026thinsp;1.412\u0026ndash;2.163, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), chemotherapy (no/unknown vs yes: HR\u0026thinsp;=\u0026thinsp;1.299, 95%CI\u0026thinsp;=\u0026thinsp;1.141\u0026ndash;1.479, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and AJCC stage (II vs I: HR\u0026thinsp;=\u0026thinsp;1.931, 95%CI\u0026thinsp;=\u0026thinsp;1.758\u0026ndash;2.120, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; III vs I: HR\u0026thinsp;=\u0026thinsp;3.129, 95%CI\u0026thinsp;=\u0026thinsp;2.789\u0026ndash;3.510, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; IV vs I: HR\u0026thinsp;=\u0026thinsp;2.985, 95%CI\u0026thinsp;=\u0026thinsp;2.471\u0026ndash;3.602, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were significantly correlated with the risk of esophageal cancer.\u003c/p\u003e \u003cp\u003eIn the LND\u0026thinsp;\u0026ge;\u0026thinsp;0.12 cohort, age at diagnosis (HR\u0026thinsp;=\u0026thinsp;1.012, 95%CI\u0026thinsp;=\u0026thinsp;1.008\u0026ndash;1.016, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), marital status (unmarried vs married: HR\u0026thinsp;=\u0026thinsp;1.169, 95%CI\u0026thinsp;=\u0026thinsp;1.034\u0026ndash;1.322, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013; separated, widowed or divorced [SDW] vs married: HR\u0026thinsp;=\u0026thinsp;1.179, 95%CI\u0026thinsp;=\u0026thinsp;1.056\u0026ndash;1.317, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), surgery (no/unknown vs yes: HR\u0026thinsp;=\u0026thinsp;1.669, 95%CI\u0026thinsp;=\u0026thinsp;1.484\u0026ndash;1.877, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), chemotherapy (no/unknown vs yes: HR\u0026thinsp;=\u0026thinsp;1.821, 95%CI\u0026thinsp;=\u0026thinsp;1.599\u0026ndash;2.074, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and AJCC stage (III vs II: HR\u0026thinsp;=\u0026thinsp;1.497, 95%CI\u0026thinsp;=\u0026thinsp;1.322\u0026ndash;1.682, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; IV vs II: HR\u0026thinsp;=\u0026thinsp;1.766, 95%CI\u0026thinsp;=\u0026thinsp;1.533\u0026ndash;2.035, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were significantly increased the risk of esophageal cancer. Patients in LND\u0026thinsp;\u0026lt;\u0026thinsp;0.12 subgroup were in a lower burden of deaths and hazard ratio than in LND\u0026thinsp;\u0026ge;\u0026thinsp;0.12 subgroup deaths and hazard ratio during the study period \u003cb\u003e(Fig.\u0026nbsp;1C)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Identification of Biomarkers of Lymph Node Metastasis in Esophageal Squamous Cell Carcinoma\u003c/h2\u003e \u003cp\u003eIn this study, we conducted a comprehensive analysis using advanced computational methods to examine the key roles and prognostic significance of genes between patient in N0 or NX (lymph node metastasis). We obtained ESCC datasets from TCGA, comprising 77 ESCC tumor samples. Data processing and identification of differentially expressed genes (DEGs) were performed using R software packages. Out of 16,190 genes analyzed, 382 genes met the screening criteria of this study (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, qValue\u0026thinsp;\u0026lt;\u0026thinsp;0.2, |log2FC\u0026gt;|1.0), including 168 upregulated genes and 214 downregulated genes.. \u003cb\u003e(Fig.\u0026nbsp;2A)\u003c/b\u003e. To investigate the function of the identified genes, we performed functional enrichment analysis on the differentially expressed genes (DEGs). The results indicated that pathways related to glutathione metabolism were enriched in both Gene Ontology Biological Processes (GO BP) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis \u003cb\u003e(Fig.\u0026nbsp;2B and 2C)\u003c/b\u003e. Following that, we developed a genetic risk score model for predicting prognosis in lymph node metastasis samples of esophageal squamous cell carcinoma \u003cb\u003e(Fig.\u0026nbsp;2D)\u003c/b\u003e. This model was constructed using 26 candidate hub genes associated with prognosis, identified through a unique stepwise Cox regression analysis. We performed further analysis using multiple stepwise Cox regression to assess their impact on patient survival status, seven of these hub genes were found to be independent predictors of lymph node metastasis in ESCC \u003cb\u003e(Fig.\u0026nbsp;2E)\u003c/b\u003e. The survival analysis was performed to assess the predictive ability of the prognostic model, which indicated that patients in the high-risk subgroup exhibited significantly poorer overall survival (OS) compared to those in the low-risk subgroup \u003cb\u003e(Fig.\u0026nbsp;2F)\u003c/b\u003e. Subsequently, a total of 77 LUAD patients were divided into train or test groups, each group was stratified into low-risk and high-risk subgroups based on the median risk score. Consistent with previous findings, the high-risk subgroup demonstrated significantly reduced OS relative to the low-risk subgroup \u003cb\u003e(Fig.\u0026nbsp;2G and 2J).\u003c/b\u003e To further validate the prognostic efficacy of the seven biomarkers, a time-dependent ROC analysis was conducted, revealing moderate diagnostic performance \u003cb\u003e(Fig.\u0026nbsp;2H and 2K)\u003c/b\u003e. An expression heatmap and survival status plot of patients, incorporating seven genes, were generated for both subgroups \u003cb\u003e(Fig.\u0026nbsp;2I and 2L)\u003c/b\u003e. These findings underscore that the prognostic model possesses robust sensitivity and specificity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.3. The transcription factors CTCFL and AHR collaboratively regulate Glutathione S-Transferase (GSTs)-mediated lymph node density.\u003c/h2\u003e \u003cp\u003eTo investigate the regulatory mechanisms underlying lymph node metastasis in esophageal cancer, we delved into the downstream pathways associated with this process. Among the seven identified hub genes, CTCFL and AHR were notable as transcription factors, implying that lymph node metastasis in esophageal squamous cell carcinoma patients may be driven by transcriptional regulatory mechanisms. \u003cb\u003e(Fig.\u0026nbsp;3A)\u003c/b\u003e. Notably, CTCFL and AHR, known for their elevated expression across various tumor types \u003cb\u003e(Fig.\u0026nbsp;3B)\u003c/b\u003e. Besides, analysis demonstrated a significant positive correlation in patients with esophageal squamous cell carcinoma (ESCC) \u003cb\u003e(Fig.\u0026nbsp;3C)\u003c/b\u003e. Given the consistent expression and functional patterns of CTCFL and AHR, we endeavored to elucidate their intrinsic relationship. To investigate potential interactions among these two transcription factors promoting lymph node density (LND), we employed a protein-protein interaction docking prediction model merging the three-dimensional structures of Alpha Fold2 and ZDOCK docking. Our findings revealed a stable interaction between CTCFL and AHR, with a binding free energy of 32.8 kcal/mol. This stability is facilitated by five hydrogen bond connections and one halogen bond connection between the two proteins \u003cb\u003e(Fig.\u0026nbsp;3D)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eFollowing that, we conducted Co-IP experiments to validate the interaction between CTCFL and AHR \u003cb\u003e(Fig.\u0026nbsp;3E)\u003c/b\u003e. Subsequently, immunofluorescence staining confirmed the colocalization of CTCFL and AHR, a finding further supported by overexpression using distinct tags \u003cb\u003e(Fig.\u0026nbsp;3F)\u003c/b\u003e. It appears that these two factors interact to execute specific biological functions. To delve into the regulatory role of their interaction in the glutathione metabolism pathway, we considered the close relationship between esophageal squamous cell carcinoma's lymph node density and GSTs expression.\u003c/p\u003e \u003cp\u003eHence, we conducted CUT\u0026amp;Tag sequencing analysis on CTCFL, AHR, and their combined form to investigate their role in activating downstream genes within this pathway. The data revealed that CTCFL or AHR individually can bind to the transcription initiation regions of GSTA1, GSTA2, GSTM3, and GSTM4 \u003cb\u003e(Fig.\u0026nbsp;3G)\u003c/b\u003e. Surprisingly, upon interaction between CTCFL and AHR, there was a notable enhancement in their binding to the promoter regions of GSTs \u003cb\u003e(Fig.\u0026nbsp;3G)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.4. CTCFL and AHR condensates enrich the transcription apparatus to promote GSTs transactivation.\u003c/h2\u003e \u003cp\u003eIt's notable that nuclei in ESCC cells displayed a droplet-like distribution of co-localized CTCFL and AHR staining, suggesting the self-aggregation capability of these proteins. To delve into the structural basis of CTCFL and AHR condensates, we employed IUPred2 and PONDR to analyze their amino acid sequences. The results uncovered a classic intrinsically disordered region (IDR) from amino acids 37\u0026ndash;252 within CTCFL protein, but the IDRs of AHR is not obvious \u003cb\u003e(Fig.\u0026nbsp;4A and 4B)\u003c/b\u003e. To validate the formation of CTCFL and AHR condensates, we conducted overexpression and purification of EGFP-tagged CTCFL and mCherry-tagged AHR proteins in HEK293T cells. Fluorescence phase contrast microscopy revealed that at 37\u0026deg;C, CTCFL exhibited protein concentration-dependent condensation-forming ability, whereas AHR did not. However, the fusion proteins of CTCFL and AHR exhibited droplet-forming ability \u003cb\u003e(Fig.\u0026nbsp;4C)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eTo assess whether CTCFL or AHR demonstrate liquid-like properties within cells, we conducted in vivo experiments using the fluorescence recovery after photobleaching (FRAP) assay. The results indicated rapid recovery of CTCFL and the fusion proteins of CTCFL and AHR in solution, while AHR protein alone did not exhibit this ability. This suggests the significance of the intrinsically disordered region (IDR) domain for CTCFL's phase separation potential. Moreover, the fusion of CTCFL with AHR enabled the latter to recover after bleaching, emphasizing the collaborative role in this process \u003cb\u003e(Fig.\u0026nbsp;4D)\u003c/b\u003e. The findings above indicate that CTCFL and AHR interact through phase separation, with AHR's phase-separation ability contingent on CTCFL. Additionally, the fusion protein of CTCFL and AHR notably enhances their capacity to transcribe downstream genes.\u003c/p\u003e \u003cp\u003eWe next employed siRNAs to downregulate CTCFL, AHR, and both of two TFs in KYSE150 esophageal squamous cell carcinoma cells. The combined Wound Healing and Transwell experiments demonstrated that CTCFL along with AHR collectively governs the migration capability of esophageal cancer cells \u003cb\u003e(Fig.\u0026nbsp;4E and 4F)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.5. GSTs impact LND in esophageal squamous cell carcinoma by controlling Reactive Oxygen Species (ROS) levels.\u003c/h2\u003e \u003cp\u003eThe results above indicate that CTCFL undergoes liquid-liquid phase separation, leading to the formation of droplets with AHR and the aggregation into a CTCFL-AHR complex. AHR likely functions as a transcriptional regulator of CTCFL, enhancing its transcriptional activity. For instance, AHR facilitates CTCFL in transcribing GSTs \u003cb\u003e(Fig.\u0026nbsp;5A)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eTo validate the regulatory role of CTCFL and AHR in the transcription of GSTs, we employed siRNA to knock down CTCFL, AHR, and the combination of CTCFL with AHR in KYSE150 cells. The results revealed that reducing CTCFL expression led to decreased expression levels of GSTA1, GSTA2, GSTM3, and GSTM4 within the GST family, resulting in varying degrees of transcriptional inhibition. Knocking down AHR individually showed minimal impact. However, when both transcription factors were downregulated, there was a dramatically inhibiting in GST expression levels \u003cb\u003e(Fig.\u0026nbsp;5B)\u003c/b\u003e. The protein expression levels of GSTs corresponded with the observed transcriptional trends \u003cb\u003e(Fig.\u0026nbsp;5C)\u003c/b\u003e. To delve into the mechanism by which GSTA1, GSTA2, GSTM3, and GSTM4 regulate lymph node density (LND), we constructed a protein-protein interaction (PPI) network using Cytoscape software. This network integrated 1065 nodes sourced from the STRING database. Leveraging the MODE tool, we analyzed the co-expression network to identify potential key modules. The primary module obtained genes in pivotal subgroups enriched in processes related to glutathione metabolism and transferase pathways \u003cb\u003e(Fig.\u0026nbsp;5D)\u003c/b\u003e, Suggesting interactions between GSTA1, GSTA2, GSTM3, and GSTM4. To validate their direct interactions, we investigated the relationships between these four transsulfurases in KYSE150 cells. GSTA1-GSTM3 and GSTA2-GSTM4 exhibited co-localization with each other \u003cb\u003e(Fig.\u0026nbsp;5E and 5F)\u003c/b\u003e. Indeed, these proteins demonstrated potential co-localization and played essential roles in the transsulfurization pathway of glutathione synthesis.\u003c/p\u003e \u003cp\u003eBased on previous bioinformatics analyses, glutathione metabolism was notably enriched in the high LND group. GSTs serve as pivotal roles in this metabolic pathway. The levels of glutathione (GSH) and its biosynthetic capacity are influenced by the enzymatic activity of GSTs, consequently impacting tumor malignancy through the regulation of ROS levels.\u003csup\u003e\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e Therefore, we assessed glutathione (GSH) levels \u003cb\u003e(Fig.\u0026nbsp;5G)\u003c/b\u003e and reactive oxygen species (ROS) levels \u003cb\u003e(Fig.\u0026nbsp;5H)\u003c/b\u003e in KYSE 150 cells by overexpressing GSTA1, GSTA2, GSTM3, and GSTM4 in AHR, CTCFL, AHR combined with CTCFL knockdown cell lines, respectively. The findings revealed that knockdown of AHR and CTCFL significantly reduced GSH levels while increasing ROS levels. Supplementing any of these four enzymes individually could partially restore GSH content and reduce ROS levels. However, supplementing a single type of GST alone was insufficient to normalize ROS levels, suggesting a comprehensive regulation of ROS levels by GSTs.\u003c/p\u003e \u003cp\u003eThese results highlight the role of CTCFL-AHR in regulating GST expression in esophageal cancer cells and mediating ROS changes, which in turn influence the lymph node density of ESCC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.6. CTCFL and AHR collaboratively activate GSTA1 and GSTA2, promoting LND of ESCC in mice.\u003c/h2\u003e \u003cp\u003eTo confirm lymph node density (LND) in esophageal cancer relies on CTCFL, AHR and GSTs in vivo, we established a spontaneous ESCC model in p53-/-, ED-L2cre mice and generated organoids from mouse esophageal tumor tissues \u003cb\u003e(Fig.\u0026nbsp;6A and 6B)\u003c/b\u003e. These were then compared with organoids from low-LND mice \u003cb\u003e(Fig.\u0026nbsp;6C)\u003c/b\u003e. Remarkably, the organoids from high-LND mice exhibited clear growth advantages \u003cb\u003e(Fig.\u0026nbsp;6D)\u003c/b\u003e, accompanied by a significant upregulation in protein expression levels of CTCFL, AHR and GSTs \u003cb\u003e(Fig.\u0026nbsp;6E)\u003c/b\u003e. These indicate that the transcriptional factors CTCFL and AHR regulate GSTs expression, thereby influencing the lymph node density in ESCC.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIt is well recognized that lymph node metastasis is one of the most important prognostic factors for cancer patients.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e Increasing studies have shown that lymph node ratio (LNR is the ratio of the number of positive lymph nodes to the number of total lymph nodes examined) or lymph node density (LND) has been correlated with the prognosis\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. LNR or LND has been found to be more reliable than the AJCC N stage in predicting gastric cancer prognosis.\u003csup\u003e\u003cspan additionalcitationids=\"CR29 CR30\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e The study by Han et al. showed that a prognostic scoring system containing lymph node ratio could predict the survival ratio of IIIA-N2 patients after surgery and postoperative chemotherapy, and the lymph node ratio might be a useful complement to TNM staging in IIIA-N2 patients.\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e However, correlation between lymph node density and esophageal cancer patients' survival was still unclear. Thus, our study indicates that lymph node density is an prognostic factor for the survival of esophageal cancer patients for the first time. Therefore, our study elucidates the affect of LND in ESCC patients\u0026rsquo; prognosis via clinical analysis and validation of molecule phenotype.\u003c/p\u003e \u003cp\u003eWe included 8716 cases of esophageal cancer from the SEER database. The age of diagnosis was a risk factor for patients with esophageal cancer (HR\u0026thinsp;=\u0026thinsp;1.020, 95%CI\u0026thinsp;=\u0026thinsp;1.017\u0026ndash;1.023, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In another study regarding distant organ metastasis in esophageal cancer, age was also proved to be a prognostic factor (HR\u0026thinsp;=\u0026thinsp;1.016, 95%CI\u0026thinsp;=\u0026thinsp;1.012\u0026ndash;1.020, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003csup\u003e33\u003c/sup\u003e Esophageal cancer is a hostile disease with poor prognosis and the surgery is the best treatment option for esophageal cancer patients till now.\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e After we included LND as a stratified variable in the model, whether received surgery was found to be a risk factor for survival in patients with esophageal cancer (no / unknown vs yes: HR\u0026thinsp;=\u0026thinsp;1.696, 95%CI\u0026thinsp;=\u0026thinsp;1.531\u0026ndash;1.879, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Radiotherapy plays an important role in the comprehensive treatment of esophageal cancer, and has been recognized as a treatment which can improve the therapeutic outcome.\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e Radiotherapy and chemotherapy can improve overall survival, yet might also enhance the risk of postoperative death in patients with locally advanced resectable esophageal cancer.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e Therefore, in our study, whether received radiation therapy was excluded from multivariate analysis. Importantly, chemotherapy was found to significantly affect esophageal cancer patients' survival, failure to receive chemotherapy was dangerous for esophageal cancer patients. Finally, compared with AJCC I, the HR in higher AJCC patients was also constantly increasing, indicating that the higher the AJCC level, the poor prognosis for the patient's survival. Furthermore, we used R software to divide the continuous LND into two groups, with 0.12 as the cutoff point. Compared with the LND\u0026thinsp;\u0026lt;\u0026thinsp;0.12 group, the group with higher lymph node density was indeed a risk factor for esophageal cancer patients (LND\u0026thinsp;\u0026ge;\u0026thinsp;0.12 vs LND\u0026thinsp;\u0026lt;\u0026thinsp;0.12: HR\u0026thinsp;=\u0026thinsp;1.629, 95% CI\u0026thinsp;=\u0026thinsp;1.521\u0026ndash;1.745, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). So far, the prognostic significance of lymph node density in esophageal cancer has not been fully understood. Therefore, assessing the value of LND as a prognostic factor for esophageal cancer is a meaningful attempt. In addition, we further performed subgroup analysis with LND as the stratification. We also performed a subgroup analysis using LND as a stratification, and identified different risk factors for esophageal cancer patients in different LND subgroups.\u003c/p\u003e \u003cp\u003eLymph node metastasis plays a crucial role in the treatment and prognosis of esophageal squamous cell carcinoma (ESCC), and how to prevent which is particularly crucial in the early intervention process of patients. The role implications of glutathione metabolism in relation to ESCC have remained unexplored thus far. In this study, we undertook a comprehensive transcriptome analysis encompassing both high-LND and low-LND samples derived from TCGA ESCC patients. The findings from this analysis have unveiled biophysical properties of CTCFL and AHR condensate as a pivotal orchestrator of lympha node density. One open question in this study pertains to the mechanism by which glutathione metabolic process, as well as the role played the specific enzymes in this process. Our investigation has demonstrated that the interation of GSTA1, GSTA2, GSTM3 and GSTM4 can heighten lymphatic metastasis risk by inhibiting ROS level across diverse ESCC models.\u003c/p\u003e \u003cp\u003eIn conclusion, our study suggests lymph node density has a significant correlation with poor disease-specific survival for ESCC patients. Importantly, unlike the LND\u0026thinsp;\u0026lt;\u0026thinsp;0.12 group, the group with higher lymph node density is a prognostic factor for ESCC whose LND\u0026thinsp;\u0026ge;\u0026thinsp;0.12. The dysregulated expression of CTCFL and AHR leads to decreased ROS levels mediated by GSTs, thereby expediting process of lymph node density (LND) in ESCC.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthorship contributions:\u0026nbsp;\u003c/strong\u003eConceptualization, H.-R.Z.; methodology, H.-R.Z. and R.W.; software, H.-R.Z. and H.-H.Z; resources, H.-R.Z. and R.W.; data curation, P.-G.G. and R.W.; writing\u0026mdash;original draft preparation, P.-G.G..; writing-original draft, H.-R.Z. and H.-H.Z.; writing review and editing, H.-R.Z. ; supervision, H.-R.Z.. All authors have read and agreed to the published version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eNo.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe clinical data of ESCC patients can gain from the Surveillance, Epidemiology, and End Results (SEER) (https://seer.cancer.gov/) and the Cancer Genome Atlas (TCGA) (https://www.cancer.gov/). The RNA sequencing data of ESCC patients can gain from The Cancer Genome Atlas (TCGA) (https://www.cancer.gov/). The other datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThis study was performed with full institutional ethical approval via the Meizhou people\u0026rsquo;s hospital Institutional Review Board. Approval reference number TOP-IACUC-2022-0205.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAi D, Chen Y, Liu Q, Deng J, Zhao K. 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Int J Cancer. 2018; 143: 430-7.10.1002/ijc.31312\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"esophageal squamous cell carcinoma, lymph node density, optimal cutoff, cox regression, glutathione metabolism, phase separation, Glutathione S-Transferase","lastPublishedDoi":"10.21203/rs.3.rs-4675218/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4675218/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eEsophageal squamous cell carcinoma (ESCC) prognosis is closely associated with lymph node density (LND). The identification of biomarkers and regulatory mechanisms influencing LND could enhance our understanding of ESCC progression and inform therapeutic strategies.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis study analyzed 8,716 esophageal cancer patients to determine the prognostic significance of LND. Univariate and multivariate Cox regression analyses were performed to assess clinical factors. Gene expression data from The Cancer Genome Atlas (TCGA) were used to identify differentially expressed genes (DEGs) between LND\u0026thinsp;\u0026lt;\u0026thinsp;0.12 and LND\u0026thinsp;\u0026ge;\u0026thinsp;0.12 groups. Functional enrichment, protein-protein interactions, and transcriptional regulation were investigated using advanced computational tools, immunoprecipitation, immunofluorescence, CUT\u0026amp;Tag sequencing, and phase separation assays.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eHigher LND (\u0026ge;\u0026thinsp;0.12) was associated with poorer survival outcomes. DEGs analysis revealed significant enrichment in glutathione metabolic pathways. CTCFL and AHR transcription factors were identified as key regulators of glutathione S-Transferase (GSTs) genes. These transcription factors exhibited phase separation properties, enhancing GSTs transcription. Knockdown experiments confirmed that CTCFL and AHR collaboratively regulate GSTs, affecting reactive oxygen species (ROS) levels and LND. In vivo, ESCC models demonstrated upregulation of CTCFL, AHR, and GSTs in high-LND mice, corroborating the regulatory role of these factors in tumor progression.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe transcription factors CTCFL and AHR regulate GST-mediated glutathione metabolism, influencing LND and ESCC progression. Targeting these regulatory pathways may offer novel therapeutic approaches for managing ESCC.\u003c/p\u003e","manuscriptTitle":"Biophysical Properties of CTCFL and AHR Condensate Regulate Glutathione S-Transferase Mediated Lymph Node Density in Esophageal Squamous Cell Carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-10 11:48:07","doi":"10.21203/rs.3.rs-4675218/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b6493155-ce1d-4526-baf2-380cfb799c2c","owner":[],"postedDate":"August 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-01T15:23:50+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-10 11:48:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4675218","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4675218","identity":"rs-4675218","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}