The Tumor-Promoting Role of Neutrophil Extracellular Traps in Esophageal Squamous Cell Carcinoma and Their Interaction with the Gut Microbiota

The Tumor-Promoting Role of Neutrophil Extracellular Traps in Esophageal Squamous Cell Carcinoma and Their Interaction with the Gut Microbiota | 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 The Tumor-Promoting Role of Neutrophil Extracellular Traps in Esophageal Squamous Cell Carcinoma and Their Interaction with the Gut Microbiota Ziqiang Hong, Qing Liu, Yi Zhang, Xiang Shi, Dacheng Jing, Tao Cheng, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6203794/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 and Objectives In recent years, the role of neutrophil extracellular traps (NETs) in the tumor microenvironment has garnered increasing attention, yet their relationship with esophageal squamous cell carcinoma (ESCC) remains poorly understood. Additionally, the interplay between gut microbiota and the tumor immune microenvironment may influence the progression of ESCC. This study aims to investigate the diagnostic value of NETs-related markers (CitH3, MPO, and NE) in ESCC patients, their correlation with clinical characteristics, and the impact of NETs levels on patient prognosis. Furthermore, we seek to elucidate the pro-tumorigenic mechanisms of NETs in ESCC. By analyzing gut microbiota composition, we also aim to uncover differences in microbial communities between ESCC patients and healthy individuals and explore their association with NETs levels, thereby providing novel theoretical foundations for the early diagnosis and treatment of ESCC. Methods Peripheral blood, surgical specimens, fecal samples, and clinical data were collected from 60 ESCC patients undergoing surgical treatment, along with peripheral blood and fecal samples from 60 healthy controls. ELISA was employed to measure plasma levels of CitH3, MPO, and NE in both groups, and their correlations with clinical features were analyzed. The diagnostic efficacy of NETs markers was evaluated using ROC curves, and the 3-year survival rates of patients with high versus low CitH3 levels were compared. Changes in NETs levels pre- and post-surgery, as well as the impact of different surgical approaches on NETs, were assessed. 16S rDNA gene sequencing was utilized to analyze differences in gut microbial composition, and its correlation with plasma NETs levels was explored. Immunohistochemistry, Western blot (WB), and qRT-PCR were performed to detect the expression of CitH3, MPO, and NE in surgical specimens. In vitro experiments involved stimulating neutrophils with phorbol esters to generate NETs, followed by functional assays and pathway analyses to evaluate the effects of NETs on ESCC cells. A subcutaneous xenograft model in nude mice was established to validate the pro-tumorigenic mechanisms of NETs. Results The plasma levels of CitH3, MPO, and NE in ESCC patients were significantly elevated compared to those in healthy controls and were correlated with clinical characteristics. The AUC value for diagnosing ESCC using NETs was 0.981, demonstrating high sensitivity and specificity. Elevated CitH3 levels were indicative of lower survival rates. Postoperative levels of CitH3, MPO, and NE increased, with robot-assisted minimally invasive esophagectomy (RAMIE) showing lower levels of these markers compared to video-assisted minimally invasive esophagectomy (VAMIE). Dysbiosis of the gut microbiota in ESCC patients was associated with NETs levels. In vitro experiments revealed that NETs promoted ESCC cell proliferation, migration, invasion, and angiogenesis. WB analysis indicated that NETs facilitated epithelial-mesenchymal transition (EMT) and angiogenesis by upregulating the protein expression levels of N-Cadherin, Vimentin, MMP2, MMP9, HIF-1α, TNF-α, VEGF, VEGFA, Ang-1, and Ang-2. In vivo experiments confirmed that NETs promoted tumor growth, and DNase1 partially reversed this effect. Conclusions This study elucidates the tumor-promoting role of NETs in ESCC and their association with gut microbiota. NETs markers (CitH3, MPO, and NE) were significantly elevated in ESCC patients, offering diagnostic and prognostic value. NETs promote tumor progression by regulating EMT and angiogenesis pathways, with DNase1 partially reversing this effect. Dysbiosis of the gut microbiota in ESCC patients is linked to NETs levels. These findings provide novel insights into the early diagnosis and targeted therapy of ESCC, warranting further exploration into the regulatory mechanisms of NETs and microbiota. Esophageal squamous cell carcinoma Neutrophil extracellular traps Gut microbiota Epithelial-mesenchymal transition Angiogenesis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background Esophageal Squamous Cell Carcinoma (ESCC) represents a significant global health burden, particularly in Asian countries, where it exhibits high incidence and mortality rates, ranking as one of the leading causes of cancer-related deaths [ 1 , 2 ]. Despite recent advancements in diagnostic techniques and therapeutic modalities for ESCC, the overall prognosis for patients remains suboptimal, with five-year survival rates persistently low. This underscores the pressing need to elucidate the underlying mechanisms of ESCC pathogenesis and to explore novel diagnostic and therapeutic strategies. Within the realm of cancer research, the tumor microenvironment has garnered increasing attention for its pivotal role in cancer progression. As a burgeoning research hotspot in the tumor microenvironment, the role of NETs in the process of tumorigenesis and development has gradually become the focus of research [ 3 , 4 ]. NETs are web-like structures released by activated neutrophils, composed of DNA and a variety of proteins, including citrullinated histone H3 (CitH3), myeloperoxidase (MPO), and neutrophil elastase (NE) [ 5 , 6 ]. Accumulating evidence suggests that NETs play a significant role in various cancers, influencing tumor cell proliferation, migration, invasion, and angiogenesis through multiple pathways [ 7 , 8 ]. However, the relationship between NETs and ESCC remains largely unexplored, with no definitive conclusions drawn to date. Simultaneously, the intricate and dynamic interplay between the gut microbiota and the host immune system has increasingly been implicated in the initiation and progression of tumors [ 9 ]. A growing body of evidence suggests that gut dysbiosis is closely associated with the development and progression of various cancers, potentially influencing tumor evolution through mechanisms such as modulation of immune homeostasis, generation of metabolic byproducts, and regulation of inflammatory responses [ 10 , 11 ]. However, the precise mechanistic role of the gut microbiota in ESCC remains poorly understood. Furthermore, whether a relationship exists between the gut microbiota and NETs, and how such an interaction might impact the pathogenesis and progression of ESCC, warrants further investigation. This study aims to comprehensively and systematically evaluate the diagnostic utility of NETs-related biomarkers in ESCC patients, elucidate their intrinsic associations with clinical characteristics and patient prognosis, and further delineate the tumor-promoting mechanisms of NETs in ESCC progression. Additionally, by comparing the gut microbiota composition between ESCC patients and healthy individuals, we seek to uncover potential correlations between microbial profiles and NETs levels. The findings of this research are anticipated to provide novel insights into the pathogenesis of ESCC, identify clinically relevant biomarkers and therapeutic targets, and contribute to the advancement of early diagnosis and precision medicine in ESCC management. Materials and Methods Study Subjects Case Group: A total of 60 patients with pathologically confirmed ESCC, who were scheduled for surgical treatment at our institution, were enrolled in this study. Peripheral venous blood, surgically resected tumor tissue specimens, and fecal samples were collected from these patients. Control Group: Sixty healthy individuals undergoing routine health examinations during the same period were selected as controls, and their peripheral venous blood and fecal samples were collected for comparative analysis. Ethical Statement: This study was approved by the Ethics Committee of our institution, and written informed consent was obtained from all participants prior to their inclusion. Experimental Methods Specimen Collection and Processing Peripheral Blood Collection and Processing: Fasting venous blood samples (2 mL) were collected from the antecubital vein in the morning using EDTA-coated anticoagulant tubes. The tubes were gently inverted to ensure proper mixing and transported to the laboratory within 15 minutes. The samples were centrifuged at 3000 rpm for 20 minutes at 4°C to separate the plasma, which was then aliquoted into 1 mL cryovials and stored at -80°C until further analysis. Tissue Specimen Processing: Tumor tissues and adjacent normal tissues were collected from ESCC patients during surgical resection. A portion of the tissues was immediately fixed for immunohistochemical analysis, while the remaining samples were snap-frozen in liquid nitrogen for subsequent experiments. Fecal Sample Collection and Processing: Fecal samples were collected from 20 ESCC patients and 20 healthy controls. Approximately 5 g of fresh fecal material was collected in the morning using a sealed specimen container and transported to the laboratory under cold chain conditions. The samples were stored at -80°C. For microbial DNA extraction, the surface layer of the fecal sample was removed, and 0.2 g of material from 0.3 cm below the surface was used. The remaining samples were retained for additional analyses. Surgical Procedures RAMIE: Thirty ESCC patients underwent RAMIE using the da Vinci robotic platform (McKeown technique). The procedure was performed under general anesthesia with single-lumen endotracheal intubation and bilateral lung ventilation. The surgery was conducted in three phases: thoracic, abdominal, and cervical. Detailed procedural steps are described in our previous study [ 12 ]. VAMIE: Another 30 ESCC patients underwent VAMIE using thoracoscopy and laparoscopy (McKeown technique). The remaining procedural steps were identical to those of RAMIE. Isolation of Neutrophils and Extraction of NETs Neutrophil Isolation: Five milliliters of fasting peripheral venous blood were collected into EDTA-coated tubes. Five milliliters of neutrophil isolation medium were layered at the bottom, followed by the slow addition of 5 mL of blood. The mixture was centrifuged at 450×g for 40 minutes at 20°C. The neutrophil layer was collected and treated with red blood cell lysis buffer, followed by centrifugation at 450×g for 5 minutes at 24°C. This step was repeated until complete red blood cell lysis was achieved. The neutrophils were washed twice with PBS, centrifuged at 300×g for 10 minutes, and resuspended in RPMI-1640 medium supplemented with 10% fetal bovine serum. Cell viability was assessed using trypan blue staining, ensuring a viability rate exceeding 95%. Induction and Extraction of NETs: Purified neutrophils (1×10⁶ cells) were seeded into six-well plates and stimulated with 100 nM phorbol 12-myristate 13-acetate (PMA; MCE, USA) for 4 hours in a 37°C, 5% CO₂ incubator. The supernatant was gently aspirated, leaving behind NETs and neutrophils. The NETs were washed with pre-cooled calcium- and magnesium-free PBS, and the wash solution was collected. The samples were centrifuged at 450×g for 10 minutes at 4°C, and the supernatant was further centrifuged at 15,000×g for 15 minutes at 4°C. The pellet was resuspended in PBS, and NETs formation was confirmed by microscopy using Sytox Green staining. The concentration of NETs was quantified using a microvolume spectrophotometer. ELISA Assay The levels of NETs-related biomarkers (CitH3, MPO, and NE) and inflammatory cytokines (IL-6 and IL-8) in plasma were quantified using enzyme-linked immunosorbent assay (ELISA) kits (Hangzhou Lianke Biotechnology Co., Ltd.) in strict accordance with the manufacturer’s instructions. The diagnostic efficacy of NETs biomarkers for ESCC was evaluated by receiver operating characteristic (ROC) curve analysis. Additionally, the 3-year survival rates were compared between patients with high and low CitH3 levels. Changes in plasma levels of CitH3, MPO, NE, IL-6, and IL-8 were assessed preoperatively and postoperatively in ESCC patients, and the impact of different surgical approaches (RAMIE vs. VAMIE) on NETs levels was analyzed. Gut Microbiota Analysis The differences in gut microbial composition between ESCC patients and healthy controls were analyzed using 16S rDNA gene sequencing. Microbial DNA was extracted from fecal samples, and sequencing was performed on the Illumina platform. Bioinformatics tools such as QIIME2 and LEfSe were employed to analyze microbial diversity and identify differentially abundant taxa. Furthermore, the correlation between gut microbiota composition and plasma NETs levels was explored. Immunohistochemistry ESCC tumor tissues and adjacent normal tissues were fixed in 4% paraformaldehyde, embedded in paraffin, and sectioned at a thickness of 4µm. Tissue sections were subjected to antigen retrieval by heating in citrate buffer (pH 6.0) at 95°C for 20 minutes. Non-specific binding was blocked with 3% bovine serum albumin (BSA), followed by incubation with primary antibodies against CitH3, MPO, and NE (1:200 dilution) at 4°C overnight. Horseradish peroxidase (HRP)-conjugated secondary antibodies (1:500 dilution) were applied and incubated at room temperature for 1 hour. Color development was achieved using 3,3'-diaminobenzidine (DAB), and nuclei were counterstained with hematoxylin. Staining results were visualized under a microscope, and the intensity of positive staining was quantified using ImageJ software. Cell Culture and Experimental Grouping Cell Culture: KYSE-30 and TE-1 cells were cultured in RPMI-1640 medium supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin. Human umbilical vein endothelial cells (HUVECs) were maintained in endothelial cell-specific EGM-2 medium, enriched with 10% FBS, endothelial cell growth supplements, and 1% penicillin-streptomycin. All cell lines were incubated at 37°C in a humidified atmosphere containing 5% CO₂. Experimental Grouping: Cells were divided into the following experimental groups: (1) Control group: ESCC cells without treatment; (2) NETs group: ESCC cells treated with 0.5 µg/mL NETs; and (3) NETs + DNase1 group: ESCC cells treated with 0.5 µg/mL NETs and 100 U/mL DNase1. CCK-8 Assay Cells from different treatment groups were seeded into 96-well plates, and cell proliferation was assessed at 0, 24, 48, and 72 hours. After adding the CCK-8 reagent, the optical density (OD) at 450 nm was measured 2 hours later to evaluate cell viability. Colony Formation Assay A total of 100–200 cells were seeded into 6-well plates and cultured for 7 days. The colonies were fixed with 4% paraformaldehyde and stained with crystal violet. Colonies consisting of 50 or more cells were counted to determine clonogenic survival. Wound Healing Assay Cells were seeded into culture dishes until they reached 90% confluency. A sterile 200 µL pipette tip was used to create a scratch, and the cells were gently washed with PBS to remove debris. Serum-free medium was added, and the width of the scratch was measured at 0 and 36 hours to evaluate cell migration capability. Transwell Migration and Invasion Assay For the migration assay, 2×10 4 cells were seeded into the upper chamber of a Transwell insert, while the lower chamber was filled with RPMI-1640 medium supplemented with 10% FBS. After 36 hours of incubation, the cells that migrated to the lower surface of the membrane were fixed and stained with crystal violet. For the invasion assay, the Transwell inserts were pre-coated with Matrigel, and the same procedure was followed. The number of migrated or invaded cells was quantified under a microscope. Tube Formation Assay HUVECs were seeded onto Matrigel-coated 24-well plates and incubated for 6 hours. The formation of capillary-like structures was observed under a microscope, and the total tube length and number of branches were quantified using ImageJ software. Quantitative Real-Time PCR (qRT-PCR) Total RNA was extracted using TRIzol reagent and reverse-transcribed into cDNA. The relative expression levels of target genes were calculated using the 2^-ΔΔCt method, with GAPDH serving as the internal reference gene. The PCR cycling conditions were as follows: initial denaturation at 95°C for 5 minutes, followed by 40 cycles of denaturation at 95°C for 10 seconds, annealing at 60°C for 10 seconds, and extension at 72°C for 20 seconds. Primer sequences are provided in Supplementary Table S1 . Western Blot Analysis Proteins were extracted using RIPA lysis buffer, and 10 µg of protein was separated by SDS-PAGE. After transferring to a PVDF membrane, the membrane was blocked for 2 hours and subsequently incubated with primary antibodies (overnight at 4°C) and secondary antibodies (2 hours at room temperature). Protein bands were visualized using enhanced chemiluminescence (ECL), and band intensities were quantified using ImageJ software. Detailed antibody information is provided in Supplementary Table S2 . Subcutaneous Tumor Formation in Nude Mice Cells from different treatment groups (KYSE30, KYSE30 + NETs, and KYSE30 + NETs + DNase1) were subcutaneously injected into the right flank of male BALB/c nude mice (5 weeks old, n = 5 per group). Tumor volume was measured every 5 days using a caliper, and mice were euthanized on day 27. Tumors were excised and weighed for further analysis. Statistical Analysis Data were analyzed using SPSS or GraphPad Prism software. Continuous variables are presented as mean ± standard deviation (SD), and comparisons between groups were performed using Student’s t-test or one-way ANOVA. Categorical variables were analyzed using the chi-square test. The Anosim algorithm was used to evaluate the significance of differences in β-diversity. The Linear discriminant analysis Effect Size (LEfSe) tool was employed to screen for differential species between groups. Survival analysis was conducted using the Kaplan-Meier method, and diagnostic efficacy was evaluated using ROC curve analysis. A P-value < 0.05 was considered statistically significant. Results Expression and Clinical Significance of NETs-Related Biomarkers in ESCC Patients The experimental workflow of this study is illustrated in Fig. 1A. No significant differences were observed in baseline characteristics between the ESCC group and the healthy control group (Table 1). The ELISA detection process for plasma samples from both ESCC patients and healthy controls is depicted in Fig. 1B. The results revealed that the plasma levels of CitH3, MPO, and NE were significantly elevated in ESCC patients compared to healthy controls (Fig. 1C, Table 2), suggesting a potential role of NETs in the pathogenesis and progression of ESCC. ROC curve analysis demonstrated that the combination of CitH3, MPO, and NE achieved an area under the curve (AUC) of 0.981 for diagnosing ESCC, with a sensitivity of 92.3% and specificity of 94.7% (Fig. 1D, Table 3). These findings indicate that these biomarkers, when used in combination, hold substantial promise for clinical application. Furthermore, a significant positive correlation was observed among the levels of CitH3, MPO, and NE (P < 0.001) (Fig. 1E), underscoring their synergistic role in NETs formation. NETs Levels and Their Association with Clinical Characteristics No significant differences in NETs levels were observed between genders (P > 0.05) (Fig. 1F), suggesting that their expression may not be influenced by sex-related factors. Further analysis revealed that the plasma levels of CitH3, MPO, and NE in ESCC patients were closely associated with several clinical characteristics (Supplementary Table S3 ). Patients aged > 60 years exhibited significantly higher plasma levels of CitH3, MPO, and NE compared to those aged ≤ 60 years ( P < 0.05) (Fig. 1G), implying that older patients may be more prone to NETs formation due to immunosenescence or chronic inflammatory states. Additionally, patients with a tumor diameter ≥ 3 cm demonstrated significantly elevated plasma levels of CitH3, MPO, and NE compared to those with a tumor diameter < 3 cm ( P < 0.05) (Fig. 1H), indicating a potential correlation between NETs levels and tumor burden. Moreover, patients with lymph node metastasis exhibited significantly higher plasma levels of CitH3, MPO, and NE than those without metastasis ( P < 0.05) (Fig. 1I), suggesting that NETs may facilitate tumor cell invasion and metastasis, thereby contributing to disease progression. Notably, stage III patients displayed significantly higher plasma levels of CitH3, MPO, and NE compared to stage I patients ( P < 0.05) (Fig. 1J), further supporting the association between NETs levels and disease severity. Prognostic analysis revealed that patients with high CitH3 levels had a significantly lower 3-year overall survival rate compared to those with low CitH3 levels ( P = 0.034) (Fig. 1K), indicating that CitH3 may serve as an independent prognostic predictor for ESCC patients. Table 1 Comparison of Baseline Characteristics Between ESCC Patients and Healthy Controls Characteristic ESCC Group (n = 60) Healthy Control Group (n = 60) P-value Gender 0.581 Male 36 32 Female 24 28 Age (years) 0.709 ≤ 60 22 25 > 60 38 35 Smoking History 0.144 Yes 35 26 No 25 34 Drinking history 0.262 Yes 40 33 No 20 27 Table 2 Comparison of NETs Levels Between ESCC Patients and Healthy Controls Characteristic ESCC Group (n = 60) Healthy Control Group (n = 60) P-value CitH3 (ng/mL) 203.33 ± 15.76 177.99 ± 12.03 < 0.001 MPO (ng/mL) 22.81 ± 2.79 18.10 ± 3.02 < 0.001 NE (ng/mL) 1.65 ± 0.59 0.97 ± 0.28 < 0.001 Table 3 Diagnostic Value of CitH3, MPO, and NE for ESCC Characteristic Optimal cut-off value Sensitivity (%) Specificity (%) AUC value Maximum Youden index CitH3 192.621 78.3 93.3 0.902 0.717 MPO 19.653 88.3 75.0 0.874 0.633 NE 0.986 93.3 66.7 0.865 0.600 CitH3 + MPO + NE / 95.0 91.7 0.981 0.867 Changes in NETs Levels Pre- and Post-Surgery and the Impact of Surgical Approaches Postoperative plasma levels of CitH3, MPO, and NE in ESCC patients were significantly elevated compared to preoperative levels (POD1, POD3, POD6; P < 0.05), peaking around POD3 (Fig. 2A). This phenomenon is likely attributable to systemic inflammatory responses and neutrophil activation triggered by surgical trauma. To investigate the underlying mechanisms driving the postoperative increase in NETs levels, we measured the levels of key cytokines known to induce NETs formation—interleukin-6 (IL-6) and interleukin-8 (IL-8). The results revealed a significant postoperative elevation in both IL-6 and IL-8 levels ( P < 0.05) (Fig. 2B, 2C), suggesting that surgical trauma may promote NETs formation by inducing the release of IL-6 and IL-8, thereby activating neutrophils. Further analysis comparing 30 patients who underwent RAMIE and 30 patients who underwent VAMIE demonstrated that postoperative levels of CitH3, MPO, and NE were significantly lower in the RAMIE group compared to the VAMIE group at POD1, POD3, and POD6 (P < 0.05) (Fig. 2D, 2E). This indicates that RAMIE may mitigate postoperative NETs release by reducing surgical trauma and inflammatory responses. Correlation Between Gut Microbiota and NETs Levels The workflow for gut microbiota analysis is illustrated in Fig. 3A, encompassing sample collection, DNA extraction, 16S rDNA sequencing, and data analysis. Taxonomic composition analysis at the phylum level revealed that both ESCC patients and healthy controls were predominantly colonized by Firmicutes. However, the abundance of Proteobacteria was significantly elevated in the ESCC group (Fig. 3B, 3C, 3D). At the genus level, significant differences were observed between the ESCC group and healthy controls (Fig. 3E, 3F). In the ESCC group, the abundance of opportunistic pathogens such as Clostridium_P was markedly increased, while beneficial genera like Akkermansia were significantly reduced. Alpha diversity analysis indicated no significant differences between the two groups in terms of richness, diversity, evenness, or coverage (Fig. 3G). Rarefaction curves demonstrated that the sequencing depth was sufficient to capture the microbial diversity (Fig. 3K). Beta diversity analysis, based on Jaccard distance, revealed distinct clustering of bacterial communities between the two groups, as visualized by principal coordinate analysis (PCoA) and non-metric multidimensional scaling (NMDS) (Fig. 3H, 3I). LEfSe analysis identified significantly differentially abundant taxa: in the ESCC group, the phyla Proteobacteria , Fusobacteriota , and Chloroflexota were significantly enriched, while at the genus level, opportunistic pathogens such as Clostridium_P and Faecalimonas were more abundant. In contrast, the healthy control group exhibited a higher abundance of beneficial taxa, including the phylum Verrucomicrobiota and the genus Akkermansia (Fig. 3J). Spearman correlation analysis (Table 4) demonstrated that the abundance of Klebsiella , Streptococcus , and Veillonella was positively correlated with plasma NETs levels. Conversely, the abundance of Akkermansia , Roseburia , Blautia , Eggerthella , and Actinomyces showed a significant negative correlation with plasma NETs levels. Table 4 Correlation Analysis Between Differential Genus Abundance and Plasma NETs Levels Characteristic NETs-CitH3 NETs-MPO NETs-NE r* p r* p r* p Akkermansia -0.50 <0.001 -0.51 <0.001 -0.55 <0.001 Roseburia -0.70 <0.001 -0.60 <0.001 -0.78 <0.001 Blautia -0.58 <0.001 -0.60 <0.001 -0.55 <0.001 Eggerthella -0.77 <0.001 -0.74 <0.001 -0.76 <0.001 Actinomyces -0.61 <0.001 -0.61 <0.001 -0.58 <0.001 Klebsiella 0.66 <0.001 0.67 <0.001 0.63 <0.001 Streptococcus 0.60 <0.001 0.55 <0.001 0.72 0 and negatively correlated when r < 0. The closer the absolute value of r is to 1, the stronger the linear correlation between the two variables. Expression of NETs in Surgical Specimens from ESCC Patients Immunohistochemical Analysis Immunohistochemical analysis revealed that the expression levels of CitH3, MPO, and NE were significantly higher in ESCC tissues compared to adjacent non-tumor tissues (Fig. 4A), suggesting a pronounced enrichment of NETs in the tumor microenvironment of ESCC. To further validate these findings, we performed WB analysis on 18 pairs of ESCC tissues and matched adjacent non-tumor tissues. The results demonstrated that the protein expression levels of CitH3, MPO, and NE were significantly elevated in ESCC tissues compared to adjacent tissues (Fig. 4B), consistent with the immunohistochemical results. Additionally, qRT-PCR analysis showed that the mRNA expression levels of CitH3, MPO, and NE were also significantly upregulated in ESCC tissues (Fig. 4C), indicating that NETs-related genes are transcriptionally activated in ESCC. Collectively, these findings demonstrate that NETs are significantly enriched in the tumor microenvironment of ESCC and may contribute to tumor progression and metastasis through their histone, protease, and oxidase components. This discovery provides histological evidence supporting the tumor-promoting role of NETs in ESCC and lays the groundwork for further investigation into the molecular mechanisms underlying NETs-mediated tumorigenesis. Impact of NETs on ESCC Cell Function Expression of NETs in ESCC Cells The workflow for NETs extraction is illustrated in Fig. 4D. WB analysis revealed that the protein expression levels of CitH3, MPO, and NE were significantly higher in the NETs-treated group compared to the Control group ( P < 0.05) (Fig. 4E), indicating that NETs markedly induce the expression of these biomarkers. In the NETs + DNase1 group, the protein levels of CitH3, MPO, and NE were reduced compared to the NETs group ( P < 0.05) but remained higher than those in the Control group ( P < 0.05), suggesting that DNase1 partially attenuates, but does not completely abolish, the induction effect of NETs. qRT-PCR results demonstrated that the mRNA expression levels of CitH3, MPO, and NE were significantly elevated in the NETs group compared to the Control group ( P < 0.05), indicating that NETs exert a regulatory effect at the transcriptional level (Fig. 4F). In the NETs + DNase1 group, the mRNA levels of these markers were reduced compared to the NETs group ( P < 0.05) but remained higher than those in the Control group ( P < 0.05), further supporting the partial reversal of NETs' effects by DNase1. These findings collectively demonstrate that NETs significantly upregulate the expression of CitH3, MPO, and NE in ESCC cells, while DNase1 partially reverses this effect through NETs degradation. Cell Proliferation and Colony Formation Assays Cell proliferation was assessed at 0, 24, 48, and 72 hours using the CCK-8 assay. The results showed that the OD450 values in the NETs group were significantly higher than those in the Control group at all time points ( P < 0.05) (Fig. 4G), indicating that NETs markedly enhance the proliferative capacity of ESCC cells. In the NETs + DNase1 group, the OD450 values were significantly lower than those in the NETs group ( P < 0.05) but remained higher than those in the Control group, suggesting that DNase1 partially reverses the pro-proliferative effects of NETs. Colony formation assays revealed that the number of colonies consisting of 50 or more cells was significantly higher in the NETs group compared to the Control group ( P < 0.05) (Fig. 4H), demonstrating that NETs significantly enhance the clonogenic potential of ESCC cells. The number of colonies in the NETs + DNase1 group was intermediate between the Control and NETs groups, further confirming that DNase1 partially inhibits the pro-clonogenic effects of NETs. Cell Migration and Invasion Assays The wound healing assay demonstrated that, at 36 hours, the migration rate of cells in the NETs group was significantly higher than that in the Control group ( P < 0.05) (Fig. 4I), indicating that NETs markedly enhance the migratory capacity of ESCC cells. The migration rate in the NETs + DNase1 group was intermediate between the Control and NETs groups. Transwell assays revealed that the number of migrating and invading cells in the NETs group was significantly higher than that in the Control group ( P < 0.05) (Fig. 4J), suggesting that NETs significantly promote the migratory and invasive abilities of ESCC cells. In the NETs + DNase1 group, the number of migrating and invading cells was significantly reduced compared to the NETs group but remained higher than that in the Control group, indicating that DNase1 partially reverses the pro-migratory and pro-invasive effects of NETs. Angiogenesis Assay The tube formation assay demonstrated that the number and length of tubes formed by cells in the NETs group were significantly greater than those in the Control group ( P < 0.05) (Fig. 4J), indicating that NETs significantly enhance the angiogenic capacity of ESCC cells. In the NETs + DNase1 group, the number and length of tubes were significantly reduced compared to the NETs group but remained higher than those in the Control group, suggesting that DNase1 partially reverses the pro-angiogenic effects of NETs. Molecular Mechanisms Underlying NETs-Mediated ESCC Progression NETs Regulate the Expression of EMT-Related Proteins WB analysis revealed that in ESCC cells treated with NETs, the expression of mesenchymal markers N-Cadherin and Vimentin, as well as matrix metalloproteinases MMP2 and MMP9, was significantly elevated compared to the Control group ( P < 0.05). Conversely, the expression of the epithelial marker E-Cadherin was significantly reduced ( P < 0.05) (Fig. 5A, 5B). In the NETs + DNase1 group, the expression of N-Cadherin, Vimentin, MMP2, and MMP9 was significantly lower than that in the NETs group ( P < 0.05) but remained higher than that in the Control group ( P < 0.05). Meanwhile, the expression of E-Cadherin was partially restored. These findings suggest that NETs promote the invasion and metastasis of ESCC cells by inducing EMT, while DNase1 partially reverses this effect through the degradation of NETs. NETs Activate Inflammatory Signaling Pathways WB analysis revealed that the expression of hypoxia-inducible factor HIF-1α and tumor necrosis factor TNF-α was significantly elevated in ESCC cells treated with NETs compared to the Control group ( P < 0.05) (Fig. 5C). In the NETs group, the expression of p-NF-κB (p-p65), p-Ikkβ, and p-IκBα was markedly upregulated ( P < 0.05) (Fig. 5C), indicating activation of the NF-κB signaling pathway. In the NETs + DNase1 group, the expression of HIF-1α, TNF-α, and NF-κB pathway-related proteins was significantly reduced compared to the NETs group ( P < 0.05) but remained higher than that in the Control group ( P < 0.05). These results suggest that NETs promote inflammatory responses and angiogenesis in the tumor microenvironment by activating the HIF-1α/TNF-α/NF-κB signaling axis, while DNase1 partially inhibits this process. Molecular Mechanisms of NETs-Mediated Angiogenesis NETs treatment significantly upregulated the protein expression of angiogenesis-related factors, including VEGF, Ang-1, VEGFA, and Ang-2 in ESCC cells (Fig. 5D). This finding further underscores the critical role of NETs in promoting tumor angiogenesis and microenvironment remodeling. The addition of DNase1 partially reversed this effect, as evidenced by a reduction in the expression levels of these angiogenic factors, although they remained higher than those in the Control group. This indicates that DNase1 attenuates, but does not completely abolish, NETs-induced angiogenesis through the degradation of NETs. Validation of the Tumor-Promoting Role of NETs in a Nude Mouse Xenograft Model In the subcutaneous xenograft tumor model using nude mice, the tumor-promoting effects of NETs were validated (experimental schematic shown in Fig. 5E). The results demonstrated that both tumor volume and weight in the NETs group were significantly greater than those in the Control group ( P < 0.05) (Fig. 5F, 5G, 5H), indicating that NETs significantly enhance tumor growth. In the NETs + DNase1 group, tumor volume and weight were significantly reduced compared to the NETs group ( P < 0.05) but remained higher than those in the Control group ( P < 0.05), suggesting that DNase1 partially inhibits tumor growth by degrading NETs. qRT-PCR analysis revealed that the mRNA expression levels of CitH3, MPO, and NE in tumor tissues from the NETs group were significantly higher than those in the Control group ( P < 0.05) (Fig. 5I), indicating that NETs exert a significant regulatory effect on these markers at the transcriptional level. WB analysis further confirmed that the protein expression levels of CitH3, MPO, and NE were significantly elevated in the NETs group compared to the Control group ( P < 0.05) (Fig. 5J). Additionally, the expression of mesenchymal markers N-Cadherin and Vimentin, as well as matrix metalloproteinases MMP2 and MMP9, was significantly upregulated in the NETs group ( P < 0.05), while the expression of the epithelial marker E-Cadherin was significantly downregulated ( P < 0.05) (Fig. 5K, 5L). These findings suggest that NETs promote tumor invasion and metastasis by inducing EMT. Collectively, these results comprehensively elucidate the critical role of NETs in promoting tumor growth and inducing EMT at both the phenotypic and molecular levels, providing substantial experimental evidence for understanding the mechanisms underlying NETs-mediated tumorigenesis and progression. Discussion This study is the first to systematically elucidate the tumor-promoting role of NETs in ESCC and their potential association with gut microbiota. Through clinical sample analysis, molecular mechanism exploration, and animal model validation, we have demonstrated that NETs-related biomarkers (CitH3, MPO, and NE) hold significant diagnostic and prognostic value in ESCC. Furthermore, we have delineated the molecular mechanisms by which NETs promote tumor progression through the regulation of EMT and angiogenesis pathways. Additionally, the dysbiosis of gut microbiota in ESCC patients was found to be closely associated with NETs levels, suggesting that the intricate interplay within the microbiota-immune-tumor axis may represent a novel direction for future ESCC research. 1.NETs as Novel Diagnostic and Prognostic Biomarkers in ESCC This study demonstrates that plasma levels of CitH3, MPO, and NE in ESCC patients are significantly elevated compared to those in healthy individuals. Notably, elevated CitH3 levels are closely associated with advanced tumor staging, lymph node metastasis, and poor prognosis. ROC curve analysis further reveals that the combination of these three biomarkers achieves an impressive AUC of 0.981, underscoring their potential as non-invasive diagnostic markers. These findings align with previous studies indicating that NETs are implicated in the progression of various malignancies, including gastric cancer and breast cancer [ 13 , 14 ]. However, the specific role of NETs in ESCC remains to be fully elucidated. We hypothesize that NETs may facilitate tumor cell invasion through the release of proteases such as NE and MPO, which directly degrade the basement membrane, or via CitH3-mediated chromatin decondensation, thereby promoting tumor aggressiveness. 2. Impact of Surgical Approaches on NETs Levels This study observed a significant postoperative increase in plasma levels of CitH3, MPO, and NE in ESCC patients compared to preoperative levels, which may be closely associated with systemic inflammatory responses and neutrophil activation triggered by surgical trauma. Further analysis revealed that the postoperative levels of CitH3, MPO, and NE were significantly lower in the RAMIE group compared to the traditional VAMIE group (P < 0.05), suggesting that RAMIE may reduce postoperative NETs release by minimizing surgical trauma and inflammatory responses. Additionally, this study found that postoperative plasma levels of inflammatory cytokines IL-6 and IL-8 were significantly elevated compared to preoperative levels ( P < 0.01), with their trends aligning closely with the increase in NETs biomarkers. As known activators of neutrophils, IL-6 and IL-8 may promote NETs release by activating the NF-κB signaling pathway [ 15 – 17 ]. This phenomenon suggests that surgical trauma may induce the release of IL-6 and IL-8, thereby activating neutrophils and promoting NETs formation, which could potentially impact postoperative recovery. These findings provide critical insights for selecting appropriate surgical approaches in clinical practice. RAMIE, as a novel minimally invasive surgical technique, may reduce tissue damage and inflammatory responses, thereby lowering postoperative NETs levels. Future large-scale clinical studies are warranted to validate the impact of different surgical approaches on the prognosis of ESCC patients and to explore the potential value of perioperative anti-inflammatory therapies (e.g., IL-6/IL-8 inhibitors) in reducing NETs formation and improving patient outcomes. 3. Interaction Between Gut Microbiota Dysbiosis and NETs 16S rDNA sequencing revealed a significant increase in the abundance of opportunistic pathogens, such as Proteobacteria , Fusobacteriota , Klebsiella , and Streptococcus , in the gut microbiota of ESCC patients. Conversely, the abundance of beneficial bacteria with anti-inflammatory and barrier-protective functions, such as Akkermansia and Roseburia , was markedly reduced. Notably, a positive correlation was observed between the abundance of opportunistic pathogens and NETs levels, while a negative correlation was found between beneficial bacteria and NETs levels. This suggests that gut microbiota may influence NETs formation by modulating neutrophil activity, thereby contributing to the progression of ESCC. This study is the first to reveal a significant correlation between gut microbiota dysbiosis and plasma NETs levels in ESCC patients, providing critical evidence for understanding the role of the "microbiota-immune-tumor" axis in ESCC. However, the specific regulatory mechanisms underlying the interaction between gut microbiota and NETs remain to be elucidated. Future studies could employ metagenomics and metabolomics approaches to investigate the impact of gut microbiota-derived metabolites on NETs formation and ESCC progression, offering new insights for the prevention and treatment of ESCC. 4. Molecular Mechanisms Underlying NETs-Mediated ESCC Progression In vitro experiments demonstrated that NETs significantly enhance the proliferative, migratory, invasive, and angiogenic capacities of ESCC cells, and these effects were partially reversed by DNase1. Extensive prior literature has shown that NETs promote the EMT process in various cancers. Building on this, we first investigated whether NETs similarly induce EMT in ESCC. The results unequivocally confirmed that NETs significantly promote EMT in ESCC, consistent with findings in other tumor types, thereby expanding our understanding of the role of NETs in tumor biology. Concurrently, in angiogenesis assays, we observed that NETs significantly enhance vascular generation in ESCC. Given the close interplay between EMT and angiogenesis in tumor progression, both of which critically drive malignant behaviors such as invasion and metastasis, it is of great significance to explore the signaling pathways associated with these processes. We focused on the HIF-1α/TNF-α/NF-κB signaling pathway because HIF-1α, activated under hypoxic conditions, can induce the expression of multiple genes related to angiogenesis and EMT [ 18 , 19 ]. TNF-α, as an inflammatory cytokine, promotes EMT and angiogenesis through the NF-κB signaling pathway [ 20 , 21 ]. Our WB results validated this hypothesis: in ESCC cells treated with NETs, the expression of HIF-1α and TNF-α was significantly upregulated, and key molecules of the NF-κB signaling pathway (e.g., p-NF-κB, p-Ikkβ, and p-IκBα) were activated. These findings align closely with the NETs-induced EMT and angiogenesis phenotypes. Furthermore, NETs treatment significantly increased the protein expression of angiogenesis-related factors, including VEGF, Ang-1, VEGFA, and Ang-2, underscoring the critical role of NETs in promoting tumor angiogenesis and microenvironment remodeling. The addition of DNase1 partially reversed these effects, as evidenced by a reduction in the expression levels of these angiogenic factors, although they remained higher than those in the control group. This indicates that DNase1 attenuates, but does not completely abolish, NETs-induced angiogenesis through the degradation of NETs. By validating the HIF-1α/TNF-α/NF-κB signaling pathway, we have gained a more comprehensive and in-depth understanding of how NETs coordinately promote EMT and angiogenesis by modulating key signaling pathways, thereby driving the malignant progression of ESCC. The nude mouse xenograft experiments further validated the tumor-promoting role of NETs, while the tumor-suppressive effects of DNase1 suggest that targeting NETs degradation may represent a potential therapeutic strategy for ESCC. These findings provide a solid theoretical foundation for the development of future therapeutic strategies targeting NETs and related signaling pathways in ESCC. Despite the significant insights provided by this study, several limitations remain: (1) This is a single-center study with a relatively small sample size, which may limit the generalizability and representativeness of the findings; (2) The study only explored the correlation between gut microbiota and NETs levels without delving into the specific regulatory mechanisms; (3) The tumor-suppressive effects of DNase1 were only partially effective, suggesting that NETs may exert their effects through multiple pathways, necessitating the exploration of combination targeting strategies (e.g., DNase1 + immune checkpoint inhibitors) in future research. Additionally, the interactions between NETs and other immune cells (e.g., macrophages, T cells) and their overall impact on the tumor immune microenvironment warrant further investigation. Conclusion This study represents the first systematic elucidation of the tumor-promoting role of NETs in ESCC and their potential interplay with the gut microbiota. The identification of CitH3, MPO, and NE as novel diagnostic biomarkers offers a promising avenue for the early detection of ESCC, while their correlation with prognosis provides a foundation for the development of personalized therapeutic strategies. Furthermore, the mechanisms by which NETs facilitate tumor progression through the regulation of EMT and angiogenesis pathways, coupled with the tumor-suppressive effects of DNase1, establish a robust experimental basis for the development of NETs-targeted therapies. Future investigations should focus on unraveling the intricate regulatory mechanisms between the gut microbiota and NETs, as well as elucidating the role of NETs in immune evasion in ESCC. These efforts will provide critical theoretical insights and technical support for the precise prevention and treatment of ESCC. Abbreviations ESCC Esophageal squamous cell carcinoma NETs Neutrophil extracellular traps ELISA Enzyme linked immunosorbent assay CitH3 Citrullinated histone H3 MPO Myeloperoxidase NE Neutrophil elastase RAMIE robot-assisted minimally invasive esophagectomy VAMIE Video-assisted minimally invasive esophagectomy WB Western blot EMT Epithelial-mesenchymal transition ROC Receiver operating characteristic qRT-PCR Quantitative Real-Time PCR HUVECs Human umbilical vein endothelial cells Declarations Acknowledgements The authors have nothing to report. Authors’ contributions ZQH, QL, HXL, HLP and YJG: conception and design and administrative support; QL, YZ and XS: provision of study materials or patients; DCJ: collection and assembly of the data; TC: data analysis and interpretation; ZQH, QL, HXL, HLP and YJG: revise the manuscript; manuscript writing: all authors. The authors read and approved the final manuscript. Funding This research was supported by Gansu Province Key R&D Project (22YF7FA095). Data availability Data is provided within the manuscript or supplementary information fles. Ethics approval and consent to participate The present study was approved by the Ethics Committee of Gansu Provincial Hospital, approval number: 2024-019 (Lanzhou, China). Consent for publication All the authors have read and approved the final manuscript for publication. Competing interests The authors declare no competing interests. Author details 1 Department of Thoracic Surgery, Liaoning Cancer Hospital & Institute, Cancer Hospital of China Medical University, Shenyang 110042, China; 2 Department of thoracic surgery, The Affiliated Huizhou Hospital, Guangzhou Medical University, Huizhou 516000, China; 3 Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China; 4 Department of thoracic surgery, Gansu Provincial Hospital, Lanzhou 730000, China; 5 Department of thoracic surgery, Zunyi Medical University Qianxinan Affiliated Hospital, Xingyi 562400, China. References Jing ZQ, Wu,Yifei, Xie et al. Ribonucleotide reductase small subunit M2 promotes the proliferation of esophageal squamous cell carcinoma cells via HuR-mediated mRNA stabilization. Acta Pharm Sin B. 2024;14:0. Jin Tao M, Mao Y, Lu, et al. ∆Np63α promotes radioresistance in esophageal squamous cell carcinoma through the PLEC-KEAP1-NRF2 feedback loop. Cell Death Dis. 2024;15:0. Till JE, Seewald NJ, Yazdani Z, et al. Corticosteroid-dependent association between prognostic peripheral blood cell-free DNA levels and neutrophil-mediated NETosis in patients with glioblastoma. Clin Cancer Res Published online January. 2025;31. 10.1158/1078-0432.CCR-24-3169 . Xu L, Kong Y, Li K, et al. Neutrophil extracellular traps promote growth of lung adenocarcinoma by mediating the stability of m6A-mediated SLC2A3 mRNA-induced ferroptosis resistance and CD8(+) T cell inhibition. Clin Transl Med. 2025;15(2):e70192. Tan C, Aziz M, Wang P. The vitals of NETs. J Leukoc Biol. 2021;110(4):797–808. Honda M, Kubes P. Neutrophils and neutrophil extracellular traps in the liver and gastrointestinal system. Nat Rev Gastroenterol Hepatol. 2018;15(4):206–21. Khan U, Chowdhury S, Billah MM, et al. Neutrophil Extracellular Traps in Colorectal Cancer Progression and Metastasis. Int J Mol Sci. 2021;22(14):7260. Xiao Y, Cong M, Li J, et al. Cathepsin C promotes breast cancer lung metastasis by modulating neutrophil infiltration and neutrophil extracellular trap formation. Cancer Cell. 2021;39(3):423–e4377. Cune D, Pitasi CL, Rubiola A, et al. Inhibition of Atg7 in intestinal epithelial cells drives resistance against Citrobacter rodentium. Cell Death Dis. 2025;16(1):112. Tsenkova M, Brauer M, Pozdeev VI, et al. Ketogenic diet suppresses colorectal cancer through the gut microbiome long chain fatty acid stearate. Nat Commun. 2025;16(1):1792. Liu WT, Hu XW, Choy YN, et al. Investigating the role of inflammatory cytokines in mediating the effect of gut microbiota on gastrointestinal cancers: a mendelian randomization study. Gastric Cancer Published online Febr. 2025;17. 10.1007/s10120-025-01587-w . Hong Z, Cui B, Wang K, et al. Comparison of Clinical Efficacy Between Da Vinci Robot-Assisted Ivor Lewis Esophagectomy and McKeown Esophagectomy for Middle and Lower Thoracic Esophageal Cancer: A Multicenter Propensity Score-Matched Study. Ann Surg Oncol. 2023;30(13):8271–7. Li J, Xia Y, Sun B, et al. Neutrophil extracellular traps induced by the hypoxic microenvironment in gastric cancer augment tumour growth. Cell Commun Signal. 2023;21(1):86. Zhao H, Liang Y, Sun C, et al. Dihydrotanshinone I Inhibits the Lung Metastasis of Breast Cancer by Suppressing Neutrophil Extracellular Traps Formation. Int J Mol Sci. 2022;23(23):15180. Joshi MB, Lad A, Bharath Prasad AS, Balakrishnan A, Ramachandra L, Satyamoorthy K. High glucose modulates IL-6 mediated immune homeostasis through impeding neutrophil extracellular trap formation. FEBS Lett. 2013;587(14):2241–6. Yang L, Liu L, Zhang R, et al. IL-8 mediates a positive loop connecting increased neutrophil extracellular traps (NETs) and colorectal cancer liver metastasis. J Cancer. 2020;11(15):4384–96. de Andrea CE, Ochoa MC, Villalba-Esparza M, et al. Heterogenous presence of neutrophil extracellular traps in human solid tumours is partially dependent on IL-8. J Pathol. 2021;255(2):190–201. Wang M, Zhao X, Zhu D, et al. HIF-1α promoted vasculogenic mimicry formation in hepatocellular carcinoma through LOXL2 up-regulation in hypoxic tumor microenvironment. J Exp Clin Cancer Res. 2017;36(1):60. Zeng X, Liu S, Yang H, et al. Synergistic anti-tumour activity of ginsenoside Rg3 and doxorubicin on proliferation, metastasis and angiogenesis in osteosarcoma by modulating mTOR/HIF-1α/VEGF and EMT signalling pathways. J Pharm Pharmacol. 2023;75(11):1405–17. Liu BX, Xie Y, Zhang J, et al. SERPINB5 promotes colorectal cancer invasion and migration by promoting EMT and angiogenesis via the TNF-α/NF-κB pathway. Int Immunopharmacol. 2024;131:111759. Ma Q, Hao S, Hong W, et al. Versatile function of NF-ĸB in inflammation and cancer. Exp Hematol Oncol. 2024;13(1):68. Additional Declarations No competing interests reported. Supplementary Files RawimagesofWesternBlotexperiment.pptx ColonyFormationAssayTranswellMigrationandInvasionAssayTubeFormationAssayoriginalfigure.tiff WoundHealingAssayoriginalfigure.tiff SupplementaryTable1.docx SupplementaryTable2.docx SupplementaryTable3.docx 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6203794","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":427727114,"identity":"412034b3-3c06-4a98-b8d1-2bace572f965","order_by":0,"name":"Ziqiang Hong","email":"","orcid":"","institution":"Liaoning Cancer Hospital \u0026 Institute, Cancer Hospital of China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ziqiang","middleName":"","lastName":"Hong","suffix":""},{"id":427727115,"identity":"49e238fb-7f5a-4b98-94cb-5347c4ae525b","order_by":1,"name":"Qing Liu","email":"","orcid":"","institution":"The Affiliated Huizhou Hospital, Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Qing","middleName":"","lastName":"Liu","suffix":""},{"id":427727118,"identity":"d305ec05-8bee-43a7-8553-d0c6ae9661b5","order_by":2,"name":"Yi Zhang","email":"","orcid":"","institution":"Liaoning Cancer Hospital \u0026 Institute, Cancer Hospital of China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Zhang","suffix":""},{"id":427727119,"identity":"5e32c138-8e78-4db7-9038-bd0ed9b0d99e","order_by":3,"name":"Xiang Shi","email":"","orcid":"","institution":"Liaoning Cancer Hospital \u0026 Institute, Cancer Hospital of China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiang","middleName":"","lastName":"Shi","suffix":""},{"id":427727120,"identity":"f5e9e41e-c164-41ad-8f36-bbc7222e93af","order_by":4,"name":"Dacheng Jing","email":"","orcid":"","institution":"Gansu Provincial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Dacheng","middleName":"","lastName":"Jing","suffix":""},{"id":427727121,"identity":"6205a893-7c2b-4ba9-a6b9-a8f7dba4a334","order_by":5,"name":"Tao Cheng","email":"","orcid":"","institution":"Zunyi Medical University Qianxinan Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Cheng","suffix":""},{"id":427727122,"identity":"9b4f776c-fa80-4493-929c-7dd4bca9202b","order_by":6,"name":"Hongxu Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYBACAwhlwcPA3tj48EOFhBw/kVokeBh4Dh82ljhjYSzZQKQWIEpLk+Btq0jcQEiLOXvv4de8bRIyBgdyDCQk50kwbmBgfvjoBh4tlj3n0ixntknwGBw4Y2BQuE2C2ZyBzdg4B5/DbuSYGXwEaTnYY5AguU2CzbKBh02aoJZEkJbDQIt454CsI6zF+AHYlmNsiQ28DRIShLWcOWPGOOOcBI/kGebDzBLHJAwkmwn55XiP8WeeMht7vvsP239+qKmr72dvfvgYnxYgYJNA5TPjVw5W8oGwmlEwCkbBKBjRAAAxokmMvdn0mgAAAABJRU5ErkJggg==","orcid":"","institution":"Liaoning Cancer Hospital \u0026 Institute, Cancer Hospital of China Medical University","correspondingAuthor":true,"prefix":"","firstName":"Hongxu","middleName":"","lastName":"Liu","suffix":""},{"id":427727123,"identity":"d3a34c2a-63f6-4555-8257-73e920e9c0dc","order_by":7,"name":"Hailong Piao","email":"","orcid":"","institution":"Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Hailong","middleName":"","lastName":"Piao","suffix":""},{"id":427727124,"identity":"6dd0d17c-ba30-4642-826f-114fef17e1ea","order_by":8,"name":"Yunjiu Gou","email":"","orcid":"","institution":"Gansu Provincial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yunjiu","middleName":"","lastName":"Gou","suffix":""}],"badges":[],"createdAt":"2025-03-11 14:08:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6203794/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6203794/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":78688016,"identity":"9c8bab80-ef15-4544-a155-029cec172a0e","added_by":"auto","created_at":"2025-03-17 15:50:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2647676,"visible":true,"origin":"","legend":"\u003cp\u003eExpression and Clinical Significance of NETs in ESCC. \u003cstrong\u003eA.\u003c/strong\u003eSchematic flowchart of the study design. \u003cstrong\u003eB.\u003c/strong\u003e Diagram illustrating the ELISA-based detection of plasma biomarkers in ESCC patients and healthy controls. \u003cstrong\u003eC.\u003c/strong\u003e Plasma levels of CitH3, MPO, and NE in ESCC patients compared to healthy controls. \u003cstrong\u003eD.\u003c/strong\u003e ROC curve analysis evaluating the diagnostic efficacy of CitH3, MPO, and NE for ESCC. \u003cstrong\u003eE.\u003c/strong\u003e Correlation analysis of CitH3, MPO, and NE expression levels. \u003cstrong\u003eF, G, H, I, J.\u003c/strong\u003eAssociations between plasma levels of CitH3, MPO, and NE and clinical characteristics, including gender, age, tumor diameter, lymph node metastasis, and pathological stage in ESCC patients. \u003cstrong\u003eK.\u003c/strong\u003e Three-year overall survival (OS) rates in ESCC patients stratified by high and low CitH3 levels.\u003c/p\u003e","description":"","filename":"Fig.1.png","url":"https://assets-eu.researchsquare.com/files/rs-6203794/v1/ee3438bb4bccb8c801246130.png"},{"id":78688025,"identity":"5c872550-0f35-49ae-badf-b35669ef4d3d","added_by":"auto","created_at":"2025-03-17 15:50:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2974246,"visible":true,"origin":"","legend":"\u003cp\u003eImpact of Preoperative, Postoperative, and Surgical Approaches on NETs Levels. \u003cstrong\u003eA.\u003c/strong\u003e Changes in plasma levels of CitH3, MPO, and NE before surgery and at postoperative day 1 (POD1), day 3 (POD3), and day 6 (POD6). \u003cstrong\u003eB, C.\u003c/strong\u003eChanges in plasma levels of IL-6 and IL-8 before surgery and at POD1, POD3, and POD6. \u003cstrong\u003eD.\u003c/strong\u003e Intraoperative images comparing RAMIE and VAMIE. \u003cstrong\u003eE.\u003c/strong\u003eComparison of plasma levels of CitH3, MPO, and NE between RAMIE and VAMIE groups before surgery and at POD1, POD3, and POD6.\u003c/p\u003e","description":"","filename":"Fig.2.png","url":"https://assets-eu.researchsquare.com/files/rs-6203794/v1/e4795ab4e5f012464578745a.png"},{"id":78688778,"identity":"88e1f88d-e072-4f9c-a247-dc7033b719e8","added_by":"auto","created_at":"2025-03-17 15:58:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":5097442,"visible":true,"origin":"","legend":"\u003cp\u003eGut Microbiota Analysis in ESCC Patients and Healthy Controls. \u003cstrong\u003eA.\u003c/strong\u003eSchematic diagram of the gut microbiota analysis workflow for ESCC patients and healthy controls. \u003cstrong\u003eB.\u003c/strong\u003e Venn diagram illustrating the overlap and uniqueness of operational taxonomic units (OTUs) between ESCC patients and healthy controls. \u003cstrong\u003eC.\u003c/strong\u003e Stacked bar plot showing the relative abundance of bacterial phyla in the combined ESCC and control groups. \u003cstrong\u003eD.\u003c/strong\u003e Stacked bar plot displaying the relative abundance of bacterial phyla in individual samples. \u003cstrong\u003eE.\u003c/strong\u003e Stacked bar plot depicting the relative abundance of bacterial genera in the combined ESCC and control groups. \u003cstrong\u003eF.\u003c/strong\u003e Stacked bar plot presenting the relative abundance of bacterial genera in individual samples. \u003cstrong\u003eG.\u003c/strong\u003e Boxplots of alpha diversity indices (richness, diversity, evenness, and coverage) comparing the ESCC and control groups. \u003cstrong\u003eH.\u003c/strong\u003eTwo-dimensional principal coordinate analysis (PCoA) plot based on Jaccard distance, illustrating the clustering of bacterial communities. \u003cstrong\u003eI.\u003c/strong\u003eTwo-dimensional non-metric multidimensional scaling (NMDS) plot based on Jaccard distance, demonstrating the separation of bacterial communities. \u003cstrong\u003eJ.\u003c/strong\u003eLinear discriminant analysis (LDA) effect size (LEfSe) bar plot highlighting differentially abundant taxa. The length of the bars (LDA score) represents the magnitude of significant differences in taxonomic units. \u003cstrong\u003eK.\u003c/strong\u003e Rarefaction curves of alpha diversity indices, indicating the sufficiency of sequencing depth to capture microbial diversity.\u003c/p\u003e","description":"","filename":"Fig.3.png","url":"https://assets-eu.researchsquare.com/files/rs-6203794/v1/f6fc5b13c4692ad5b5649849.png"},{"id":78688896,"identity":"cf15abd9-2680-4eb9-98cf-933b27bf215e","added_by":"auto","created_at":"2025-03-17 16:06:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":15282198,"visible":true,"origin":"","legend":"\u003cp\u003eExpression of NETs in ESCC Tissues and Cells and Their Impact on ESCC Cell Function. \u003cstrong\u003eA.\u003c/strong\u003e Immunohistochemical analysis of CitH3, MPO, and NE expression in ESCC tissues and adjacent non-tumor tissues. \u003cstrong\u003eB, C.\u003c/strong\u003e Protein and mRNA expression levels of CitH3, MPO, and NE in 18 pairs of ESCC tissues and matched adjacent non-tumor tissues. \u003cstrong\u003eD.\u003c/strong\u003e Schematic diagram illustrating the extraction of NETs. \u003cstrong\u003eE, F.\u003c/strong\u003e Protein and mRNA expression levels of CitH3, MPO, and NE in ESCC cells. \u003cstrong\u003eG.\u003c/strong\u003e CCK-8 assay evaluating the effect of NETs on ESCC cell proliferation. \u003cstrong\u003eH.\u003c/strong\u003e Colony formation assay assessing the impact of NETs on ESCC cell proliferation. \u003cstrong\u003eI.\u003c/strong\u003e Wound healing assay analyzing the effect of NETs on ESCC cell migration. \u003cstrong\u003eJ.\u003c/strong\u003e Transwell migration, invasion, and tube formation assays investigating the influence of NETs on ESCC cell migration, invasion, and angiogenesis.\u003c/p\u003e","description":"","filename":"Fig.4.png","url":"https://assets-eu.researchsquare.com/files/rs-6203794/v1/081dffca6e78d00cf1fbc182.png"},{"id":78688022,"identity":"6f3b2af2-0017-4d2a-8bff-712f482f0c17","added_by":"auto","created_at":"2025-03-17 15:50:09","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":4122594,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular Mechanisms Underlying NETs-Mediated ESCC Progression. \u003cstrong\u003eA.\u003c/strong\u003eNETs promote EMT. \u003cstrong\u003eB.\u003c/strong\u003e NETs enhance the invasion and metastasis of ESCC cells by upregulating the expression of MMP2 and MMP9. \u003cstrong\u003eC.\u003c/strong\u003e NETs activate the HIF-1α/TNF-α/NF-κB signaling axis. \u003cstrong\u003eD.\u003c/strong\u003e Molecular mechanisms by which NETs promote angiogenesis. \u003cstrong\u003eE.\u003c/strong\u003e Schematic diagram of subcutaneous tumor formation in nude mice. \u003cstrong\u003eF.\u003c/strong\u003e Subcutaneous tumors from different treatment groups. \u003cstrong\u003eG.\u003c/strong\u003e Tumor volume growth curves over time. \u003cstrong\u003eH.\u003c/strong\u003e Tumor weight at the endpoint of the experiment. \u003cstrong\u003eI.\u003c/strong\u003e mRNA expression levels of CitH3, MPO, and NE in tumor tissues from different treatment groups. \u003cstrong\u003eJ.\u003c/strong\u003e Protein expression levels of CitH3, MPO, and NE in tumor tissues from different treatment groups. \u003cstrong\u003eK.\u003c/strong\u003e Expression of EMT-related proteins in tumor tissues from different treatment groups.\u003c/p\u003e","description":"","filename":"Fig.5.png","url":"https://assets-eu.researchsquare.com/files/rs-6203794/v1/e7a0bca6f079d878d210cbe1.png"},{"id":84746667,"identity":"2a373600-504d-4adf-ae6e-257132351004","added_by":"auto","created_at":"2025-06-17 01:16:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":30356471,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6203794/v1/113b25e0-46aa-4ae0-b085-9e3aeb0564ee.pdf"},{"id":78688050,"identity":"44a6a80b-e099-48ba-bd47-21c4b301ba1b","added_by":"auto","created_at":"2025-03-17 15:50:10","extension":"pptx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14016231,"visible":true,"origin":"","legend":"","description":"","filename":"RawimagesofWesternBlotexperiment.pptx","url":"https://assets-eu.researchsquare.com/files/rs-6203794/v1/e8cd9bb646dc8c46568a3456.pptx"},{"id":78688063,"identity":"9d5bddc1-0ea1-48fb-8f15-9d6bca76dd6a","added_by":"auto","created_at":"2025-03-17 15:50:11","extension":"tiff","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":14832382,"visible":true,"origin":"","legend":"","description":"","filename":"ColonyFormationAssayTranswellMigrationandInvasionAssayTubeFormationAssayoriginalfigure.tiff","url":"https://assets-eu.researchsquare.com/files/rs-6203794/v1/01916ac73ae287dd6a4de545.tiff"},{"id":78688056,"identity":"aa18f63b-3b3a-4cd5-b92a-50a7dade6126","added_by":"auto","created_at":"2025-03-17 15:50:10","extension":"tiff","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":19658378,"visible":true,"origin":"","legend":"","description":"","filename":"WoundHealingAssayoriginalfigure.tiff","url":"https://assets-eu.researchsquare.com/files/rs-6203794/v1/08eb3edfcad8cc40d7d57efa.tiff"},{"id":78688895,"identity":"eeb04860-cbd2-498b-9c45-3e5339ef1acf","added_by":"auto","created_at":"2025-03-17 16:06:09","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":16170,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6203794/v1/7dc4ed9cd605af12adae9c19.docx"},{"id":78688774,"identity":"7f477a36-396a-435e-a454-da8b06e3a5c7","added_by":"auto","created_at":"2025-03-17 15:58:09","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":21201,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2.docx","url":"https://assets-eu.researchsquare.com/files/rs-6203794/v1/b38b85effc3ad6459793e57e.docx"},{"id":78688028,"identity":"8479fa9e-9bbe-4a2a-a1a7-0ac795c0dd40","added_by":"auto","created_at":"2025-03-17 15:50:09","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":21195,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable3.docx","url":"https://assets-eu.researchsquare.com/files/rs-6203794/v1/27805f8d5b61a9cf6366341b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Tumor-Promoting Role of Neutrophil Extracellular Traps in Esophageal Squamous Cell Carcinoma and Their Interaction with the Gut Microbiota","fulltext":[{"header":"Background","content":"\u003cp\u003eEsophageal Squamous Cell Carcinoma (ESCC) represents a significant global health burden, particularly in Asian countries, where it exhibits high incidence and mortality rates, ranking as one of the leading causes of cancer-related deaths [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite recent advancements in diagnostic techniques and therapeutic modalities for ESCC, the overall prognosis for patients remains suboptimal, with five-year survival rates persistently low. This underscores the pressing need to elucidate the underlying mechanisms of ESCC pathogenesis and to explore novel diagnostic and therapeutic strategies.\u003c/p\u003e \u003cp\u003eWithin the realm of cancer research, the tumor microenvironment has garnered increasing attention for its pivotal role in cancer progression. As a burgeoning research hotspot in the tumor microenvironment, the role of NETs in the process of tumorigenesis and development has gradually become the focus of research [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. NETs are web-like structures released by activated neutrophils, composed of DNA and a variety of proteins, including citrullinated histone H3 (CitH3), myeloperoxidase (MPO), and neutrophil elastase (NE) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Accumulating evidence suggests that NETs play a significant role in various cancers, influencing tumor cell proliferation, migration, invasion, and angiogenesis through multiple pathways [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, the relationship between NETs and ESCC remains largely unexplored, with no definitive conclusions drawn to date.\u003c/p\u003e \u003cp\u003eSimultaneously, the intricate and dynamic interplay between the gut microbiota and the host immune system has increasingly been implicated in the initiation and progression of tumors [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. A growing body of evidence suggests that gut dysbiosis is closely associated with the development and progression of various cancers, potentially influencing tumor evolution through mechanisms such as modulation of immune homeostasis, generation of metabolic byproducts, and regulation of inflammatory responses [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, the precise mechanistic role of the gut microbiota in ESCC remains poorly understood. Furthermore, whether a relationship exists between the gut microbiota and NETs, and how such an interaction might impact the pathogenesis and progression of ESCC, warrants further investigation.\u003c/p\u003e \u003cp\u003eThis study aims to comprehensively and systematically evaluate the diagnostic utility of NETs-related biomarkers in ESCC patients, elucidate their intrinsic associations with clinical characteristics and patient prognosis, and further delineate the tumor-promoting mechanisms of NETs in ESCC progression. Additionally, by comparing the gut microbiota composition between ESCC patients and healthy individuals, we seek to uncover potential correlations between microbial profiles and NETs levels. The findings of this research are anticipated to provide novel insights into the pathogenesis of ESCC, identify clinically relevant biomarkers and therapeutic targets, and contribute to the advancement of early diagnosis and precision medicine in ESCC management.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Subjects\u003c/h2\u003e \u003cp\u003eCase Group: A total of 60 patients with pathologically confirmed ESCC, who were scheduled for surgical treatment at our institution, were enrolled in this study. Peripheral venous blood, surgically resected tumor tissue specimens, and fecal samples were collected from these patients. Control Group: Sixty healthy individuals undergoing routine health examinations during the same period were selected as controls, and their peripheral venous blood and fecal samples were collected for comparative analysis. Ethical Statement: This study was approved by the Ethics Committee of our institution, and written informed consent was obtained from all participants prior to their inclusion.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExperimental Methods\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSpecimen Collection and Processing\u003c/h2\u003e \u003cp\u003ePeripheral Blood Collection and Processing: Fasting venous blood samples (2 mL) were collected from the antecubital vein in the morning using EDTA-coated anticoagulant tubes. The tubes were gently inverted to ensure proper mixing and transported to the laboratory within 15 minutes. The samples were centrifuged at 3000 rpm for 20 minutes at 4\u0026deg;C to separate the plasma, which was then aliquoted into 1 mL cryovials and stored at -80\u0026deg;C until further analysis.\u003c/p\u003e \u003cp\u003eTissue Specimen Processing: Tumor tissues and adjacent normal tissues were collected from ESCC patients during surgical resection. A portion of the tissues was immediately fixed for immunohistochemical analysis, while the remaining samples were snap-frozen in liquid nitrogen for subsequent experiments.\u003c/p\u003e \u003cp\u003eFecal Sample Collection and Processing: Fecal samples were collected from 20 ESCC patients and 20 healthy controls. Approximately 5 g of fresh fecal material was collected in the morning using a sealed specimen container and transported to the laboratory under cold chain conditions. The samples were stored at -80\u0026deg;C. For microbial DNA extraction, the surface layer of the fecal sample was removed, and 0.2 g of material from 0.3 cm below the surface was used. The remaining samples were retained for additional analyses.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSurgical Procedures\u003c/h3\u003e\n\u003cp\u003eRAMIE: Thirty ESCC patients underwent RAMIE using the da Vinci robotic platform (McKeown technique). The procedure was performed under general anesthesia with single-lumen endotracheal intubation and bilateral lung ventilation. The surgery was conducted in three phases: thoracic, abdominal, and cervical. Detailed procedural steps are described in our previous study [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eVAMIE: Another 30 ESCC patients underwent VAMIE using thoracoscopy and laparoscopy (McKeown technique). The remaining procedural steps were identical to those of RAMIE.\u003c/p\u003e\n\u003ch3\u003eIsolation of Neutrophils and Extraction of NETs\u003c/h3\u003e\n\u003cp\u003eNeutrophil Isolation: Five milliliters of fasting peripheral venous blood were collected into EDTA-coated tubes. Five milliliters of neutrophil isolation medium were layered at the bottom, followed by the slow addition of 5 mL of blood. The mixture was centrifuged at 450\u0026times;g for 40 minutes at 20\u0026deg;C. The neutrophil layer was collected and treated with red blood cell lysis buffer, followed by centrifugation at 450\u0026times;g for 5 minutes at 24\u0026deg;C. This step was repeated until complete red blood cell lysis was achieved. The neutrophils were washed twice with PBS, centrifuged at 300\u0026times;g for 10 minutes, and resuspended in RPMI-1640 medium supplemented with 10% fetal bovine serum. Cell viability was assessed using trypan blue staining, ensuring a viability rate exceeding 95%.\u003c/p\u003e \u003cp\u003eInduction and Extraction of NETs: Purified neutrophils (1\u0026times;10⁶ cells) were seeded into six-well plates and stimulated with 100 nM phorbol 12-myristate 13-acetate (PMA; MCE, USA) for 4 hours in a 37\u0026deg;C, 5% CO₂ incubator. The supernatant was gently aspirated, leaving behind NETs and neutrophils. The NETs were washed with pre-cooled calcium- and magnesium-free PBS, and the wash solution was collected. The samples were centrifuged at 450\u0026times;g for 10 minutes at 4\u0026deg;C, and the supernatant was further centrifuged at 15,000\u0026times;g for 15 minutes at 4\u0026deg;C. The pellet was resuspended in PBS, and NETs formation was confirmed by microscopy using Sytox Green staining. The concentration of NETs was quantified using a microvolume spectrophotometer.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eELISA Assay\u003c/h2\u003e \u003cp\u003eThe levels of NETs-related biomarkers (CitH3, MPO, and NE) and inflammatory cytokines (IL-6 and IL-8) in plasma were quantified using enzyme-linked immunosorbent assay (ELISA) kits (Hangzhou Lianke Biotechnology Co., Ltd.) in strict accordance with the manufacturer\u0026rsquo;s instructions. The diagnostic efficacy of NETs biomarkers for ESCC was evaluated by receiver operating characteristic (ROC) curve analysis. Additionally, the 3-year survival rates were compared between patients with high and low CitH3 levels. Changes in plasma levels of CitH3, MPO, NE, IL-6, and IL-8 were assessed preoperatively and postoperatively in ESCC patients, and the impact of different surgical approaches (RAMIE vs. VAMIE) on NETs levels was analyzed.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGut Microbiota Analysis\u003c/h3\u003e\n\u003cp\u003eThe differences in gut microbial composition between ESCC patients and healthy controls were analyzed using 16S rDNA gene sequencing. Microbial DNA was extracted from fecal samples, and sequencing was performed on the Illumina platform. Bioinformatics tools such as QIIME2 and LEfSe were employed to analyze microbial diversity and identify differentially abundant taxa. Furthermore, the correlation between gut microbiota composition and plasma NETs levels was explored.\u003c/p\u003e\n\u003ch3\u003eImmunohistochemistry\u003c/h3\u003e\n\u003cp\u003eESCC tumor tissues and adjacent normal tissues were fixed in 4% paraformaldehyde, embedded in paraffin, and sectioned at a thickness of 4\u0026micro;m. Tissue sections were subjected to antigen retrieval by heating in citrate buffer (pH 6.0) at 95\u0026deg;C for 20 minutes. Non-specific binding was blocked with 3% bovine serum albumin (BSA), followed by incubation with primary antibodies against CitH3, MPO, and NE (1:200 dilution) at 4\u0026deg;C overnight. Horseradish peroxidase (HRP)-conjugated secondary antibodies (1:500 dilution) were applied and incubated at room temperature for 1 hour. Color development was achieved using 3,3'-diaminobenzidine (DAB), and nuclei were counterstained with hematoxylin. Staining results were visualized under a microscope, and the intensity of positive staining was quantified using ImageJ software.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCell Culture and Experimental Grouping\u003c/h2\u003e \u003cp\u003eCell Culture: KYSE-30 and TE-1 cells were cultured in RPMI-1640 medium supplemented with 10% fetal bovine serum and 1% penicillin-streptomycin. Human umbilical vein endothelial cells (HUVECs) were maintained in endothelial cell-specific EGM-2 medium, enriched with 10% FBS, endothelial cell growth supplements, and 1% penicillin-streptomycin. All cell lines were incubated at 37\u0026deg;C in a humidified atmosphere containing 5% CO₂.\u003c/p\u003e \u003cp\u003eExperimental Grouping: Cells were divided into the following experimental groups: (1) Control group: ESCC cells without treatment; (2) NETs group: ESCC cells treated with 0.5 \u0026micro;g/mL NETs; and (3) NETs\u0026thinsp;+\u0026thinsp;DNase1 group: ESCC cells treated with 0.5 \u0026micro;g/mL NETs and 100 U/mL DNase1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eCCK-8 Assay\u003c/h2\u003e \u003cp\u003eCells from different treatment groups were seeded into 96-well plates, and cell proliferation was assessed at 0, 24, 48, and 72 hours. After adding the CCK-8 reagent, the optical density (OD) at 450 nm was measured 2 hours later to evaluate cell viability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eColony Formation Assay\u003c/h2\u003e \u003cp\u003eA total of 100\u0026ndash;200 cells were seeded into 6-well plates and cultured for 7 days. The colonies were fixed with 4% paraformaldehyde and stained with crystal violet. Colonies consisting of 50 or more cells were counted to determine clonogenic survival.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eWound Healing Assay\u003c/h2\u003e \u003cp\u003eCells were seeded into culture dishes until they reached 90% confluency. A sterile 200 \u0026micro;L pipette tip was used to create a scratch, and the cells were gently washed with PBS to remove debris. Serum-free medium was added, and the width of the scratch was measured at 0 and 36 hours to evaluate cell migration capability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eTranswell Migration and Invasion Assay\u003c/h2\u003e \u003cp\u003eFor the migration assay, 2\u0026times;10\u003csup\u003e4\u003c/sup\u003e cells were seeded into the upper chamber of a Transwell insert, while the lower chamber was filled with RPMI-1640 medium supplemented with 10% FBS. After 36 hours of incubation, the cells that migrated to the lower surface of the membrane were fixed and stained with crystal violet. For the invasion assay, the Transwell inserts were pre-coated with Matrigel, and the same procedure was followed. The number of migrated or invaded cells was quantified under a microscope.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eTube Formation Assay\u003c/h2\u003e \u003cp\u003eHUVECs were seeded onto Matrigel-coated 24-well plates and incubated for 6 hours. The formation of capillary-like structures was observed under a microscope, and the total tube length and number of branches were quantified using ImageJ software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eQuantitative Real-Time PCR (qRT-PCR)\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted using TRIzol reagent and reverse-transcribed into cDNA. The relative expression levels of target genes were calculated using the 2^-ΔΔCt method, with GAPDH serving as the internal reference gene. The PCR cycling conditions were as follows: initial denaturation at 95\u0026deg;C for 5 minutes, followed by 40 cycles of denaturation at 95\u0026deg;C for 10 seconds, annealing at 60\u0026deg;C for 10 seconds, and extension at 72\u0026deg;C for 20 seconds. Primer sequences are provided in Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eWestern Blot Analysis\u003c/h2\u003e \u003cp\u003eProteins were extracted using RIPA lysis buffer, and 10 \u0026micro;g of protein was separated by SDS-PAGE. After transferring to a PVDF membrane, the membrane was blocked for 2 hours and subsequently incubated with primary antibodies (overnight at 4\u0026deg;C) and secondary antibodies (2 hours at room temperature). Protein bands were visualized using enhanced chemiluminescence (ECL), and band intensities were quantified using ImageJ software. Detailed antibody information is provided in Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eSubcutaneous Tumor Formation in Nude Mice\u003c/h2\u003e \u003cp\u003eCells from different treatment groups (KYSE30, KYSE30\u0026thinsp;+\u0026thinsp;NETs, and KYSE30\u0026thinsp;+\u0026thinsp;NETs\u0026thinsp;+\u0026thinsp;DNase1) were subcutaneously injected into the right flank of male BALB/c nude mice (5 weeks old, n\u0026thinsp;=\u0026thinsp;5 per group). Tumor volume was measured every 5 days using a caliper, and mice were euthanized on day 27. Tumors were excised and weighed for further analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eData were analyzed using SPSS or GraphPad Prism software. Continuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), and comparisons between groups were performed using Student\u0026rsquo;s t-test or one-way ANOVA. Categorical variables were analyzed using the chi-square test. The Anosim algorithm was used to evaluate the significance of differences in β-diversity. The Linear discriminant analysis Effect Size (LEfSe) tool was employed to screen for differential species between groups. Survival analysis was conducted using the Kaplan-Meier method, and diagnostic efficacy was evaluated using ROC curve analysis. A P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eExpression and Clinical Significance of NETs-Related Biomarkers in ESCC Patients\u003c/h2\u003e \u003cp\u003eThe experimental workflow of this study is illustrated in Fig.\u0026nbsp;1A. No significant differences were observed in baseline characteristics between the ESCC group and the healthy control group (Table\u0026nbsp;1). The ELISA detection process for plasma samples from both ESCC patients and healthy controls is depicted in Fig.\u0026nbsp;1B. The results revealed that the plasma levels of CitH3, MPO, and NE were significantly elevated in ESCC patients compared to healthy controls (Fig.\u0026nbsp;1C, Table\u0026nbsp;2), suggesting a potential role of NETs in the pathogenesis and progression of ESCC.\u003c/p\u003e \u003cp\u003eROC curve analysis demonstrated that the combination of CitH3, MPO, and NE achieved an area under the curve (AUC) of 0.981 for diagnosing ESCC, with a sensitivity of 92.3% and specificity of 94.7% (Fig.\u0026nbsp;1D, Table\u0026nbsp;3). These findings indicate that these biomarkers, when used in combination, hold substantial promise for clinical application. Furthermore, a significant positive correlation was observed among the levels of CitH3, MPO, and NE (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;1E), underscoring their synergistic role in NETs formation.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eNETs Levels and Their Association with Clinical Characteristics\u003c/h2\u003e \u003cp\u003eNo significant differences in NETs levels were observed between genders (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;1F), suggesting that their expression may not be influenced by sex-related factors. Further analysis revealed that the plasma levels of CitH3, MPO, and NE in ESCC patients were closely associated with several clinical characteristics (Supplementary Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePatients aged\u0026thinsp;\u0026gt;\u0026thinsp;60 years exhibited significantly higher plasma levels of CitH3, MPO, and NE compared to those aged\u0026thinsp;\u0026le;\u0026thinsp;60 years (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;1G), implying that older patients may be more prone to NETs formation due to immunosenescence or chronic inflammatory states. Additionally, patients with a tumor diameter\u0026thinsp;\u0026ge;\u0026thinsp;3 cm demonstrated significantly elevated plasma levels of CitH3, MPO, and NE compared to those with a tumor diameter\u0026thinsp;\u0026lt;\u0026thinsp;3 cm (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;1H), indicating a potential correlation between NETs levels and tumor burden.\u003c/p\u003e \u003cp\u003eMoreover, patients with lymph node metastasis exhibited significantly higher plasma levels of CitH3, MPO, and NE than those without metastasis (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;1I), suggesting that NETs may facilitate tumor cell invasion and metastasis, thereby contributing to disease progression. Notably, stage III patients displayed significantly higher plasma levels of CitH3, MPO, and NE compared to stage I patients (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;1J), further supporting the association between NETs levels and disease severity.\u003c/p\u003e \u003cp\u003ePrognostic analysis revealed that patients with high CitH3 levels had a significantly lower 3-year overall survival rate compared to those with low CitH3 levels (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034) (Fig.\u0026nbsp;1K), indicating that CitH3 may serve as an independent prognostic predictor for ESCC patients.\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\u003eComparison of Baseline Characteristics Between ESCC Patients and Healthy Controls\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eESCC Group (n\u0026thinsp;=\u0026thinsp;60)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHealthy Control Group (n\u0026thinsp;=\u0026thinsp;60)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.581\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32\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\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28\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 (years)\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.709\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\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\u003e\u0026gt;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35\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\u003eSmoking History\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.144\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26\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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34\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\u003eDrinking history\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=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.262\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33\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\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\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\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\u003eComparison of NETs Levels Between ESCC Patients and Healthy Controls\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=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" 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\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eESCC Group (n\u0026thinsp;=\u0026thinsp;60)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHealthy Control Group (n\u0026thinsp;=\u0026thinsp;60)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCitH3 (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e203.33\u0026thinsp;\u0026plusmn;\u0026thinsp;15.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e177.99\u0026thinsp;\u0026plusmn;\u0026thinsp;12.03\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\u003eMPO (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e22.81\u0026thinsp;\u0026plusmn;\u0026thinsp;2.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e18.10\u0026thinsp;\u0026plusmn;\u0026thinsp;3.02\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\u003eNE (ng/mL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e0.97\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\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 \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\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\u003eDiagnostic Value of CitH3, MPO, and NE for ESCC\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOptimal cut-off value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSensitivity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpecificity (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAUC value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMaximum Youden index\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCitH3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e192.621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.717\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.633\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.600\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCitH3\u0026thinsp;+\u0026thinsp;MPO\u0026thinsp;+\u0026thinsp;NE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e91.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.867\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\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eChanges in NETs Levels Pre- and Post-Surgery and the Impact of Surgical Approaches\u003c/h2\u003e \u003cp\u003ePostoperative plasma levels of CitH3, MPO, and NE in ESCC patients were significantly elevated compared to preoperative levels (POD1, POD3, POD6; P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), peaking around POD3 (Fig.\u0026nbsp;2A). This phenomenon is likely attributable to systemic inflammatory responses and neutrophil activation triggered by surgical trauma. To investigate the underlying mechanisms driving the postoperative increase in NETs levels, we measured the levels of key cytokines known to induce NETs formation\u0026mdash;interleukin-6 (IL-6) and interleukin-8 (IL-8). The results revealed a significant postoperative elevation in both IL-6 and IL-8 levels (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;2B, 2C), suggesting that surgical trauma may promote NETs formation by inducing the release of IL-6 and IL-8, thereby activating neutrophils.\u003c/p\u003e \u003cp\u003eFurther analysis comparing 30 patients who underwent RAMIE and 30 patients who underwent VAMIE demonstrated that postoperative levels of CitH3, MPO, and NE were significantly lower in the RAMIE group compared to the VAMIE group at POD1, POD3, and POD6 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;2D, 2E). This indicates that RAMIE may mitigate postoperative NETs release by reducing surgical trauma and inflammatory responses.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eCorrelation Between Gut Microbiota and NETs Levels\u003c/h2\u003e \u003cp\u003eThe workflow for gut microbiota analysis is illustrated in Fig.\u0026nbsp;3A, encompassing sample collection, DNA extraction, 16S rDNA sequencing, and data analysis. Taxonomic composition analysis at the phylum level revealed that both ESCC patients and healthy controls were predominantly colonized by Firmicutes. However, the abundance of Proteobacteria was significantly elevated in the ESCC group (Fig.\u0026nbsp;3B, 3C, 3D). At the genus level, significant differences were observed between the ESCC group and healthy controls (Fig.\u0026nbsp;3E, 3F). In the ESCC group, the abundance of opportunistic pathogens such as \u003cem\u003eClostridium_P\u003c/em\u003e was markedly increased, while beneficial genera like \u003cem\u003eAkkermansia\u003c/em\u003e were significantly reduced.\u003c/p\u003e \u003cp\u003eAlpha diversity analysis indicated no significant differences between the two groups in terms of richness, diversity, evenness, or coverage (Fig.\u0026nbsp;3G). Rarefaction curves demonstrated that the sequencing depth was sufficient to capture the microbial diversity (Fig.\u0026nbsp;3K). Beta diversity analysis, based on Jaccard distance, revealed distinct clustering of bacterial communities between the two groups, as visualized by principal coordinate analysis (PCoA) and non-metric multidimensional scaling (NMDS) (Fig.\u0026nbsp;3H, 3I).\u003c/p\u003e \u003cp\u003eLEfSe analysis identified significantly differentially abundant taxa: in the ESCC group, the phyla \u003cem\u003eProteobacteria\u003c/em\u003e, \u003cem\u003eFusobacteriota\u003c/em\u003e, and \u003cem\u003eChloroflexota\u003c/em\u003e were significantly enriched, while at the genus level, opportunistic pathogens such as \u003cem\u003eClostridium_P\u003c/em\u003e and \u003cem\u003eFaecalimonas\u003c/em\u003e were more abundant. In contrast, the healthy control group exhibited a higher abundance of beneficial taxa, including the phylum \u003cem\u003eVerrucomicrobiota\u003c/em\u003e and the genus \u003cem\u003eAkkermansia\u003c/em\u003e (Fig.\u0026nbsp;3J).\u003c/p\u003e \u003cp\u003eSpearman correlation analysis (Table\u0026nbsp;4) demonstrated that the abundance of \u003cem\u003eKlebsiella\u003c/em\u003e, \u003cem\u003eStreptococcus\u003c/em\u003e, and \u003cem\u003eVeillonella\u003c/em\u003e was positively correlated with plasma NETs levels. Conversely, the abundance of \u003cem\u003eAkkermansia\u003c/em\u003e, \u003cem\u003eRoseburia\u003c/em\u003e, \u003cem\u003eBlautia\u003c/em\u003e, \u003cem\u003eEggerthella\u003c/em\u003e, and \u003cem\u003eActinomyces\u003c/em\u003e showed a significant negative correlation with plasma NETs levels.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation Analysis Between Differential Genus Abundance and Plasma NETs Levels\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eNETs-CitH3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNETs-MPO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eNETs-NE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003er*\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003er* p\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003er*\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAkkermansia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.51 \u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoseburia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.60 \u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlautia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.60 \u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEggerthella\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.74 \u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActinomyces\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.61 \u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKlebsiella\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.67 \u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStreptococcus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.55 \u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVeillonella\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.49 0.0016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0068\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\u003e*: r represents the correlation coefficient, where two variables are positively correlated when r\u0026thinsp;\u0026gt;\u0026thinsp;0 and negatively correlated when r\u0026thinsp;\u0026lt;\u0026thinsp;0. The closer the absolute value of r is to 1, the stronger the linear correlation between the two variables.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eExpression of NETs in Surgical Specimens from ESCC Patients\u003c/h2\u003e \u003cdiv id=\"Sec27\" class=\"Section4\"\u003e \u003ch2\u003eImmunohistochemical Analysis\u003c/h2\u003e \u003cp\u003eImmunohistochemical analysis revealed that the expression levels of CitH3, MPO, and NE were significantly higher in ESCC tissues compared to adjacent non-tumor tissues (Fig.\u0026nbsp;4A), suggesting a pronounced enrichment of NETs in the tumor microenvironment of ESCC. To further validate these findings, we performed WB analysis on 18 pairs of ESCC tissues and matched adjacent non-tumor tissues. The results demonstrated that the protein expression levels of CitH3, MPO, and NE were significantly elevated in ESCC tissues compared to adjacent tissues (Fig.\u0026nbsp;4B), consistent with the immunohistochemical results. Additionally, qRT-PCR analysis showed that the mRNA expression levels of CitH3, MPO, and NE were also significantly upregulated in ESCC tissues (Fig.\u0026nbsp;4C), indicating that NETs-related genes are transcriptionally activated in ESCC.\u003c/p\u003e \u003cp\u003eCollectively, these findings demonstrate that NETs are significantly enriched in the tumor microenvironment of ESCC and may contribute to tumor progression and metastasis through their histone, protease, and oxidase components. This discovery provides histological evidence supporting the tumor-promoting role of NETs in ESCC and lays the groundwork for further investigation into the molecular mechanisms underlying NETs-mediated tumorigenesis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eImpact of NETs on ESCC Cell Function\u003c/h2\u003e \u003cdiv id=\"Sec29\" class=\"Section3\"\u003e \u003ch2\u003eExpression of NETs in ESCC Cells\u003c/h2\u003e \u003cp\u003eThe workflow for NETs extraction is illustrated in Fig.\u0026nbsp;4D. WB analysis revealed that the protein expression levels of CitH3, MPO, and NE were significantly higher in the NETs-treated group compared to the Control group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;4E), indicating that NETs markedly induce the expression of these biomarkers. In the NETs\u0026thinsp;+\u0026thinsp;DNase1 group, the protein levels of CitH3, MPO, and NE were reduced compared to the NETs group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) but remained higher than those in the Control group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting that DNase1 partially attenuates, but does not completely abolish, the induction effect of NETs.\u003c/p\u003e \u003cp\u003eqRT-PCR results demonstrated that the mRNA expression levels of CitH3, MPO, and NE were significantly elevated in the NETs group compared to the Control group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that NETs exert a regulatory effect at the transcriptional level (Fig.\u0026nbsp;4F). In the NETs\u0026thinsp;+\u0026thinsp;DNase1 group, the mRNA levels of these markers were reduced compared to the NETs group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) but remained higher than those in the Control group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), further supporting the partial reversal of NETs' effects by DNase1. These findings collectively demonstrate that NETs significantly upregulate the expression of CitH3, MPO, and NE in ESCC cells, while DNase1 partially reverses this effect through NETs degradation.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eCell Proliferation and Colony Formation Assays\u003c/h3\u003e\n\u003cp\u003eCell proliferation was assessed at 0, 24, 48, and 72 hours using the CCK-8 assay. The results showed that the OD450 values in the NETs group were significantly higher than those in the Control group at all time points (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;4G), indicating that NETs markedly enhance the proliferative capacity of ESCC cells. In the NETs\u0026thinsp;+\u0026thinsp;DNase1 group, the OD450 values were significantly lower than those in the NETs group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) but remained higher than those in the Control group, suggesting that DNase1 partially reverses the pro-proliferative effects of NETs.\u003c/p\u003e \u003cp\u003eColony formation assays revealed that the number of colonies consisting of 50 or more cells was significantly higher in the NETs group compared to the Control group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;4H), demonstrating that NETs significantly enhance the clonogenic potential of ESCC cells. The number of colonies in the NETs\u0026thinsp;+\u0026thinsp;DNase1 group was intermediate between the Control and NETs groups, further confirming that DNase1 partially inhibits the pro-clonogenic effects of NETs.\u003c/p\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003eCell Migration and Invasion Assays\u003c/h2\u003e \u003cp\u003eThe wound healing assay demonstrated that, at 36 hours, the migration rate of cells in the NETs group was significantly higher than that in the Control group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;4I), indicating that NETs markedly enhance the migratory capacity of ESCC cells. The migration rate in the NETs\u0026thinsp;+\u0026thinsp;DNase1 group was intermediate between the Control and NETs groups.\u003c/p\u003e \u003cp\u003eTranswell assays revealed that the number of migrating and invading cells in the NETs group was significantly higher than that in the Control group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;4J), suggesting that NETs significantly promote the migratory and invasive abilities of ESCC cells. In the NETs\u0026thinsp;+\u0026thinsp;DNase1 group, the number of migrating and invading cells was significantly reduced compared to the NETs group but remained higher than that in the Control group, indicating that DNase1 partially reverses the pro-migratory and pro-invasive effects of NETs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003eAngiogenesis Assay\u003c/h2\u003e \u003cp\u003eThe tube formation assay demonstrated that the number and length of tubes formed by cells in the NETs group were significantly greater than those in the Control group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;4J), indicating that NETs significantly enhance the angiogenic capacity of ESCC cells. In the NETs\u0026thinsp;+\u0026thinsp;DNase1 group, the number and length of tubes were significantly reduced compared to the NETs group but remained higher than those in the Control group, suggesting that DNase1 partially reverses the pro-angiogenic effects of NETs.\u003c/p\u003e \u003cdiv id=\"Sec33\" class=\"Section3\"\u003e \u003ch2\u003eMolecular Mechanisms Underlying NETs-Mediated ESCC Progression\u003c/h2\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section3\"\u003e \u003ch2\u003eNETs Regulate the Expression of EMT-Related Proteins\u003c/h2\u003e \u003cp\u003eWB analysis revealed that in ESCC cells treated with NETs, the expression of mesenchymal markers N-Cadherin and Vimentin, as well as matrix metalloproteinases MMP2 and MMP9, was significantly elevated compared to the Control group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Conversely, the expression of the epithelial marker E-Cadherin was significantly reduced (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;5A, 5B). In the NETs\u0026thinsp;+\u0026thinsp;DNase1 group, the expression of N-Cadherin, Vimentin, MMP2, and MMP9 was significantly lower than that in the NETs group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) but remained higher than that in the Control group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Meanwhile, the expression of E-Cadherin was partially restored.\u003c/p\u003e \u003cp\u003eThese findings suggest that NETs promote the invasion and metastasis of ESCC cells by inducing EMT, while DNase1 partially reverses this effect through the degradation of NETs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eNETs Activate Inflammatory Signaling Pathways\u003c/h3\u003e\n\u003cp\u003eWB analysis revealed that the expression of hypoxia-inducible factor HIF-1α and tumor necrosis factor TNF-α was significantly elevated in ESCC cells treated with NETs compared to the Control group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;5C). In the NETs group, the expression of p-NF-κB (p-p65), p-Ikkβ, and p-IκBα was markedly upregulated (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;5C), indicating activation of the NF-κB signaling pathway. In the NETs\u0026thinsp;+\u0026thinsp;DNase1 group, the expression of HIF-1α, TNF-α, and NF-κB pathway-related proteins was significantly reduced compared to the NETs group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) but remained higher than that in the Control group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These results suggest that NETs promote inflammatory responses and angiogenesis in the tumor microenvironment by activating the HIF-1α/TNF-α/NF-κB signaling axis, while DNase1 partially inhibits this process.\u003c/p\u003e\n\u003ch3\u003eMolecular Mechanisms of NETs-Mediated Angiogenesis\u003c/h3\u003e\n\u003cp\u003eNETs treatment significantly upregulated the protein expression of angiogenesis-related factors, including VEGF, Ang-1, VEGFA, and Ang-2 in ESCC cells (Fig.\u0026nbsp;5D). This finding further underscores the critical role of NETs in promoting tumor angiogenesis and microenvironment remodeling. The addition of DNase1 partially reversed this effect, as evidenced by a reduction in the expression levels of these angiogenic factors, although they remained higher than those in the Control group. This indicates that DNase1 attenuates, but does not completely abolish, NETs-induced angiogenesis through the degradation of NETs.\u003c/p\u003e \u003cdiv id=\"Sec37\" class=\"Section2\"\u003e \u003ch2\u003eValidation of the Tumor-Promoting Role of NETs in a Nude Mouse Xenograft Model\u003c/h2\u003e \u003cp\u003eIn the subcutaneous xenograft tumor model using nude mice, the tumor-promoting effects of NETs were validated (experimental schematic shown in Fig.\u0026nbsp;5E). The results demonstrated that both tumor volume and weight in the NETs group were significantly greater than those in the Control group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;5F, 5G, 5H), indicating that NETs significantly enhance tumor growth. In the NETs\u0026thinsp;+\u0026thinsp;DNase1 group, tumor volume and weight were significantly reduced compared to the NETs group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) but remained higher than those in the Control group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting that DNase1 partially inhibits tumor growth by degrading NETs.\u003c/p\u003e \u003cp\u003eqRT-PCR analysis revealed that the mRNA expression levels of CitH3, MPO, and NE in tumor tissues from the NETs group were significantly higher than those in the Control group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;5I), indicating that NETs exert a significant regulatory effect on these markers at the transcriptional level. WB analysis further confirmed that the protein expression levels of CitH3, MPO, and NE were significantly elevated in the NETs group compared to the Control group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;5J). Additionally, the expression of mesenchymal markers N-Cadherin and Vimentin, as well as matrix metalloproteinases MMP2 and MMP9, was significantly upregulated in the NETs group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while the expression of the epithelial marker E-Cadherin was significantly downregulated (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;5K, 5L). These findings suggest that NETs promote tumor invasion and metastasis by inducing EMT.\u003c/p\u003e \u003cp\u003eCollectively, these results comprehensively elucidate the critical role of NETs in promoting tumor growth and inducing EMT at both the phenotypic and molecular levels, providing substantial experimental evidence for understanding the mechanisms underlying NETs-mediated tumorigenesis and progression.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study is the first to systematically elucidate the tumor-promoting role of NETs in ESCC and their potential association with gut microbiota. Through clinical sample analysis, molecular mechanism exploration, and animal model validation, we have demonstrated that NETs-related biomarkers (CitH3, MPO, and NE) hold significant diagnostic and prognostic value in ESCC. Furthermore, we have delineated the molecular mechanisms by which NETs promote tumor progression through the regulation of EMT and angiogenesis pathways. Additionally, the dysbiosis of gut microbiota in ESCC patients was found to be closely associated with NETs levels, suggesting that the intricate interplay within the microbiota-immune-tumor axis may represent a novel direction for future ESCC research.\u003c/p\u003e \u003cdiv id=\"Sec39\" class=\"Section2\"\u003e \u003ch2\u003e1.NETs as Novel Diagnostic and Prognostic Biomarkers in ESCC\u003c/h2\u003e \u003cp\u003eThis study demonstrates that plasma levels of CitH3, MPO, and NE in ESCC patients are significantly elevated compared to those in healthy individuals. Notably, elevated CitH3 levels are closely associated with advanced tumor staging, lymph node metastasis, and poor prognosis. ROC curve analysis further reveals that the combination of these three biomarkers achieves an impressive AUC of 0.981, underscoring their potential as non-invasive diagnostic markers. These findings align with previous studies indicating that NETs are implicated in the progression of various malignancies, including gastric cancer and breast cancer [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, the specific role of NETs in ESCC remains to be fully elucidated. We hypothesize that NETs may facilitate tumor cell invasion through the release of proteases such as NE and MPO, which directly degrade the basement membrane, or via CitH3-mediated chromatin decondensation, thereby promoting tumor aggressiveness.\u003c/p\u003e \u003cdiv id=\"Sec40\" class=\"Section3\"\u003e \u003ch2\u003e2. Impact of Surgical Approaches on NETs Levels\u003c/h2\u003e \u003cp\u003eThis study observed a significant postoperative increase in plasma levels of CitH3, MPO, and NE in ESCC patients compared to preoperative levels, which may be closely associated with systemic inflammatory responses and neutrophil activation triggered by surgical trauma. Further analysis revealed that the postoperative levels of CitH3, MPO, and NE were significantly lower in the RAMIE group compared to the traditional VAMIE group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting that RAMIE may reduce postoperative NETs release by minimizing surgical trauma and inflammatory responses.\u003c/p\u003e \u003cp\u003eAdditionally, this study found that postoperative plasma levels of inflammatory cytokines IL-6 and IL-8 were significantly elevated compared to preoperative levels (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), with their trends aligning closely with the increase in NETs biomarkers. As known activators of neutrophils, IL-6 and IL-8 may promote NETs release by activating the NF-κB signaling pathway [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This phenomenon suggests that surgical trauma may induce the release of IL-6 and IL-8, thereby activating neutrophils and promoting NETs formation, which could potentially impact postoperative recovery.\u003c/p\u003e \u003cp\u003eThese findings provide critical insights for selecting appropriate surgical approaches in clinical practice. RAMIE, as a novel minimally invasive surgical technique, may reduce tissue damage and inflammatory responses, thereby lowering postoperative NETs levels. Future large-scale clinical studies are warranted to validate the impact of different surgical approaches on the prognosis of ESCC patients and to explore the potential value of perioperative anti-inflammatory therapies (e.g., IL-6/IL-8 inhibitors) in reducing NETs formation and improving patient outcomes.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003e3. Interaction Between Gut Microbiota Dysbiosis and NETs\u003c/h3\u003e\n\u003cp\u003e16S rDNA sequencing revealed a significant increase in the abundance of opportunistic pathogens, such as \u003cem\u003eProteobacteria\u003c/em\u003e, \u003cem\u003eFusobacteriota\u003c/em\u003e, \u003cem\u003eKlebsiella\u003c/em\u003e, and \u003cem\u003eStreptococcus\u003c/em\u003e, in the gut microbiota of ESCC patients. Conversely, the abundance of beneficial bacteria with anti-inflammatory and barrier-protective functions, such as \u003cem\u003eAkkermansia\u003c/em\u003e and \u003cem\u003eRoseburia\u003c/em\u003e, was markedly reduced. Notably, a positive correlation was observed between the abundance of opportunistic pathogens and NETs levels, while a negative correlation was found between beneficial bacteria and NETs levels. This suggests that gut microbiota may influence NETs formation by modulating neutrophil activity, thereby contributing to the progression of ESCC.\u003c/p\u003e \u003cp\u003eThis study is the first to reveal a significant correlation between gut microbiota dysbiosis and plasma NETs levels in ESCC patients, providing critical evidence for understanding the role of the \"microbiota-immune-tumor\" axis in ESCC. However, the specific regulatory mechanisms underlying the interaction between gut microbiota and NETs remain to be elucidated. Future studies could employ metagenomics and metabolomics approaches to investigate the impact of gut microbiota-derived metabolites on NETs formation and ESCC progression, offering new insights for the prevention and treatment of ESCC.\u003c/p\u003e\n\u003ch3\u003e4. Molecular Mechanisms Underlying NETs-Mediated ESCC Progression\u003c/h3\u003e\n\u003cp\u003eIn vitro experiments demonstrated that NETs significantly enhance the proliferative, migratory, invasive, and angiogenic capacities of ESCC cells, and these effects were partially reversed by DNase1. Extensive prior literature has shown that NETs promote the EMT process in various cancers. Building on this, we first investigated whether NETs similarly induce EMT in ESCC. The results unequivocally confirmed that NETs significantly promote EMT in ESCC, consistent with findings in other tumor types, thereby expanding our understanding of the role of NETs in tumor biology.\u003c/p\u003e \u003cp\u003eConcurrently, in angiogenesis assays, we observed that NETs significantly enhance vascular generation in ESCC. Given the close interplay between EMT and angiogenesis in tumor progression, both of which critically drive malignant behaviors such as invasion and metastasis, it is of great significance to explore the signaling pathways associated with these processes.\u003c/p\u003e \u003cp\u003eWe focused on the HIF-1α/TNF-α/NF-κB signaling pathway because HIF-1α, activated under hypoxic conditions, can induce the expression of multiple genes related to angiogenesis and EMT [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. TNF-α, as an inflammatory cytokine, promotes EMT and angiogenesis through the NF-κB signaling pathway [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Our WB results validated this hypothesis: in ESCC cells treated with NETs, the expression of HIF-1α and TNF-α was significantly upregulated, and key molecules of the NF-κB signaling pathway (e.g., p-NF-κB, p-Ikkβ, and p-IκBα) were activated. These findings align closely with the NETs-induced EMT and angiogenesis phenotypes.\u003c/p\u003e \u003cp\u003eFurthermore, NETs treatment significantly increased the protein expression of angiogenesis-related factors, including VEGF, Ang-1, VEGFA, and Ang-2, underscoring the critical role of NETs in promoting tumor angiogenesis and microenvironment remodeling. The addition of DNase1 partially reversed these effects, as evidenced by a reduction in the expression levels of these angiogenic factors, although they remained higher than those in the control group. This indicates that DNase1 attenuates, but does not completely abolish, NETs-induced angiogenesis through the degradation of NETs.\u003c/p\u003e \u003cp\u003eBy validating the HIF-1α/TNF-α/NF-κB signaling pathway, we have gained a more comprehensive and in-depth understanding of how NETs coordinately promote EMT and angiogenesis by modulating key signaling pathways, thereby driving the malignant progression of ESCC. The nude mouse xenograft experiments further validated the tumor-promoting role of NETs, while the tumor-suppressive effects of DNase1 suggest that targeting NETs degradation may represent a potential therapeutic strategy for ESCC. These findings provide a solid theoretical foundation for the development of future therapeutic strategies targeting NETs and related signaling pathways in ESCC.\u003c/p\u003e \u003cp\u003eDespite the significant insights provided by this study, several limitations remain: (1) This is a single-center study with a relatively small sample size, which may limit the generalizability and representativeness of the findings; (2) The study only explored the correlation between gut microbiota and NETs levels without delving into the specific regulatory mechanisms; (3) The tumor-suppressive effects of DNase1 were only partially effective, suggesting that NETs may exert their effects through multiple pathways, necessitating the exploration of combination targeting strategies (e.g., DNase1\u0026thinsp;+\u0026thinsp;immune checkpoint inhibitors) in future research. Additionally, the interactions between NETs and other immune cells (e.g., macrophages, T cells) and their overall impact on the tumor immune microenvironment warrant further investigation.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study represents the first systematic elucidation of the tumor-promoting role of NETs in ESCC and their potential interplay with the gut microbiota. The identification of CitH3, MPO, and NE as novel diagnostic biomarkers offers a promising avenue for the early detection of ESCC, while their correlation with prognosis provides a foundation for the development of personalized therapeutic strategies. Furthermore, the mechanisms by which NETs facilitate tumor progression through the regulation of EMT and angiogenesis pathways, coupled with the tumor-suppressive effects of DNase1, establish a robust experimental basis for the development of NETs-targeted therapies. Future investigations should focus on unraveling the intricate regulatory mechanisms between the gut microbiota and NETs, as well as elucidating the role of NETs in immune evasion in ESCC. These efforts will provide critical theoretical insights and technical support for the precise prevention and treatment of ESCC.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cp\u003eESCC Esophageal squamous cell carcinoma\u003c/p\u003e \u003cp\u003eNETs Neutrophil extracellular traps\u003c/p\u003e \u003cp\u003eELISA Enzyme linked immunosorbent assay\u003c/p\u003e \u003cp\u003eCitH3 Citrullinated histone H3\u003c/p\u003e \u003cp\u003eMPO Myeloperoxidase\u003c/p\u003e \u003cp\u003eNE Neutrophil elastase\u003c/p\u003e \u003cp\u003eRAMIE robot-assisted minimally invasive esophagectomy\u003c/p\u003e \u003cp\u003eVAMIE Video-assisted minimally invasive esophagectomy\u003c/p\u003e \u003cp\u003eWB Western blot\u003c/p\u003e \u003cp\u003eEMT Epithelial-mesenchymal transition\u003c/p\u003e \u003cp\u003eROC Receiver operating characteristic\u003c/p\u003e \u003cp\u003eqRT-PCR Quantitative Real-Time PCR\u003c/p\u003e \u003cp\u003eHUVECs Human umbilical vein endothelial cells\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have nothing to report.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZQH, QL, HXL, HLP and YJG: conception and design and administrative support; QL, YZ and XS: provision of study materials or patients; DCJ: collection and assembly of the data; TC: data analysis and interpretation; ZQH, QL, HXL, HLP and YJG: revise the manuscript; manuscript writing: all authors. The authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by Gansu Province Key R\u0026amp;D Project (22YF7FA095).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is provided within the manuscript or supplementary information fles.\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate The present study was approved by the Ethics Committee of Gansu Provincial Hospital, approval number: 2024-019 (Lanzhou, China).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the authors have read and approved the final manuscript for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eDepartment of Thoracic Surgery, Liaoning Cancer Hospital \u0026amp; Institute, Cancer Hospital of China Medical University, Shenyang 110042, China; \u003csup\u003e2\u003c/sup\u003eDepartment of thoracic surgery, The Affiliated Huizhou Hospital, Guangzhou Medical University, Huizhou 516000, China; \u003csup\u003e3\u003c/sup\u003eDalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China; \u003csup\u003e4\u003c/sup\u003eDepartment of thoracic surgery, Gansu Provincial Hospital, Lanzhou 730000, China; \u003csup\u003e5\u003c/sup\u003eDepartment of thoracic surgery,\u0026nbsp;Zunyi Medical University Qianxinan Affiliated Hospital, Xingyi 562400, China.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJing ZQ, Wu,Yifei, Xie et al. Ribonucleotide reductase small subunit M2 promotes the proliferation of esophageal squamous cell carcinoma cells via HuR-mediated mRNA stabilization. Acta Pharm Sin B. 2024;14:0.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJin Tao M, Mao Y, Lu, et al. ∆Np63α promotes radioresistance in esophageal squamous cell carcinoma through the PLEC-KEAP1-NRF2 feedback loop. Cell Death Dis. 2024;15:0.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTill JE, Seewald NJ, Yazdani Z, et al. Corticosteroid-dependent association between prognostic peripheral blood cell-free DNA levels and neutrophil-mediated NETosis in patients with glioblastoma. Clin Cancer Res Published online January. 2025;31. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1158/1078-0432.CCR-24-3169\u003c/span\u003e\u003cspan address=\"10.1158/1078-0432.CCR-24-3169\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu L, Kong Y, Li K, et al. Neutrophil extracellular traps promote growth of lung adenocarcinoma by mediating the stability of m6A-mediated SLC2A3 mRNA-induced ferroptosis resistance and CD8(+) T cell inhibition. Clin Transl Med. 2025;15(2):e70192.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTan C, Aziz M, Wang P. The vitals of NETs. J Leukoc Biol. 2021;110(4):797\u0026ndash;808.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHonda M, Kubes P. Neutrophils and neutrophil extracellular traps in the liver and gastrointestinal system. Nat Rev Gastroenterol Hepatol. 2018;15(4):206\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhan U, Chowdhury S, Billah MM, et al. Neutrophil Extracellular Traps in Colorectal Cancer Progression and Metastasis. Int J Mol Sci. 2021;22(14):7260.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiao Y, Cong M, Li J, et al. Cathepsin C promotes breast cancer lung metastasis by modulating neutrophil infiltration and neutrophil extracellular trap formation. Cancer Cell. 2021;39(3):423\u0026ndash;e4377.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCune D, Pitasi CL, Rubiola A, et al. Inhibition of Atg7 in intestinal epithelial cells drives resistance against Citrobacter rodentium. Cell Death Dis. 2025;16(1):112.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsenkova M, Brauer M, Pozdeev VI, et al. Ketogenic diet suppresses colorectal cancer through the gut microbiome long chain fatty acid stearate. Nat Commun. 2025;16(1):1792.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu WT, Hu XW, Choy YN, et al. Investigating the role of inflammatory cytokines in mediating the effect of gut microbiota on gastrointestinal cancers: a mendelian randomization study. Gastric Cancer Published online Febr. 2025;17. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10120-025-01587-w\u003c/span\u003e\u003cspan address=\"10.1007/s10120-025-01587-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHong Z, Cui B, Wang K, et al. Comparison of Clinical Efficacy Between Da Vinci Robot-Assisted Ivor Lewis Esophagectomy and McKeown Esophagectomy for Middle and Lower Thoracic Esophageal Cancer: A Multicenter Propensity Score-Matched Study. Ann Surg Oncol. 2023;30(13):8271\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi J, Xia Y, Sun B, et al. Neutrophil extracellular traps induced by the hypoxic microenvironment in gastric cancer augment tumour growth. Cell Commun Signal. 2023;21(1):86.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao H, Liang Y, Sun C, et al. Dihydrotanshinone I Inhibits the Lung Metastasis of Breast Cancer by Suppressing Neutrophil Extracellular Traps Formation. Int J Mol Sci. 2022;23(23):15180.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJoshi MB, Lad A, Bharath Prasad AS, Balakrishnan A, Ramachandra L, Satyamoorthy K. High glucose modulates IL-6 mediated immune homeostasis through impeding neutrophil extracellular trap formation. FEBS Lett. 2013;587(14):2241\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang L, Liu L, Zhang R, et al. IL-8 mediates a positive loop connecting increased neutrophil extracellular traps (NETs) and colorectal cancer liver metastasis. J Cancer. 2020;11(15):4384\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Andrea CE, Ochoa MC, Villalba-Esparza M, et al. Heterogenous presence of neutrophil extracellular traps in human solid tumours is partially dependent on IL-8. J Pathol. 2021;255(2):190\u0026ndash;201.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang M, Zhao X, Zhu D, et al. HIF-1α promoted vasculogenic mimicry formation in hepatocellular carcinoma through LOXL2 up-regulation in hypoxic tumor microenvironment. J Exp Clin Cancer Res. 2017;36(1):60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeng X, Liu S, Yang H, et al. Synergistic anti-tumour activity of ginsenoside Rg3 and doxorubicin on proliferation, metastasis and angiogenesis in osteosarcoma by modulating mTOR/HIF-1α/VEGF and EMT signalling pathways. J Pharm Pharmacol. 2023;75(11):1405\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu BX, Xie Y, Zhang J, et al. SERPINB5 promotes colorectal cancer invasion and migration by promoting EMT and angiogenesis via the TNF-α/NF-κB pathway. Int Immunopharmacol. 2024;131:111759.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa Q, Hao S, Hong W, et al. Versatile function of NF-ĸB in inflammation and cancer. Exp Hematol Oncol. 2024;13(1):68.\u003c/span\u003e\u003c/li\u003e\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, Neutrophil extracellular traps, Gut microbiota, Epithelial-mesenchymal transition, Angiogenesis","lastPublishedDoi":"10.21203/rs.3.rs-6203794/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6203794/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground and Objectives \u003c/strong\u003eIn recent years, the role of neutrophil extracellular traps (NETs) in the tumor microenvironment has garnered increasing attention, yet their relationship with esophageal squamous cell carcinoma (ESCC) remains poorly understood. Additionally, the interplay between gut microbiota and the tumor immune microenvironment may influence the progression of ESCC. This study aims to investigate the diagnostic value of NETs-related markers (CitH3, MPO, and NE) in ESCC patients, their correlation with clinical characteristics, and the impact of NETs levels on patient prognosis. Furthermore, we seek to elucidate the pro-tumorigenic mechanisms of NETs in ESCC. By analyzing gut microbiota composition, we also aim to uncover differences in microbial communities between ESCC patients and healthy individuals and explore their association with NETs levels, thereby providing novel theoretical foundations for the early diagnosis and treatment of ESCC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003ePeripheral blood, surgical specimens, fecal samples, and clinical data were collected from 60 ESCC patients undergoing surgical treatment, along with peripheral blood and fecal samples from 60 healthy controls. ELISA was employed to measure plasma levels of CitH3, MPO, and NE in both groups, and their correlations with clinical features were analyzed. The diagnostic efficacy of NETs markers was evaluated using ROC curves, and the 3-year survival rates of patients with high versus low CitH3 levels were compared. Changes in NETs levels pre- and post-surgery, as well as the impact of different surgical approaches on NETs, were assessed. 16S rDNA gene sequencing was utilized to analyze differences in gut microbial composition, and its correlation with plasma NETs levels was explored. Immunohistochemistry, Western blot (WB), and qRT-PCR were performed to detect the expression of CitH3, MPO, and NE in surgical specimens. In vitro experiments involved stimulating neutrophils with phorbol esters to generate NETs, followed by functional assays and pathway analyses to evaluate the effects of NETs on ESCC cells. A subcutaneous xenograft model in nude mice was established to validate the pro-tumorigenic mechanisms of NETs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eThe plasma levels of CitH3, MPO, and NE in ESCC patients were significantly elevated compared to those in healthy controls and were correlated with clinical characteristics. The AUC value for diagnosing ESCC using NETs was 0.981, demonstrating high sensitivity and specificity. Elevated CitH3 levels were indicative of lower survival rates. Postoperative levels of CitH3, MPO, and NE increased, with robot-assisted minimally invasive esophagectomy (RAMIE) showing lower levels of these markers compared to video-assisted minimally invasive esophagectomy (VAMIE). Dysbiosis of the gut microbiota in ESCC patients was associated with NETs levels. In vitro experiments revealed that NETs promoted ESCC cell proliferation, migration, invasion, and angiogenesis. WB analysis indicated that NETs facilitated epithelial-mesenchymal transition (EMT) and angiogenesis by upregulating the protein expression levels of N-Cadherin, Vimentin, MMP2, MMP9, HIF-1α, TNF-α, VEGF, VEGFA, Ang-1, and Ang-2. In vivo experiments confirmed that NETs promoted tumor growth, and DNase1 partially reversed this effect.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions \u003c/strong\u003eThis study elucidates the tumor-promoting role of NETs in ESCC and their association with gut microbiota. NETs markers (CitH3, MPO, and NE) were significantly elevated in ESCC patients, offering diagnostic and prognostic value. NETs promote tumor progression by regulating EMT and angiogenesis pathways, with DNase1 partially reversing this effect. Dysbiosis of the gut microbiota in ESCC patients is linked to NETs levels. These findings provide novel insights into the early diagnosis and targeted therapy of ESCC, warranting further exploration into the regulatory mechanisms of NETs and microbiota.\u003c/p\u003e","manuscriptTitle":"The Tumor-Promoting Role of Neutrophil Extracellular Traps in Esophageal Squamous Cell Carcinoma and Their Interaction with the Gut Microbiota","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-17 15:50:04","doi":"10.21203/rs.3.rs-6203794/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":"b7baffac-dc5b-4368-8751-2e6d55266321","owner":[],"postedDate":"March 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-17T01:08:27+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-17 15:50:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6203794","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6203794","identity":"rs-6203794","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}