Association of Preoperative Inflammatory Markers with Prognosis in Esophageal Squamous Cell Carcinoma: Development and Validation of a Survival Prognostic Model in a Two-Center Study

Association of Preoperative Inflammatory Markers with Prognosis in Esophageal Squamous Cell Carcinoma: Development and Validation of a Survival Prognostic Model in a Two-Center Study | 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 Article Association of Preoperative Inflammatory Markers with Prognosis in Esophageal Squamous Cell Carcinoma: Development and Validation of a Survival Prognostic Model in a Two-Center Study ZhengWei Chen, Gaoxiang Wang, Tianyang Xia, Wei Shao, Changqing Liu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5262158/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 Objective This study evaluates the prognostic value of preoperative inflammatory markers—Neutrophil-to-Lymphocyte Ratio (NLR), Platelet-to-Lymphocyte Ratio (PLR), Systemic Immune-Inflammation Index (SII), and Fibrinogen-to-Prealbumin Ratio (FPR)—in patients with resectable esophageal squamous cell carcinoma (ESCC). A survival prognostic model integrating these markers with TNM staging was developed and validated. Methods Clinical data from 224 ESCC patients who underwent surgical resection between January 2017 and December 2017 at the First Affiliated Hospital of the University of Science and Technology of China were retrospectively analyzed as a training set, and data from 87 patients at Tongling People's Hospital (January 2018 to September 2019) served as the validation set. ROC analysis determined optimal cut-off values for NLR, PLR, SII, and FPR. Survival was analyzed using the Kaplan-Meier method, and prognostic factors were identified through Cox regression. A nomogram was constructed using R software to predict overall survival (OS) and disease-free survival (DFS). Model performance was assessed via ROC, calibration curves, and decision curve analysis (DCA). Results The optimal cut-off values for NLR, PLR, SII, and FPR were 2.70, 140.34, 360.73, and 0.015, respectively. Higher NLR, PLR, and FPR levels were associated with significantly poorer 5-year OS and DFS (all p < 0.01), while higher SII levels were associated with improved outcomes (p = 0.008 for OS, p = 0.013 for DFS). Multivariate Cox analysis identified age, T stage, N stage, differentiation, and NLR as independent prognostic factors. The nomogram demonstrated strong predictive accuracy, with ROC AUCs of 0.966 (3-year OS), 0.907 (5-year OS), 0.960 (3-year DFS), and 0.919 (5-year DFS). Calibration curves confirmed model reliability, and DCA indicated high clinical utility. Conclusions Preoperative NLR, PLR, SII, and FPR are significant predictors of ESCC prognosis, with NLR serving as an independent marker. The nomogram based on inflammatory markers and clinicopathological factors accurately predicts patient outcomes, aiding preoperative decision-making and postoperative management. Health sciences/Oncology Health sciences/Oncology/Surgical oncology Esophageal squamous cell carcinoma Prognosis Inflammatory markers A nomogram Calibration curve Double center Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Esophageal cancer (EC) is a highly aggressive malignancy originating from the esophageal epithelium, with esophageal squamous cell carcinoma (ESCC) being the predominant histological subtype in China, accounting for over 95% of cases [ 1 ]. Despite advancements in surgical techniques and adjuvant therapies, the prognosis of ESCC remains poor. The 5-year overall survival (OS) rate for early-stage ESCC can reach approximately 90%, yet for patients with locally advanced disease, this rate plummets to less than 30%, with postoperative recurrence rates as high as 47%[ 2 ].These stark statistics highlight the pressing need for improved prognostic tools and treatment strategies. In recent years, it has become increasingly evident that the prognosis of cancer patients is influenced not only by tumor-related factors but also by the patient’s nutritional, inflammatory, and immune status [3; 4; 5]. Malnutrition is particularly common in cancer patients and has been associated with poor treatment responses, increased toxicity from therapeutic interventions, and diminished long-term survival[6; 7; 8]. As a result, markers of systemic inflammation and nutrition have emerged as potential prognostic tools in various malignancies, including ESCC. Among these, the Neutrophil-to-Lymphocyte Ratio (NLR), Platelet-to-Lymphocyte Ratio (PLR), Systemic Immune-Inflammation Index (SII), and Fibrinogen-to-Prealbumin Ratio (FPR) have been identified as key indicators of systemic inflammation and immune status. Although several studies have suggested that inflammatory markers may be predictive of survival outcomes in cancer patients, the prognostic value of these markers in ESCC remains a topic of ongoing debate[ 9 ]. Existing studies on inflammatory markers in ESCC have produced inconsistent results, and comprehensive survival prediction models incorporating these markers are limited. Moreover, few studies have attempted to validate these models across independent patient cohorts, leaving their clinical applicability uncertain. Given the high recurrence rates and poor survival outcomes associated with ESCC, there is a clear need for reliable, easily accessible prognostic models. This study aims to fill this gap by analyzing the prognostic significance of preoperative inflammatory markers—NLR, PLR, SII, and FPR—in patients with resectable ESCC. Furthermore, we seek to construct and validate a nomogram-based survival prediction model that integrates these markers with TNM staging and other clinicopathological factors. By developing this model, we hope to provide clinicians with a robust tool for assessing patient prognosis, guiding preoperative decision-making, and optimizing postoperative management. Materials and Methods 1. Study Design and Population This retrospective cohort study included patients diagnosed with resectable esophageal squamous cell carcinoma (ESCC) who underwent surgical treatment at two medical centers: the First Affiliated Hospital of the University of Science and Technology of China and Tongling People’s Hospital. The study period spanned from January 2017 to December 2017 for the training cohort (n = 224) and from January 2018 to September 2019 for the validation cohort (n = 87). Inclusion criteria were: 1. All patients with esophageal cancer met the diagnostic criteria of the standardized diagnosis and treatment Guidelines for esophageal cancer, and signed the informed consent for surgery; Two patients underwent R0 radical surgery for thoracic esophageal cancer, and the postoperative pathology was squamous cell carcinoma. 3. No preoperative neoadjuvant therapy, no acute infectious lesions; 4 Complete clinical data. Exclusion criteria: 1. Lack of clinical and follow-up data; 2. Preoperative infection; 3 complicated with malignant tumors of other organs; 4. Reoperation due to tumor recurrence. 2. Clinical Data and Specimen Collection Clinical data, including demographic information, tumor characteristics (such as TNM stage and tumor differentiation), and laboratory test results, were collected from the hospital's medical records. Preoperative blood samples were drawn within one week before surgery to measure key inflammatory markers, including Neutrophil-to-Lymphocyte Ratio (NLR), Platelet-to-Lymphocyte Ratio (PLR), Systemic Immune-Inflammation Index (SII), and Fibrinogen-to-Prealbumin Ratio (FPR). All patients were followed up for at least 5 years postoperatively, and overall survival (OS) and disease-free survival (DFS) were calculated from the date of surgery to the date of death, recurrence, or last follow-up. The following data were specifically recorded: NLR: Neutrophil count divided by lymphocyte count. PLR: Platelet count divided by lymphocyte count. SII: Neutrophil count × platelet count / lymphocyte count. FPR: Fibrinogen level divided by prealbumin level. OS : the time from the initiation of treatment to the date of death from any cause or the last follow-up. PFS : the time from the start of treatment to the first occurrence of disease recurrence, metastasis, or death due to disease progression. 3. Ethical Considerations The study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of the First Affiliated Hospital of University of Science and Technology of China (approval numbers:2024-RE-243) and the Ethics Committee of Tongling People's Hospital, Anhui Province (approval numbers:2024021). All patient data were anonymized to ensure confidentiality prior to analysis. Written or oral informed consent was obtained from all patients included in this study, depending on the clinical condition of the patients at the time of data collection. 4. Follow-up Methods Patients were followed up through a combination of regular outpatient visits and telephone consultations. Follow-up commenced immediately after the completion of treatment. During the first two years, follow-up assessments were conducted every 3 months. In the third and fourth years, follow-up was conducted every 6 months, and annually thereafter until the 5-year mark or until patient death. 5. Analytical Methods 5.1 Cut-off Value Determination Receiver operating characteristic (ROC) curve analysis was used to determine the optimal cut-off values for NLR, PLR, SII, and FPR in predicting survival outcomes. These cut-off values were subsequently applied to classify patients into high and low groups for each marker. 5.2 Descriptive and Comparative Analysis Categorical variables were analyzed using the chi-square test. Kaplan-Meier survival curves were generated for OS and PFS, and comparisons between survival curves were performed using the log-rank test. Univariate analysis was initially conducted to identify factors significantly associated with survival outcomes. 5.3 Survival Analysis Kaplan-Meier analysis was performed to estimate OS and DFS, and log-rank tests were used to compare survival curves between different inflammatory marker groups. Univariate and multivariate Cox proportional hazards models were applied to identify independent prognostic factors for OS and DFS. 5.4 Nomogram Construction and Validation Based on the results of multivariate Cox analysis, a nomogram was constructed to predict 3-year and 5-year OS and DFS. The predictive performance of the nomogram was evaluated by calculating the area under the ROC curve (AUC). Calibration curves were plotted to assess the consistency between predicted and observed survival outcomes, and decision curve analysis (DCA) was used to evaluate the clinical utility of the model. 6. Statistical Analysis All statistical analyses were conducted using SPSS software version 26.0 (IBM Corp., Armonk, NY, USA) and R software version 4.4.1 (http://www.r-project.org/). Additional R packages such as "survival", "rms", "foreign", and "survivalROC" were employed to facilitate the analyses. Results 1. Determination of Optimal Cut-off Values for Inflammatory Indicators and Grouping The optimal cut-off values for preoperative inflammatory markers to predict postoperative overall survival (OS) in patients with resectable esophageal squamous cell carcinoma (ESCC) were determined using ROC curve analysis. The AUC for NLR, PLR, SII, FPR, tumor size, shortest distance from tumor to resection margin, and the number of lymph node metastases were 0.678 (95% CI: 0.607–0.749), 0.640 (95% CI: 0.558–0.722), 0.677 (95% CI: 0.598–0.756), 0.712 (95% CI: 0.630–0.793), 0.842 (95% CI: 0.780–0.905), 0.456 (95% CI: 0.371–0.542), and 0.721 (95% CI: 0.652–0.790), respectively. The corresponding maximum Youden indices were 0.375, 0.258, 0.298, 0.338, 0.581, 0.436, and 0.073. The optimal cut-off values were established as 2.70 for NLR, 140.34 for PLR, 360.73 for SII, 0.015 for FPR, 3.1 cm for tumor size, 0.5 cm for shortest distance to resection margin, and 4.25 for the number of lymph node metastases. Patients were then categorized into high and low groups based on these thresholds for further survival analysis(Figure 1 ). 2. Relationship between inflammatory indicators and clinicopathologic features of patients with ESCC Significant differences were observed in gender and tumor location between the low and high NLR groups (p < 0.05), while no significant differences were found for age, smoking history, family history of tumors, underlying diseases, tumor size, T stage, N stage, number of metastatic lymph nodes, or postoperative adjuvant therapy (all p > 0.05). In the PLR analysis, age and tumor size differed significantly between the low and high groups (p < 0.05), whereas gender, smoking history, family history of tumors, underlying diseases, tumor location, differentiation type, T stage, N stage, lymph node metastasis, and postoperative adjuvant therapy showed no significant differences (all p > 0.05). For SII, tumor size exhibited a significant difference between the groups (p < 0.05), but other factors, including gender, age, smoking history, family history of tumors, underlying diseases, tumor location, differentiation type, T stage, N stage, lymph node metastasis, and postoperative adjuvant therapy, did not vary significantly (all p > 0.05). Similarly, in the FPR analysis, tumor location showed a significant difference (p < 0.05), while other variables, such as gender, age, smoking history, family history of tumors, underlying diseases, tumor size, differentiation type, T stage, N stage, lymph node metastasis, and postoperative adjuvant therapy, remained consistent (all p > 0.05)(Table1). These findings suggest selective correlations between specific inflammatory markers and certain clinicopathologic features. 3. Relationship between inflammatory markers and prognosis of patients with ESCC Patients in the high NLR group exhibited significantly lower 5-year overall survival (OS) and disease-free survival (DFS) rates compared to those in the low NLR group (both p < 0.001). Similarly, the high PLR group had lower 5-year OS and DFS rates compared to the low PLR group (p = 0.005 and p = 0.009, respectively). In contrast, patients in the high SII group demonstrated higher 5-year OS and DFS rates than those in the low SII group (p = 0.008 and p = 0.018, respectively). Additionally, the high FPR group showed significantly lower 5-year OS and DFS rates compared to the low FPR group (both p < 0.001) (Table 2, Figure 2, and Figure 3). These findings suggest that elevated NLR, PLR, and FPR levels are associated with poorer prognosis, while higher SII levels correlate with improved survival outcomes in ESCC patients. 4. Analysis of prognostic factors affecting patients with esophageal squamous cell carcinoma 1. Analysis of factors affecting OS in patients with esophageal squamous cell carcinoma: Univariate COX regression analysis revealed that age, tumor size, type of differentiation, T stage, N stage, number of cases of lymph node metastasis, NLR, PLR, SII, FPR, and adjuvant therapy were associated with OS in patients with esophageal squamous cell carcinoma (all p < 0.05, Table 3 ). Multifactorial analysis regression analysis showed that patient age (p=0.001), tumor size (p=0.003), type of differentiation (p<0.001), T-stage (p=0.001), N-stage (p<0.001), NLR subgroups (p=0.001), and FPR subgroups (p=0.008) (Table 4). 2. Analysis of factors affecting PFS in patients with esophageal squamous cell carcinoma: Univariate COX regression analysis revealed that age, tumor size, differentiation type, T stage, N stage, number of lymph node metastasis cases, NLR, PLR, SII, FPR, FPR, and adjuvant therapy were associated with DFS in patients with esophageal squamous cell carcinoma (all p < 0.05, Table 3 ). Multifactorial analysis regression analysis showed that patient age (p=0.001), tumor size (p=0.021), type of differentiation (p<0.001), T stage (p<0.001), N stage (p<0.001), NLR subgroups (p=0.001), and FPR subgroups (p=0.009) (Table 4). 5. A column-line graphical prediction model for overall and disease-free survival in patients with ESCC Multifactorial COX regression analysis identified patient age, tumor size, differentiation type, T stage, N stage, NLR grouping, and FPR grouping (p < 0.05) as independent risk factors for overall survival (OS) and progression-free survival (PFS) in patients with ESCC. To develop a more concise and practical prognostic model, the variables with the strongest predictive power—age, differentiation type, T stage, N stage, and NLR grouping (p ≤ 0.001)—were incorporated into the construction of a nomogram for predicting OS and PFS in ESCC patients (Figure 4). In this nomogram, each risk factor is assigned a score based on its impact on the outcome. The scores are then summed to generate a total score, which can be used to estimate the 3- and 5-year survival probabilities, as well as the likelihood of recurrence, for individual patients with ESCC. 6. Predictive model validation and effectiveness evaluation Predictive Model Performance：After incorporating the results from multifactorial analysis into the predictive model, the area under the ROC curve (AUC) for 3-year overall survival (OS) in patients with esophageal squamous cell carcinoma (ESCC) was calculated at 0.966 (95% CI: 0.932-1.000). For 5-year OS, the AUC was 0.907 (95% CI: 0.870-0.944). Additionally, the AUC for 3-year disease-free survival (DFS) was 0.960 (95% CI: 0.926-0.994), while the AUC for 5-year DFS was 0.919 (95% CI: 0.875-0.947).To validate the model externally, the external validation set was utilized. The AUC for 3-year OS in this validation set was 0.830 (95% CI: 0.731-0.929), and for 5-year OS, it was 0.931 (95% CI: 0.880-0.981). The model also predicted a 3-year DFS AUC of 0.875 (95% CI: 0.792-0.958) and a 5-year DFS AUC of 0.915 (95% CI: 0.855-0.975)(Figure 5). Calibration of the Prediction Model：Internal and external validations were conducted using the enhanced Bootstrap method with 1,000 repetitions. The calibration curves demonstrated a strong correlation between the predicted values for 3-year and 5-year OS and PFS and the actual observed values. This indicates a significant alignment between the model's predictions and the actual survival rates in both the training and external validation sets (Figure 6). Applicability of the Predictive Model: Decision curve analysis (DCA) illustrated that the model exhibited a threshold probability ranging from 0 to 0.95 (Figure 7), indicating a positive net clinical benefit. The graph includes four curves: "None," representing the net clinical benefit without any intervention, and "All," showing the net benefit with clinical intervention for all patients. The curves labeled "time=36" and "time=60" reflect the net clinical benefits gained by patients at 3 years and 5 years, respectively, under the nomogram predictive model intervention. The external validation cohort demonstrated positive net clinical benefits for both OS and PFS at 3 and 5 years. Discussion Esophageal cancer (EC) ranks among the most lethal malignancies globally, with unsatisfactory prognosis despite advances in surgical and systemic treatments [10; 11]. The systemic inflammatory response is a recognized hallmark of malignant tumors, where inflammatory cells significantly contribute to tumorigenesis and progression[ 12 ]. Recent studies have highlighted the prognostic value of preoperative inflammatory indexes, yet the comparative advantages and limitations of various inflammatory markers in predicting survival outcomes for esophageal squamous cell carcinoma (ESCC) remain inadequately addressed[ 13 ]. Existing literature, both domestic and international, on survival prognosis models for ESCC is scarce, and previous models have not undergone thorough validation[14; 15]. In this study, we examined the impact of several common preoperative inflammatory indicators—including Neutrophil-to-Lymphocyte Ratio (NLR), Platelet-to-Lymphocyte Ratio (PLR), Systemic Immune-Inflammation Index (SII), and Fibrinogen-to-Platelet Ratio (FPR)—on the survival prognosis of patients with EC. We constructed a survival prognosis model integrating these inflammatory markers alongside the patients' TNM stage. This model aims to assist clinicians in making individualized survival predictions and treatment recommendations, thereby supporting preoperative clinical decision-making. NLR has emerged as a crucial indicator of systemic inflammatory response and is consistently associated with poor prognosis across various malignancies. Elevated NLR may signify a pro-tumorigenic inflammatory milieu coupled with suppressed anti-tumor immune responses within the tumor microenvironment[ 16 ]. Our retrospective analysis of 354 patients with EC and 220 patients with early-stage EC revealed significantly higher preoperative NLR values in the EC group compared to healthy controls (p < 0.001). Furthermore, elevated NLR was correlated with poorer overall survival (OS) (p = 0.004) and disease-free survival (DFS) (p = 0.001). These findings align with previous research demonstrating that patients with NLR > 2.7 had significantly worse OS and DFS outcomes[ 17 ]. The underlying mechanisms for the prognostic significance of NLR may be explained as follows:(1) Neutrophils play a dual role in cancer biology; they can remodel the tumor microenvironment through the production of cytokines and chemokines, thereby fostering tumor cell proliferation and metastasis. Neutrophils can also enhance inflammatory responses via the secretion of reactive oxygen species and pro-inflammatory cytokines such as IL-6 and TNF-α[ 18 ]. (2) Lymphocytes, particularly T cells, B cells, and natural killer cells, are integral components of the anti-tumor immune response. A diminished lymphocyte count, often observed in patients with high NLR, compromises the host’s ability to inhibit tumor progression[ 19 ]. Given this context, the correlation between elevated NLR and poor prognosis underscores its potential as a robust independent prognostic factor for ESCC. PLR, indicative of increased platelet counts relative to lymphocytes, has similarly been linked to poor prognostic outcomes. An increase in PLR is often indicative of heightened pro-tumor inflammatory responses and diminished anti-tumor immunity. A retrospective study involving 317 patients who underwent radical esophageal cancer surgery found that those with elevated NLR and PLR had significantly poorer survival outcomes[ 20 ]. Specifically, a PLR > 150 was associated with reduced DFS (p = 0.018). The mechanisms contributing to the prognostic implications of PLR include: (1) Elevated platelet counts facilitate tumor growth and metastasis through mechanisms such as enhancing tumor cell adhesion via platelet-leukocyte aggregation and promoting angiogenesis, thereby sustaining tumor development[ 21 ]. (2) High PLR values imply a decrease in lymphocytes, especially T cells, which weakens the host's anti-tumor immune response and makes it easier for tumor cells to evade the surveillance and clearance by the immune system, thereby exacerbating disease progression[ 22 ]. (3) Chronic systemic inflammation, as reflected in elevated PLR, not only promotes cancer development but also fosters tumor cell proliferation and metastasis by augmenting the expression of pro-inflammatory cytokines[ 23 ] . SII is a composite index calculated from three parameters: neutrophils, platelets, and lymphocytes, and this index is designed to comprehensively reflect the systemic inflammatory response and immune status of the patient. Jun Yang et al retrospectively analyzed 160 EC patients, and after comparing the low SII group with the high SII group, it was found that OS was prolonged in the low SII group (p = 0.036, HR = 0.59) and prolonged PFS (p = 0.041, HR = 0.60) [ 24 ]. This is similar to the results of our study, and the reasons for this may be analyzed as follows: 1. The high values of SII mainly reflect an increase in neutrophil and platelet counts, as well as a decrease in lymphocytes. An increase in neutrophils indicates the presence of an active inflammatory response in the body, which usually promotes the formation and development of the tumor microenvironment, thus supporting the growth and spread of tumor cells [ 25 ].2. Elevated SII implies a relative decrease in lymphocyte counts, especially T lymphocytes, which weakens the host's anti-tumor immune response. Decreased lymphocytes are associated with an increased ability of tumor cells to evade immune surveillance and attack, a process that further promotes tumor progression and metastasis.3. Elevated SII may represent a more active state of systemic inflammation, which not only promotes tumor progression, but may also make patients more susceptible to inflammation-related complications, which may affect survival, 4. Decreased lymphocytes weaken the anti-tumor immune response, whereas increased neutrophils and platelets may enhance immune escape from the tumor, and this combination of immunosuppression and a pro-inflammatory state may be the central mechanism underlying the relationship between elevated SII and poor prognosis [ 26 ]. Fibrinogen is an acute phase response protein synthesized by the liver that plays a key role in the coagulation process. FPR is calculated from the ratio of Fibrinogen to platelet count, reflecting the activity of the blood coagulation system as well as the inflammatory state in the body. Previous studies have shown that FPR can reflect the prognostic value of tumors. Takashi Suzuki et al measured preoperative plasma fibrinogen levels in 315 patients undergoing surgery for gastric cancer, and found that plasma fibrinogen levels decreased significantly after radical surgery, and multivariate analysis showed that hyperfibrinogenemia was an independent prognostic factor affecting survival (p = 0.018), and preoperative high fibrinogenemia was associated with gastric tumor progression, inflammatory mediators, and low overall survival [ 27 ].Dr Ji-Feng Feng retrospectively analyzed 372 patients with resectable esophageal squamous carcinoma and found that the optimal critical value of FPR was 0.014, and that patients with lower levels of FPR had a cancer specific survival (CSS) ( 50.7% vs. 18.0%, p < 0.001) and OS (48.0% vs. 17.6%, p < 0.001) were better than those with higher levels of FPR, and multivariate Cox analysis showed that FPR was an independent prognostic factor for CSS and OS independently[ 28 ]. Conclusion In summary, preoperative inflammatory indicators such as NLR, PLR, SII, and FPR exhibit significant correlations with the prognosis of esophageal squamous cell carcinoma, each reflecting varying degrees of tumor invasion and progression. Among these, NLR has demonstrated the most robust predictive value. Our column-line graph prediction model, which combines NLR with clinicopathological factors, provides a reliable tool for accurately predicting the prognosis of ESCC patients. The model’s calibration curves show a high degree of alignment with actual survival rates, affirming its predictive utility. Nonetheless, this study is limited by its retrospective design, which introduces potential biases. Future research should focus on prospective, multicenter studies that enroll larger cohorts to further validate the predictive model and explore its clinical applicability in guiding treatment decisions. These efforts will be crucial in enhancing the prognostic stratification of ESCC patients and improving their overall management. Declarations Author Contribution Zhengwei-chen :Article design and writing, data collection and analysis, polishing and submissionGaoxiang-Wang:Article design, writing and revisionTianyang-Xia:Article Article Data collection and calculationWei-Shao:Article polishing, translation and revisionChangqing-Liu:Article design guidance and paper revisionWeiguo-Zhang:Article design guidance and paper revisionFangqin-Wang:Article design guidance and paper revisionMingran-Xie:Article design and writing, analysis, polishing and submissionAll authors reviewed the manuscript Data Availability The data supporting the results of this study are available from the electronic medical record systems of the First Affiliated Hospital of USTC and Tongling People's Hospital, but the availability of these data is limited, and the data are used under license for the current study and therefore are not publicly available. 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Prognostic value of pretreatment systemic immune-inflammation index in Chinese esophageal squamous cell carcinoma patients receiving radical radiotherapy: A meta-analysis. Med. (Baltim). 102 , e34117 (2023). Jhunjhunwala, S., Hammer, C. & Delamarre, L. Antigen presentation in cancer: insights into tumour immunogenicity and immune evasion. Nat. Rev. Cancer . 21 , 298–312 (2021). Suzuki, T. et al. Hyperfibrinogenemia is associated with inflammatory mediators and poor prognosis in patients with gastric cancer. Surg. Today . 46 , 1394–1401 (2016). Feng, J. F., Wang, L., Jiang, Y. H. & Yang, X. A Novel Prognostic Index in Patients with Resectable Esophageal Squamous Cell Carcinoma: Fibrinogen/Prealbumin Ratio. Rev. Invest. Clin. 72 , 46–54 (2020). Tables Table 1 to 4 are available in the Supplementary Files section. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5262158","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":376867719,"identity":"14ae02bd-210d-4d54-8fbd-33d4e239094a","order_by":0,"name":"ZhengWei Chen","email":"","orcid":"","institution":"Tongling People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"ZhengWei","middleName":"","lastName":"Chen","suffix":""},{"id":376867724,"identity":"5e4b55b3-ded2-4376-9be6-ce62f73f76b2","order_by":1,"name":"Gaoxiang Wang","email":"","orcid":"","institution":"The First Affiliated Hospital of USTC, University of Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Gaoxiang","middleName":"","lastName":"Wang","suffix":""},{"id":376867725,"identity":"af65f906-3dce-4e75-83f3-0c1e255a8e17","order_by":2,"name":"Tianyang Xia","email":"","orcid":"","institution":"The First Affiliated Hospital of USTC, University of Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Tianyang","middleName":"","lastName":"Xia","suffix":""},{"id":376867726,"identity":"b046d9d5-036a-4a22-84e7-0c4afbac6759","order_by":3,"name":"Wei Shao","email":"","orcid":"","institution":"Affiliated Hangzhou First People’s Hospital, School of Medicine, Westlake University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Shao","suffix":""},{"id":376867727,"identity":"71c4e218-3dda-4077-a424-fb28c7a7b4e5","order_by":4,"name":"Changqing Liu","email":"","orcid":"","institution":"The First Affiliated Hospital of USTC, University of Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Changqing","middleName":"","lastName":"Liu","suffix":""},{"id":376867728,"identity":"a94cb501-46c7-4b28-893d-1d77c85ef062","order_by":5,"name":"Weiguo Zhang","email":"","orcid":"","institution":"Tongling People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Weiguo","middleName":"","lastName":"Zhang","suffix":""},{"id":376867729,"identity":"062260fd-8672-4e2e-8c34-376566c8f6fd","order_by":6,"name":"Fangqin Wang","email":"","orcid":"","institution":"Tongling People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Fangqin","middleName":"","lastName":"Wang","suffix":""},{"id":376867730,"identity":"3273fd15-7371-4f51-8e9e-82454311df0b","order_by":7,"name":"Mingran Xie","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIie3PMQrCMBSA4ZRAu7S6poh6hUgXoZdpKTSLOmcQydTVtVIP0SM0FJxSuknBRXeHnEBsoYuDIW4O+ac3vI/HA8Bk+tMqSdFinKGWsHgu1sG4rUdg7WU0ZtoEX5t77QpEyrblEtAwZk5TKYlfEMzPFG3LLoEICBIzdxcpyXSWguopBgJtYGV1zJCLlcQeiJchgtu6Jy8NMlzhPYlwlfSEaRC/6Eku0OrUJQGKLiTI3I2a4FsKpaSH5aTlDyn34fzoCDX5LBq++2HfZDKZTF96A+CQRUoOYQaeAAAAAElFTkSuQmCC","orcid":"","institution":"The First Affiliated Hospital of USTC, University of Science and Technology of China","correspondingAuthor":true,"prefix":"","firstName":"Mingran","middleName":"","lastName":"Xie","suffix":""}],"badges":[],"createdAt":"2024-10-14 15:08:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5262158/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5262158/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69443380,"identity":"cb572f60-2b49-49d5-a319-1b6ee7a7c64a","added_by":"auto","created_at":"2024-11-20 11:38:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":71392,"visible":true,"origin":"","legend":"\u003cp\u003eSubject working characteristic curves of preoperative NLR, PLR , SII and FPR levels and postoperative overall survival in patients with esophageal squamous cell carcinoma\u003c/p\u003e","description":"","filename":"figure145.png","url":"https://assets-eu.researchsquare.com/files/rs-5262158/v1/c4d164d57244f0ce5f3e4e6e.png"},{"id":69443377,"identity":"e55d2855-a000-480d-8c7b-f148e47c0027","added_by":"auto","created_at":"2024-11-20 11:38:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":81121,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between NLR, PLR, SII, FPR and OS prognosis in patients with esophageal squamous cell carcinoma\u003c/p\u003e","description":"","filename":"Figure2.OSPLR1.png","url":"https://assets-eu.researchsquare.com/files/rs-5262158/v1/3f9193ac1ce442dd6c0844ff.png"},{"id":69442922,"identity":"64469137-031e-4e89-8cab-1da2071ea812","added_by":"auto","created_at":"2024-11-20 11:30:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":80053,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between NLR, PLR, SII, FPR and PFS prognosis in patients with esophageal squamous cell carcinoma\u003c/p\u003e","description":"","filename":"Figure3.PFSNLR.png","url":"https://assets-eu.researchsquare.com/files/rs-5262158/v1/c135561365a6c4b5a8c33a90.png"},{"id":69444818,"identity":"88f6378d-0416-426d-8d4e-8ea437e75709","added_by":"auto","created_at":"2024-11-20 11:46:02","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":770301,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram prediction model for overall survival (picture a) and disease-free survival (picture b)in patients with esophageal squamous cell carcinoma\u003c/p\u003e","description":"","filename":"Figure4.OS.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5262158/v1/6d0a68e80541b5797f9235dc.jpg"},{"id":69442924,"identity":"c04784c4-0745-499a-9d0f-df1f5f3e918f","added_by":"auto","created_at":"2024-11-20 11:30:02","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":289466,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve analysis of overall survival rate and disease-free survival rate of patients with esophageal squamous cell carcinoma\u003c/p\u003e\n\u003cp\u003eNote: picture a ROC curve analysis of overall survival in patients with esophageal squamous cell carcinoma in the training set,picture b ROC curve analysis of disease-free survival in patients with esophageal squamous cell carcinoma in the training set, picture c ROC curve analysis of overall survival in patients with esophageal squamous cell carcinoma in the validation set, picture d ROC curve analysis of disease-free survival in patients with esophageal squamous cell carcinoma in the validation set\u003c/p\u003e","description":"","filename":"Figure5.os.validation00.png","url":"https://assets-eu.researchsquare.com/files/rs-5262158/v1/f5c0417251236f61c51c13cf.png"},{"id":69443379,"identity":"171903ef-7470-4fce-835b-f9c50eb13918","added_by":"auto","created_at":"2024-11-20 11:38:02","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1749471,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration curve analysis of overall and disease-free survival in patients with esophageal squamous cell carcinoma\u003c/p\u003e\n\u003cp\u003eNote: Figure a Calibration curve analysis of overall survival in patients with esophageal squamous cell carcinoma in the training set, Figure bCalibration curve analysis of disease-free survival in patients with esophageal squamous cell carcinoma in the training set; Figure c Calibration curve analysis of overall survival in patients with esophageal squamous cell carcinoma in the validation set, Figure d Calibration curve analysis of disease-free survival in patients with esophageal squamous cell carcinoma in the validation set\u003c/p\u003e","description":"","filename":"Figure6.PFS.Training..jpg","url":"https://assets-eu.researchsquare.com/files/rs-5262158/v1/6d48e587e4d9c3d4bccdb50c.jpg"},{"id":69442927,"identity":"f3ce42ac-685f-4e8c-8527-bfc34d919f40","added_by":"auto","created_at":"2024-11-20 11:30:02","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1406258,"visible":true,"origin":"","legend":"\u003cp\u003eDCA curve analysis of overall and disease-free survival in patients with esophageal squamous cell carcinoma\u003c/p\u003e\n\u003cp\u003eNote: Figure a DCA curve analysis of overall survival in patients with esophageal squamous cell carcinoma in the training set, Figure b DCA curve analysis of disease-free survival in patients with esophageal squamous cell carcinoma in the training set; Figure c DCA curve analysis of overall survival in patients with esophageal squamous cell carcinoma in the validation set, Figure d DCA curve analysis of disease-free survival in patients with esophageal squamous cell carcinoma in the validation set.\u003c/p\u003e","description":"","filename":"Figure7.OS.Training.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5262158/v1/f9b8ce1e79a5172bf873697b.jpg"},{"id":71066368,"identity":"3063a6c0-775d-4bc6-91ea-9038c375ad81","added_by":"auto","created_at":"2024-12-10 20:01:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4828616,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5262158/v1/9c8777f4-3062-40be-bcc0-19e95984085f.pdf"},{"id":69442921,"identity":"f962422c-b9a1-400a-ac19-c5c0e1b20165","added_by":"auto","created_at":"2024-11-20 11:30:02","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":22669,"visible":true,"origin":"","legend":"","description":"","filename":"Table.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5262158/v1/b8b65ad098d7952018c4172b.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of Preoperative Inflammatory Markers with Prognosis in Esophageal Squamous Cell Carcinoma: Development and Validation of a Survival Prognostic Model in a Two-Center Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEsophageal cancer (EC) is a highly aggressive malignancy originating from the esophageal epithelium, with esophageal squamous cell carcinoma (ESCC) being the predominant histological subtype in China, accounting for over 95% of cases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Despite advancements in surgical techniques and adjuvant therapies, the prognosis of ESCC remains poor. The 5-year overall survival (OS) rate for early-stage ESCC can reach approximately 90%, yet for patients with locally advanced disease, this rate plummets to less than 30%, with postoperative recurrence rates as high as 47%[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].These stark statistics highlight the pressing need for improved prognostic tools and treatment strategies.\u003c/p\u003e \u003cp\u003eIn recent years, it has become increasingly evident that the prognosis of cancer patients is influenced not only by tumor-related factors but also by the patient\u0026rsquo;s nutritional, inflammatory, and immune status [3; 4; 5]. Malnutrition is particularly common in cancer patients and has been associated with poor treatment responses, increased toxicity from therapeutic interventions, and diminished long-term survival[6; 7; 8]. As a result, markers of systemic inflammation and nutrition have emerged as potential prognostic tools in various malignancies, including ESCC. Among these, the Neutrophil-to-Lymphocyte Ratio (NLR), Platelet-to-Lymphocyte Ratio (PLR), Systemic Immune-Inflammation Index (SII), and Fibrinogen-to-Prealbumin Ratio (FPR) have been identified as key indicators of systemic inflammation and immune status.\u003c/p\u003e \u003cp\u003eAlthough several studies have suggested that inflammatory markers may be predictive of survival outcomes in cancer patients, the prognostic value of these markers in ESCC remains a topic of ongoing debate[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Existing studies on inflammatory markers in ESCC have produced inconsistent results, and comprehensive survival prediction models incorporating these markers are limited. Moreover, few studies have attempted to validate these models across independent patient cohorts, leaving their clinical applicability uncertain.\u003c/p\u003e \u003cp\u003eGiven the high recurrence rates and poor survival outcomes associated with ESCC, there is a clear need for reliable, easily accessible prognostic models. This study aims to fill this gap by analyzing the prognostic significance of preoperative inflammatory markers\u0026mdash;NLR, PLR, SII, and FPR\u0026mdash;in patients with resectable ESCC. Furthermore, we seek to construct and validate a nomogram-based survival prediction model that integrates these markers with TNM staging and other clinicopathological factors. By developing this model, we hope to provide clinicians with a robust tool for assessing patient prognosis, guiding preoperative decision-making, and optimizing postoperative management.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003e1.\u0026nbsp; \u0026nbsp;Study Design and Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective cohort study included patients diagnosed with resectable esophageal squamous cell carcinoma (ESCC) who underwent surgical treatment at two medical centers: the First Affiliated Hospital of the University of Science and Technology of China and Tongling People\u0026rsquo;s Hospital. The study period spanned from January 2017 to December 2017 for the training cohort (n = 224) and from January 2018 to September 2019 for the validation cohort (n = 87). Inclusion criteria were: 1. All patients with esophageal cancer met the diagnostic criteria of the standardized diagnosis and treatment Guidelines for esophageal cancer, and signed the informed consent for surgery; Two patients underwent R0 radical surgery for thoracic esophageal cancer, and the postoperative pathology was squamous cell carcinoma. 3. No preoperative neoadjuvant therapy, no acute infectious lesions; 4 Complete clinical data. Exclusion criteria: 1. Lack of clinical and follow-up data; 2. Preoperative infection; 3 complicated with malignant tumors of other organs; 4. Reoperation due to tumor recurrence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.\u0026nbsp; \u0026nbsp;\u0026nbsp;Clinical Data and Specimen Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinical data, including demographic information, tumor characteristics (such as TNM stage and tumor differentiation), and laboratory test results, were collected from the hospital\u0026apos;s medical records. Preoperative blood samples were drawn within one week before surgery to measure key inflammatory markers, including Neutrophil-to-Lymphocyte Ratio (NLR), Platelet-to-Lymphocyte Ratio (PLR), Systemic Immune-Inflammation Index (SII), and Fibrinogen-to-Prealbumin Ratio (FPR). All patients were followed up for at least 5 years postoperatively, and overall survival (OS) and disease-free survival (DFS) were calculated from the date of surgery to the date of death, recurrence, or last follow-up.\u003c/p\u003e\n\u003cp\u003eThe following data were specifically recorded:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNLR:\u003c/strong\u003e Neutrophil count divided by lymphocyte count.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePLR:\u0026nbsp;\u003c/strong\u003ePlatelet count divided by lymphocyte count.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSII:\u0026nbsp;\u003c/strong\u003eNeutrophil count \u0026times; platelet count / lymphocyte count.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFPR:\u003c/strong\u003e Fibrinogen level divided by prealbumin level.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOS\u003c/strong\u003e: the time from the initiation of treatment to the date of death from any cause or the last follow-up.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePFS\u003c/strong\u003e: the time from the start of treatment to the first occurrence of disease recurrence, metastasis, or death due to disease progression. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.\u0026nbsp; \u0026nbsp;\u0026nbsp;Ethical Considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of the First Affiliated Hospital of University of Science and Technology of China (approval numbers:2024-RE-243) and the Ethics Committee of Tongling People\u0026apos;s Hospital, Anhui Province (approval numbers:2024021). All patient data were anonymized to ensure confidentiality prior to analysis. Written or oral informed consent was obtained from all patients included in this study, depending on the clinical condition of the patients at the time of data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.\u0026nbsp; \u0026nbsp;\u0026nbsp;Follow-up Methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients were followed up through a combination of regular outpatient visits and telephone consultations. Follow-up commenced immediately after the completion of treatment. During the first two years, follow-up assessments were conducted every 3 months. In the third and fourth years, follow-up was conducted every 6 months, and annually thereafter until the 5-year mark or until patient death.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.\u0026nbsp; \u0026nbsp;\u0026nbsp;Analytical Methods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.1\u0026nbsp;Cut-off Value Determination\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eReceiver operating characteristic (ROC) curve analysis was used to determine the optimal cut-off values for NLR, PLR, SII, and FPR in predicting survival outcomes. These cut-off values were subsequently applied to classify patients into high and low groups for each marker.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.2 Descriptive and Comparative Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCategorical variables were analyzed using the chi-square test. Kaplan-Meier survival curves were generated for OS and PFS, and comparisons between survival curves were performed using the log-rank test. Univariate analysis was initially conducted to identify factors significantly associated with survival outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.3 Survival Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKaplan-Meier analysis was performed to estimate OS and DFS, and log-rank tests were used to compare survival curves between different inflammatory marker groups. Univariate and multivariate Cox proportional hazards models were applied to identify independent prognostic factors for OS and DFS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5.4 Nomogram Construction and Validation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the results of multivariate Cox analysis, a nomogram was constructed to predict 3-year and 5-year OS and DFS. The predictive performance of the nomogram was evaluated by calculating the area under the ROC curve (AUC). Calibration curves were plotted to assess the consistency between predicted and observed survival outcomes, and decision curve analysis (DCA) was used to evaluate the clinical utility of the model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6.\u0026nbsp; \u0026nbsp;\u0026nbsp;Statistical Analysis \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were conducted using SPSS software version 26.0 (IBM Corp., Armonk, NY, USA) and R software version 4.4.1 (http://www.r-project.org/). Additional R packages such as \u0026quot;survival\u0026quot;, \u0026quot;rms\u0026quot;, \u0026quot;foreign\u0026quot;, and \u0026quot;survivalROC\u0026quot; were employed to facilitate the analyses.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e1. Determination of Optimal Cut-off Values for Inflammatory Indicators and Grouping\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe optimal cut-off values for preoperative inflammatory markers to predict postoperative overall survival (OS) in patients with resectable esophageal squamous cell carcinoma (ESCC) were determined using ROC curve analysis. The AUC for NLR, PLR, SII, FPR, tumor size, shortest distance from tumor to resection margin, and the number of lymph node metastases were 0.678 (95% CI: 0.607\u0026ndash;0.749), 0.640 (95% CI: 0.558\u0026ndash;0.722), 0.677 (95% CI: 0.598\u0026ndash;0.756), 0.712 (95% CI: 0.630\u0026ndash;0.793), 0.842 (95% CI: 0.780\u0026ndash;0.905), 0.456 (95% CI: 0.371\u0026ndash;0.542), and 0.721 (95% CI: 0.652\u0026ndash;0.790), respectively. The corresponding maximum Youden indices were 0.375, 0.258, 0.298, 0.338, 0.581, 0.436, and 0.073. The optimal cut-off values were established as 2.70 for NLR, 140.34 for PLR, 360.73 for SII, 0.015 for FPR, 3.1 cm for tumor size, 0.5 cm for shortest distance to resection margin, and 4.25 for the number of lymph node metastases. Patients were then categorized into high and low groups based on these thresholds for further survival analysis(Figure 1 ).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Relationship between inflammatory indicators and clinicopathologic features of patients with ESCC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSignificant differences were observed in gender and tumor location between the low and high NLR groups (p \u0026lt; 0.05), while no significant differences were found for age, smoking history, family history of tumors, underlying diseases, tumor size, T stage, N stage, number of metastatic lymph nodes, or postoperative adjuvant therapy (all p \u0026gt; 0.05). In the PLR analysis, age and tumor size differed significantly between the low and high groups (p \u0026lt; 0.05), whereas gender, smoking history, family history of tumors, underlying diseases, tumor location, differentiation type, T stage, N stage, lymph node metastasis, and postoperative adjuvant therapy showed no significant differences (all p \u0026gt; 0.05). For SII, tumor size exhibited a significant difference between the groups (p \u0026lt; 0.05), but other factors, including gender, age, smoking history, family history of tumors, underlying diseases, tumor location, differentiation type, T stage, N stage, lymph node metastasis, and postoperative adjuvant therapy, did not vary significantly (all p \u0026gt; 0.05). Similarly, in the FPR analysis, tumor location showed a significant difference (p \u0026lt; 0.05), while other variables, such as gender, age, smoking history, family history of tumors, underlying diseases, tumor size, differentiation type, T stage, N stage, lymph node metastasis, and postoperative adjuvant therapy, remained consistent (all p \u0026gt; 0.05)(Table1). These findings suggest selective correlations between specific inflammatory markers and certain clinicopathologic features.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3. Relationship between inflammatory markers and prognosis of patients with ESCC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients in the high NLR group exhibited significantly lower 5-year overall survival (OS) and disease-free survival (DFS) rates compared to those in the low NLR group (both p \u0026lt; 0.001). Similarly, the high PLR group had lower 5-year OS and DFS rates compared to the low PLR group (p = 0.005 and p = 0.009, respectively). In contrast, patients in the high SII group demonstrated higher 5-year OS and DFS rates than those in the low SII group (p = 0.008 and p = 0.018, respectively). Additionally, the high FPR group showed significantly lower 5-year OS and DFS rates compared to the low FPR group (both p \u0026lt; 0.001) (Table 2, Figure 2, and Figure 3). These findings suggest that elevated NLR, PLR, and FPR levels are associated with poorer prognosis, while higher SII levels correlate with improved survival outcomes in ESCC patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Analysis of prognostic factors affecting patients with esophageal squamous cell carcinoma\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1. Analysis of factors affecting OS in patients with esophageal squamous cell carcinoma: Univariate COX regression analysis revealed that age, tumor size, type of differentiation, T stage, N stage, number of cases of lymph node metastasis, NLR, PLR, SII, FPR, and adjuvant therapy were associated with OS in patients with esophageal squamous cell carcinoma (all p \u0026lt; 0.05, Table 3 ). Multifactorial analysis regression analysis showed that patient age (p=0.001), tumor size (p=0.003), type of differentiation (p\u0026lt;0.001), T-stage (p=0.001), N-stage (p\u0026lt;0.001), NLR subgroups (p=0.001), and FPR subgroups (p=0.008) (Table 4).\u003c/p\u003e\n\u003cp\u003e2. Analysis of factors affecting PFS in patients with esophageal squamous cell carcinoma: Univariate COX regression analysis revealed that age, tumor size, differentiation type, T stage, N stage, number of lymph node metastasis cases, NLR, PLR, SII, FPR, FPR, and adjuvant therapy were associated with DFS in patients with esophageal squamous cell carcinoma (all p \u0026lt; 0.05, Table 3 ). Multifactorial analysis regression analysis showed that patient age (p=0.001), tumor size (p=0.021), type of differentiation (p\u0026lt;0.001), T stage (p\u0026lt;0.001), N stage (p\u0026lt;0.001), NLR subgroups (p=0.001), and FPR subgroups (p=0.009) (Table 4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e5. A column-line graphical prediction model for overall and disease-free survival in patients with ESCC\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMultifactorial COX regression analysis identified patient age, tumor size, differentiation type, T stage, N stage, NLR grouping, and FPR grouping (p \u0026lt; 0.05) as independent risk factors for overall survival (OS) and progression-free survival (PFS) in patients with ESCC. To develop a more concise and practical prognostic model, the variables with the strongest predictive power\u0026mdash;age, differentiation type, T stage, N stage, and NLR grouping (p \u0026le; 0.001)\u0026mdash;were incorporated into the construction of a nomogram for predicting OS and PFS in ESCC patients (Figure 4). In this nomogram, each risk factor is assigned a score based on its impact on the outcome. The scores are then summed to generate a total score, which can be used to estimate the 3- and 5-year survival probabilities, as well as the likelihood of recurrence, for individual patients with ESCC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e6. Predictive model validation and effectiveness evaluation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePredictive Model Performance：After incorporating the results from multifactorial analysis into the predictive model, the area under the ROC curve (AUC) for 3-year overall survival (OS) in patients with esophageal squamous cell carcinoma (ESCC) was calculated at 0.966 (95% CI: 0.932-1.000). For 5-year OS, the AUC was 0.907 (95% CI: 0.870-0.944). Additionally, the AUC for 3-year disease-free survival (DFS) was 0.960 (95% CI: 0.926-0.994), while the AUC for 5-year DFS was 0.919 (95% CI: 0.875-0.947).To validate the model externally, the external validation set was utilized. The AUC for 3-year OS in this validation set was 0.830 (95% CI: 0.731-0.929), and for 5-year OS, it was 0.931 (95% CI: 0.880-0.981). The model also predicted a 3-year DFS AUC of 0.875 (95% CI: 0.792-0.958) and a 5-year DFS AUC of 0.915 (95% CI: 0.855-0.975)(Figure 5).\u003c/p\u003e\n\u003cp\u003eCalibration of the Prediction Model：Internal and external validations were conducted using the enhanced Bootstrap method with 1,000 repetitions. The calibration curves demonstrated a strong correlation between the predicted values for 3-year and 5-year OS and PFS and the actual observed values. This indicates a significant alignment between the model\u0026apos;s predictions and the actual survival rates in both the training and external validation sets (Figure 6).\u003c/p\u003e\n\u003cp\u003eApplicability of the Predictive Model: Decision curve analysis (DCA) illustrated that the model exhibited a threshold probability ranging from 0 to 0.95 (Figure 7), indicating a positive net clinical benefit. The graph includes four curves: \u0026quot;None,\u0026quot; representing the net clinical benefit without any intervention, and \u0026quot;All,\u0026quot; showing the net benefit with clinical intervention for all patients. The curves labeled \u0026quot;time=36\u0026quot; and \u0026quot;time=60\u0026quot; reflect the net clinical benefits gained by patients at 3 years and 5 years, respectively, under the nomogram predictive model intervention. The external validation cohort demonstrated positive net clinical benefits for both OS and PFS at 3 and 5 years.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eEsophageal cancer (EC) ranks among the most lethal malignancies globally, with unsatisfactory prognosis despite advances in surgical and systemic treatments [10; 11]. The systemic inflammatory response is a recognized hallmark of malignant tumors, where inflammatory cells significantly contribute to tumorigenesis and progression[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Recent studies have highlighted the prognostic value of preoperative inflammatory indexes, yet the comparative advantages and limitations of various inflammatory markers in predicting survival outcomes for esophageal squamous cell carcinoma (ESCC) remain inadequately addressed[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Existing literature, both domestic and international, on survival prognosis models for ESCC is scarce, and previous models have not undergone thorough validation[14; 15]. In this study, we examined the impact of several common preoperative inflammatory indicators\u0026mdash;including Neutrophil-to-Lymphocyte Ratio (NLR), Platelet-to-Lymphocyte Ratio (PLR), Systemic Immune-Inflammation Index (SII), and Fibrinogen-to-Platelet Ratio (FPR)\u0026mdash;on the survival prognosis of patients with EC. We constructed a survival prognosis model integrating these inflammatory markers alongside the patients' TNM stage. This model aims to assist clinicians in making individualized survival predictions and treatment recommendations, thereby supporting preoperative clinical decision-making.\u003c/p\u003e \u003cp\u003eNLR has emerged as a crucial indicator of systemic inflammatory response and is consistently associated with poor prognosis across various malignancies. Elevated NLR may signify a pro-tumorigenic inflammatory milieu coupled with suppressed anti-tumor immune responses within the tumor microenvironment[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Our retrospective analysis of 354 patients with EC and 220 patients with early-stage EC revealed significantly higher preoperative NLR values in the EC group compared to healthy controls (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Furthermore, elevated NLR was correlated with poorer overall survival (OS) (p\u0026thinsp;=\u0026thinsp;0.004) and disease-free survival (DFS) (p\u0026thinsp;=\u0026thinsp;0.001). These findings align with previous research demonstrating that patients with NLR\u0026thinsp;\u0026gt;\u0026thinsp;2.7 had significantly worse OS and DFS outcomes[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The underlying mechanisms for the prognostic significance of NLR may be explained as follows:(1) Neutrophils play a dual role in cancer biology; they can remodel the tumor microenvironment through the production of cytokines and chemokines, thereby fostering tumor cell proliferation and metastasis. Neutrophils can also enhance inflammatory responses via the secretion of reactive oxygen species and pro-inflammatory cytokines such as IL-6 and TNF-α[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. (2) Lymphocytes, particularly T cells, B cells, and natural killer cells, are integral components of the anti-tumor immune response. A diminished lymphocyte count, often observed in patients with high NLR, compromises the host\u0026rsquo;s ability to inhibit tumor progression[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Given this context, the correlation between elevated NLR and poor prognosis underscores its potential as a robust independent prognostic factor for ESCC.\u003c/p\u003e \u003cp\u003ePLR, indicative of increased platelet counts relative to lymphocytes, has similarly been linked to poor prognostic outcomes. An increase in PLR is often indicative of heightened pro-tumor inflammatory responses and diminished anti-tumor immunity. A retrospective study involving 317 patients who underwent radical esophageal cancer surgery found that those with elevated NLR and PLR had significantly poorer survival outcomes[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Specifically, a PLR\u0026thinsp;\u0026gt;\u0026thinsp;150 was associated with reduced DFS (p\u0026thinsp;=\u0026thinsp;0.018). The mechanisms contributing to the prognostic implications of PLR include: (1) Elevated platelet counts facilitate tumor growth and metastasis through mechanisms such as enhancing tumor cell adhesion via platelet-leukocyte aggregation and promoting angiogenesis, thereby sustaining tumor development[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. (2) High PLR values imply a decrease in lymphocytes, especially T cells, which weakens the host's anti-tumor immune response and makes it easier for tumor cells to evade the surveillance and clearance by the immune system, thereby exacerbating disease progression[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. (3) Chronic systemic inflammation, as reflected in elevated PLR, not only promotes cancer development but also fosters tumor cell proliferation and metastasis by augmenting the expression of pro-inflammatory cytokines[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] .\u003c/p\u003e \u003cp\u003eSII is a composite index calculated from three parameters: neutrophils, platelets, and lymphocytes, and this index is designed to comprehensively reflect the systemic inflammatory response and immune status of the patient. Jun Yang et al retrospectively analyzed 160 EC patients, and after comparing the low SII group with the high SII group, it was found that OS was prolonged in the low SII group (p\u0026thinsp;=\u0026thinsp;0.036, HR\u0026thinsp;=\u0026thinsp;0.59) and prolonged PFS (p\u0026thinsp;=\u0026thinsp;0.041, HR\u0026thinsp;=\u0026thinsp;0.60) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This is similar to the results of our study, and the reasons for this may be analyzed as follows: 1. The high values of SII mainly reflect an increase in neutrophil and platelet counts, as well as a decrease in lymphocytes. An increase in neutrophils indicates the presence of an active inflammatory response in the body, which usually promotes the formation and development of the tumor microenvironment, thus supporting the growth and spread of tumor cells [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].2. Elevated SII implies a relative decrease in lymphocyte counts, especially T lymphocytes, which weakens the host's anti-tumor immune response. Decreased lymphocytes are associated with an increased ability of tumor cells to evade immune surveillance and attack, a process that further promotes tumor progression and metastasis.3. Elevated SII may represent a more active state of systemic inflammation, which not only promotes tumor progression, but may also make patients more susceptible to inflammation-related complications, which may affect survival, 4. Decreased lymphocytes weaken the anti-tumor immune response, whereas increased neutrophils and platelets may enhance immune escape from the tumor, and this combination of immunosuppression and a pro-inflammatory state may be the central mechanism underlying the relationship between elevated SII and poor prognosis [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Fibrinogen is an acute phase response protein synthesized by the liver that plays a key role in the coagulation process. FPR is calculated from the ratio of Fibrinogen to platelet count, reflecting the activity of the blood coagulation system as well as the inflammatory state in the body. Previous studies have shown that FPR can reflect the prognostic value of tumors. Takashi Suzuki et al measured preoperative plasma fibrinogen levels in 315 patients undergoing surgery for gastric cancer, and found that plasma fibrinogen levels decreased significantly after radical surgery, and multivariate analysis showed that hyperfibrinogenemia was an independent prognostic factor affecting survival (p\u0026thinsp;=\u0026thinsp;0.018), and preoperative high fibrinogenemia was associated with gastric tumor progression, inflammatory mediators, and low overall survival [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].Dr Ji-Feng Feng retrospectively analyzed 372 patients with resectable esophageal squamous carcinoma and found that the optimal critical value of FPR was 0.014, and that patients with lower levels of FPR had a cancer specific survival (CSS) ( 50.7% vs. 18.0%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and OS (48.0% vs. 17.6%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were better than those with higher levels of FPR, and multivariate Cox analysis showed that FPR was an independent prognostic factor for CSS and OS independently[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, preoperative inflammatory indicators such as NLR, PLR, SII, and FPR exhibit significant correlations with the prognosis of esophageal squamous cell carcinoma, each reflecting varying degrees of tumor invasion and progression. Among these, NLR has demonstrated the most robust predictive value. Our column-line graph prediction model, which combines NLR with clinicopathological factors, provides a reliable tool for accurately predicting the prognosis of ESCC patients. The model\u0026rsquo;s calibration curves show a high degree of alignment with actual survival rates, affirming its predictive utility.\u003c/p\u003e \u003cp\u003eNonetheless, this study is limited by its retrospective design, which introduces potential biases. Future research should focus on prospective, multicenter studies that enroll larger cohorts to further validate the predictive model and explore its clinical applicability in guiding treatment decisions. These efforts will be crucial in enhancing the prognostic stratification of ESCC patients and improving their overall management.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZhengwei-chen :Article design and writing, data collection and analysis, polishing and submissionGaoxiang-Wang:Article design, writing and revisionTianyang-Xia:Article Article Data collection and calculationWei-Shao:Article polishing, translation and revisionChangqing-Liu:Article design guidance and paper revisionWeiguo-Zhang:Article design guidance and paper revisionFangqin-Wang:Article design guidance and paper revisionMingran-Xie:Article design and writing, analysis, polishing and submissionAll authors reviewed the manuscript\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data supporting the results of this study are available from the electronic medical record systems of the First Affiliated Hospital of USTC and Tongling People's Hospital, but the availability of these data is limited, and the data are used under license for the current study and therefore are not publicly available. However, data can be obtained from the authors upon reasonable request and with the permission of the ethics Committee of the First Affiliated Hospital of the University of Science and Technology of China and the People's Hospital of Tongling.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHe, S. et al. Cancer profiles in China and comparisons with the USA: a comprehensive analysis in the incidence, mortality, survival, staging, and attribution to risk factors. \u003cem\u003eSci. China Life Sci.\u003c/em\u003e \u003cb\u003e67\u003c/b\u003e, 122\u0026ndash;131 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, W. et al. Current and future perspectives in unresectable locally advanced esophageal squamous cell cancer (Review). \u003cem\u003eOncol. 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The role of the systemic inflammatory response in predicting outcomes in patients with operable cancer: Systematic review and meta-analysis. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e, 16717 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaba, Y. et al. Tumor immune microenvironment and immune checkpoint inhibitors in esophageal squamous cell carcinoma. \u003cem\u003eCancer Sci.\u003c/em\u003e \u003cb\u003e111\u003c/b\u003e, 3132\u0026ndash;3141 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe, Y. F. et al. Preoperative NLR and PLR in the middle or lower ESCC patients with radical operation. \u003cem\u003eEur. J. Cancer Care (Engl)\u003c/em\u003e 26 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, C. et al. 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Systemic Immune-Inflammatory Index, Tumor-Infiltrating Lymphocytes, and Clinical Outcomes in Esophageal Squamous Cell Carcinoma Receiving Concurrent Chemoradiotherapy. J Immunol Res (2023) 4275998. (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeng, X., Ye, L., Luo, M., Zeng, D. \u0026amp; Chen, Y. Prognostic value of pretreatment systemic immune-inflammation index in Chinese esophageal squamous cell carcinoma patients receiving radical radiotherapy: A meta-analysis. \u003cem\u003eMed. (Baltim).\u003c/em\u003e \u003cb\u003e102\u003c/b\u003e, e34117 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJhunjhunwala, S., Hammer, C. \u0026amp; Delamarre, L. Antigen presentation in cancer: insights into tumour immunogenicity and immune evasion. \u003cem\u003eNat. Rev. Cancer\u003c/em\u003e. \u003cb\u003e21\u003c/b\u003e, 298\u0026ndash;312 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuzuki, T. et al. Hyperfibrinogenemia is associated with inflammatory mediators and poor prognosis in patients with gastric cancer. \u003cem\u003eSurg. Today\u003c/em\u003e. \u003cb\u003e46\u003c/b\u003e, 1394\u0026ndash;1401 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeng, J. F., Wang, L., Jiang, Y. H. \u0026amp; Yang, X. A Novel Prognostic Index in Patients with Resectable Esophageal Squamous Cell Carcinoma: Fibrinogen/Prealbumin Ratio. \u003cem\u003eRev. Invest. Clin.\u003c/em\u003e \u003cb\u003e72\u003c/b\u003e, 46\u0026ndash;54 (2020).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 to 4 are available in the Supplementary Files section.\u003c/p\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, Prognosis, Inflammatory markers, A nomogram, Calibration curve, Double center","lastPublishedDoi":"10.21203/rs.3.rs-5262158/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5262158/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study evaluates the prognostic value of preoperative inflammatory markers\u0026mdash;Neutrophil-to-Lymphocyte Ratio (NLR), Platelet-to-Lymphocyte Ratio (PLR), Systemic Immune-Inflammation Index (SII), and Fibrinogen-to-Prealbumin Ratio (FPR)\u0026mdash;in patients with resectable esophageal squamous cell carcinoma (ESCC). A survival prognostic model integrating these markers with TNM staging was developed and validated.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eClinical data from 224 ESCC patients who underwent surgical resection between January 2017 and December 2017 at the First Affiliated Hospital of the University of Science and Technology of China were retrospectively analyzed as a training set, and data from 87 patients at Tongling People's Hospital (January 2018 to September 2019) served as the validation set. ROC analysis determined optimal cut-off values for NLR, PLR, SII, and FPR. Survival was analyzed using the Kaplan-Meier method, and prognostic factors were identified through Cox regression. A nomogram was constructed using R software to predict overall survival (OS) and disease-free survival (DFS). Model performance was assessed via ROC, calibration curves, and decision curve analysis (DCA).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe optimal cut-off values for NLR, PLR, SII, and FPR were 2.70, 140.34, 360.73, and 0.015, respectively. Higher NLR, PLR, and FPR levels were associated with significantly poorer 5-year OS and DFS (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while higher SII levels were associated with improved outcomes (p\u0026thinsp;=\u0026thinsp;0.008 for OS, p\u0026thinsp;=\u0026thinsp;0.013 for DFS). Multivariate Cox analysis identified age, T stage, N stage, differentiation, and NLR as independent prognostic factors. The nomogram demonstrated strong predictive accuracy, with ROC AUCs of 0.966 (3-year OS), 0.907 (5-year OS), 0.960 (3-year DFS), and 0.919 (5-year DFS). Calibration curves confirmed model reliability, and DCA indicated high clinical utility.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003ePreoperative NLR, PLR, SII, and FPR are significant predictors of ESCC prognosis, with NLR serving as an independent marker. The nomogram based on inflammatory markers and clinicopathological factors accurately predicts patient outcomes, aiding preoperative decision-making and postoperative management.\u003c/p\u003e","manuscriptTitle":"Association of Preoperative Inflammatory Markers with Prognosis in Esophageal Squamous Cell Carcinoma: Development and Validation of a Survival Prognostic Model in a Two-Center Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-20 11:29:56","doi":"10.21203/rs.3.rs-5262158/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":"d419d61e-1811-4807-a2d7-185f4a2be949","owner":[],"postedDate":"November 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":40118355,"name":"Health sciences/Oncology"},{"id":40118356,"name":"Health sciences/Oncology/Surgical oncology"}],"tags":[],"updatedAt":"2024-12-10T19:53:24+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-20 11:29:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5262158","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5262158","identity":"rs-5262158","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}