Development and Validation of a Nomogram Based on Albumin-to-Neutrophil-to-Lymphocyte Ratio for Prognosis in Gastric Cancer

Development and Validation of a Nomogram Based on Albumin-to-Neutrophil-to-Lymphocyte Ratio for Prognosis in Gastric Cancer | 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 Development and Validation of a Nomogram Based on Albumin-to-Neutrophil-to-Lymphocyte Ratio for Prognosis in Gastric Cancer Nuo Xu, Hailun Xie, Shuyao Wang, Changhong Xu, Yibo Chen, Siyu Lin, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8999159/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background The albumin-to-neutrophil-to-lymphocyte ratio (ANLR) is a novel inflammatory composite indicator with potential prognostic value in cancer. This study aimed to assess the association of ANLR with overall survival (OS) and progression-free survival (PFS) in patients with gastric cancer and evaluate its clinical relevance. Methods A retrospective analysis was performed on 2,051 patients undergoing radical gastrectomy for gastric cancer from 2012 to 2021 in the INSCOC database. The optimal ANLR cutoff was determined using receiver operating characteristic (ROC) analysis. Survival was estimated by Kaplan–Meier curves, and independent prognostic value was assessed with Cox proportional hazards models. A nomogram was constructed for OS and PFS prediction, with internal and external validations using ROC curves, calibration plots, and decision curve analysis (DCA) to evaluate model performance. Results A total of 1,766 patients were included (internal cohort, n = 1,203; external cohort, n = 563). The optimal ANLR cutoff was 20.39. Patients with high ANLR (≥ 20.39) had better OS and PFS than those with low ANLR (< 20.39). Multivariate Cox analysis confirmed ANLR as an independent prognostic factor for OS (HR = 0.623) and PFS (HR = 0.589, P < 0.001). The nomogram predicted OS and PFS with areas under the curve (AUCs) of 0.660 and 0.710. External validation demonstrated good calibration and discrimination (C-index: OS 0.664, PFS 0.883). Conclusion ANLR is an effective biomarker for prognostic assessment in gastric cancer. The nomogram provides individualized survival prediction and supports clinical decision-making. Health sciences/Biomarkers Biological sciences/Cancer Health sciences/Gastroenterology Health sciences/Oncology Health sciences/Risk factors Gastric cancer Albumin/Neutrophil-to-Lymphocyte Ratio Prognosis Overall survival Progression-free survival Nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction GC is a major global health challenge, ranking as the fourth most common cancer worldwide, following lung cancer, colorectal cancer and liver cancer[ 1 ]. According to the data from GLOBOCAN 2022, gastric cancer ranks fifth in terms of both mortality and incidence rates. Nearly 1 million new cases are diagnosed each year, resulting in over 650,000 deaths worldwide[ 2 ].The systemic treatment for gastric cancer includes chemotherapy, immunotherapy and targeted therapy. The combination of immune checkpoint inhibitors and chemotherapy has become the standard treatment for patients with advanced gastric cancer[ 3 ].The prognosis of gastric cancer depends on the stage of the cancer, the treatment, the biological characteristics, as well as patient-related factors such as nutrition and gender. Systemic inflammation and nutritional status play a significant role in the occurrence, progression and prognosis of tumors[ 4 – 7 ].The NLR inflammatory state impairs the immune response, promotes tumor immune escape, and ultimately drives tumor progression and invasion[ 8 , 9 ].The elevation of NLR and PLR is associated with poorer OS and DFS in GC patients, suggesting that they may have potential utility for risk stratification in clinical practice[ 10 , 11 ].In the context of systemic inflammation, liver protein synthesis undergoes reprogramming, with the liver prioritizing the production of acute-phase proteins - thus, albumin has recently been proposed as a biomarker for systemic inflammation in cancer patients[ 12 ].It has been confirmed that peripheral blood inflammatory markers such as NLR, PLR and LMR have been shown to reflect the overall inflammatory status and are potential indicators that can assist in the clinical diagnosis and prognosis assessment of gastric cancer[ 13 , 14 ].Shufa Tan et al. meta-analysis suggests that NLR, PLR, and LMR are significant independent risk predictors for GC patients receiving immune checkpoint inhibitors[ 15 ]. Ogata et al. [ 16 ] conducted a study which indicated that the high NLR before and after nab-paclitaxel treatment was significantly associated with a shortened OS for patients with unresectable or recurrent gastric cancer. The ANLR, as a composite indicator that integrates nutritional status with inflammatory response, may provide a more comprehensive reflection of a patient's pathophysiological condition compared to single indicators. However, the prognostic value of ANLR in gastric cancer patients remains uncertain, and its associations with clinical pathological features and stability across different subgroups necessitate systematic verification. This study, based on a large retrospective cohort, aims to evaluate the association between ANLR and OS as well as PFS and to establish its value as an independent prognostic factor. Additionally, we intend to explore the prognostic stability of ANLR in various clinical pathological subgroups, construct and validate a nomogram prognostic model that includes ANLR, and provide a novel tool for the prognosis assessment of gastric cancer. Materials and Methods 2.1. Research design and patients This multicenter cohort study utilized data from the INSCOC database (registration number: ChiCTR1800020329). We employed the previously mentioned design and methods to prospectively collect cohort data from multiple medical centers across China. This investigation was a retrospective cohort study that included 1,766 patients with gastric cancer who underwent radical gastrectomy at various medical centers in China between January 2012 and December 2021. The patients were divided into an internal validation cohort (n = 1,203) and an external validation cohort (n = 563).Inclusion criteria: age ≥ 18 years; pathologically diagnosed with gastric cancer; underwent radical gastrectomy (R0 resection); complete clinical data. Exclusion criteria: received neoadjuvant radiotherapy and chemotherapy before surgery; had concurrent malignant tumors; had a history of autoimmune diseases; had possible acute or chronic inflammatory diseases that may affect neutrophil or albumin levels; had missing clinical or follow-up data. This study adhered to the principles outlined in the Helsinki Declaration and received approval from the ethics committees of each participating local center. All participants provided written informed consent for the use of their clinical data, and their personal information was kept confidential. 2.2. Data collection Clinical and pathological data were comprehensively collected from the hospital's electronic medical record system. This collection included baseline information of the patients, such as gender, age, height, weight, body mass index (BMI), history of hypertension, diabetes, heart disease, smoking, drinking, chronic hepatitis, tuberculosis, adjuvant therapy, and length of hospital stay. Tumor-related information encompassed the TNM stage (determined according to the 8th edition of the American Joint Committee on Cancer (AJCC) staging system), T stage, N stage, M stage, and differentiation grade. Baseline fasting blood samples were obtained from all patients as recorded in the medical record system on the morning of the second day following admission. Laboratory parameters assessed included white blood cell count, neutrophil count, platelet count, lymphocyte count, hemoglobin, albumin, total bilirubin, direct bilirubin, total cholesterol, triglycerides, alanine aminotransferase (ALT), aspartate aminotransferase (AST), creatinine, urea nitrogen, C-reactive protein, ANLR, neutrophil/lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and prognostic nutritional index (PNI). The NLR was calculated as the neutrophil count (×10⁹/L) divided by the lymphocyte count (×10⁹/L), while the PLR was defined as the platelet count (×10⁹/L) divided by the lymphocyte count (×10⁹/L). The PNI was defined as albumin (g/L) + 5 × lymphocyte (×10⁹/L). The ANLR was calculated as albumin (g/L) divided by NLR, or as albumin (g/dL) divided by (neutrophil count (×10⁹/L) divided by lymphocyte count (×10⁹/L)). 2.3. Follow-up Postoperative follow-up for the patients was conducted regularly within 5 years. In the first 2 years, it was done every 3 months, then every 6 months for the following 3 years, and annually thereafter. Each follow-up included a detailed inquiry about symptoms and signs to detect potential recurrence or metastasis, supplemented by basic examinations: routine blood tests, biochemical analysis, measurement of tumor markers, gastroscopy, and imaging studies (CT or MRI) for a comprehensive health assessment. All 1766 eligible patients (treated from 2012 to 2021) from the INSCOC database underwent long-term follow-up through telephone interviews and outpatient visits. The follow-up period was 65 (interquartile range: 40–84) months, and no deaths occurred. OS was measured as the time from surgery to any cause of death or the last follow-up date; PFS was measured as the time from surgery to tumor progression or any cause of death (whichever occurred first) or the last follow-up date. 2.4 Outcomes The primary endpoint of this study was to evaluate the prognostic value of ANLR in predicting OS and PFS in gastric cancer, to differentiate the impact of high and low ANLR groups on survival prognosis, and to facilitate the early screening of patients. Secondary endpoints included the analysis of independent prognostic risk factors influencing OS and PFS in gastric cancer patients, as well as their correlation with ANLR. These factors were determined to be unaffected by gender, age, or BMI. 2.5 Model validation We developed a risk prediction model and performed internal validation using data from 36 hospitals collected between December 2012 and December 2021 (n = 1,203). External validation was conducted using data from one independent hospital (n = 553). The internal validation assessed the stability of the predictive model against random variations in sample composition by performing 1,000 bootstrap resampling iterations. ROC curves were generated, followed by calibration curves, DCA, and clinical impact curves for the nomogram. External validation was conducted in a similar manner, generating the nomogram, ROC curve, and calibration curve, with predictive variables transformed consistently with those in the model derivation cohort. The results of both internal and external validations were statistically analyzed, and model performance was evaluated using the area under the receiver operating characteristic curve (AUROC), overall R² goodness-of-fit, D statistic, Harrell’s C-index, and Brier score. 2.6 Statistical analysis Statistical analysis was conducted using R version 4.0.2. Measurement data were presented as mean ± standard deviation (SD) or median (interquartile range, IQR), with comparisons between groups performed using t-tests or Kruskal-Wallis tests. Count data were expressed as frequencies (percentages), and group comparisons were conducted using χ² tests. Survival curves were generated using the Kaplan-Meier method, and differences between groups were assessed using the log-rank test. The Cox proportional hazards regression model was used to evaluate the association between ANLR and both OS and PFS, and to calculate HR and 95% CI. Three models were employed to adjust for confounding factors. Independent prognostic factors were included in the construction of nomogram models for OS and PFS. Model efficacy was evaluated using receiver ROC curves, calibration curves, and DCA, and was subsequently validated in an external cohort. A two-sided p-value of < 0.05 was considered statistically significant. Results 3.1. Patient characteristics This study included a total of 1,766 patients who underwent radical gastrectomy for gastric cancer between December 2012 and December 2021. Of these, 1,203 patients were included in the initial research cohort and subsequently entered the model development cohort, while 563 patients were allocated to the external validation cohort ( Fig. 1 ) . The baseline characteristics of patients in the main cohort, internal cohort, and external validation cohort. The median age was 65.72 years, with 67.6% of the patients being male ( p < 0.05) ( Table 1 ) . 3.2 Comparison of ANLR cutoff values and grouping characteristics The AUC was 0.594, with a 95% confidence interval of 0.564 to 0.624. The optimal cut-off value was established at 20.39, which yielded a sensitivity of 43.1% and a specificity of 72.8% (Figure S1 and Fig. 2 ) .We divided the cohort of 1,766 patients into two groups: low ANLR (< 20.39, n = 716) and high ANLR (≥ 20.39, n = 482) ( Table 2 ) . 3.3 Kaplan-Meier and Survival Prognosis Analysis Based on the ANLR cutoff, Kaplan–Meier analysis demonstrated that patients with low ANLR had significantly poorer survival than those with high ANLR, with lower OS (55.82% vs. 44.18%) and PFS (53.27% vs. 46.73%) (both Log-rank p < 0.001), as well as reduced 5-year survival ( Fig. 3 ) . Subgroup analysis by tumor differentiation showed consistently worse outcomes in the low ANLR group, including OS and PFS differences across high/moderate and low/undifferentiated grades (all p < 0.001; Fig. 4, Figure S2 ). Similarly, TNM stage stratification revealed inferior OS and PFS in the low ANLR group across all stages, particularly in stage III–IV disease ( p < 0.0001; Fig. 5, Figure S3 ). These findings confirm the stable prognostic value of ANLR across differentiation grades and TNM stages. 3.4 The non-linear relationship between ANLR and the survival outcomes of GC patients Multivariate restricted cubic spline (RCS) analyses were conducted using three progressively adjusted models to evaluate the association between ANLR and OS and PFS in GC patients. All models demonstrated a significant non-linear, L-shaped relationship (p 1 indicated increased risk and poorer prognosis. Higher ANLR levels were associated with improved survival outcomes ( Fig. 6 ) . 3.5 Correlation Analysis of ANLR and GC Patients' Survival Outcomes After full adjustment (Model c), the high ANLR group had significantly lower risks of mortality (HR = 0.623, 95% CI: 0.490–0.792, p < 0.001) and progression (HR = 0.589, 95% CI: 0.439–0.791, p < 0.001) compared with the low ANLR group. Consistent results were observed in cut-off and quartile analyses, showing a significant decreasing trend in OS and PFS risk with increasing ANLR ( p < 0.001). Overall, higher ANLR remained a robust protective factor for OS and PFS after multivariable adjustment (Table S8-S9) . 3.6 Subgroup analysis of survival outcomes in ANLR and GC patients In the subgroup analysis of progression-free survival (PFS)-related factors, ANLR remained an independent risk factor for most subgroups ( P 24 days), smoking, alcohol consumption, diabetes, hypertension, chronic hepatitis, tumor differentiation (Medium and Undifferentiated), TNM stage (III-IV), T stages (T1 and T4), N stages (N0, N2, N3), and M1 stage (Figure S4) . 3.7 The prognostic performance of ANLR in comparison with established inflammatory nutrition indicators In terms of OS prediction, ANLR demonstrated higher predictive efficacy, with AUC values exceeding those of the control indices (ANLR: 0.592 vs. NLR: 0.567 vs. PLR: 0.572 vs. PNI: 0.549) (Figure S5) ;In the 1-year, 3-year, and 5-year predictions, OS (1-year: 0.543 vs. 3-year: 0.531 vs. 5-year: 0.518) and PFS (1-year: 0.541 vs. 3-year: 0.533 vs. 5-year: 0.528) all demonstrated good predictive effects (Figure S6) ;During the 1-year, 3-year, and 5-year survival periods, for predicting the OS of patients, the area under the ROC curve of ANLR was the largest (ANLR: 0.519 vs. NLR: 0.471 vs. PLR: 0.517 vs. PNI: 0.498) (Figure S7) . 3.8 The establishment of the nomogram Multivariate Cox proportional hazards regression analysis demonstrated that age, BMI, diabetes, TNM stage, N stage, and ANLR were independent risk factors for OS in patients with GC ( P < 0.05). For PFS, age, adjuvant chemotherapy, TNM stage, N stage, and ANLR were identified as independent risk factors ( P < 0.05) (Table S6-S7) . Based on these key variables, a nomogram was constructed to predict 1-, 3-, and 5-year OS and PFS in GC patients (Figure S8) . The predicted probabilities of OS and PFS at these time points were calculated by summing the scores assigned to each variable. 3.9 Internal validation of the nomogram The predictive performance of the nomogram was assessed using ROC analysis with bootstrap internal validation. The AUCs for predicting OS and PFS were 0.660 and 0.710, respectively (Figure S9) . Calibration curves for 1-, 3-, and 5-year survival showed good agreement between predicted and observed outcomes, particularly in the high-probability range, with slight deviation in the mid-low range (Figure S10) . Decision curve analysis demonstrated that the nomogram provided greater net clinical benefit within the key risk threshold range of 1.5%–4% and reduced unnecessary interventions for patients with a risk below 1.5% (Figure S11) . 3.10 Verification of the external cohort External validation using an independent hospital cohort confirmed the internal findings, with Cox regression identifying the same independent risk factors and a nomogram constructed (Figure S12) . Bootstrap internal validation yielded AUCs of 0.690 for OS and 0.920 for PFS (Figure S13) . Calibration curves for 1-, 3-, and 5-year survival demonstrated high accuracy, with predicted 5-year OS and PFS closely matching observed outcomes (Figure S14) . Decision curve analysis showed that in the external cohort, the nomogram substantially improved net clinical benefit within the key risk threshold of 0.05–0.15, outperforming standard thresholds across different risk levels (Figure S15) . 3.11 Comparison of verification efficiency between internal queue and external queue We further validated the models of the internal and external queues. In the OS model, we compared the performance of each algorithm for the internal queue and the external queue. The Brier score was (0.189 vs. 0.218), the overall R2 fitting effect was (0.087 vs. 0.167), Harrell's C index was (0.655 vs. 0.664), and the calibration slope was all 1.000 (0.920–1.080), indicating that the model has good discrimination and calibration. Similarly, in the model PFS, we compared the performance of each algorithm for the two queues. The Brier score was (0.150 vs. 0.099), the overall R 2 fitting effect was (0.133 vs. 0.578), Harrell's C index was (0.667 vs. 0.510), and the calibration slope was all 1.000 (0.920–1.080), all indicating that the model has good discrimination and calibration (Table S10-S12) . Discussion GC remains a significant global health issue, characterized by high incidence and mortality rates, especially in certain parts of the world. Although the incidence of gastric cancer has generally decreased in many countries, it remains the leading cause of cancer-related deaths, with over 1 million new cases diagnosed each year[ 17 ]. In terms of treatment, surgery remains the cornerstone for achieving a cure, but the role of systemic treatments, including neoadjuvant chemotherapy (NAC), chemotherapy, targeted therapy and immunotherapy, is becoming increasingly important[ 18 , 19 ]. Inflammation plays a crucial role in the development and progression of cancer[ 20 ]. Inflammatory markers, such as the PLR, the monocyte-to-lymphocyte ratio (MLR), the systemic inflammatory response index (SIRI), and the Glasgow Prognostic Score (GPS), have demonstrated the potential to provide valuable prognostic information in GC[ 21 – 25 ].In patients with gastric cancer, higher levels of NLR and PLR are associated with poorer OS and PFS, while a higher lymphocyte-to-monocyte ratio (LMR) is associated with a better prognosis [ 26 ]. Studies have shown that a high NLR is associated with a poorer clinical outcome and can serve as a prognostic biomarker for patients with colorectal cancer[ 27 ].Furthermore, the level of serum albumin reflects the inflammation and malnutrition of the cancer host[ 28 ]. A retrospective study involving 1023 GC patients showed that the pre-treatment serum albumin level was an important prognostic factor[ 29 ].A prospective study involving 500 GC patients showed that preoperative NLR/Alb was a prognostic factor for survival after radical surgery [ 30 ].The ratio of neutrophils to lymphocytes / serum albumin. Multiple studies have reported that preoperative NLR and PNI are prognostic factors for patients after GC surgery[ 31 – 35 ]. Furthermore, a retrospective study demonstrated that the OS of ESCC patients with high NLR/pre-Alb was worse than that of patients with low NLR/pre-Alb ( p = 0.043) [ 36 ]. Additionally, a retrospective study indicated that in advanced RCC cases, patients with high NLR/Alb and CRP/Alb ratios had significantly poorer PFS and OS compared to those with low NLR/Alb and CRP/Alb ratios[ 37 ]. The results of this study suggest that ANLR is an effective biomarker for prognosis assessment in patients with gastric cancer. Patients with ANLR ≥ 20.39 had better 5-year OS and PFS compared to those with ANLR < 20.39 ( P < 0.001). The multivariate Cox regression analysis showed that a high ANLR was an independent protective factor for OS and PFS. Subgroup analysis indicated that ANLR had stable prognostic value in different TNM stages, differentiation degrees, and clinical characteristic subgroups. The Nomogram model constructed based on ANLR showed good calibration and discrimination efficacy (C-index: OS 0.664, PFS 0.883). This result has been previously validated in studies regarding the prognostic ability of ANLR under different conditions: an elevated peripheral ANLR can effectively predict adverse outcomes of coronary artery disease and diabetic foot ulcers[ 38 – 40 ]. This predictive ability extends to gastrointestinal cancers, as demonstrated by Onuma et al. (in a small sample from a single center), where preoperative ANLR was identified as an important prognostic indicator for gastric cancer patients after radical gastrectomy[ 41 ]. Compared to existing inflammation-nutrition indicators (NLR, PLR, PNI), ANLR demonstrates superior performance in prognosis assessment. For OS prediction, the AUC for ANLR (0.592) is higher than that for NLR (0.567), PLR (0.572), and PNI (0.549). In PFS prediction, the AUC for ANLR (0.616) is also significantly superior to that of the other indicators. Subgroup analysis shows that ANLR can effectively differentiate prognosis risk in patients with various clinical characteristics. Notably, for patients with advanced and poorly differentiated tumors, the risk stratification value is particularly significant. The nomogram constructed in this study incorporated key factors, including ANLR, age, BMI, and TNM stage. It was validated through both internal (C-index OS = 0.727, PFS = 0.719) and external validation (AUC OS = 0.690, PFS = 0.920), demonstrating good discrimination and calibration. DCA indicated that within the risk threshold range of 1.5% to 4% for OS and 0.05 to 0.15 for PFS, the net benefit of the nomogram was significantly higher than that of traditional staging tools, effectively reducing excessive interventions for low-risk patients. For high-risk patients (e.g., ANLR ≥ 20.39 and stage III), a more intensive postoperative follow-up (e.g., imaging examinations every 3 months) should be emphasized, and intensified adjuvant therapy may be considered. Conversely, for low-risk patients, interventions can be appropriately reduced to prevent unnecessary treatment. The limitations of this study include its retrospective design, which may introduce selection bias; the absence of data on postoperative dynamic changes in ANLR; the lack of exploration into the association between ANLR and the efficacy of immunotherapy; and the failure to fully address potential confounding factors (such as Helicobacter pylori infection and dietary structure). Future prospective cohort studies, combined with multi-omics data, are needed to further validate the prognostic value and underlying mechanisms of ANLR. Conclusion ANLR serves as an independent prognostic factor for OS and PFS in patients with gastric cancer. A high ANLR score indicates a poor prognosis. The nomogram model constructed based on ANLR demonstrates strong prognostic prediction efficacy and can serve as a straightforward and reliable tool in clinical practice Declarations Ethical approval and consent to participate This study adhered to the Helsinki Declaration. All participants signed the informed consent form, and the study was approved by the hospital's institutional review board (registration number: ChiCTR1800020329). Consent to publish All authors have agreed to publish. Availability of data and materials The datasets used and/or analyzed during the study can be obtained from the corresponding authors upon reasonable request. Declaration of competing interests There are no competing interests. Funding This study was supported by the Youth Science Foundation of Guangxi Medical University (GXMUYSF 202548), the 18th batch of Special Funding from the China Postdoctoral Science Foundation (2025T180639), the National Key Research and Development Program (2022YFC2009600, 2022YFC2009601), Laboratory for Clinical Medicine, Capital Medical University (2023-SYJCLC01). Author contributions XN: Draft writing, data collection, data analysis. HL: Review and editing, visualization, supervision, methodology, conceptualization. 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Clinical effects of the neutrophil-to-lymphocyte ratio/serum albumin ratio in patients with gastric cancer after gastrectomy. J Pers Med. (2023). 2023;13(3) https://doi.org/10.3390/jpm13030432 Zhang, L. X., Wei, Z. J., Xu, A. M. & Zang, J. H. Can the neutrophil-lymphocyte ratio and platelet-lymphocyte ratio be beneficial in predicting lymph node metastasis and promising prognostic markers of gastric cancer patients? Tumor maker retrospective study. Int J Surg. (2018). 2018;56:320 – 27 https://doi.org/10.1016/j.ijsu.2018.06.037 Li, Z. et al. The clinical value and usage of inflammatory and nutritional markers in survival prediction for gastric cancer patients with neoadjuvant chemotherapy and d2 lymphadenectomy. Gastric Cancer 2020 . 23 (3), 540–549. https://doi.org/10.1007/s10120-019-01027-6 (2020). Hirahara, N. et al. Comparison of the prognostic value of immunoinflammation-based biomarkers in patients with gastric cancer. Oncotarget 2020 . 11 (27), 2625–2635. https://doi.org/10.18632/oncotarget.27653 (2020). Takechi, H. et al. Using the preoperative prognostic nutritional index as a predictive factor for non-cancer-related death in post-curative resection gastric cancer patients: a retrospective cohort study. Bmc Gastroenterol. (2020). 2020;20(1):256 https://doi.org/10.1186/s12876-020-01402-z Xishan, Z., Ye, Z., Feiyan, M., Liang, X. & Shikai, W. The role of prognostic nutritional index for clinical outcomes of gastric cancer after total gastrectomy. Sci. Rep. 2020 . 10 (1), 17373. https://doi.org/10.1038/s41598-020-74525-8 (2020). Lv, Y., Zhang, J., Liu, Z., Tian, Y. & Liu, F. A novel inflammation-based prognostic index for patients with esophageal squamous cell carcinoma: neutrophil lymphocyte ratio/prealbumin ratio. Med. (Baltimore) 2019 . 98 (7), e14562. https://doi.org/10.1097/MD.0000000000014562 (2019). Ueda, K. et al. The prognostic value of systemic inflammatory markers in advanced renal cell carcinoma patients treated with molecular targeted therapies. Anticancer Res. 2020 . 40 (3), 1739–1745. https://doi.org/10.21873/anticanres.14127 (2020). Lin, Z., Zhuang, W., Wang, L. & Lan, W. Association between nutritional inflammation index and diabetic foot ulcers: a population-based study. Front Nutr. (2025). 2025;12:1532131 https://doi.org/10.3389/fnut.2025.1532131 Yang, S. B. & Zhao, H. W. Associations between albumin/neutrophil-to-lymphocyte ratio score and new-onset atrial fibrillation in patients with acute myocardial infarction undergoing pci. J. Inflamm. Res. 2025 . 18 , 61–71. https://doi.org/10.2147/JIR.S500743 (2025). Wei, C. et al. Nomograms based on the albumin/neutrophil-to-lymphocyte ratio score for predicting coronary artery disease or subclinical coronary artery disease. J. Inflamm. Res. 2023 . 16 , 169–182. https://doi.org/doi: 10.2147/JIR.S392482 (2023). Onuma, S. et al. Clinical effects of the neutrophil-to-lymphocyte ratio/serum albumin ratio in patients with gastric cancer after gastrectomy. J Pers Med. (2023). 2023;13(3) https://doi.org/10.3390/jpm13030432 Tables Tables are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.doc Table2.doc SupplementaryMaterial.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 17 Apr, 2026 Editor assigned by journal 14 Apr, 2026 Editor invited by journal 10 Mar, 2026 Submission checks completed at journal 04 Mar, 2026 First submitted to journal 04 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8999159","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":625242568,"identity":"153bea20-6927-4ce7-9cca-08a70a783873","order_by":0,"name":"Nuo Xu","email":"","orcid":"","institution":"Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Nuo","middleName":"","lastName":"Xu","suffix":""},{"id":625242569,"identity":"233c731f-f551-4e63-bfcd-7f8579eb5c53","order_by":1,"name":"Hailun Xie","email":"","orcid":"","institution":"Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hailun","middleName":"","lastName":"Xie","suffix":""},{"id":625242571,"identity":"19b6ddf0-c76c-463f-94f0-bc3e27e057c1","order_by":2,"name":"Shuyao Wang","email":"","orcid":"","institution":"Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shuyao","middleName":"","lastName":"Wang","suffix":""},{"id":625242574,"identity":"00be39fc-b746-4385-ba97-81cf2b033735","order_by":3,"name":"Changhong Xu","email":"","orcid":"","institution":"Guangxi Medical University","correspondingAuthor":false,"prefix":"","firstName":"Changhong","middleName":"","lastName":"Xu","suffix":""},{"id":625242576,"identity":"1cb35405-8bbe-48c1-ae94-b7776f1ec747","order_by":4,"name":"Yibo Chen","email":"","orcid":"","institution":"Guangxi Medical 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University","correspondingAuthor":true,"prefix":"","firstName":"Hanping","middleName":"","lastName":"Shi","suffix":""}],"badges":[],"createdAt":"2026-03-01 04:23:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8999159/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8999159/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107838955,"identity":"0f70e901-a7da-42c3-aaae-52588d6210d5","added_by":"auto","created_at":"2026-04-26 17:14:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":706986,"visible":true,"origin":"","legend":"\u003cp\u003eStudy design and flow chart\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8999159/v1/7004c378dc654f26aba119aa.png"},{"id":107870597,"identity":"62883d31-4f81-449d-be8f-672d48641433","added_by":"auto","created_at":"2026-04-27 07:40:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":821444,"visible":true,"origin":"","legend":"\u003cp\u003eThe optimal threshold cutoff of ANLR by ROC curve.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8999159/v1/a46184995eeb65878743f0e0.png"},{"id":107838959,"identity":"0c665b3f-1375-4a1b-a653-a108f338c3f5","added_by":"auto","created_at":"2026-04-26 17:14:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1861271,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier curve of ANLR in GC patients.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8999159/v1/0bb68187ef524dbbd2adaa0a.png"},{"id":107838961,"identity":"eb938e50-1ed9-41a5-b783-0c99f08727c0","added_by":"auto","created_at":"2026-04-26 17:14:18","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":989217,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier curve of ANLR based on differentiation degree subgroup in GC patients.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8999159/v1/0408f0590eaef7a10711a6ae.png"},{"id":107872299,"identity":"cc6468df-995a-4ab9-aebc-8266cf48d0e5","added_by":"auto","created_at":"2026-04-27 07:56:34","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":980080,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier curve of ANLR based on degree TNM subgroup in GC patients.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-8999159/v1/88f60a0cdb72b61d8b1b267b.png"},{"id":107838963,"identity":"98b98e66-80fb-4399-b53b-6990f26187dd","added_by":"auto","created_at":"2026-04-26 17:14:18","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":728686,"visible":true,"origin":"","legend":"\u003cp\u003eThe association between ANLR and survival in GC patients.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-8999159/v1/6a05d3f4db6df6095a1ee4b7.png"},{"id":109252176,"identity":"8e02a481-e05d-4dbb-b11a-09c33179ed32","added_by":"auto","created_at":"2026-05-14 09:21:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5850483,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8999159/v1/e9586efb-3d1a-45f5-a8a4-b63b3dfbfe13.pdf"},{"id":107870565,"identity":"48358699-2b2e-4ca1-9028-62ffb4915834","added_by":"auto","created_at":"2026-04-27 07:39:56","extension":"doc","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":68096,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.doc","url":"https://assets-eu.researchsquare.com/files/rs-8999159/v1/9754d1de3cc69f2fe45177b7.doc"},{"id":107870200,"identity":"1e79dfef-1d84-4eca-9c93-366d7fa00885","added_by":"auto","created_at":"2026-04-27 07:39:07","extension":"doc","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":65024,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.doc","url":"https://assets-eu.researchsquare.com/files/rs-8999159/v1/7cf12383fe8e5864d67218b1.doc"},{"id":107869960,"identity":"8801ecff-a315-45ae-89d7-79b3306557da","added_by":"auto","created_at":"2026-04-27 07:38:33","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":4516440,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-8999159/v1/3e5f1f7276b0e8f9237dc294.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and Validation of a Nomogram Based on Albumin-to-Neutrophil-to-Lymphocyte Ratio for Prognosis in Gastric Cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGC is a major global health challenge, ranking as the fourth most common cancer worldwide, following lung cancer, colorectal cancer and liver cancer[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. According to the data from GLOBOCAN 2022, gastric cancer ranks fifth in terms of both mortality and incidence rates. Nearly 1\u0026nbsp;million new cases are diagnosed each year, resulting in over 650,000 deaths worldwide[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].The systemic treatment for gastric cancer includes chemotherapy, immunotherapy and targeted therapy. The combination of immune checkpoint inhibitors and chemotherapy has become the standard treatment for patients with advanced gastric cancer[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].The prognosis of gastric cancer depends on the stage of the cancer, the treatment, the biological characteristics, as well as patient-related factors such as nutrition and gender.\u003c/p\u003e \u003cp\u003eSystemic inflammation and nutritional status play a significant role in the occurrence, progression and prognosis of tumors[\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].The NLR inflammatory state impairs the immune response, promotes tumor immune escape, and ultimately drives tumor progression and invasion[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].The elevation of NLR and PLR is associated with poorer OS and DFS in GC patients, suggesting that they may have potential utility for risk stratification in clinical practice[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].In the context of systemic inflammation, liver protein synthesis undergoes reprogramming, with the liver prioritizing the production of acute-phase proteins - thus, albumin has recently been proposed as a biomarker for systemic inflammation in cancer patients[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].It has been confirmed that peripheral blood inflammatory markers such as NLR, PLR and LMR have been shown to reflect the overall inflammatory status and are potential indicators that can assist in the clinical diagnosis and prognosis assessment of gastric cancer[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].Shufa Tan et al. meta-analysis suggests that NLR, PLR, and LMR are significant independent risk predictors for GC patients receiving immune checkpoint inhibitors[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Ogata et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] conducted a study which indicated that the high NLR before and after nab-paclitaxel treatment was significantly associated with a shortened OS for patients with unresectable or recurrent gastric cancer.\u003c/p\u003e \u003cp\u003eThe ANLR, as a composite indicator that integrates nutritional status with inflammatory response, may provide a more comprehensive reflection of a patient's pathophysiological condition compared to single indicators. However, the prognostic value of ANLR in gastric cancer patients remains uncertain, and its associations with clinical pathological features and stability across different subgroups necessitate systematic verification. This study, based on a large retrospective cohort, aims to evaluate the association between ANLR and OS as well as PFS and to establish its value as an independent prognostic factor. Additionally, we intend to explore the prognostic stability of ANLR in various clinical pathological subgroups, construct and validate a nomogram prognostic model that includes ANLR, and provide a novel tool for the prognosis assessment of gastric cancer.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Research design and patients\u003c/h2\u003e \u003cp\u003eThis multicenter cohort study utilized data from the INSCOC database (registration number: ChiCTR1800020329). We employed the previously mentioned design and methods to prospectively collect cohort data from multiple medical centers across China. This investigation was a retrospective cohort study that included 1,766 patients with gastric cancer who underwent radical gastrectomy at various medical centers in China between January 2012 and December 2021. The patients were divided into an internal validation cohort (n\u0026thinsp;=\u0026thinsp;1,203) and an external validation cohort (n\u0026thinsp;=\u0026thinsp;563).Inclusion criteria: age\u0026thinsp;\u0026ge;\u0026thinsp;18 years; pathologically diagnosed with gastric cancer; underwent radical gastrectomy (R0 resection); complete clinical data. Exclusion criteria: received neoadjuvant radiotherapy and chemotherapy before surgery; had concurrent malignant tumors; had a history of autoimmune diseases; had possible acute or chronic inflammatory diseases that may affect neutrophil or albumin levels; had missing clinical or follow-up data. This study adhered to the principles outlined in the Helsinki Declaration and received approval from the ethics committees of each participating local center. All participants provided written informed consent for the use of their clinical data, and their personal information was kept confidential.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.2. Data collection\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eClinical and pathological data were comprehensively collected from the hospital's electronic medical record system. This collection included baseline information of the patients, such as gender, age, height, weight, body mass index (BMI), history of hypertension, diabetes, heart disease, smoking, drinking, chronic hepatitis, tuberculosis, adjuvant therapy, and length of hospital stay. Tumor-related information encompassed the TNM stage (determined according to the 8th edition of the American Joint Committee on Cancer (AJCC) staging system), T stage, N stage, M stage, and differentiation grade.\u003c/p\u003e \u003cp\u003eBaseline fasting blood samples were obtained from all patients as recorded in the medical record system on the morning of the second day following admission. Laboratory parameters assessed included white blood cell count, neutrophil count, platelet count, lymphocyte count, hemoglobin, albumin, total bilirubin, direct bilirubin, total cholesterol, triglycerides, alanine aminotransferase (ALT), aspartate aminotransferase (AST), creatinine, urea nitrogen, C-reactive protein, ANLR, neutrophil/lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and prognostic nutritional index (PNI). The NLR was calculated as the neutrophil count (\u0026times;10⁹/L) divided by the lymphocyte count (\u0026times;10⁹/L), while the PLR was defined as the platelet count (\u0026times;10⁹/L) divided by the lymphocyte count (\u0026times;10⁹/L). The PNI was defined as albumin (g/L)\u0026thinsp;+\u0026thinsp;5 \u0026times; lymphocyte (\u0026times;10⁹/L). The ANLR was calculated as albumin (g/L) divided by NLR, or as albumin (g/dL) divided by (neutrophil count (\u0026times;10⁹/L) divided by lymphocyte count (\u0026times;10⁹/L)).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.3. Follow-up\u003c/b\u003e\u003c/h2\u003e \u003cp\u003ePostoperative follow-up for the patients was conducted regularly within 5 years. In the first 2 years, it was done every 3 months, then every 6 months for the following 3 years, and annually thereafter. Each follow-up included a detailed inquiry about symptoms and signs to detect potential recurrence or metastasis, supplemented by basic examinations: routine blood tests, biochemical analysis, measurement of tumor markers, gastroscopy, and imaging studies (CT or MRI) for a comprehensive health assessment. All 1766 eligible patients (treated from 2012 to 2021) from the INSCOC database underwent long-term follow-up through telephone interviews and outpatient visits. The follow-up period was 65 (interquartile range: 40\u0026ndash;84) months, and no deaths occurred. OS was measured as the time from surgery to any cause of death or the last follow-up date; PFS was measured as the time from surgery to tumor progression or any cause of death (whichever occurred first) or the last follow-up date.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Outcomes\u003c/h2\u003e \u003cp\u003eThe primary endpoint of this study was to evaluate the prognostic value of ANLR in predicting OS and PFS in gastric cancer, to differentiate the impact of high and low ANLR groups on survival prognosis, and to facilitate the early screening of patients. Secondary endpoints included the analysis of independent prognostic risk factors influencing OS and PFS in gastric cancer patients, as well as their correlation with ANLR. These factors were determined to be unaffected by gender, age, or BMI.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.5 Model validation\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eWe developed a risk prediction model and performed internal validation using data from 36 hospitals collected between December 2012 and December 2021 (n\u0026thinsp;=\u0026thinsp;1,203). External validation was conducted using data from one independent hospital (n\u0026thinsp;=\u0026thinsp;553). The internal validation assessed the stability of the predictive model against random variations in sample composition by performing 1,000 bootstrap resampling iterations. ROC curves were generated, followed by calibration curves, DCA, and clinical impact curves for the nomogram. External validation was conducted in a similar manner, generating the nomogram, ROC curve, and calibration curve, with predictive variables transformed consistently with those in the model derivation cohort. The results of both internal and external validations were statistically analyzed, and model performance was evaluated using the area under the receiver operating characteristic curve (AUROC), overall R\u0026sup2; goodness-of-fit, D statistic, Harrell\u0026rsquo;s C-index, and Brier score.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.6 Statistical analysis\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eStatistical analysis was conducted using R version 4.0.2. Measurement data were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) or median (interquartile range, IQR), with comparisons between groups performed using t-tests or Kruskal-Wallis tests. Count data were expressed as frequencies (percentages), and group comparisons were conducted using χ\u0026sup2; tests. Survival curves were generated using the Kaplan-Meier method, and differences between groups were assessed using the log-rank test. The Cox proportional hazards regression model was used to evaluate the association between ANLR and both OS and PFS, and to calculate HR and 95% CI. Three models were employed to adjust for confounding factors. Independent prognostic factors were included in the construction of nomogram models for OS and PFS. Model efficacy was evaluated using receiver ROC curves, calibration curves, and DCA, and was subsequently validated in an external cohort. A two-sided p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003e\u003cstrong\u003e3.1. Patient characteristics\u003c/strong\u003e\u003c/h2\u003e\n \u003cp\u003eThis study included a total of 1,766 patients who underwent radical gastrectomy for gastric cancer between December 2012 and December 2021. Of these, 1,203 patients were included in the initial research cohort and subsequently entered the model development cohort, while 563 patients were allocated to the external validation cohort \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;1\u003cstrong\u003e)\u003c/strong\u003e. The baseline characteristics of patients in the main cohort, internal cohort, and external validation cohort. The median age was 65.72 years, with 67.6% of the patients being male (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) \u003cstrong\u003e(\u003c/strong\u003eTable\u0026nbsp;1\u003cstrong\u003e)\u003c/strong\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003e3.2 Comparison of ANLR cutoff values and grouping characteristics\u003c/h2\u003e\n \u003cp\u003eThe AUC was 0.594, with a 95% confidence interval of 0.564 to 0.624. The optimal cut-off value was established at 20.39, which yielded a sensitivity of 43.1% and a specificity of 72.8% \u003cstrong\u003e(Figure S1 and\u003c/strong\u003e Fig. 2\u003cstrong\u003e)\u003c/strong\u003e.We divided the cohort of 1,766 patients into two groups: low ANLR (\u0026lt; 20.39, n = 716) and high ANLR (≥ 20.39, n = 482)\u003cstrong\u003e(\u003c/strong\u003eTable\u0026nbsp;2\u003cstrong\u003e)\u003c/strong\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003e3.3 Kaplan-Meier and Survival Prognosis Analysis\u003c/h2\u003e\n \u003cp\u003eBased on the ANLR cutoff, Kaplan–Meier analysis demonstrated that patients with low ANLR had significantly poorer survival than those with high ANLR, with lower OS (55.82% vs. 44.18%) and PFS (53.27% vs. 46.73%) (both Log-rank p \u0026lt; 0.001), as well as reduced 5-year survival \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;3\u003cstrong\u003e)\u003c/strong\u003e. Subgroup analysis by tumor differentiation showed consistently worse outcomes in the low ANLR group, including OS and PFS differences across high/moderate and low/undifferentiated grades (all \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001; Fig.\u0026nbsp;4, \u003cstrong\u003eFigure S2\u003c/strong\u003e). Similarly, TNM stage stratification revealed inferior OS and PFS in the low ANLR group across all stages, particularly in stage III–IV disease ( \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001; Fig.\u0026nbsp;5, \u003cstrong\u003eFigure S3\u003c/strong\u003e). These findings confirm the stable prognostic value of ANLR across differentiation grades and TNM stages.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003e3.4 The non-linear relationship between ANLR and the survival outcomes of GC patients\u003c/h2\u003e\n \u003cp\u003eMultivariate restricted cubic spline (RCS) analyses were conducted using three progressively adjusted models to evaluate the association between ANLR and OS and PFS in GC patients. All models demonstrated a significant non-linear, L-shaped relationship (p \u0026lt; 0.001). A turning point was identified at an ANLR value of 20.39. When ANLR was below 20.39, HR \u0026gt; 1 indicated increased risk and poorer prognosis. Higher ANLR levels were associated with improved survival outcomes \u003cstrong\u003e(\u003c/strong\u003eFig.\u0026nbsp;6\u003cstrong\u003e)\u003c/strong\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003e3.5 Correlation Analysis of ANLR and GC Patients' Survival Outcomes\u003c/h2\u003e\n \u003cp\u003eAfter full adjustment (Model c), the high ANLR group had significantly lower risks of mortality (HR = 0.623, 95% CI: 0.490–0.792, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) and progression (HR = 0.589, 95% CI: 0.439–0.791, p \u0026lt; 0.001) compared with the low ANLR group. Consistent results were observed in cut-off and quartile analyses, showing a significant decreasing trend in OS and PFS risk with increasing ANLR ( \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). Overall, higher ANLR remained a robust protective factor for OS and PFS after multivariable adjustment \u003cstrong\u003e(Table S8-S9)\u003c/strong\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\"\u003e\n \u003ch2\u003e3.6 Subgroup analysis of survival outcomes in ANLR and GC patients\u003c/h2\u003e\n \u003cp\u003eIn the subgroup analysis of progression-free survival (PFS)-related factors, ANLR remained an independent risk factor for most subgroups (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05), except in the following cases: BMI (Normal), length of hospital stay (\u0026gt; 24 days), smoking, alcohol consumption, diabetes, hypertension, chronic hepatitis, tumor differentiation (Medium and Undifferentiated), TNM stage (III-IV), T stages (T1 and T4), N stages (N0, N2, N3), and M1 stage \u003cstrong\u003e(Figure S4)\u003c/strong\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\"\u003e\n \u003ch2\u003e3.7 The prognostic performance of ANLR in comparison with established inflammatory nutrition indicators\u003c/h2\u003e\n \u003cp\u003eIn terms of OS prediction, ANLR demonstrated higher predictive efficacy, with AUC values exceeding those of the control indices (ANLR: 0.592 vs. NLR: 0.567 vs. PLR: 0.572 vs. PNI: 0.549)\u003cstrong\u003e(Figure S5)\u003c/strong\u003e;In the 1-year, 3-year, and 5-year predictions, OS (1-year: 0.543 vs. 3-year: 0.531 vs. 5-year: 0.518) and PFS (1-year: 0.541 vs. 3-year: 0.533 vs. 5-year: 0.528) all demonstrated good predictive effects \u003cstrong\u003e(Figure S6)\u003c/strong\u003e;During the 1-year, 3-year, and 5-year survival periods, for predicting the OS of patients, the area under the ROC curve of ANLR was the largest (ANLR: 0.519 vs. NLR: 0.471 vs. PLR: 0.517 vs. PNI: 0.498) \u003cstrong\u003e(Figure S7)\u003c/strong\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\"\u003e\n \u003ch2\u003e3.8 The establishment of the nomogram\u003c/h2\u003e\n \u003cp\u003eMultivariate Cox proportional hazards regression analysis demonstrated that age, BMI, diabetes, TNM stage, N stage, and ANLR were independent risk factors for OS in patients with GC (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). For PFS, age, adjuvant chemotherapy, TNM stage, N stage, and ANLR were identified as independent risk factors (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) \u003cstrong\u003e(Table S6-S7)\u003c/strong\u003e. Based on these key variables, a nomogram was constructed to predict 1-, 3-, and 5-year OS and PFS in GC patients \u003cstrong\u003e(Figure S8)\u003c/strong\u003e. The predicted probabilities of OS and PFS at these time points were calculated by summing the scores assigned to each variable.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\"\u003e\n \u003ch2\u003e\u003cstrong\u003e3.9 Internal validation of the nomogram\u003c/strong\u003e\u003c/h2\u003e\n \u003cp\u003eThe predictive performance of the nomogram was assessed using ROC analysis with bootstrap internal validation. The AUCs for predicting OS and PFS were 0.660 and 0.710, respectively \u003cstrong\u003e(Figure S9)\u003c/strong\u003e. Calibration curves for 1-, 3-, and 5-year survival showed good agreement between predicted and observed outcomes, particularly in the high-probability range, with slight deviation in the mid-low range \u003cstrong\u003e(Figure S10)\u003c/strong\u003e. Decision curve analysis demonstrated that the nomogram provided greater net clinical benefit within the key risk threshold range of 1.5%–4% and reduced unnecessary interventions for patients with a risk below 1.5% \u003cstrong\u003e(Figure S11)\u003c/strong\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\"\u003e\n \u003ch2\u003e\u003cstrong\u003e3.10 Verification of the external cohort\u003c/strong\u003e\u003c/h2\u003e\n \u003cp\u003eExternal validation using an independent hospital cohort confirmed the internal findings, with Cox regression identifying the same independent risk factors and a nomogram constructed \u003cstrong\u003e(Figure S12)\u003c/strong\u003e. Bootstrap internal validation yielded AUCs of 0.690 for OS and 0.920 for PFS \u003cstrong\u003e(Figure S13)\u003c/strong\u003e. Calibration curves for 1-, 3-, and 5-year survival demonstrated high accuracy, with predicted 5-year OS and PFS closely matching observed outcomes \u003cstrong\u003e(Figure S14)\u003c/strong\u003e. Decision curve analysis showed that in the external cohort, the nomogram substantially improved net clinical benefit within the key risk threshold of 0.05–0.15, outperforming standard thresholds across different risk levels \u003cstrong\u003e(Figure S15)\u003c/strong\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\"\u003e\n \u003ch2\u003e3.11 Comparison of verification efficiency between internal queue and external queue\u003c/h2\u003e\n \u003cp\u003eWe further validated the models of the internal and external queues. In the OS model, we compared the performance of each algorithm for the internal queue and the external queue. The Brier score was (0.189 vs. 0.218), the overall R2 fitting effect was (0.087 vs. 0.167), Harrell's C index was (0.655 vs. 0.664), and the calibration slope was all 1.000 (0.920–1.080), indicating that the model has good discrimination and calibration. Similarly, in the model PFS, we compared the performance of each algorithm for the two queues. The Brier score was (0.150 vs. 0.099), the overall R\u003csup\u003e2\u003c/sup\u003e fitting effect was (0.133 vs. 0.578), Harrell's C index was (0.667 vs. 0.510), and the calibration slope was all 1.000 (0.920–1.080), all indicating that the model has good discrimination and calibration \u003cstrong\u003e(Table S10-S12)\u003c/strong\u003e.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eGC remains a significant global health issue, characterized by high incidence and mortality rates, especially in certain parts of the world. Although the incidence of gastric cancer has generally decreased in many countries, it remains the leading cause of cancer-related deaths, with over 1\u0026nbsp;million new cases diagnosed each year[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In terms of treatment, surgery remains the cornerstone for achieving a cure, but the role of systemic treatments, including neoadjuvant chemotherapy (NAC), chemotherapy, targeted therapy and immunotherapy, is becoming increasingly important[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInflammation plays a crucial role in the development and progression of cancer[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Inflammatory markers, such as the PLR, the monocyte-to-lymphocyte ratio (MLR), the systemic inflammatory response index (SIRI), and the Glasgow Prognostic Score (GPS), have demonstrated the potential to provide valuable prognostic information in GC[\u003cspan additionalcitationids=\"CR22 CR23 CR24\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].In patients with gastric cancer, higher levels of NLR and PLR are associated with poorer OS and PFS, while a higher lymphocyte-to-monocyte ratio (LMR) is associated with a better prognosis [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Studies have shown that a high NLR is associated with a poorer clinical outcome and can serve as a prognostic biomarker for patients with colorectal cancer[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].Furthermore, the level of serum albumin reflects the inflammation and malnutrition of the cancer host[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. A retrospective study involving 1023 GC patients showed that the pre-treatment serum albumin level was an important prognostic factor[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].A prospective study involving 500 GC patients showed that preoperative NLR/Alb was a prognostic factor for survival after radical surgery [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].The ratio of neutrophils to lymphocytes / serum albumin. Multiple studies have reported that preoperative NLR and PNI are prognostic factors for patients after GC surgery[\u003cspan additionalcitationids=\"CR32 CR33 CR34\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Furthermore, a retrospective study demonstrated that the OS of ESCC patients with high NLR/pre-Alb was worse than that of patients with low NLR/pre-Alb (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.043) [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Additionally, a retrospective study indicated that in advanced RCC cases, patients with high NLR/Alb and CRP/Alb ratios had significantly poorer PFS and OS compared to those with low NLR/Alb and CRP/Alb ratios[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe results of this study suggest that ANLR is an effective biomarker for prognosis assessment in patients with gastric cancer. Patients with ANLR\u0026thinsp;\u0026ge;\u0026thinsp;20.39 had better 5-year OS and PFS compared to those with ANLR\u0026thinsp;\u0026lt;\u0026thinsp;20.39 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The multivariate Cox regression analysis showed that a high ANLR was an independent protective factor for OS and PFS. Subgroup analysis indicated that ANLR had stable prognostic value in different TNM stages, differentiation degrees, and clinical characteristic subgroups. The Nomogram model constructed based on ANLR showed good calibration and discrimination efficacy (C-index: OS 0.664, PFS 0.883). This result has been previously validated in studies regarding the prognostic ability of ANLR under different conditions: an elevated peripheral ANLR can effectively predict adverse outcomes of coronary artery disease and diabetic foot ulcers[\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. This predictive ability extends to gastrointestinal cancers, as demonstrated by Onuma et al. (in a small sample from a single center), where preoperative ANLR was identified as an important prognostic indicator for gastric cancer patients after radical gastrectomy[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCompared to existing inflammation-nutrition indicators (NLR, PLR, PNI), ANLR demonstrates superior performance in prognosis assessment. For OS prediction, the AUC for ANLR (0.592) is higher than that for NLR (0.567), PLR (0.572), and PNI (0.549). In PFS prediction, the AUC for ANLR (0.616) is also significantly superior to that of the other indicators. Subgroup analysis shows that ANLR can effectively differentiate prognosis risk in patients with various clinical characteristics. Notably, for patients with advanced and poorly differentiated tumors, the risk stratification value is particularly significant.\u003c/p\u003e \u003cp\u003eThe nomogram constructed in this study incorporated key factors, including ANLR, age, BMI, and TNM stage. It was validated through both internal (C-index OS\u0026thinsp;=\u0026thinsp;0.727, PFS\u0026thinsp;=\u0026thinsp;0.719) and external validation (AUC OS\u0026thinsp;=\u0026thinsp;0.690, PFS\u0026thinsp;=\u0026thinsp;0.920), demonstrating good discrimination and calibration. DCA indicated that within the risk threshold range of 1.5% to 4% for OS and 0.05 to 0.15 for PFS, the net benefit of the nomogram was significantly higher than that of traditional staging tools, effectively reducing excessive interventions for low-risk patients. For high-risk patients (e.g., ANLR\u0026thinsp;\u0026ge;\u0026thinsp;20.39 and stage III), a more intensive postoperative follow-up (e.g., imaging examinations every 3 months) should be emphasized, and intensified adjuvant therapy may be considered. Conversely, for low-risk patients, interventions can be appropriately reduced to prevent unnecessary treatment.\u003c/p\u003e \u003cp\u003eThe limitations of this study include its retrospective design, which may introduce selection bias; the absence of data on postoperative dynamic changes in ANLR; the lack of exploration into the association between ANLR and the efficacy of immunotherapy; and the failure to fully address potential confounding factors (such as Helicobacter pylori infection and dietary structure). Future prospective cohort studies, combined with multi-omics data, are needed to further validate the prognostic value and underlying mechanisms of ANLR.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eANLR serves as an independent prognostic factor for OS and PFS in patients with gastric cancer. A high ANLR score indicates a poor prognosis. The nomogram model constructed based on ANLR demonstrates strong prognostic prediction efficacy and can serve as a straightforward and reliable tool in clinical practice\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study adhered to the Helsinki Declaration. All participants signed the informed consent form, and the study was approved by the hospital\u0026apos;s institutional review board (registration number: ChiCTR1800020329).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have agreed to publish.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the study can be obtained from the corresponding authors upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere are no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Youth Science Foundation of Guangxi Medical University (GXMUYSF 202548), the 18th batch of Special Funding from the China Postdoctoral Science Foundation (2025T180639), the National Key Research and Development Program (2022YFC2009600, 2022YFC2009601), Laboratory for Clinical Medicine, Capital Medical University (2023-SYJCLC01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXN: Draft writing, data collection, data analysis. HL: Review and editing, visualization, supervision, methodology, conceptualization. SW: Data investigation, supervision, methodology. CX: Software, methodology, supervision. YC: Data organization. SL: Data collection. HS: Review and editing, supervision, conceptualization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the INSCOC project members for their substantial work on data collection and patient follow-up.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel, R. L., Giaquinto, A. N., Jemal, A. \u0026amp; Cancer statistics Ca Cancer J Clin. 2024 2024 Jan-Feb;74(1):12\u0026ndash;49. 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(2023). 2023;13(3) \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/jpm13030432\u003c/span\u003e\u003cspan address=\"10.3390/jpm13030432\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables are available in the Supplementary Files section.\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Gastric cancer, Albumin/Neutrophil-to-Lymphocyte Ratio, Prognosis, Overall survival, Progression-free survival, Nomogram","lastPublishedDoi":"10.21203/rs.3.rs-8999159/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8999159/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe albumin-to-neutrophil-to-lymphocyte ratio (ANLR) is a novel inflammatory composite indicator with potential prognostic value in cancer. This study aimed to assess the association of ANLR with overall survival (OS) and progression-free survival (PFS) in patients with gastric cancer and evaluate its clinical relevance.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA retrospective analysis was performed on 2,051 patients undergoing radical gastrectomy for gastric cancer from 2012 to 2021 in the INSCOC database. The optimal ANLR cutoff was determined using receiver operating characteristic (ROC) analysis. Survival was estimated by Kaplan\u0026ndash;Meier curves, and independent prognostic value was assessed with Cox proportional hazards models. A nomogram was constructed for OS and PFS prediction, with internal and external validations using ROC curves, calibration plots, and decision curve analysis (DCA) to evaluate model performance.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA total of 1,766 patients were included (internal cohort, n\u0026thinsp;=\u0026thinsp;1,203; external cohort, n\u0026thinsp;=\u0026thinsp;563). The optimal ANLR cutoff was 20.39. Patients with high ANLR (\u0026ge;\u0026thinsp;20.39) had better OS and PFS than those with low ANLR (\u0026lt;\u0026thinsp;20.39). Multivariate Cox analysis confirmed ANLR as an independent prognostic factor for OS (HR\u0026thinsp;=\u0026thinsp;0.623) and PFS (HR\u0026thinsp;=\u0026thinsp;0.589, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The nomogram predicted OS and PFS with areas under the curve (AUCs) of 0.660 and 0.710. External validation demonstrated good calibration and discrimination (C-index: OS 0.664, PFS 0.883).\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eANLR is an effective biomarker for prognostic assessment in gastric cancer. The nomogram provides individualized survival prediction and supports clinical decision-making.\u003c/p\u003e","manuscriptTitle":"Development and Validation of a Nomogram Based on Albumin-to-Neutrophil-to-Lymphocyte Ratio for Prognosis in Gastric Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-26 17:14:13","doi":"10.21203/rs.3.rs-8999159/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-17T13:47:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-14T13:19:26+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-10T04:32:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-04T08:58:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-03-04T07:10:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"58b89bcc-9369-4122-ab49-deb503b36a00","owner":[],"postedDate":"April 26th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":66552179,"name":"Health sciences/Biomarkers"},{"id":66552180,"name":"Biological sciences/Cancer"},{"id":66552181,"name":"Health sciences/Gastroenterology"},{"id":66552182,"name":"Health sciences/Oncology"},{"id":66552183,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-04-26T17:14:13+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-26 17:14:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8999159","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8999159","identity":"rs-8999159","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}