Development and Validation of a Predictive Model for Gastric Cancer Based on the Albumin-to-Neutrophil-to-Lymphocyte Ratio: A Retrospective Cohort Study

Development and Validation of a Predictive Model for Gastric Cancer Based on the Albumin-to-Neutrophil-to-Lymphocyte Ratio: A Retrospective Cohort 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 Research Article Development and Validation of a Predictive Model for Gastric Cancer Based on the Albumin-to-Neutrophil-to-Lymphocyte Ratio: A Retrospective Cohort Study Nuo Xu, Hailun Xie, Pei Zhong, Changhong Xu, Shuyao Wang, Siyu Lin, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7845089/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 aims to examine the correlation between ANLR and OS, PFS in patients diagnosed with gastric cancer, with the goal of elucidating its predictive value and clinical relevance. Method A retrospective analysis was conducted on clinical case data from 2,051 patients who underwent radical gastrectomy for gastric cancer between 2012 and 2021, as recorded in the INSCOC database. Determine the optimal cut-off value of ANLR through the ROC curve. The survival curve was generated using the Kaplan-Meier method, the Cox proportional hazards regression model was utilized to analyze the association between ANLR and both OS and PFS. The nomogram prognostic model was constructed, with internal and external validations performed through ROC curve, calibration curve and DCA to evaluate the model's performance. Result This study ultimately included 1766 patients, with 1203 patients in the internal validation cohort and 563 patients in the external validation cohort. The optimal cutoff value of ANLR was 20.39. Patients with high ANLR (≥ 20.39) had better OS and PFS than those with low ANLR (< 20.39). Multivariate Cox regression showed that ANLR was an independent prognostic factor for OS (HR = 0.623, 95% CI: 0.490–0.792, p < 0.001) and PFS (HR = 0.589, 95% CI: 0.439–0.791, p < 0.001). The Nomogram model predicted OS and PFS with AUCs of 0.660 and 0.710. The external validation showed good calibration and discriminatory efficacy (C-index: OS 0.664, PFS 0.883). Conclusion The ANLR can serve as an effective biomarker for the prognostic assessment of patients with gastric cancer. The nomogram model is beneficial for individualized prognostic prediction and clinical decision-making. 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 Figure 7 1. 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. 2. 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. 3. 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) . Table 1 presents 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. Tumor differentiation was as follows: well-differentiated (14.4% vs. 4.6%), moderately-differentiated (28.4% vs. 25.8%), poorly-differentiated (53.0% vs. 65.2%), and undifferentiated (4.2% vs. 4.4%). The TNM stage distribution was as follows: stage I-II (46.5%) and stage III-IV (53.5%). No significant differences were observed in clinical characteristics (e.g., age, BMI, tumor stage) between the internal and external cohorts (p > 0.05). Patients in the high ANLR group (≥ 20.39) were older, had a lower BMI, longer hospital stays, and a higher proportion of advanced tumors (stage III-IV) (p < 0.05). 3.2 Comparison of ANLR cutoff values and grouping characteristics The ROC curve for predicting survival duration using patient survival rate as the outcome variable was plotted. 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% (Fig. 2A-B) . We divided the cohort of 1,766 patients into two groups: low ANLR (< 20.39, n = 716) and high ANLR (≥ 20.39, n = 482). Statistically significant differences were observed between the two groups regarding age, BMI, length of hospital stay, smoking status, TNM stage, white blood cell count, neutrophil count, platelet count, lymphocyte count, hemoglobin levels, albumin levels, total bilirubin, direct bilirubin, AST, C-reactive protein, ANLR, NLR, PLR, and prognostic nutritional index (PNI) (p < 0.05). Conversely, no statistically significant differences were found between the two groups regarding gender, alcohol consumption, diabetes, hypertension, heart disease, chronic hepatitis, tuberculosis, tumor differentiation, T stage, N stage, M stage, total cholesterol, triglycerides, ALT, creatinine, and urea nitrogen (p > 0.05) ( Table 2 ) . 3.3 Kaplan-Meier and Survival Prognosis Analysis Based on the ANLR cutoff value grouping, the Kaplan-Meier analysis showed that the survival outcomes of patients with low ANLR were significantly worse than those of patients with high ANLR. The OS (55.82% vs. 44.18%, Log-rank p < 0.001), PFS (53.27% vs. 46.73%, Log-rank p < 0.001), and 5-year survival rate were all significantly lower (Fig. 3) . In the subgroup analysis based on tumor differentiation degree, patients in the high ANLR group had a better prognosis than those in the low ANLR group, with OS (75.96% vs. 24.04%) and PFS (68.35% vs. 31.65%) (Fig. 4) ; further analysis of high and moderate differentiation degrees yielded the same results, OS (73.47% vs. 26.53%) and PFS (75.81% vs. 24.19%); for low and undifferentiated degrees, OS (78.82% vs. 21.17%) and PFS (69.70% vs. 30.30%), it can be seen that the prognosis of patients with low ANLR was significantly worse than that of patients with high ANLR, and the statistical significance was all P < 0.0001 (Figure S1 ) . In the TNM stage subgroup analysis, patients with low ANLR all showed poorer survival rates, OS (55.82% vs. 44.18%) and PFS (54.27% vs. 45.73%), and the results were consistent when comparing each stage (Fig. 5) ; patients in the low ANLR group had lower OS and PFS than those in the high ANLR group, especially in stage III-IV (I-II stage: OS 56.19% vs. 43.81%, p < 0.0001; PFS 53.75% vs. 46.25%, p < 0.0001; stage III-IV: OS 63.19% vs. 36.81%, PFS 54.62% vs. 45.38%, all p < 0.0001) (Figure S2) . 3.4 The non-linear relationship between ANLR and the survival outcomes of GC patients We utilized multivariate restricted cubic splines (RCS) across three models to analyze the association between the ANLR score in GC patients and OS and PFS. The results of our analysis demonstrated that models a, b, and c progressively incorporated corresponding variables, revealing a non-linear association between ANLR and both OS and PFS, with an L-shaped pattern. The RCS plots all showed statistical significance (p < 0.001). The risk of OS and PFS in GC patients did not consistently decrease with increasing ANLR; instead, a turning point was observed at an ANLR value of 20.39. Specifically, when ANLR was below 20.39, the HR was greater than 1, indicating a higher risk and poorer prognosis. Beyond this turning point, as ANLR increased above 20.39, the HR decreased significantly, and the risk correspondingly declined. When ANLR exceeded 40, the HR stabilized around 0.5 (p < 0.001). In conclusion, as ANLR increases, the survival prognosis of GC patients improves (Fig. 6) . 3.5 Correlation Analysis of ANLR and GC Patients' Survival Outcomes Multivariate Cox regression analysis revealed that in the analysis of poor OS prognosis, without adjusting for any confounding factors (Model a), every one standard deviation increase in ANLR was associated with a 1.7% reduction in the risk of disease progression (HR = 0.983, 95%CI: 0.976–0.991, p < 0.001). Similarly, in Model c, which adjusted for confounding factors, the mortality risk in the high ANLR group was 1.605 times that of the low ANLR group (HR = 0.623, 95% CI: 0.490–0.792, p < 0.001). In the analysis of poor PFS risk, Model c showed that the progression risk in the high ANLR group was 1.698 times that of the low ANLR group (HR = 0.589, 95% CI: 0.439–0.791, p < 0.001).The analysis of the cut-off points for the high and low ANLR groups revealed that, compared to the low ANLR group, patients in the high ANLR group had a significantly lower risk of adverse OS (HR = 0.538, 95% confidence interval [CI]: 0.120–0.620, p < 0.001). Even after adjusting for potential confounding factors using models b and c, the risk remained reduced by 0.3% (HR = 0.997, 95% CI: 0.992–1.006, p < 0.001). In the quartile analysis of ANLR, as ANLR increased, the risks of OS (HR = 0.554, 95% CI: 0.401–0.767, p < 0.001) and PFS (HR = 0.690, 95% CI: 0.444–1.073, p = 0.009) exhibited a decreasing trend (trend p < 0.001). Therefore, whether based on continuous variables, cut-off point grouping, or quartile grouping, higher ANLR levels are significantly associated with better OS and PFS. Even after adjusting for multiple potential confounding factors, this protective association remains significant (HR in models b and c are still significantly less than 1) ( Table 3 – 4 ) . Table 3 Association between ANLR and OS of GC patients. ANLR Model a p value Model b p value Model c p value Continuous (per SD) 0.983 (0.976,0.991) < 0.001 0.986 (0.978,0.994) < 0.001 0.987 (0.978,0.995) 0.001 Cutoff value (High) 0.538 (0.120,0.620) < 0.001 0.998(0.991,1.005) < 0.001 0.997 (0.992,1.006) < 0.001 Quartiles Q1 (~ 9.24) ref ref ref Q2 (9.24 ~ 17.21) 0.838 (0.638,1.100) 0.202 0.848 (0.645,1.115) 0.237 0.918 (0.695,1.211) 0.545 Q3 (17.21 ~ 27.69) 0.568 (0.420,0.767) < 0.001 0.605 (0.446,0.819) 0.001 0.632 (0.463,0.863) 0.004 Q4 (27.69~) 0.505 (0.372,0.685) < 0.001 0.562 (0.412,0.769) < 0.001 0.554 (0.401,0.767) < 0.001 p for trend < 0.001 < 0.001 < 0.001 Model a: No adjusted. Model b: Adjusted for Gender, Age, BMI, TNM stage. Model c: Adjusted for Gender, Age, BMI, Hospitalization, Smoking, Alcohol, Diabetes, Hypertension Cardiovascular, Chronic hepatitis, Tuberculosis, Chemotherapy, Differentiation, TNM Stage, T Stage, N Stage, M Stage. Table 4 Association between ANLR and PFS of GC patients. ANLR Model a p value Model b p value Model c p value Continuous (per SD) 0.981 (0.971,0.991) < 0.001 0.988 (0.978,0.999) 0.028 0.998 (0.990,1.005) 0.508 Cutoff value (High) 0.462 (0.143,0.771) < 0.001 0.999(0.993,1.004) < 0.001 0.999 (0.994,1.004) < 0.001 Quartiles Q1 (~ 9.24) ref ref ref Q2 (9.24 ~ 17.21) 1.051 (0.762,1.450) 0.760 0.881 (0.628,1.236) 0.463 0.914 (0.641,1.303) 0.618 Q3 (17.21 ~ 27.69) 0.568 (0.420,0.767) 0.002 0.705 (0.474,1.048) 0.084 0.942 (0.609,1.455) 0.787 Q4 (27.69~) 0.451 (0.303,0.670) < 0.001 0.514 (0.344,0.768) 0.001 0.690 (0.444,1.073) 0.009 p for trend < 0.001 < 0.001 < 0.001 Model a: No adjusted. Model b: Adjusted for Gender, Age, BMI, TNM stage. Model c: Adjusted for Gender, Age, BMI, Hospitalization, Smoking, Alcohol, Diabetes, Hypertension Cardiovascular, Chronic hepatitis, Tuberculosis, Chemotherapy, Differentiation, TNM Stage, T Stage, N Stage, M Stage. 3.6 Subgroup analysis of survival outcomes in ANLR and GC patients The multivariate forest plot analysis indicated that the following subgroups related to OS factors showed statistical significance (P < 0.05): age, body mass index (BMI) categories (Normal and High), length of hospital stay (< 24 days), gender, smoking status, absence of alcohol consumption, absence of diabetes, hypertension, absence of heart disease, absence of chronic hepatitis, adjuvant chemotherapy, degree of differentiation (high and low differentiation), TNM stage (I-II), T stages (T1 and T3), N stage (N0-N3), and M stage (M0). These results suggest that ANLR level is an independent risk factor for most subgroups. 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 S3 A) . Furthermore, as shown in the heat map, it presents the numerical distribution of multiple variables under different groups (Figure S3 B-C) . 3.7 The prognostic performance of ANLR in comparison with established inflammatory nutrition indicators We conducted a ROC analysis to evaluate the prognostic utility of ANLR compared to the established biomarkers NLR, PLR, and PNI. 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). For PFS prediction, the AUC values also indicated consistent predictive effects (ANLR: 0.616 vs. NLR: 0.532 vs. PLR: 0.558 vs. PNI: 0.589) (Figure S4) ;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 S5) ;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); Similarly, for predicting the PFS of patients, the area under the ROC curve of ANLR was also the best (ANLR: 0.528 vs. NLR: 0.466 vs. PLR: 0.486 vs. PNI: 0.515) (Figure S6) . 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 5 – 6 ) . Based on these key variables, a nomogram was constructed to predict 1-, 3-, and 5-year OS and PFS in GC patients (Fig. 7A-B) . The predicted probabilities of OS and PFS at these time points were calculated by summing the scores assigned to each variable. Table 5 Univariate and multivariate Cox regression analysis of clinicopathological characteristics associated with Overall Survival in GC patients.---internal cohort Variables Univariate analysis Multivariate analysis HR (95%CI) P value HR (95%CI) P value Age(years) 1.012(1.002–1.021) 0.014 1.010(1.003–1.020) 0.044 BMI(kg/m 2 ) 0.946(0.914–0.980) 0.002 0.962(0.928–0.997) 0.034 Hospitalization 0.999(0.992–1.007) 0.875 Genger (female/male) 0.820(0.650–1.034) 0.094 Smoking (Yes/No) 1.202(0.972–1.487) 0.089 Alcohol (Yes/No) 1.049(0.817–1.347) 0.709 Diabetes (Yes/No) 1.634(1.116–2.393) 0.012 1.632(1.105–2.412) 0.014 Hypertension (Yes/No) 1.150(0.860–1.538) 0.347 Cardiovascular (Yes/No) 0.713(0.295–1.724) 0.452 Chronic hepatitis (Yes/No) 1.259(0.649–2.443) 0.496 Tuberculosis (Yes/No) 1.028(0.144–7.325) 0.978 Chemotherapy (Yes/No) 1.028(0.144–7.325) 0.978 Differentiation (Undifferentiated Poor/ Medium/ High ) 1.227(1.069–1.409) 0.004 1.123(0.972–1.296) 0.115 TNM Stage (Ⅰ/Ⅱ/Ⅲ/IV) 1.591(1.402–1.806) < 0.000 1.448(1.247–1.681) < 0.001 T Stage (T1/T2/T3/T4) 1.222(1.102–1.356) < 0.000 0.970(0.850–1.107) 0.653 N Stage (N0/N1/N2/N3) 1.298(1.174–1.434) < 0.000 1.177(1.035–1.337) 0.013 M Stage (M0/M1) 1.409(1.115–1.780) 0.004 0.928(0.718–1.201) 0.571 Leukocyte (10 9 /L) 0.996(0.971–1.021) 0.726 Neutrophil (10 9 /L) 1.001(0.994–1.009) 0.714 Platelet (10 9 /L) 1.000(0.999–1.001) 0.747 Lymphocyte (I10 9 /L) 0.982(0.964–1.001) 0.062 Hemoglobin (g/L) 0.998(0.993–1.002) 0.251 Albumin (g/dL) 0.977(0.960–0.993) 0.007 0.997(0.978–1.017) 0.794 Total bilirubin 1.005(1.001–1.010) 0.024 1.007(0.996–1.018) 0.221 Direct bilirubin 1.008(1.001–1.014) 0.020 0.996(0.981–1.012) 0.640 Total cholesterol (-) Triglycerides (-) AST 1.000(0.997–1.003) 0.910 ALT 0.999(0.996–1.003) 0.700 Creatinine 0.998(0.993–1.002) 0.328 Urea nitrogen 1.000(0.997–1.003) 0.921 C-reactive protein (-) ANLR 0.983(0.976–0.991) < 0.000 0.623(0.490–0.792) < 0.001 NLR 1.003(0.992–1.015) 0.600 PLR 1.000(0.999–1.001) 0.339 Table 6 Univariate and multivariate Cox regression analysis of clinicopathological characteristics associated with Progression-free survival in GC patients.---development cohort Variables Univariate analysis Multivariate analysis HR (95%CI) P value HR (95%CI) P value Age(years) 1.014(1.002–1.026) 0.020 1.012(1.000-1.024) 0.044 BMI(kg/m 2 ) 0.925(0.886–0.967) < 0.000 0.958(0.917–1.002) 0.059 Hospitalization 0.999(0.990–1.008) 0.865 Genger (female/male) 0.835(0.628–1.112) 0.217 Smoking (Yes/No) 1.048(0.804–1.365) 0.729 Alcohol (Yes/No) 0.864(0.612–1.222) 0.409 Diabetes (Yes/No) 1.529(0.918–2.547) 0.103 Hypertension (Yes/No) 0.786(0.510–1.210) 0.274 Cardiovascular (Yes/No) 0.816(0.304–2.195) 0.688 Chronic hepatitis (Yes/No) 1.422(0.702–2.881) 0.329 Tuberculosis (Yes/No) 2.217(0.550–8.932) 0.263 Chemotherapy (Yes/No) 1.572(1.191–2.076) 0.001 0.294(0.012–0.576) 0.041 Differentiation (Undifferentiated Poor/ Medium/ High ) 1.198(1.008–1.423) 0.040 1.114(0.939–1.322) 0.214 TNM Stage (Ⅰ/Ⅱ/Ⅲ/IV) 1.828(1.556–2.149) < 0.000 1.570(1.319–1.869) < 0.000 T Stage (T1/T2/T3/T4) 1.321(1.155–1.511) < 0.000 0.987(0.833–1.169) 0.877 N Stage (N0/N1/N2/N3) 1.352(1.194–1.532) < 0.000 1.180(1.012–1.377) 0.035 M Stage (M0/M1) 1.290(0.960–1.733) 0.091 Leukocyte (10 9 /L) 1.003(0.977–1.031) 0.807 Neutrophil (10 9 /L) 0.993(0.981–1.005) 0.276 Platelet (10 9 /L) 1.000(0.999–1.001) 0.983 Lymphocyte (I10 9 /L) 0.968(0.939–0.998) 0.038 0.982(0.954–1.010) 0.200 Hemoglobin (g/L) 0.995(0.990-1.000) 0.051 Albumin (g/dL) 1.003(0.988–1.019) 0.682 Total bilirubin 1.004(0.999–1.010) 0.126 Direct bilirubin 1.006(0.999–1.014) 0.116 Total cholesterol (-) Triglycerides (-) AST 1.001(0.998–1.004) 0.506 ALT 1.000(0.997–1.003) 0.905 Creatinine 1.000(0.995–1.005) 0.934 Urea nitrogen 0.996(0.988–1.005) 0.392 C-reactive protein (-) ANLR(17.55分) 0.462(0.349–0.612) < 0.000 0.589(0.439–0.791) < 0.000 NLR 1.003(0.990–1.017) 0.662 PLR 1.000(0.980–1.001) 0.059 3.9 Internal validation of the nomogram The predictive performance of the original model-derived cohort was evaluated using ROC analysis, based on the nomogram. Internal validation was conducted through enhanced bootstrap resampling. The AUC for the nomogram in predicting OS and PFS was 0.660 and 0.710, respectively (Figure S7) . Calibration curves were plotted for 1-, 3-, and 5-year survival predictions. As shown in the figures, the model demonstrated high accuracy in predicting survival probabilities in the higher range (0.8–1.0), but slight deviation was observed in the medium-low range (0.6–0.7). Overall, the predicted 1-,3-,5-year OS and PFS values for GC patients were highly consistent with actual observations (Figure S8) . DCA was used to assess the relative clinical utility of the nomogram, and decision curve graphs were plotted for 1-, 3- and 5-year predictions, with net benefit corrected using standardized settings. The results indicated that, in the internal validation cohort, the nomogram model significantly improved net benefit within the key clinical decision range (risk threshold 1.5%–4%) and reduced unnecessary interventions for patients with a risk of < 1.5% (Figure S9) . 3.10 Verification of the external cohort We conducted external validation through another hospital in the database, using the Cox proportional hazards regression model to screen independent risk factors, and obtained the same results as the internal validation. We also drew a nomogram (Figure S10). We used the enhanced bootstrap resampling for internal validation, and the area under the OS and PFS ROC curves was 0.690 and 0.920 (Figure S11) . We drew calibration curves for 1, 3, and 5 years. It can be seen that the model has accurate prediction in the survival probability interval (0.9-1.0). Overall, the predicted values of 5-year OS and PFS for GC patients are highly consistent with the actual observations (Figure S12) . Further, we drew the decision curve of the column chart, and drew 5-year decision curves. The results show that in the external validation cohort, the column chart model significantly improved the net benefit in the critical clinical decision interval (risk threshold 0.05–0.15). For different risk thresholds, the clinical decision curve based on the column chart model is superior to the threshold (Figure S13) . 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 (Tables S6-S8) . 4. 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 ].For instance, 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 ].Furthermore, the prognostic value of NLR and PLR in colorectal cancer has also been confirmed. 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 (OS: 55.82% vs. 44.18%, P < 0.001; PFS: 53.27% vs. 46.73%, P < 0.001). The multivariate Cox regression analysis showed that a high ANLR was an independent protective factor for OS (HR = 0.739, 95% CI: 0.622–0.878, P = 0.001) and PFS (HR = 0.745, 95% CI: 0.630–0.880, P = 0.001). 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 (Figs. 4–5) . Additionally, ANLR is calculated based on routine diagnostic indicators and does not require additional testing. It is capable of capturing the complex interactions between the host and the tumor within the tumor microenvironment more effectively than individual biomarkers. 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 research was supported by the National Key Research and Development Program (2022YFC2009600). Author contributions XN: Draft writing, data collection, data analysis. 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Wei C, Fan W, Zhang Y, Liu Y, Ding Z, Si Y, 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. 2023;16:169–82. https://doi.org/doi: 10.2147/JIR.S392482 . Onuma S, Hashimoto I, Suematsu H, Nagasawa S, Kanematsu K, Aoyama T 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 1 and 2 are available in the Supplementary Files section. Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":706986,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7845089/v1/fc59ce96645485f29070ba82.png"},{"id":94759052,"identity":"eee5e6c5-fe77-410e-8215-5671267ec7ad","added_by":"auto","created_at":"2025-10-30 11:52:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":160131,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7845089/v1/cc89ac680871c32f92ef5623.png"},{"id":94759055,"identity":"d4777da6-625d-429b-a81e-c7c219ebacf2","added_by":"auto","created_at":"2025-10-30 11:52:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":306503,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7845089/v1/8bd30702a5d55ff1eab08457.png"},{"id":94824778,"identity":"a7fc34de-f9ea-44b1-b5fc-3a8726fa68a1","added_by":"auto","created_at":"2025-10-31 06:49:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":989217,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7845089/v1/419da566837b4130eab3f163.png"},{"id":94823462,"identity":"0135ccfe-721d-4fe6-b7e7-ac0400abd7f9","added_by":"auto","created_at":"2025-10-31 06:47:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":980080,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7845089/v1/3f99e062f1a2598fbb6263fd.png"},{"id":94823625,"identity":"0e43a3ed-76f4-485c-b988-1005f4a795c5","added_by":"auto","created_at":"2025-10-31 06:47:40","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":728686,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-7845089/v1/7c10251b637c8443ab12b961.png"},{"id":94759065,"identity":"8c3e9c10-d080-47bc-8a81-bc5ab25b4c2f","added_by":"auto","created_at":"2025-10-30 11:52:56","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":230711,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-7845089/v1/a35e5c7022762c1d1ef93a51.png"},{"id":103556000,"identity":"68dd6809-06fe-4d1c-b169-543a6834f6ed","added_by":"auto","created_at":"2026-02-27 03:55:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5439982,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7845089/v1/a6a7a418-f92b-440c-9056-21a2de80ab76.pdf"},{"id":94823406,"identity":"7500bc4d-fcc7-4bfd-b089-166ff1388d50","added_by":"auto","created_at":"2025-10-31 06:47:18","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-7845089/v1/63051aa7914805443c6589f9.doc"},{"id":94824076,"identity":"2be320f4-6847-4ca1-934e-00b9271dbc9c","added_by":"auto","created_at":"2025-10-31 06:48:26","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-7845089/v1/8e5ca33dfffc1c63eea1c952.doc"},{"id":94759068,"identity":"ed0b0f23-2d44-47d6-b624-2274a58ac328","added_by":"auto","created_at":"2025-10-30 11:52:56","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":3543266,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7845089/v1/592128cc54b797513b482e84.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and Validation of a Predictive Model for Gastric Cancer Based on the Albumin-to-Neutrophil-to-Lymphocyte Ratio: A Retrospective Cohort Study","fulltext":[{"header":"1. 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":"2. 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":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003e3.1. Patient characteristics\u003c/b\u003e\u003c/h2\u003e\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 \u003cb\u003e(Fig.\u0026nbsp;1)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents 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. Tumor differentiation was as follows: well-differentiated (14.4% vs. 4.6%), moderately-differentiated (28.4% vs. 25.8%), poorly-differentiated (53.0% vs. 65.2%), and undifferentiated (4.2% vs. 4.4%). The TNM stage distribution was as follows: stage I-II (46.5%) and stage III-IV (53.5%). No significant differences were observed in clinical characteristics (e.g., age, BMI, tumor stage) between the internal and external cohorts (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Patients in the high ANLR group (\u0026ge;\u0026thinsp;20.39) were older, had a lower BMI, longer hospital stays, and a higher proportion of advanced tumors (stage III-IV) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Comparison of ANLR cutoff values and grouping characteristics\u003c/h2\u003e\u003cp\u003eThe ROC curve for predicting survival duration using patient survival rate as the outcome variable was plotted. 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% \u003cb\u003e(Fig.\u0026nbsp;2A-B)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eWe divided the cohort of 1,766 patients into two groups: low ANLR (\u0026lt;\u0026thinsp;20.39, n\u0026thinsp;=\u0026thinsp;716) and high ANLR (\u0026ge;\u0026thinsp;20.39, n\u0026thinsp;=\u0026thinsp;482). Statistically significant differences were observed between the two groups regarding age, BMI, length of hospital stay, smoking status, TNM stage, white blood cell count, neutrophil count, platelet count, lymphocyte count, hemoglobin levels, albumin levels, total bilirubin, direct bilirubin, AST, C-reactive protein, ANLR, NLR, PLR, and prognostic nutritional index (PNI) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Conversely, no statistically significant differences were found between the two groups regarding gender, alcohol consumption, diabetes, hypertension, heart disease, chronic hepatitis, tuberculosis, tumor differentiation, T stage, N stage, M stage, total cholesterol, triglycerides, ALT, creatinine, and urea nitrogen (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Kaplan-Meier and Survival Prognosis Analysis\u003c/h2\u003e\u003cp\u003eBased on the ANLR cutoff value grouping, the Kaplan-Meier analysis showed that the survival outcomes of patients with low ANLR were significantly worse than those of patients with high ANLR. The OS (55.82% vs. 44.18%, Log-rank p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), PFS (53.27% vs. 46.73%, Log-rank p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and 5-year survival rate were all significantly lower \u003cb\u003e(Fig.\u0026nbsp;3)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003eIn the subgroup analysis based on tumor differentiation degree, patients in the high ANLR group had a better prognosis than those in the low ANLR group, with OS (75.96% vs. 24.04%) and PFS (68.35% vs. 31.65%) \u003cb\u003e(Fig.\u0026nbsp;4)\u003c/b\u003e; further analysis of high and moderate differentiation degrees yielded the same results, OS (73.47% vs. 26.53%) and PFS (75.81% vs. 24.19%); for low and undifferentiated degrees, OS (78.82% vs. 21.17%) and PFS (69.70% vs. 30.30%), it can be seen that the prognosis of patients with low ANLR was significantly worse than that of patients with high ANLR, and the statistical significance was all P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001 \u003cb\u003e(Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e)\u003c/b\u003e. In the TNM stage subgroup analysis, patients with low ANLR all showed poorer survival rates, OS (55.82% vs. 44.18%) and PFS (54.27% vs. 45.73%), and the results were consistent when comparing each stage \u003cb\u003e(Fig.\u0026nbsp;5)\u003c/b\u003e; patients in the low ANLR group had lower OS and PFS than those in the high ANLR group, especially in stage III-IV (I-II stage: OS 56.19% vs. 43.81%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; PFS 53.75% vs. 46.25%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001; stage III-IV: OS 63.19% vs. 36.81%, PFS 54.62% vs. 45.38%, all p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) \u003cb\u003e(Figure S2)\u003c/b\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.4 The non-linear relationship between ANLR and the survival outcomes of GC patients\u003c/h2\u003e\u003cp\u003eWe utilized multivariate restricted cubic splines (RCS) across three models to analyze the association between the ANLR score in GC patients and OS and PFS. The results of our analysis demonstrated that models a, b, and c progressively incorporated corresponding variables, revealing a non-linear association between ANLR and both OS and PFS, with an L-shaped pattern. The RCS plots all showed statistical significance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The risk of OS and PFS in GC patients did not consistently decrease with increasing ANLR; instead, a turning point was observed at an ANLR value of 20.39. Specifically, when ANLR was below 20.39, the HR was greater than 1, indicating a higher risk and poorer prognosis. Beyond this turning point, as ANLR increased above 20.39, the HR decreased significantly, and the risk correspondingly declined. When ANLR exceeded 40, the HR stabilized around 0.5 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In conclusion, as ANLR increases, the survival prognosis of GC patients improves \u003cb\u003e(Fig.\u0026nbsp;6)\u003c/b\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Correlation Analysis of ANLR and GC Patients' Survival Outcomes\u003c/h2\u003e\u003cp\u003eMultivariate Cox regression analysis revealed that in the analysis of poor OS prognosis, without adjusting for any confounding factors (Model a), every one standard deviation increase in ANLR was associated with a 1.7% reduction in the risk of disease progression (HR\u0026thinsp;=\u0026thinsp;0.983, 95%CI: 0.976\u0026ndash;0.991, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, in Model c, which adjusted for confounding factors, the mortality risk in the high ANLR group was 1.605 times that of the low ANLR group (HR\u0026thinsp;=\u0026thinsp;0.623, 95% CI: 0.490\u0026ndash;0.792, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In the analysis of poor PFS risk, Model c showed that the progression risk in the high ANLR group was 1.698 times that of the low ANLR group (HR\u0026thinsp;=\u0026thinsp;0.589, 95% CI: 0.439\u0026ndash;0.791, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).The analysis of the cut-off points for the high and low ANLR groups revealed that, compared to the low ANLR group, patients in the high ANLR group had a significantly lower risk of adverse OS (HR\u0026thinsp;=\u0026thinsp;0.538, 95% confidence interval [CI]: 0.120\u0026ndash;0.620, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Even after adjusting for potential confounding factors using models b and c, the risk remained reduced by 0.3% (HR\u0026thinsp;=\u0026thinsp;0.997, 95% CI: 0.992\u0026ndash;1.006, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In the quartile analysis of ANLR, as ANLR increased, the risks of OS (HR\u0026thinsp;=\u0026thinsp;0.554, 95% CI: 0.401\u0026ndash;0.767, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and PFS (HR\u0026thinsp;=\u0026thinsp;0.690, 95% CI: 0.444\u0026ndash;1.073, p\u0026thinsp;=\u0026thinsp;0.009) exhibited a decreasing trend (trend p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Therefore, whether based on continuous variables, cut-off point grouping, or quartile grouping, higher ANLR levels are significantly associated with better OS and PFS. Even after adjusting for multiple potential confounding factors, this protective association remains significant (HR in models b and c are still significantly less than 1)\u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation between ANLR and OS of GC patients.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eANLR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel a\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel b\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eModel c\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eContinuous (per SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.983 (0.976,0.991)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.986 (0.978,0.994)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.987 (0.978,0.995)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCutoff value (High)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.538 (0.120,0.620)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.998(0.991,1.005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.997 (0.992,1.006)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuartiles\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1 (~\u0026thinsp;9.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2 (9.24\u0026thinsp;~\u0026thinsp;17.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.838 (0.638,1.100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.202\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.848 (0.645,1.115)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.237\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.918 (0.695,1.211)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.545\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3 (17.21\u0026thinsp;~\u0026thinsp;27.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.568 (0.420,0.767)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.605 (0.446,0.819)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.632 (0.463,0.863)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4 (27.69~)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.505 (0.372,0.685)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.562 (0.412,0.769)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.554 (0.401,0.767)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ep for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel a: No adjusted.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel b: Adjusted for Gender, Age, BMI, TNM stage.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel c: Adjusted for Gender, Age, BMI, Hospitalization, Smoking, Alcohol, Diabetes, Hypertension Cardiovascular, Chronic hepatitis, Tuberculosis, Chemotherapy, Differentiation, TNM Stage, T Stage, N Stage, M Stage.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation between ANLR and PFS of GC patients.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eANLR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel a\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel b\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eModel c\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ep value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eContinuous (per SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.981 (0.971,0.991)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.988 (0.978,0.999)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.998 (0.990,1.005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.508\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCutoff value (High)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.462 (0.143,0.771)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.999(0.993,1.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.999 (0.994,1.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQuartiles\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ1 (~\u0026thinsp;9.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eref\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ2 (9.24\u0026thinsp;~\u0026thinsp;17.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.051 (0.762,1.450)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.760\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.881 (0.628,1.236)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.463\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.914 (0.641,1.303)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.618\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ3 (17.21\u0026thinsp;~\u0026thinsp;27.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.568 (0.420,0.767)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.705 (0.474,1.048)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.942 (0.609,1.455)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.787\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQ4 (27.69~)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.451 (0.303,0.670)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.514 (0.344,0.768)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.690 (0.444,1.073)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ep for trend\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel a: No adjusted.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel b: Adjusted for Gender, Age, BMI, TNM stage.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eModel c: Adjusted for Gender, Age, BMI, Hospitalization, Smoking, Alcohol, Diabetes, Hypertension Cardiovascular, Chronic hepatitis, Tuberculosis, Chemotherapy, Differentiation, TNM Stage, T Stage, N Stage, M Stage.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Subgroup analysis of survival outcomes in ANLR and GC patients\u003c/h2\u003e\u003cp\u003eThe multivariate forest plot analysis indicated that the following subgroups related to OS factors showed statistical significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05): age, body mass index (BMI) categories (Normal and High), length of hospital stay (\u0026lt;\u0026thinsp;24 days), gender, smoking status, absence of alcohol consumption, absence of diabetes, hypertension, absence of heart disease, absence of chronic hepatitis, adjuvant chemotherapy, degree of differentiation (high and low differentiation), TNM stage (I-II), T stages (T1 and T3), N stage (N0-N3), and M stage (M0). These results suggest that ANLR level is an independent risk factor for most subgroups. In the subgroup analysis of progression-free survival (PFS)-related factors, ANLR remained an independent risk factor for most subgroups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), except in the following cases: BMI (Normal), length of hospital stay (\u0026gt;\u0026thinsp;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 \u003cb\u003e(Figure S3 A)\u003c/b\u003e. Furthermore, as shown in the heat map, it presents the numerical distribution of multiple variables under different groups\u003cb\u003e(Figure S3 B-C)\u003c/b\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.7 The prognostic performance of ANLR in comparison with established inflammatory nutrition indicators\u003c/h2\u003e\u003cp\u003eWe conducted a ROC analysis to evaluate the prognostic utility of ANLR compared to the established biomarkers NLR, PLR, and PNI. 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). For PFS prediction, the AUC values also indicated consistent predictive effects (ANLR: 0.616 vs. NLR: 0.532 vs. PLR: 0.558 vs. PNI: 0.589) \u003cb\u003e(Figure S4)\u003c/b\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 \u003cb\u003e(Figure S5)\u003c/b\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); Similarly, for predicting the PFS of patients, the area under the ROC curve of ANLR was also the best (ANLR: 0.528 vs. NLR: 0.466 vs. PLR: 0.486 vs. PNI: 0.515) \u003cb\u003e(Figure S6)\u003c/b\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.8 The establishment of the nomogram\u003c/h2\u003e\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 (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). For PFS, age, adjuvant chemotherapy, TNM stage, N stage, and ANLR were identified as independent risk factors (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Based on these key variables, a nomogram was constructed to predict 1-, 3-, and 5-year OS and PFS in GC patients \u003cb\u003e(Fig.\u0026nbsp;7A-B)\u003c/b\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\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariate and multivariate Cox regression analysis of clinicopathological characteristics associated with Overall Survival in GC patients.---internal cohort\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eUnivariate analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eMultivariate analysis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge(years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.012(1.002\u0026ndash;1.021)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.010(1.003\u0026ndash;1.020)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.946(0.914\u0026ndash;0.980)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.962(0.928\u0026ndash;0.997)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.034\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHospitalization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.999(0.992\u0026ndash;1.007)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.875\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenger\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(female/male)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.820(0.650\u0026ndash;1.034)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.094\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Yes/No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.202(0.972\u0026ndash;1.487)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.089\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Yes/No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.049(0.817\u0026ndash;1.347)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.709\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Yes/No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.634(1.116\u0026ndash;2.393)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.632(1.105\u0026ndash;2.412)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Yes/No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.150(0.860\u0026ndash;1.538)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.347\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCardiovascular\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Yes/No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.713(0.295\u0026ndash;1.724)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.452\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic hepatitis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Yes/No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.259(0.649\u0026ndash;2.443)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.496\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTuberculosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Yes/No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.028(0.144\u0026ndash;7.325)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.978\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChemotherapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Yes/No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.028(0.144\u0026ndash;7.325)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.978\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDifferentiation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Undifferentiated Poor/ Medium/ High )\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.227(1.069\u0026ndash;1.409)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.123(0.972\u0026ndash;1.296)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.115\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTNM Stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Ⅰ/Ⅱ/Ⅲ/IV)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.591(1.402\u0026ndash;1.806)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.448(1.247\u0026ndash;1.681)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT Stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(T1/T2/T3/T4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.222(1.102\u0026ndash;1.356)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.970(0.850\u0026ndash;1.107)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.653\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN Stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(N0/N1/N2/N3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.298(1.174\u0026ndash;1.434)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.177(1.035\u0026ndash;1.337)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM Stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(M0/M1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.409(1.115\u0026ndash;1.780)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.928(0.718\u0026ndash;1.201)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.571\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeukocyte (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.996(0.971\u0026ndash;1.021)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.726\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutrophil (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.001(0.994\u0026ndash;1.009)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.714\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelet (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.000(0.999\u0026ndash;1.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.747\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymphocyte (I10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.982(0.964\u0026ndash;1.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin (g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.998(0.993\u0026ndash;1.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.251\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlbumin (g/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.977(0.960\u0026ndash;0.993)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.997(0.978\u0026ndash;1.017)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.794\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal bilirubin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.005(1.001\u0026ndash;1.010)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.007(0.996\u0026ndash;1.018)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.221\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDirect bilirubin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.008(1.001\u0026ndash;1.014)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.996(0.981\u0026ndash;1.012)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.640\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal cholesterol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTriglycerides\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.000(0.997\u0026ndash;1.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.910\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.999(0.996\u0026ndash;1.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.700\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCreatinine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.998(0.993\u0026ndash;1.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.328\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrea nitrogen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.000(0.997\u0026ndash;1.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.921\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC-reactive protein\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eANLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.983(0.976\u0026ndash;0.991)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.623(0.490\u0026ndash;0.792)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.003(0.992\u0026ndash;1.015)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.000(0.999\u0026ndash;1.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.339\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariate and multivariate Cox regression analysis of clinicopathological characteristics associated with Progression-free survival in GC patients.---development cohort\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eUnivariate analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eMultivariate analysis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHR (95%CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge(years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.014(1.002\u0026ndash;1.026)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.012(1.000-1.024)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.044\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.925(0.886\u0026ndash;0.967)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.958(0.917\u0026ndash;1.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHospitalization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.999(0.990\u0026ndash;1.008)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.865\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenger\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(female/male)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.835(0.628\u0026ndash;1.112)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.217\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Yes/No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.048(0.804\u0026ndash;1.365)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.729\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Yes/No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.864(0.612\u0026ndash;1.222)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.409\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Yes/No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.529(0.918\u0026ndash;2.547)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypertension\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Yes/No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.786(0.510\u0026ndash;1.210)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.274\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCardiovascular\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Yes/No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.816(0.304\u0026ndash;2.195)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.688\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChronic hepatitis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Yes/No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.422(0.702\u0026ndash;2.881)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.329\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTuberculosis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Yes/No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.217(0.550\u0026ndash;8.932)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.263\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChemotherapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Yes/No)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.572(1.191\u0026ndash;2.076)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.294(0.012\u0026ndash;0.576)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDifferentiation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Undifferentiated Poor/ Medium/ High )\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.198(1.008\u0026ndash;1.423)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.040\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.114(0.939\u0026ndash;1.322)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.214\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTNM Stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(Ⅰ/Ⅱ/Ⅲ/IV)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.828(1.556\u0026ndash;2.149)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.570(1.319\u0026ndash;1.869)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT Stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(T1/T2/T3/T4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.321(1.155\u0026ndash;1.511)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.987(0.833\u0026ndash;1.169)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.877\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN Stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(N0/N1/N2/N3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.352(1.194\u0026ndash;1.532)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.180(1.012\u0026ndash;1.377)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.035\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM Stage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(M0/M1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.290(0.960\u0026ndash;1.733)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeukocyte (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.003(0.977\u0026ndash;1.031)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.807\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutrophil (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.993(0.981\u0026ndash;1.005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.276\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelet (10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.000(0.999\u0026ndash;1.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.983\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymphocyte (I10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.968(0.939\u0026ndash;0.998)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.038\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.982(0.954\u0026ndash;1.010)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.200\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin (g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.995(0.990-1.000)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.051\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlbumin (g/dL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.003(0.988\u0026ndash;1.019)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.682\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal bilirubin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.004(0.999\u0026ndash;1.010)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDirect bilirubin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.006(0.999\u0026ndash;1.014)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal cholesterol\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTriglycerides\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.001(0.998\u0026ndash;1.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.506\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.000(0.997\u0026ndash;1.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.905\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCreatinine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.000(0.995\u0026ndash;1.005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.934\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrea nitrogen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.996(0.988\u0026ndash;1.005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.392\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC-reactive protein\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(-)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eANLR(17.55分)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.462(0.349\u0026ndash;0.612)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.589(0.439\u0026ndash;0.791)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.003(0.990\u0026ndash;1.017)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.662\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.000(0.980\u0026ndash;1.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.059\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003e3.9 Internal validation of the nomogram\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eThe predictive performance of the original model-derived cohort was evaluated using ROC analysis, based on the nomogram. Internal validation was conducted through enhanced bootstrap resampling. The AUC for the nomogram in predicting OS and PFS was 0.660 and 0.710, respectively \u003cb\u003e(Figure S7)\u003c/b\u003e. Calibration curves were plotted for 1-, 3-, and 5-year survival predictions. As shown in the figures, the model demonstrated high accuracy in predicting survival probabilities in the higher range (0.8\u0026ndash;1.0), but slight deviation was observed in the medium-low range (0.6\u0026ndash;0.7). Overall, the predicted 1-,3-,5-year OS and PFS values for GC patients were highly consistent with actual observations \u003cb\u003e(Figure S8)\u003c/b\u003e. DCA was used to assess the relative clinical utility of the nomogram, and decision curve graphs were plotted for 1-, 3- and 5-year predictions, with net benefit corrected using standardized settings. The results indicated that, in the internal validation cohort, the nomogram model significantly improved net benefit within the key clinical decision range (risk threshold 1.5%\u0026ndash;4%) and reduced unnecessary interventions for patients with a risk of \u0026lt;\u0026thinsp;1.5% \u003cb\u003e(Figure S9)\u003c/b\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e\u003cb\u003e3.10 Verification of the external cohort\u003c/b\u003e\u003c/h2\u003e\u003cp\u003e We conducted external validation through another hospital in the database, using the Cox proportional hazards regression model to screen independent risk factors, and obtained the same results as the internal validation. We also drew a nomogram \u003cb\u003e(Figure S10).\u003c/b\u003e We used the enhanced bootstrap resampling for internal validation, and the area under the OS and PFS ROC curves was 0.690 and 0.920 \u003cb\u003e(Figure S11)\u003c/b\u003e. We drew calibration curves for 1, 3, and 5 years. It can be seen that the model has accurate prediction in the survival probability interval (0.9-1.0). Overall, the predicted values of 5-year OS and PFS for GC patients are highly consistent with the actual observations \u003cb\u003e(Figure S12)\u003c/b\u003e. Further, we drew the decision curve of the column chart, and drew 5-year decision curves. The results show that in the external validation cohort, the column chart model significantly improved the net benefit in the critical clinical decision interval (risk threshold 0.05\u0026ndash;0.15). For different risk thresholds, the clinical decision curve based on the column chart model is superior to the threshold \u003cb\u003e(Figure S13)\u003c/b\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e3.11 Comparison of verification efficiency between internal queue and external queue\u003c/h2\u003e\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\u0026ndash;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\u0026ndash;1.080), all indicating that the model has good discrimination and calibration \u003cb\u003e(Tables S6-S8)\u003c/b\u003e.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. 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].For instance, 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].Furthermore, the prognostic value of NLR and PLR in colorectal cancer has also been confirmed. 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 (p\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 (OS: 55.82% vs. 44.18%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; PFS: 53.27% vs. 46.73%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The multivariate Cox regression analysis showed that a high ANLR was an independent protective factor for OS (HR\u0026thinsp;=\u0026thinsp;0.739, 95% CI: 0.622\u0026ndash;0.878, P\u0026thinsp;=\u0026thinsp;0.001) and PFS (HR\u0026thinsp;=\u0026thinsp;0.745, 95% CI: 0.630\u0026ndash;0.880, P\u0026thinsp;=\u0026thinsp;0.001). 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 \u003cb\u003e(Figs.\u0026nbsp;4\u0026ndash;5)\u003c/b\u003e. Additionally, ANLR is calculated based on routine diagnostic indicators and does not require additional testing. It is capable of capturing the complex interactions between the host and the tumor within the tumor microenvironment more effectively than individual biomarkers.\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 research was supported by the National Key Research and Development Program (2022YFC2009600).\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.TZ: Data collection and organization, investigation. MZ: Data collection and organization, investigation. KP: Data collection and organization. SW: Data investigation, supervision, methodology. CX: Software, methodology, supervision. SL: Data collection. YC: Data organization. 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"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel RL, Giaquinto AN, Jemal A, Cancer statistics. 2024. 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J Pers Med. 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 1 and 2 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":"Gastric cancer, Albumin / Neutrophil-to-Lymphocyte Ratio, Prognosis, Overall survival, Progression-free survival, Nomogram","lastPublishedDoi":"10.21203/rs.3.rs-7845089/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7845089/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eThis study aims to examine the correlation between ANLR and OS, PFS in patients diagnosed with gastric cancer, with the goal of elucidating its predictive value and clinical relevance.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e\u003cp\u003eA retrospective analysis was conducted on clinical case data from 2,051 patients who underwent radical gastrectomy for gastric cancer between 2012 and 2021, as recorded in the INSCOC database. Determine the optimal cut-off value of ANLR through the ROC curve. The survival curve was generated using the Kaplan-Meier method, the Cox proportional hazards regression model was utilized to analyze the association between ANLR and both OS and PFS. The nomogram prognostic model was constructed, with internal and external validations performed through ROC curve, calibration curve and DCA to evaluate the model's performance.\u003c/p\u003e\u003ch2\u003eResult\u003c/h2\u003e\u003cp\u003eThis study ultimately included 1766 patients, with 1203 patients in the internal validation cohort and 563 patients in the external validation cohort. The optimal cutoff value of ANLR 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 regression showed that ANLR was an independent prognostic factor for OS (HR\u0026thinsp;=\u0026thinsp;0.623, 95% CI: 0.490\u0026ndash;0.792, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and PFS (HR\u0026thinsp;=\u0026thinsp;0.589, 95% CI: 0.439\u0026ndash;0.791, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The Nomogram model predicted OS and PFS with AUCs of 0.660 and 0.710. The external validation showed good calibration and discriminatory efficacy (C-index: OS 0.664, PFS 0.883).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThe ANLR can serve as an effective biomarker for the prognostic assessment of patients with gastric cancer. The nomogram model is beneficial for individualized prognostic prediction and clinical decision-making.\u003c/p\u003e","manuscriptTitle":"Development and Validation of a Predictive Model for Gastric Cancer Based on the Albumin-to-Neutrophil-to-Lymphocyte Ratio: A Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-30 11:52:51","doi":"10.21203/rs.3.rs-7845089/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":"58b89bcc-9369-4122-ab49-deb503b36a00","owner":[],"postedDate":"October 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-27T03:55:01+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-30 11:52:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7845089","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7845089","identity":"rs-7845089","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}