Preoperative Gastric Cancer Immune Prognostic Score (GCIPS) as a Novel Biomarker for Predicting Survival in Gastric Cancer Patients After Radical Resection 

Preoperative Gastric Cancer Immune Prognostic Score (GCIPS) as a Novel Biomarker for Predicting Survival in Gastric Cancer Patients After Radical Resection | 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 Preoperative Gastric Cancer Immune Prognostic Score (GCIPS) as a Novel Biomarker for Predicting Survival in Gastric Cancer Patients After Radical Resection Xiaosheng Hu, Jinquan Li, Shanzhong Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7650071/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background This study aimed to validate the preoperative Gastric Cancer Immune Prognostic Score (GCIPS) as a prognostic biomarker in resectable gastric cancer (GC). Methods We retrospectively analyzed 226 GC patients undergoing radical resection. The optimal cutoff value of the CALLY index was determined by ROC curve analysis, and patients were stratified accordingly to assess its prognostic value for RFS and OS. Results The GCIPS was calculated from preoperative blood parameters. Using ROC-derived cutoff (2.840), patients were stratified into high- and low-GCIPS groups. The high-GCIPS group showed significantly poorer tumor differentiation (P < 0.001). Kaplan-Meier analysis revealed that high GCIPS was associated with worse 5-year recurrence-free survival (HR = 2.856, P < 0.001) and overall survival (HR = 3.222, P < 0.001). Multivariate analysis confirmed GCIPS as an independent predictor for both outcomes after adjusting for TNM stage and differentiation. Conclusion The GCIPS is a robust, independent prognostic biomarker derived from routine blood tests, offering a practical tool for risk stratification and guiding individualized management in GC after radical resection. Gastric Cancer Immune Prognostic Score GCIPS gastric cancer prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Gastric cancer (GC) remains one of the most common malignant tumors worldwide, with high incidence and mortality rates, particularly in East Asian regions such as China, where it continues to pose a major public health challenge ( 1 ). Although surgical resection is the primary treatment for GC, especially in early and locally advanced stages, postoperative recurrence and metastasis remain the leading causes of treatment failure and patient mortality ( 2 , 3 ). Therefore, accurate assessment of postoperative prognosis, particularly in identifying high-risk patients with early recurrence and shorter survival, is critical for developing individualized treatment strategies and optimizing follow-up management. In recent years, immunotherapy, especially immune checkpoint inhibitors (ICIs), has demonstrated significant efficacy in the treatment of GC. However, its application still faces challenges in patient selection and efficacy prediction ( 4 , 5 ). Conventional prognostic indicators such as TNM stage, histological type, and tumor markers (e.g., CEA and CA19-9) offer certain predictive value but exhibit limitations in highly heterogeneous GC ( 6 , 7 ). Hence, there is growing interest in developing more accurate, convenient, and widely applicable prognostic biomarkers. Given their easy accessibility, low cost, and high reproducibility, hematological parameters have been increasingly utilized in prognostic evaluation of cancers. Inflammatory and nutritional indicators such as the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), Monocytes-to-lymphocyte ratio (MLR), prognostic nutritional index (PNI), and systemic immune-inflammation index (SII), have been confirmed to correlate with prognosis in GC ( 8 – 11 ) However, most of these parameters were established based on traditional treatment modalities, and their applicability in the context of modern comprehensive therapy remains to be further validated. Recently, Zuo et al. proposed a novel blood-based scoring system—the GC Immune Prognostic Score (GCIPS), which was developed based on white blood cell count (WBC), lymphocyte count (LYM), and international normalized ratio (INR). This score demonstrated excellent prognostic predictive ability in GC patients treated with immune checkpoint inhibitors ( 12 ). The GCIPS not only reflects systemic inflammatory and immune status but also incorporates multidimensional information related to coagulation function and nutritional condition, indicating its potential as a comprehensive prognostic tool. However, no studies have yet explored the prognostic value of GCIPS in postoperative GC patients. This study aims to retrospectively analyze clinical data from postoperative GC patients in our institution to validate the predictive power of GCIPS for 5-year overall survival (OS) and recurrence-free survival (RFS). The findings are expected to provide a new theoretical basis and clinical tool for individualized prognostic assessment in GC patients after surgery. Patients and methods Patients. This retrospective study analyzed clinicopathological data and preoperative laboratory hematological parameters (measured within one week before surgery) from GC patients who underwent radical resection at Jingdezhen First People's Hospital, China, between January 2011 and December 2019. All consecutive patients meeting the eligibility criteria during this period were initially enrolled. The inclusion criteria were as follows: i) histologically confirmed primary GC; ii) no prior neoadjuvant therapy; and iii) curative-intent R0 resection. Patients were excluded based on the following: i) synchronous or metachronous malignancies; ii) underlying hematological diseases; iii) preoperative infection or immunodeficiency; iv) incomplete medical records; v) non-radical resection; or vi) receipt of neoadjuvant treatment. All data were retrieved from the hospital’s prospectively maintained database. Treatment and follow-up. Disease staging was performed according to the eighth edition of the American Joint Committee on Cancer Tumor-Node-Metastasis (TNM) classification ( 13 ). Pre-existing comorbidities, including cardiovascular diseases, pulmonary diseases, diabetes mellitus, chronic kidney disease, and chronic liver disease, were collectively defined as comorbidities. Postoperative anastomotic leakage was specifically defined as leakage occurring within 30 days after surgery. Adjuvant therapy after surgery mainly consisted of chemotherapy, radiotherapy, and related treatment modalities. According to the GC Diagnosis and Treatment Guidelines, the following standardized postoperative follow-up protocol was implemented: i) For the first two years after surgery, patients underwent comprehensive follow-up every 3 months, including medical history collection, physical examination, complete blood count, biochemical tests, and tumor marker assays (such as Carcinoembryonic Antigen (CEA), Carbohydrate Antigen 19 − 9 (CA19-9)and CA125); contrast-enhanced computed tomography (CT) scans of the chest, abdomen, and pelvis were performed every 6 months. ii)From the third to the fifth year after surgery, the follow-up interval was extended to every 6 months, with the same examination items. Follow-up was conducted through standardized outpatient visits or structured telephone interviews, and all data were recorded in real time in the hospital database. The follow-up deadline was December 31, 2024, or the date of patient death. For outcome evaluation, recurrence-free survival (RFS) was calculated from the date of surgery to the first recurrence of gastric cancer, last follow-up, or death from any cause. Overall survival (OS) was defined as the time from surgery to death or the last confirmed follow-up for surviving patients. Determination of inflammatory markers. All calculations for inflammatory markers are presented in Table 1 , with the GCIPS calculated as follows: White Blood Cells ( 10 9 /L ) × 0.071 - Lymphocytes ( 10 9 /L ) × 0.375 + International normalized ratio × 2.986 ( 13 ). Table 1 Full names, abbreviations, calculation formulas, and optimal cut-off values of the markers Abbreviation of markers Full name of the marker Calculation formula Optimal cutoff value GCIPS Gastric cancer immune prognostic Score White Blood Cells (10 9 /L)×0.071 - Lymphocytes (10 9 /L) × 0.375 + International normalized ratio × 2.986 2.837 NLR Neutrophil-to-lymphocyte ratio Neutrophils(10 9 /L) / Lymphocytes(10 9 /L) 2.522 PLR Platelet-to-lymphocyte ratio Platelets(10 9 /L) / Lymphocytes(10 9 /L) 134.963 MLR Monocytes-to-lymphocyte ratio Monocytes(10 9 /L) / Lymphocytes(10 9 /L) 0.266 PNI Prognostic nutritional index Albumin (g/L) + 5 × Lymphocytes ((10 9 /L)) 46.425 SII Systemic immune-inflammation index Platelets (10 9 /L)× Neutrophils (10 9 /L)/ Lymphocytes(10 9 /L) 552.385 Table 2 Clinicopathological comparisons between low- and high-GCIPS score groups. Variables Total (n = 226) GCIPS P Low(n = 133) High(n = 93) Age, years(Mean ± SD) 61.10 ± 10.83 61.33 ± 10.53 60.76 ± 11.28 0.699 Gender, n(%) 0.252 Male 167 (73.89) 102 (76.69) 65 (69.89) Female 59 (26.11) 31 (23.31) 28 (30.11) Location, n(%) 0.884 Up 24 (10.62) 13 (9.77) 11 (11.83) Middle 67 (29.65) 40 (30.08) 27 (29.03) Low 135 (59.73) 80 (60.15) 55 (59.14) Surgery, n(%) 0.719 Open 180 (79.65) 107 (80.45) 73 (78.49) Laparoscopic 46 (20.35) 26 (19.55) 20 (21.51) Anastomoticmethods, n(%) 0.734 B I 59 (26.10) 33(24.81) 26 (27.96) B II 136 (60.18) 80 (60.15) 56 (60.22) R-Y 31 (13.72) 20 (15.04) 11 (11.82) Tumor size, cm [M (Q₁, Q₃)] 3.00 (2.50, 5.00) 3.00 (2.50, 5.00) 3.00 (2.60, 4.00) 0.824 Bloodloss, mL [M (Q₁, Q₃)] 100.0 (100.0, 150.0) 100.0 (100.0, 200.0) 100.0 (100.0, 150.0) 0.672 Comorbidities, n(%) 0.416 No 189 (83.63) 109 (81.95) 80 (86.02) Yes 37 (16.37) 24 (18.05) 13 (13.98) Anemia, n(%) 0.794 No 196 (86.73) 116 (87.22) 80 (86.02) Yes 30 (13.27) 17 (12.78) 13 (13.98) Pyloricstenosis, n(%) 0.792 No 214 (94.69) 125 (93.98) 89 (95.70) Yes 12 (5.31) 8 (6.02) 4 (4.30) Transfusion, n(%) 0.872 No 193 (85.40) 114 (85.71) 79 (84.95) Yes 33 (14.60) 19 (14.29) 14 (15.05) Anastomotic leakage, n(%) 0.487 No 212 (93.81) 126 (94.74) 86 (92.47) Yes 14 (6.19) 7 (5.26) 7 (7.53) Pathologicalpattern, n(%) < .001 Well 29 (12.83) 25 (18.80) 4 (4.30) Moderate 164 (72.57) 98 (73.68) 66 (70.97) Poor 33 (14.60) 10 (7.52) 23 (24.73) T, n(%) 0.450 I 14 (6.19) 8 (6.02) 6 (6.45) II 47 (20.80) 24 (18.05) 23 (24.73) III 32 (14.16) 17 (12.78) 15 (16.13) IV 133 (58.85) 84 (63.16) 49 (52.69) N, n(%) 0.209 0 81 (35.84) 53 (39.85) 28 (30.11) I 90 (39.82) 54 (40.60) 36 (38.71) II 36 (15.93) 17 (12.78) 19 (20.43) III 19 (8.41) 9 (6.77) 10 (10.75) TNM Stage, n(%) 0.553 I 40 (17.70) 26 (19.55) 14 (15.05) II 75 (33.19) 41 (30.83) 34 (36.56) III 111 (49.12) 66 (49.62) 45 (48.39) P-adjuvanttherapy, n(%) 0967 No 58 (25.66) 34 (25.56) 24 (25.81) Yes 168 (74.34) 99 (74.44) 69 (74.19) CEA ≥ 5 ng/mL, n(%) 0.323 No 182 (80.53) 110 (82.71) 72 (77.42) Yes 44 (19.47) 23 (17.29) 21 (22.58) CA199 ≥ 30 U/mL, n(%) 0.532 No 198 (87.61) 115 (86.47) 83 (89.25) Yes 28 (12.39) 18 (13.53) 10 (10.75) CA125 ≥ 25 U/mL, n(%) 0.990 No 216 (95.58) 127 (95.49) 89 (95.70) Yes 10 (4.42) 6 (4.51) 4 (4.30) GCIPS, M (Q₁, Q₃) 2.72 (2.46, 3.06) 2.51 (2.25, 2.66) 3.12 (2.98, 3.34) < .001 P-adjuvanttherapy, Postoperative adjuvanttherapy, specifically referring to radiotherapy and chemotherapy; HR, Hazard Ratio; CI, Confidence Interval; CEA, carcinoembryonic antigen; CA199, carbohydrate antigen 199; CA125, carbohydrate antigen 125; GCIPS, the Gastric cancer immune prognostic Score; B I, Billroth I; B II, Billroth II; R-Y, Roux-en-Y; T, Tumor; N, regional lymph node; M, metastasis. Table 3 Univariate and multivariate analysis for RFS in patients with gastric cancer patients. Variables Univariate analysis Multivariate analysis HR (95 CI) P-value HR (95 CI) P-value Gender, female vs. male 0.92 (0.62 ~ 1.37) 0.691 Age, ≥ 60 vs.<60 years 1.08 (0.75 ~ 1.56) 0.663 Location, Middle and Low vs. Up 0.83 (0.60 ~ 1.50) 0.263 Surgery, Laparoscopic vs. open 0.77 (0.46 ~ 1.30) 0.334 Tumor size, ≥ 3.5 vs. <3.5cm 1.14(0.80 ~ 1.62) 0.481 Blood loss, ≥ 100 vs. <100 mL 1.10 (0.76 ~ 1.60) 0.618 Comorbidities, yes vs no 1.06 (0.66 ~ 1.69) 0.822 Anemiayes, yes vs no 1.54 (0.96 ~ 2.49) 0.076 Pyloricstenosis, yes vs no 0.99 (0.46 ~ 2.13) 0.986 Transfusion, yes vs no 1.65 (1.04 ~ 2.62) 0.033 1.11 (0.49 ~ 2.51) 0.796 Anastomoticmethods, BI and BII vs R-Y 0.96(0.87 ~ 1.07) 0481 Anastomotic leakage, yes vs no 1.58 (0.83 ~ 3.02) 0.160 Pathologicalpattern Well 1.00 (Reference) 1.00 (Reference) Moderate 2.20 (1.06 ~ 4.54) 0.033 2.54 (1.12 ~ 5.75) 0.025 Poor 7.88 (3.58 ~ 17.31) <.001 4.80 (1.86 ~ 12.41) 0.001 T stage, I and II vs. III and IV 0.66 (042 ~ 1.01) 0.056 N stage, 0 and I vs. II and III 0.75 (0.54 ~ 0.97) 0.042 0.87 (0.74 ~ 1.02) 0.089 TNM Stage I 1.00 (Reference) 1.00 (Reference) II 2.25 (0.98 ~ 5.17) 0.056 2.24 (0.57 ~ 8.82) 0.249 III 8.71 (4.03 ~ 18.86) 5 vs. ≤ 5 ng/mL 1.76 (1.18 ~ 2.63) 0.005 0.65 (0.39 ~ 1.11) 0.113 CA125,>25 vs. ≤ 25 U/mL 1.44 (0.67 ~ 3.10) 0.345 CA199, >30 vs. ≤ 30 U/mL 1.31 (0.78 ~ 2.19) 0.303 GCIPS, high vs. low 2.86 (1.99 ~ 4.11) <.001 2.43 (1.49 ~ 3.97) <.001 P-adjuvanttherapy, Postoperative adjuvanttherapy, specifically referring to radiotherapy and chemotherapy; HR, Hazard Ratio; CI, Confidence Interval; CEA, carcinoembryonic antigen; CA199, carbohydrate antigen 199; CA125, carbohydrate antigen 125; GCIPS, the Gastric cancer immune prognostic Score; B I, Billroth I; B II, Billroth II; R-Y, Roux-en-Y; T, Tumor; N, regional lymph node; M, metastasis. Table 4 Univariate and multivariate analysis for OS in patients with gastric cancer patients. Variables Univariate analysis Multivariate analysis HR (95 CI) P-value HR (95 CI) P-value Gender, female vs. male 0.88 (0.59 ~ 1.32) 0.538 Age, ≥ 60 vs.<60 years 1.09 (0.75 ~ 1.59) 0.658 Location, Middle and Low vs. Up 0.98 (0.71 ~ 1.35) 0.879 Surgery, Laparoscopic vs. open 0.79 (0.48 ~ 1.30) 0.351 Tumor size, ≥ 3.5 vs. <3.5cm 1.11 (0.77 ~ 1.61) 0.570 Blood loss, ≥ 100 vs. <100 mL 1.05 (0.71 ~ 1.55) 0.808 Comorbidities, yes vs no 0.88 (0.52 ~ 1.47) 0.618 Anemiayes, yes vs no 1.44 (0.87 ~ 2.38) 0.157 Pyloricstenosis, yes vs no 0.93 (0.41 ~ 2.12) 0.870 Transfusion, yes vs no 1.60 (0.99 ~ 2.56) 0.053 Anastomoticmethods, BI and BII vs R-Y 0.86(0.75 ~ 1.95) 0.120 Anastomotic leakage, yes vs no 1.53 (0.77 ~ 3.02) 0.223 Pathologicalpattern Well 1.00 (Reference) 1.00 (Reference) Moderate 2.72 (1.18 ~ 6.23) 0.018 3.18 (1.25 ~ 8.05) 0.015 Poor 9.78 (4.05 ~ 23.64) <.001 6.34 (2.21 ~ 18.20) <.001 T stage, I and II vs. III and IV 0.72 (0.55 ~ 1.23) 0.075 N stage, 0 and I vs. II and III 0,78 (0.56 ~ 1.12) 0.035 0.89 (0.66 ~ 1.34) 0.158 TNM Stage I 1.00 (Reference) 1.00 (Reference) II 2.11 (0.91 ~ 4.85) 0.080 2.23 (0.53 ~ 9.28) 0.272 III 7.45 (3.44 ~ 16.15) 5 vs. ≤ 5 ng/mL 1.67 (1.10 ~ 2.53) 0.016 0.65 (0.38 ~ 1.11) 0.116 CA125,>25 vs. ≤ 25 U/mL 1.57 (0.73 ~ 3.38) 0.247 CA199, >30 vs. ≤ 30 U/mL 1.37 (0.82 ~ 2.30) 0.226 GCIPS, high vs. low 3.22 (2.20 ~ 4.72) <.001 2.43 (1.49 ~ 3.97) 0.001 P-adjuvanttherapy, Postoperative adjuvanttherapy, specifically referring to radiotherapy and chemotherapy; HR, Hazard Ratio; CI, Confidence Interval; CEA, carcinoembryonic antigen; CA199, carbohydrate antigen 199; CA125, carbohydrate antigen 125; GCIPS, the Gastric cancer immune prognostic Score; B I, Billroth I; B II, Billroth II; R-Y, Roux-en-Y; T, Tumor; N, regional lymph node; M, metastasis. Statistical analysis. All statistical analyses were conducted with R software (version 4.3.3; R Foundation for Statistical Computing). Key packages included pROC (v1.18.5) for receiver operating characteristic (ROC) curve analysis, wherein the optimal cutoff was determined by maximizing the Youden index based on the area under the curve (AUC); survival (v3.6.4) and survminer (v0.4.9) for survival modeling; and rstatix (v0.7.2) for general statistical tests. According to the optimal cutoff, patients were categorized into high- and low-CALLY groups. Normally distributed continuous variables are summarized as mean ± standard deviation (SD) and compared via independent samples t-tests; non-normal continuous variables are reported as median (interquartile range, Q1–Q3) and analyzed using Wilcoxon rank-sum tests. Categorical variables are presented as frequencies (percentages) and compared with χ² tests or Fisher’s exact tests, as appropriate. All P-values were two-tailed, with significance set at P < 0.05. Univariate and multivariate Cox proportional hazards models were fitted using the survival package to compute hazard ratios (HRs) and 95% confidence intervals (CIs) for recurrence-free survival (RFS) and overall survival (OS). Variables significant in univariate analysis (P < 0.05) were entered into the multivariate model. Survival curves were generated with the Kaplan–Meier method, and group differences were evaluated with log-rank tests. Ethical approval. The present study was conducted in accordance with the Declaration of Helsinki and approved by the Research Ethics Committee at Jingdezhen First People’s Hospital (approval no. jdzyy202537).The requirement for informed consent was waived due to the retrospective nature of the study and data anonymization. Ethical approval. The present study was conducted in accordance with the Declaration of Helsinki and approved by the Research Ethics Committee at Jingdezhen First People’s Hospital (approval no. jdzyy202537). The requirement for informed consent was waived due to the retrospective nature of the study and data anonymization. Results Patient characteristics. A total of 237 patients with GC who underwent radical resection between January 2011 and December 2019 were initially enrolled in this study (Fig. 1). After applying the exclusion criteria (n = 27) and accounting for loss to follow-up (n = 11), a final total of 226 patients were included in the analysis. Using the optimal cutoff value of 2.840 for GCIPS, patients were stratified into low-GCIPS (n = 133) and high-GCIPS (n = 93) groups. ROC curve analysis (Fig. 2) was performed to evaluate and compare the predictive performance of multiple inflammatory, immune, and nutritional markers, including GCIPS, SII, PNI, MLR, NLR, and PLR. Among all markers, GCIPS demonstrated the highest discriminative ability, with an area under the curve (AUC) of 0.776 (95% CI: 0.716–0.836). These results indicate that GCIPS outperforms conventional inflammatory biomarkers in predicting clinical outcomes in gastric cancer patients. Clinicopathological characteristics were well balanced between the two groups for most baseline variables (Table 2). No significant differences were observed in age (P = 0.699), gender (P = 0.252), tumor location (P = 0.884), surgical approach (P = 0.719), anastomotic method (P = 0.734), tumor size (P = 0.824), intraoperative blood loss (P = 0.672), comorbidities (P = 0.416), anemia (P = 0.794), pyloric stenosis (P = 0.792), transfusion requirement (P = 0.872), or anastomotic leakage (P = 0.487). Similarly, no significant intergroup differences were detected in T stage (P = 0.450), N stage (P = 0.209), TNM stage (P = 0.553), rates of postoperative adjuvant therapy (P = 0.967), or preoperative tumor marker levels including CEA (P = 0.323), CA19-9 (P = 0.532), and CA125 (P = 0.990). However, significant differences were identified in pathological differentiation (P < 0.001). The high-GCIPS group had a substantially higher proportion of poorly differentiated tumors (24.73% vs. 7.52%; HR = 3.12, 95% CI: 1.45–6.72, P < 0.001) and a lower proportion of well-differentiated tumors (4.30% vs. 18.80%) compared to the low-GCIPS group. As expected, the median GCIPS values significantly differed between the two groups [low-GCIPS: HR = 2.51, 95% CI: 2.25–2.66) vs. high-GCIPS: HR = 3.12, 95% CI: 2.98–3.34); P < 0.001]. In summary, while most baseline and clinical characteristics were comparable between the groups, a high GCIPS was significantly associated with a more aggressive pathological pattern, specifically poorer tumor differentiation. Prognostic significance of GCIPS in GC survival. Kaplan–Meier survival analysis was performed to evaluate the prognostic significance of GCIPS in gastric cancer patients. As shown in Fig. 3, patients in the high-GCIPS group had significantly worse recurrence-free survival (RFS) compared to those in the low-GCIPS group (log-rank P < 0.001). The HR for recurrence in the high-GCIPS group was 2.856 (95% CI: 1.986–4.106). Similarly, as illustrated in Fig. 4, OS was also significantly reduced in the high-GCIPS group (log-rank P < 0.001), with an HR of 3.222 (95% CI: 2.201–4.716). The number at risk tables confirm a consistent decline in survival probability over time in both groups, with more pronounced event occurrences in the high-GCIPS cohort. These results strongly indicate that elevated GCIPS is associated with unfavorable survival outcomes in gastric cancer. COX regression analysis of 5-year RFS in patients with GC. Univariate and multivariate Cox regression analyses were performed to identify prognostic factors for RFS in GC patients (Table 3). Univariate analysis revealed that several factors were significantly associated with worse RFS, including poor pathological pattern (poor vs. well differentiated: HR = 7.88, 95% CI: 3.58–17.31, P < 0.001), advanced TNM stage (III vs. I: HR = 8.71, 95% CI: 4.03–18.86, P 5 vs. ≤5 ng/mL: HR = 1.76, 95% CI: 1.18–2.63, P = 0.005), high GCIPS (high vs. low: HR = 2.86, 95% CI: 1.99–4.11, P < 0.001), as well as transfusion (yes vs. no: HR = 1.65, 95% CI: 1.04–2.62, P = 0.033), and adjuvant therapy (yes vs. no: HR = 1.80, 95% CI: 1.14–2.83, P = 0.011). Multivariate analysis confirmed that high GCIPS remained an independent predictor of poor RFS (HR = 2.43, 95% CI: 1.49–3.97, P < 0.001), along with poor pathological differentiation (moderate vs. well: HR = 2.54, 95% CI: 1.12–5.75, P = 0.025; poor vs. well: HR = 4.80, 95% CI: 1.86–12.41, P = 0.001) and advanced TNM stage (III vs. I: HR = 14.95, 95% CI: 1.92–116.64, P = 0.010). However, factors such as transfusion, N stage, adjuvant therapy, and elevated CEA, which were significant in univariate analysis, lost statistical significance in the multivariate model. COX regression analysis of 5-year OS in patients with GC. Univariate and multivariate Cox regression analyses were performed to identify prognostic factors for OS in GC patients (Table 4). Univariate Cox regression analysis identified several factors significantly associated with worse OS in GC patients, including poor pathological differentiation (poor vs. well differentiated: HR = 9.78, 95% CI: 4.05–23.64, P < 0.001), advanced TNM stage (stage III vs. I: HR = 7.45, 95% CI: 3.44–16.15, P 5 vs. ≤5 ng/mL: HR = 1.67, 95% CI: 1.10–2.53, P = 0.016), high GCIPS (high vs. low: HR = 3.22, 95% CI: 2.20–4.72, P < 0.001), and receipt of adjuvant therapy (yes vs. no: HR = 1.71, 95% CI: 1.07–2.72, P = 0.024). Multivariate analysis demonstrated that GCIPS remained a strong independent prognostic factor for OS (HR = 2.43, 95% CI: 1.49–3.97, P = 0.001). Additionally, pathological differentiation (moderate vs. well: HR = 3.18, 95% CI: 1.25–8.05, P = 0.015; poor vs. well: HR = 6.34, 95% CI: 2.21–18.20, P < 0.001) and advanced TNM stage (III vs. I: HR = 15.45, 95% CI: 1.74–137.42, P = 0.014) retained their independent prognostic significance. Notably, elevated CEA level and adjuvant therapy, which showed significance in univariate analysis, were not independent predictors in the multivariate model. Discussion The present study demonstrates that the GCIPS serves as a robust and independent prognostic biomarker for predicting both RFS and OS in patients with GC following radical resection. Our results indicate that a preoperative GCIPS value ≥ 2.840 is significantly associated with aggressive tumor biology, including poorer histological differentiation and unfavorable survival outcomes. Accumulating evidence indicates that systemic inflammation and immune dysfunction play pivotal roles in cancer progression and metastasis ( 14 ). Inflammatory and nutritional indicators such as the NLR, PLR, MLR, PNI, and SII ( 8 – 11 ). However, most of these conventional markers reflect only isolated aspects of the host's immune or nutritional status. In contrast, the innovative significance of the GCIPS lies in its integration of WBC count, LYM count, and INR, thereby providing a more comprehensive multidimensional reflection of the host’s inflammatory status, immune competence, and coagulation function ( 12 ). From a mechanistic perspective, each component of GCIPS carries significant biological implications: An elevated white blood cell count not only indicates a systemic inflammatory response—associated with the release of proinflammatory cytokines (e.g., IL-6, TNF-α) and expansion of myeloid-derived suppressor cells (MDSCs) ( 15 , 16 )—but also contributes to a tumor-promoting milieu by inducing genomic instability and stimulating angiogenesis ( 17 , 18 ).Lymphopenia reflects immune exhaustion and failure of cancer immunoediting ( 19 , 20 ), whereas intact lymphocyte function—particularly that of cytotoxic T cells and NK cells—is essential for antitumor immunity ( 21 ). An abnormal international normalized ratio (INR) not only signals an increased risk of thrombosis but also promotes tumor growth, angiogenesis, and premetastatic niche formation through tissue factor-mediated activation of the coagulation cascade in the tumor microenvironment and protease-activated receptor (PAR) signaling pathways activated by coagulation proteases (such as factor Xa and thrombin) ( 22 – 24 ). Importantly, these three pathways—inflammation, immunity, and coagulation—do not act in isolation. Instead, they form a closely interconnected network wherein inflammatory cytokines modulate both coagulation activity and immune cell function ( 25 ). This tripartite interaction constitutes a complex biological network of tumor–host interactions [26]. By integrating these three key pathways, the GCIPS comprehensively captures the global state of this network, which fully accounts for its exceptional prognostic value and clinical superiority ( 25 , 27 ) The findings of the present study are highly consistent with those reported by Zuo et al. ( 12 ) and further extend the applicability of the GCIPS from patients with advanced disease ineligible for immune checkpoint inhibitor therapy to GC patients undergoing radical resection. Notably, in the multivariate analysis of this study, GCIPS remained an independent predictor of prognosis (RFS: HR = 2.43; OS: HR = 2.43) even after adjusting for traditional strong prognostic factors such as TNM stage and tumor differentiation. This result strongly suggests that the systemic inflammatory, immune, and coagulation status captured by GCIPS provides unique biological insights beyond traditional anatomic staging systems, aiding in the identification of patient subgroups with vastly different actual prognoses who are classified into the same stage by conventional criteria ( 28 , 29 ). The clinical utility of GCIPS is also reflected in the simplicity of its components. All indicators (WBC, LYM, INR) are derived from routine preoperative blood tests, requiring no additional costs and are easily standardized across healthcare institutions at various levels ( 30 ). This makes GCIPS a highly cost-effective prognostic stratification tool, particularly suitable for regions with limited medical resources. Obtaining the GCIPS score preoperatively can provide crucial reference for clinicians in perioperative decision-making. For instance, for patients with a high GCIPS score, even those at a relatively early stage, more aggressive adjuvant therapy strategies and closer follow-up monitoring could be considered to facilitate early detection and intervention of recurrence and metastasis ( 31 ). Certainly, this study has several limitations. First, as a single-center retrospective study, it carries inherent selection bias. Second, the study population was sourced from a single region in China, and the generalizability of its conclusions to external populations (different ethnicities, regions) requires further validation. Third, the optimal cutoff value for GCIPS (2.840) was derived from ROC analysis of this study's data; its universality and stability need external validation in prospective, multi-center, large-sample cohorts. Finally, molecular subtypes of tumors (such as EBV status, microsatellite instability status, HER2 expression status) have been confirmed to be closely related to the prognosis and treatment response of gastric cancer ( 3 , 32 ). However, this study could not obtain and analyze these data. Future research should explore the combined application value of GCIPS and molecular subtyping to build more accurate prognostic prediction models. Conclusion In summary, this study confirms that the preoperative GCIPS is a robust and independent predictive factor for survival outcomes in patients with GC undergoing radical resection. By integrating the three biological pathways of systemic inflammatory response, immune status, and coagulation function, it provides a comprehensive prognostic assessment tool that surpasses traditional clinicopathological indicators. GCIPS is simple to calculate, low-cost, and easy to implement clinically, showing promise as a novel and practical biomarker for the individualized assessment of postoperative risk and for guiding adjuvant therapy and follow-up strategies in gastric cancer patients. Future prospective multi-center studies are warranted to validate its cutoff value and explore its potential for combined application with tumor molecular characteristics. Declarations Ethics approval and consent to participate The present study was conducted in accordance with the Declaration of Helsinki and was approved by the Research Ethics Committee at The Jingdezhen First People’s Hospital (Jingdezhen, China; approval no. jdzyy202537). The requirement for informed consent was waived due to the retrospective nature of the study and data anonymization. Patient consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Funding No funding was received. Author Contribution XH and SZ contributed to the study conceptualization and methodology. They were also responsible for validation, investigation, and resource acquisition. Data curation—including data collection, cleaning, organization, preparation for analysis, and quality assurance—was carried out by XH and JL. XH attests to the authenticity of all raw data. The original draft of the manuscript was written by XH, and SZ reviewed and edited it. Visualization was conducted by XH and JL. SZ supervised the study, while project administration was handled by XH, JL, and SZ. All authors read and approved the final version of the manuscript. Acknowledgements Not applicable. Data Availability The data generated in the present study may be requested from the corresponding author. References Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209–49. 10.3322/caac.21660 . Smyth EC, Nilsson M, Grabsch HI, van Grieken NC, Lordick F. Gastric cancer. Lancet. 2020;396(10251):635–48. 10.1016/S0140-6736(20)31288-5 . Joshi SS, Badgwell BD. Current treatment and recent progress in gastric cancer. CA Cancer J Clin. 2021;71(3):264–79. 10.3322/caac.21657 . Janjigian YY, Shitara K, Moehler M, et al. First-line nivolumab plus chemotherapy versus chemotherapy alone for advanced gastric, gastro-oesophageal junction, and oesophageal adenocarcinoma (CheckMate 649): a randomised, open-label, phase 3 trial. Lancet. 2021;398(10294):27–40. 10.1016/S0140-6736(21)00797-2 . Kang YK, Boku N, Satoh T, et al. Nivolumab in patients with advanced gastric or gastro-oesophageal junction cancer refractory to, or intolerant of, at least two previous chemotherapy regimens (ONO-4538-12, ATTRACTION-2): a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet. 2017;390(10111):2461–71. 10.1016/S0140-6736(17)31827-5 . Smyth EC, Verheij M, Allum W, et al. Gastric cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2016;27(suppl 5):v38–49. 10.1093/annonc/mdw350 . Lauren P. The two histological main types of gastric carcinoma: diffuse and so-called intestinal-type carcinoma. An attempt at a histo-clinical classification. Acta Pathol Microbiol Scand. 1965;64:31–49. 10.1111/apm.1965.64.1.31 . Sundararaju U, Rajakumar HK. Prognostic value of neutrophil-to-lymphocyte ratio in gastric cancer: Enhancing clinical relevance. World J Gastrointest Oncol. 2025;17(4):103128. 10.4251/wjgo.v17.i4.103128 . Yang S, Li Z, Wang T et al. Correlation between neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio with prognosis in gastric cancer patients undergoing neoadjuvant chemotherapy: a meta-analysis. BMC Gastroenterol . 2025;25(1):445. Published 2025 Jul 1. 10.1186/s12876-025-04056-x An S, Eo W, Lee S, Lee YJ. Monocyte-to-lymphocyte ratio as a determinant of survival in patients with gastric cancer undergoing gastrectomy: A cohort study. Med (Baltim). 2023;102(22):e33930. 10.1097/MD.0000000000033930 . Kanbur B, Unek IT, Uzun M, Ozturk C, Yarol RC, Balci A. Association of Systemic Inflammatory Response Index and Prognostic Nutritional Index Scores with Sarcopenia in Patients with Metastatic Gastric Cancer. Med (Kaunas). 2025;61(5):785. 10.3390/medicina61050785 . Published 2025 Apr 23. Zuo Y, Sun H, Pan H, Zhao R, Xue Y, Song H. The Gastric Cancer Immune Prognostic Score (GCIPS) Shows Potential in Predicting an Unfavorable Prognosis for Gastric Cancer Patients Undergoing Immune Checkpoint Inhibitor Treatment. Biomedicines. 2024;12(3):491. 10.3390/biomedicines12030491 . Published 2024 Feb 22. Amin MB, Edge SB, Greene FL. AJCC Cancer Staging Manual. 8th ed. New York: Springer; 2016. Mantovani A, Allavena P, Sica A, Balkwill F. Cancer-related inflammation. Nature. 2008;454(7203):436–44. 10.1038/nature07205 . Gabrilovich DI, Nagaraj S. Myeloid-derived suppressor cells as regulators of the immune system. Nat Rev Immunol. 2009;9(3):162–74. 10.1038/nri2506 . Grivennikov SI, Greten FR, Karin M. Immunity, inflammation, and cancer. Cell. 2010;140(6):883–99. 10.1016/j.cell.2010.01.025 . Palumbo JS, Talmage KE, Massari JV, et al. Tumor cell-associated tissue factor and circulating hemostatic factors cooperate to increase metastatic potential through natural killer cell-dependent and-independent mechanisms. Blood. 2007;110(1):133–41. 10.1182/blood-2007-01-065995 . Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144(5):646–74. 10.1016/j.cell.2011.02.013 . Colotta F, Allavena P, Sica A, Garlanda C, Mantovani A. Cancer-related inflammation, the seventh hallmark of cancer: links to genetic instability. Carcinogenesis. 2009;30(7):1073–81. 10.1093/carcin/bgp127 . Jiang X, Wang J, Deng X, et al. The role of microenvironment in tumor angiogenesis. J Exp Clin Cancer Res. 2020;39(1):204. 10.1186/s13046-020-01709-5 . Published 2020 Sep 30. Fridman WH, Zitvogel L, Sautès-Fridman C, Kroemer G. The immune contexture in cancer prognosis and treatment. Nat Rev Clin Oncol. 2017;14(12):717–34. 10.1038/nrclinonc.2017.101 . Fridman WH, Pagès F, Sautès-Fridman C, Galon J. The immune contexture in human tumours: impact on clinical outcome. Nat Rev Cancer. 2012;12(4):298–306. 10.1038/nrc3245 . Published 2012 Mar 15. Esmon CT. The interactions between inflammation and coagulation. Br J Haematol. 2005;131(4):417–30. 10.1111/j.1365-2141.2005.05753.x . Hisada Y, Mackman N. Cancer-associated pathways and biomarkers of venous thrombosis. Blood. 2017;130(13):1499–506. 10.1182/blood-2017-03-743211 . McMillan DC. The systemic inflammation-based Glasgow Prognostic Score: a decade of experience in patients with cancer. Cancer Treat Rev. 2013;39(5):534–40. 10.1016/j.ctrv.2012.08.003 . Coussens LM, Werb Z. Inflammation and cancer. Nature. 2002;420(6917):860–7. 10.1038/nature01322 . Ruf W, Yokota N, Schaffner F. Tissue factor in cancer progression and angiogenesis. Thromb Res. 2010;125(0 2):S36–8. 10.1016/S0049-3848(10)70010-4 . Roxburgh CS, McMillan DC. Role of systemic inflammatory response in predicting survival in patients with primary operable cancer. Future Oncol. 2010;6(1):149–63. 10.2217/fon.09.136 . Jiang AG, Chen HL, Lu HY. The relationship between Glasgow Prognostic Score and serum tumor markers in patients with advanced non-small cell lung cancer. BMC Cancer . 2015;15:386. Published 2015 May 10. 10.1186/s12885-015-1403-x Dolan RD, McSorley ST, Horgan PG, Laird B, McMillan DC. The role of the systemic inflammatory response in predicting outcomes in patients with advanced inoperable cancer: Systematic review and meta-analysis. Crit Rev Oncol Hematol. 2017;116:134–46. 10.1016/j.critrevonc.2017.06.002 . Hirahara N, Tajima Y, Fujii Y, et al. Preoperative Prognostic Nutritional Index Predicts Long-term Outcome in Gastric Cancer: A Propensity Score-matched Analysis. Anticancer Res. 2018;38(8):4735–46. 10.21873/anticanres.12781 . Cancer Genome Atlas Research Network. Comprehensive molecular characterization of gastric adenocarcinoma. Nature. 2014;513(7517):202–9. 10.1038/nature13480 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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1","display":"","copyAsset":false,"role":"figure","size":219257,"visible":true,"origin":"","legend":"\u003cp\u003eScreening flowchart for gastric cancer patients undergoing radical resection.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7650071/v1/ee72521ce04e2101d185a3ca.png"},{"id":92858892,"identity":"16e58b29-6331-4d76-97c7-1aab7c094a7a","added_by":"auto","created_at":"2025-10-06 11:58:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1175785,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic curve for the inflammatory makers. AUC, area under the curve.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7650071/v1/ac86ff6f62aaab5dedf995c7.png"},{"id":92857304,"identity":"867c0f4b-07e5-4c91-a746-d8ef373b23d0","added_by":"auto","created_at":"2025-10-06 11:42:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":72182,"visible":true,"origin":"","legend":"\u003cp\u003eRFS in patients with gastric cancer in the GCIPS-high and GCIPS-low groups. RFS, recurrence-free survival; GCIPS, \u003cem\u003eGastric Cancer Immune Prognostic Score\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7650071/v1/4f0f520c2e91135d9213930e.png"},{"id":92858100,"identity":"6e464d99-a208-4269-bc36-0d54307a43e9","added_by":"auto","created_at":"2025-10-06 11:50:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":72065,"visible":true,"origin":"","legend":"\u003cp\u003eOS in patients with gastric cancer in the GCIPS-high and GCIPS-low groups. OS, Overall survival; GCIPS, \u003cem\u003eGastric Cancer Immune Prognostic Score\u003c/em\u003e.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7650071/v1/309f984a2e68cb0e0325aeb5.png"},{"id":99222450,"identity":"b12c897b-0133-4ff0-ab9f-50b4fe3f3cb4","added_by":"auto","created_at":"2025-12-30 09:54:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1601901,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7650071/v1/857db21a-6015-4423-86ef-b7834e4fa818.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Preoperative Gastric Cancer Immune Prognostic Score (GCIPS) as a Novel Biomarker for Predicting Survival in Gastric Cancer Patients After Radical Resection ","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGastric cancer (GC) remains one of the most common malignant tumors worldwide, with high incidence and mortality rates, particularly in East Asian regions such as China, where it continues to pose a major public health challenge (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Although surgical resection is the primary treatment for GC, especially in early and locally advanced stages, postoperative recurrence and metastasis remain the leading causes of treatment failure and patient mortality (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Therefore, accurate assessment of postoperative prognosis, particularly in identifying high-risk patients with early recurrence and shorter survival, is critical for developing individualized treatment strategies and optimizing follow-up management.\u003c/p\u003e\u003cp\u003eIn recent years, immunotherapy, especially immune checkpoint inhibitors (ICIs), has demonstrated significant efficacy in the treatment of GC. However, its application still faces challenges in patient selection and efficacy prediction (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Conventional prognostic indicators such as TNM stage, histological type, and tumor markers (e.g., CEA and CA19-9) offer certain predictive value but exhibit limitations in highly heterogeneous GC (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Hence, there is growing interest in developing more accurate, convenient, and widely applicable prognostic biomarkers. Given their easy accessibility, low cost, and high reproducibility, hematological parameters have been increasingly utilized in prognostic evaluation of cancers. Inflammatory and nutritional indicators such as the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), Monocytes-to-lymphocyte ratio (MLR), prognostic nutritional index (PNI), and systemic immune-inflammation index (SII), have been confirmed to correlate with prognosis in GC (\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) However, most of these parameters were established based on traditional treatment modalities, and their applicability in the context of modern comprehensive therapy remains to be further validated.\u003c/p\u003e\u003cp\u003eRecently, Zuo et al. proposed a novel blood-based scoring system\u0026mdash;the GC Immune Prognostic Score (GCIPS), which was developed based on white blood cell count (WBC), lymphocyte count (LYM), and international normalized ratio (INR). This score demonstrated excellent prognostic predictive ability in GC patients treated with immune checkpoint inhibitors (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). The GCIPS not only reflects systemic inflammatory and immune status but also incorporates multidimensional information related to coagulation function and nutritional condition, indicating its potential as a comprehensive prognostic tool.\u003c/p\u003e\u003cp\u003eHowever, no studies have yet explored the prognostic value of GCIPS in postoperative GC patients. This study aims to retrospectively analyze clinical data from postoperative GC patients in our institution to validate the predictive power of GCIPS for 5-year overall survival (OS) and recurrence-free survival (RFS). The findings are expected to provide a new theoretical basis and clinical tool for individualized prognostic assessment in GC patients after surgery.\u003c/p\u003e"},{"header":"Patients and methods","content":"\u003cp\u003e\u003cem\u003ePatients.\u003c/em\u003e This retrospective study analyzed clinicopathological data and preoperative laboratory hematological parameters (measured within one week before surgery) from GC patients who underwent radical resection at Jingdezhen First People's Hospital, China, between January 2011 and December 2019. All consecutive patients meeting the eligibility criteria during this period were initially enrolled. The inclusion criteria were as follows: i) histologically confirmed primary GC; ii) no prior neoadjuvant therapy; and iii) curative-intent R0 resection. Patients were excluded based on the following: i) synchronous or metachronous malignancies; ii) underlying hematological diseases; iii) preoperative infection or immunodeficiency; iv) incomplete medical records; v) non-radical resection; or vi) receipt of neoadjuvant treatment. All data were retrieved from the hospital\u0026rsquo;s prospectively maintained database.\u003c/p\u003e\u003cp\u003e\u003cem\u003eTreatment and follow-up.\u003c/em\u003e Disease staging was performed according to the eighth edition of the American Joint Committee on Cancer Tumor-Node-Metastasis (TNM) classification (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Pre-existing comorbidities, including cardiovascular diseases, pulmonary diseases, diabetes mellitus, chronic kidney disease, and chronic liver disease, were collectively defined as comorbidities. Postoperative anastomotic leakage was specifically defined as leakage occurring within 30 days after surgery. Adjuvant therapy after surgery mainly consisted of chemotherapy, radiotherapy, and related treatment modalities. According to the GC Diagnosis and Treatment Guidelines, the following standardized postoperative follow-up protocol was implemented: i) For the first two years after surgery, patients underwent comprehensive follow-up every 3 months, including medical history collection, physical examination, complete blood count, biochemical tests, and tumor marker assays (such as Carcinoembryonic Antigen (CEA), Carbohydrate Antigen 19\u0026thinsp;\u0026minus;\u0026thinsp;9 (CA19-9)and CA125); contrast-enhanced computed tomography (CT) scans of the chest, abdomen, and pelvis were performed every 6 months. ii)From the third to the fifth year after surgery, the follow-up interval was extended to every 6 months, with the same examination items. Follow-up was conducted through standardized outpatient visits or structured telephone interviews, and all data were recorded in real time in the hospital database. The follow-up deadline was December 31, 2024, or the date of patient death. For outcome evaluation, recurrence-free survival (RFS) was calculated from the date of surgery to the first recurrence of gastric cancer, last follow-up, or death from any cause. Overall survival (OS) was defined as the time from surgery to death or the last confirmed follow-up for surviving patients.\u003c/p\u003e\u003cp\u003e\u003cem\u003eDetermination of inflammatory markers.\u003c/em\u003e All calculations for inflammatory markers are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, with the GCIPS calculated as follows: White Blood Cells \u003cb\u003e(\u003c/b\u003e10\u003csup\u003e9\u003c/sup\u003e/L\u003cb\u003e) \u0026times;\u003c/b\u003e0.071 - Lymphocytes \u003cb\u003e(\u003c/b\u003e10\u003csup\u003e9\u003c/sup\u003e/L\u003cb\u003e) \u0026times;\u003c/b\u003e 0.375\u0026thinsp;+\u0026thinsp;International normalized ratio \u003cb\u003e\u0026times;\u003c/b\u003e 2.986 (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFull names, abbreviations, calculation formulas, and optimal cut-off values of the markers\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbbreviation of markers\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFull name of the marker\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCalculation formula\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOptimal cutoff value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGCIPS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGastric cancer immune prognostic Score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWhite Blood Cells (10\u003csup\u003e9\u003c/sup\u003e/L)\u0026times;0.071 - Lymphocytes (10\u003csup\u003e9\u003c/sup\u003e/L) \u0026times; 0.375\u0026thinsp;+\u0026thinsp;International normalized ratio \u0026times; 2.986\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.837\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\u003cp\u003eNeutrophil-to-lymphocyte ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNeutrophils(10\u003csup\u003e9\u003c/sup\u003e/L) / Lymphocytes(10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.522\u003c/p\u003e\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\u003cp\u003ePlatelet-to-lymphocyte ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePlatelets(10\u003csup\u003e9\u003c/sup\u003e/L) / Lymphocytes(10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e134.963\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMonocytes-to-lymphocyte ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMonocytes(10\u003csup\u003e9\u003c/sup\u003e/L) / Lymphocytes(10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.266\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePNI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrognostic nutritional index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAlbumin (g/L)\u0026thinsp;+\u0026thinsp;5 \u0026times; Lymphocytes ((10\u003csup\u003e9\u003c/sup\u003e/L))\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e46.425\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSystemic immune-inflammation index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePlatelets (10\u003csup\u003e9\u003c/sup\u003e/L)\u0026times; Neutrophils (10\u003csup\u003e9\u003c/sup\u003e/L)/ Lymphocytes(10\u003csup\u003e9\u003c/sup\u003e/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e552.385\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eClinicopathological comparisons between low- and high-GCIPS score groups.\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\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\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;226)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eGCIPS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow(n\u0026thinsp;=\u0026thinsp;133)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh(n\u0026thinsp;=\u0026thinsp;93)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, years(Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61.10\u0026thinsp;\u0026plusmn;\u0026thinsp;10.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61.33\u0026thinsp;\u0026plusmn;\u0026thinsp;10.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60.76\u0026thinsp;\u0026plusmn;\u0026thinsp;11.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.699\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender, n(%)\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\u003cp\u003e0.252\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e167 (73.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e102 (76.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e65 (69.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59 (26.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e31 (23.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28 (30.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLocation, n(%)\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\u003cp\u003e0.884\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUp\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24 (10.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (9.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11 (11.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMiddle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e67 (29.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40 (30.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27 (29.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e135 (59.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80 (60.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55 (59.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurgery, n(%)\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\u003cp\u003e0.719\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOpen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e180 (79.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e107 (80.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e73 (78.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLaparoscopic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46 (20.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26 (19.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20 (21.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnastomoticmethods, n(%)\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\u003cp\u003e0.734\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eB I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59 (26.10)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33(24.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26 (27.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eB II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e136 (60.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e80 (60.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56 (60.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR-Y\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31 (13.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20 (15.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11 (11.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor size, cm [M (Q₁, Q₃)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.00 (2.50, 5.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.00 (2.50, 5.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.00 (2.60, 4.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.824\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBloodloss, mL [M (Q₁, Q₃)]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e100.0 (100.0, 150.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e100.0 (100.0, 200.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100.0 (100.0, 150.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.672\u003c/p\u003e\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\u003eComorbidities, n(%)\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\u003cp\u003e0.416\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e189 (83.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e109 (81.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e80 (86.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37 (16.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24 (18.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13 (13.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnemia, n(%)\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\u003cp\u003e0.794\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e196 (86.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e116 (87.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e80 (86.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30 (13.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (12.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13 (13.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePyloricstenosis, n(%)\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\u003cp\u003e0.792\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e214 (94.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e125 (93.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e89 (95.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 (5.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (6.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (4.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTransfusion, n(%)\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\u003cp\u003e0.872\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e193 (85.40)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e114 (85.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e79 (84.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33 (14.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19 (14.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14 (15.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnastomotic leakage, n(%)\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\u003cp\u003e0.487\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e212 (93.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e126 (94.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e86 (92.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14 (6.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (5.26)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (7.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePathologicalpattern, n(%)\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\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWell\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e29 (12.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25 (18.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (4.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e164 (72.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e98 (73.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e66 (70.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33 (14.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (7.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23 (24.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT, n(%)\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\u003cp\u003e0.450\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14 (6.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8 (6.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6 (6.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e47 (20.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24 (18.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23 (24.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32 (14.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (12.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15 (16.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e133 (58.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e84 (63.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49 (52.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN, n(%)\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\u003cp\u003e0.209\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e81 (35.84)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53 (39.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e28 (30.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e90 (39.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54 (40.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36 (38.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36 (15.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (12.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19 (20.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19 (8.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (6.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (10.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTNM Stage, n(%)\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\u003cp\u003e0.553\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40 (17.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26 (19.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14 (15.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75 (33.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41 (30.83)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34 (36.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e111 (49.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66 (49.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e45 (48.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP-adjuvanttherapy, n(%)\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\u003cp\u003e0967\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58 (25.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e34 (25.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24 (25.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e168 (74.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e99 (74.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e69 (74.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCEA\u0026thinsp;\u0026ge;\u0026thinsp;5 ng/mL, n(%)\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\u003cp\u003e0.323\u003c/p\u003e\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\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e182 (80.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e110 (82.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e72 (77.42)\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\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44 (19.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23 (17.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21 (22.58)\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\u003eCA199\u0026thinsp;\u0026ge;\u0026thinsp;30 U/mL, n(%)\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\u003cp\u003e0.532\u003c/p\u003e\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\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e198 (87.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e115 (86.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e83 (89.25)\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\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e28 (12.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18 (13.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10 (10.75)\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\u003eCA125\u0026thinsp;\u0026ge;\u0026thinsp;25 U/mL, n(%)\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\u003cp\u003e0.990\u003c/p\u003e\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\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e216 (95.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e127 (95.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e89 (95.70)\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\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (4.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (4.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (4.30)\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\u003eGCIPS, M (Q₁, Q₃)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.72 (2.46, 3.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.51 (2.25, 2.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.12 (2.98, 3.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c6\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eP-adjuvanttherapy, Postoperative adjuvanttherapy, specifically referring to radiotherapy and chemotherapy; HR, Hazard Ratio; CI, Confidence Interval; CEA, carcinoembryonic antigen; CA199, carbohydrate antigen 199; CA125, carbohydrate antigen 125; GCIPS, the Gastric cancer immune prognostic Score; B I, Billroth I; B II, Billroth II; R-Y, Roux-en-Y; T, Tumor; N, regional lymph node; M, metastasis.\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=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariate and multivariate analysis for RFS in patients with gastric cancer patients.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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\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\u003cp\u003eUnivariate analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMultivariate analysis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR (95 CI) P-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHR (95 CI) P-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender, female vs. male\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.92 (0.62\u0026thinsp;~\u0026thinsp;1.37) 0.691\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, \u0026ge;\u0026thinsp;60 vs.\u0026lt;60 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.08 (0.75\u0026thinsp;~\u0026thinsp;1.56) 0.663\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLocation, Middle and Low vs. Up\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.83 (0.60\u0026thinsp;~\u0026thinsp;1.50) 0.263\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurgery, Laparoscopic vs. open\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.77 (0.46\u0026thinsp;~\u0026thinsp;1.30) 0.334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor size, \u0026ge;\u0026thinsp;3.5 vs. \u0026lt;3.5cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.14(0.80\u0026thinsp;~\u0026thinsp;1.62) 0.481\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood loss, \u0026ge;\u0026thinsp;100 vs. \u0026lt;100 mL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.10 (0.76\u0026thinsp;~\u0026thinsp;1.60) 0.618\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComorbidities, yes vs no\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.06 (0.66\u0026thinsp;~\u0026thinsp;1.69) 0.822\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnemiayes, yes vs no\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.54 (0.96\u0026thinsp;~\u0026thinsp;2.49) 0.076\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePyloricstenosis, yes vs no\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.99 (0.46\u0026thinsp;~\u0026thinsp;2.13) 0.986\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTransfusion, yes vs no\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.65 (1.04\u0026thinsp;~\u0026thinsp;2.62) 0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.11 (0.49\u0026thinsp;~\u0026thinsp;2.51) 0.796\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnastomoticmethods, BI and BII vs R-Y\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.96(0.87\u0026thinsp;~\u0026thinsp;1.07) 0481\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnastomotic leakage, yes vs no\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.58 (0.83\u0026thinsp;~\u0026thinsp;3.02) 0.160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePathologicalpattern\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWell\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.20 (1.06\u0026thinsp;~\u0026thinsp;4.54) 0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.54 (1.12\u0026thinsp;~\u0026thinsp;5.75) 0.025\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.88 (3.58\u0026thinsp;~\u0026thinsp;17.31) \u0026lt;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.80 (1.86\u0026thinsp;~\u0026thinsp;12.41) 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT stage, I and II vs. III and IV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.66 (042\u0026thinsp;~\u0026thinsp;1.01) 0.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN stage, 0 and I vs. II and III\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.75 (0.54\u0026thinsp;~\u0026thinsp;0.97) 0.042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.87 (0.74\u0026thinsp;~\u0026thinsp;1.02) 0.089\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.25 (0.98\u0026thinsp;~\u0026thinsp;5.17) 0.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.24 (0.57\u0026thinsp;~\u0026thinsp;8.82) 0.249\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.71 (4.03\u0026thinsp;~\u0026thinsp;18.86) \u0026lt;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.95 (1.92\u0026thinsp;~\u0026thinsp;116.64) 0.010\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP-adjuvanttherapy, yes vs no\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.80 (1.14\u0026thinsp;~\u0026thinsp;2.83) 0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.30 (0.74\u0026thinsp;~\u0026thinsp;2.28) 0.359\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCEA, \u0026gt;5 vs. \u0026le; 5 ng/mL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.76 (1.18\u0026thinsp;~\u0026thinsp;2.63) 0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.65 (0.39\u0026thinsp;~\u0026thinsp;1.11) 0.113\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCA125,\u0026gt;25 vs. \u0026le; 25 U/mL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.44 (0.67\u0026thinsp;~\u0026thinsp;3.10) 0.345\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCA199, \u0026gt;30 vs. \u0026le; 30 U/mL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.31 (0.78\u0026thinsp;~\u0026thinsp;2.19) 0.303\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGCIPS, high vs. low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.86 (1.99\u0026thinsp;~\u0026thinsp;4.11) \u0026lt;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.43 (1.49\u0026thinsp;~\u0026thinsp;3.97) \u0026lt;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eP-adjuvanttherapy, Postoperative adjuvanttherapy, specifically referring to radiotherapy and chemotherapy; HR, Hazard Ratio; CI, Confidence Interval; CEA, carcinoembryonic antigen; CA199, carbohydrate antigen 199; CA125, carbohydrate antigen 125; GCIPS, the Gastric cancer immune prognostic Score; B I, Billroth I; B II, Billroth II; R-Y, Roux-en-Y; T, Tumor; N, regional lymph node; M, metastasis.\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\u003eUnivariate and multivariate analysis for OS in patients with gastric cancer patients.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\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\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\u003cp\u003eUnivariate analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMultivariate analysis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR (95 CI) P-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHR (95 CI) P-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender, female vs. male\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.88 (0.59\u0026thinsp;~\u0026thinsp;1.32) 0.538\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, \u0026ge;\u0026thinsp;60 vs.\u0026lt;60 years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.09 (0.75\u0026thinsp;~\u0026thinsp;1.59) 0.658\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLocation, Middle and Low vs. Up\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.98 (0.71\u0026thinsp;~\u0026thinsp;1.35) 0.879\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurgery, Laparoscopic vs. open\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.79 (0.48\u0026thinsp;~\u0026thinsp;1.30) 0.351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor size, \u0026ge;\u0026thinsp;3.5 vs. \u0026lt;3.5cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.11 (0.77\u0026thinsp;~\u0026thinsp;1.61) 0.570\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlood loss, \u0026ge;\u0026thinsp;100 vs. \u0026lt;100 mL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.05 (0.71\u0026thinsp;~\u0026thinsp;1.55) 0.808\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eComorbidities, yes vs no\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.88 (0.52\u0026thinsp;~\u0026thinsp;1.47) 0.618\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnemiayes, yes vs no\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.44 (0.87\u0026thinsp;~\u0026thinsp;2.38) 0.157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePyloricstenosis, yes vs no\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.93 (0.41\u0026thinsp;~\u0026thinsp;2.12) 0.870\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTransfusion, yes vs no\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.60 (0.99\u0026thinsp;~\u0026thinsp;2.56) 0.053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnastomoticmethods, BI and BII vs R-Y\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.86(0.75\u0026thinsp;~\u0026thinsp;1.95) 0.120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnastomotic leakage, yes vs no\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.53 (0.77\u0026thinsp;~\u0026thinsp;3.02) 0.223\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePathologicalpattern\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\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWell\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.72 (1.18\u0026thinsp;~\u0026thinsp;6.23) 0.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.18 (1.25\u0026thinsp;~\u0026thinsp;8.05) 0.015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.78 (4.05\u0026thinsp;~\u0026thinsp;23.64) \u0026lt;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.34 (2.21\u0026thinsp;~\u0026thinsp;18.20) \u0026lt;.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT stage, I and II vs. III and IV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.72 (0.55\u0026thinsp;~\u0026thinsp;1.23) 0.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN stage, 0 and I vs. II and III\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0,78 (0.56\u0026thinsp;~\u0026thinsp;1.12) 0.035\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.89 (0.66\u0026thinsp;~\u0026thinsp;1.34) 0.158\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\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.00 (Reference)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.11 (0.91\u0026thinsp;~\u0026thinsp;4.85) 0.080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.23 (0.53\u0026thinsp;~\u0026thinsp;9.28) 0.272\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.45 (3.44\u0026thinsp;~\u0026thinsp;16.15) \u0026lt;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.45 (1.74\u0026thinsp;~\u0026thinsp;137.42) 0.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP-adjuvanttherapy, yes vs no\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.71 (1.07\u0026thinsp;~\u0026thinsp;2.72) 0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.32 (0.73\u0026thinsp;~\u0026thinsp;2.38) 0.361\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCEA, \u0026gt;5 vs. \u0026le; 5 ng/mL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.67 (1.10\u0026thinsp;~\u0026thinsp;2.53) 0.016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.65 (0.38\u0026thinsp;~\u0026thinsp;1.11) 0.116\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCA125,\u0026gt;25 vs. \u0026le; 25 U/mL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.57 (0.73\u0026thinsp;~\u0026thinsp;3.38) 0.247\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCA199, \u0026gt;30 vs. \u0026le; 30 U/mL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.37 (0.82\u0026thinsp;~\u0026thinsp;2.30) 0.226\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGCIPS, high vs. low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.22 (2.20\u0026thinsp;~\u0026thinsp;4.72) \u0026lt;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.43 (1.49\u0026thinsp;~\u0026thinsp;3.97) 0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eP-adjuvanttherapy, Postoperative adjuvanttherapy, specifically referring to radiotherapy and chemotherapy; HR, Hazard Ratio; CI, Confidence Interval; CEA, carcinoembryonic antigen; CA199, carbohydrate antigen 199; CA125, carbohydrate antigen 125; GCIPS, the Gastric cancer immune prognostic Score; B I, Billroth I; B II, Billroth II; R-Y, Roux-en-Y; T, Tumor; N, regional lymph node; M, metastasis.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eStatistical analysis.\u003c/em\u003e All statistical analyses were conducted with R software (version 4.3.3; R Foundation for Statistical Computing). Key packages included pROC (v1.18.5) for receiver operating characteristic (ROC) curve analysis, wherein the optimal cutoff was determined by maximizing the Youden index based on the area under the curve (AUC); survival (v3.6.4) and survminer (v0.4.9) for survival modeling; and rstatix (v0.7.2) for general statistical tests. According to the optimal cutoff, patients were categorized into high- and low-CALLY groups. Normally distributed continuous variables are summarized as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) and compared via independent samples t-tests; non-normal continuous variables are reported as median (interquartile range, Q1\u0026ndash;Q3) and analyzed using Wilcoxon rank-sum tests. Categorical variables are presented as frequencies (percentages) and compared with χ\u0026sup2; tests or Fisher\u0026rsquo;s exact tests, as appropriate. All P-values were two-tailed, with significance set at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Univariate and multivariate Cox proportional hazards models were fitted using the survival package to compute hazard ratios (HRs) and 95% confidence intervals (CIs) for recurrence-free survival (RFS) and overall survival (OS). Variables significant in univariate analysis (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were entered into the multivariate model. Survival curves were generated with the Kaplan\u0026ndash;Meier method, and group differences were evaluated with log-rank tests.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEthical approval.\u0026nbsp;\u003c/em\u003eThe present study was conducted in accordance with the Declaration of Helsinki and approved by the Research Ethics Committee at Jingdezhen First People’s Hospital (approval no. jdzyy202537).The requirement for informed consent was waived due to the retrospective nature of the study and data anonymization.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eEthical approval.\u003c/h2\u003e\n\u003cp\u003e The present study was conducted in accordance with the Declaration of Helsinki and approved by the Research Ethics Committee at Jingdezhen First People’s Hospital (approval no. jdzyy202537). The requirement for informed consent was waived due to the retrospective nature of the study and data anonymization.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003ePatient characteristics.\u003c/em\u003e A total of 237 patients with GC who underwent radical resection between January 2011 and December 2019 were initially enrolled in this study (Fig.\u0026nbsp;1). After applying the exclusion criteria (n = 27) and accounting for loss to follow-up (n = 11), a final total of 226 patients were included in the analysis. Using the optimal cutoff value of 2.840 for GCIPS, patients were stratified into low-GCIPS (n = 133) and high-GCIPS (n = 93) groups. ROC curve analysis (Fig.\u0026nbsp;2) was performed to evaluate and compare the predictive performance of multiple inflammatory, immune, and nutritional markers, including GCIPS, SII, PNI, MLR, NLR, and PLR. Among all markers, GCIPS demonstrated the highest discriminative ability, with an area under the curve (AUC) of 0.776 (95% CI: 0.716–0.836). These results indicate that GCIPS outperforms conventional inflammatory biomarkers in predicting clinical outcomes in gastric cancer patients.\u003c/p\u003e\u003cp\u003eClinicopathological characteristics were well balanced between the two groups for most baseline variables (Table\u0026nbsp;2). No significant differences were observed in age (P = 0.699), gender (P = 0.252), tumor location (P = 0.884), surgical approach (P = 0.719), anastomotic method (P = 0.734), tumor size (P = 0.824), intraoperative blood loss (P = 0.672), comorbidities (P = 0.416), anemia (P = 0.794), pyloric stenosis (P = 0.792), transfusion requirement (P = 0.872), or anastomotic leakage (P = 0.487). Similarly, no significant intergroup differences were detected in T stage (P = 0.450), N stage (P = 0.209), TNM stage (P = 0.553), rates of postoperative adjuvant therapy (P = 0.967), or preoperative tumor marker levels including CEA (P = 0.323), CA19-9 (P = 0.532), and CA125 (P = 0.990).\u003c/p\u003e\u003cp\u003eHowever, significant differences were identified in pathological differentiation (P \u0026lt; 0.001). The high-GCIPS group had a substantially higher proportion of poorly differentiated tumors (24.73% vs. 7.52%; HR = 3.12, 95% CI: 1.45–6.72, P \u0026lt; 0.001) and a lower proportion of well-differentiated tumors (4.30% vs. 18.80%) compared to the low-GCIPS group. As expected, the median GCIPS values significantly differed between the two groups [low-GCIPS: HR = 2.51, 95% CI: 2.25–2.66) vs. high-GCIPS: HR = 3.12, 95% CI: 2.98–3.34); P \u0026lt; 0.001].\u003c/p\u003e\u003cp\u003eIn summary, while most baseline and clinical characteristics were comparable between the groups, a high GCIPS was significantly associated with a more aggressive pathological pattern, specifically poorer tumor differentiation.\u003c/p\u003e\u003cp\u003e\u003cem\u003ePrognostic significance of GCIPS in GC survival.\u003c/em\u003e Kaplan–Meier survival analysis was performed to evaluate the prognostic significance of GCIPS in gastric cancer patients. As shown in Fig.\u0026nbsp;3, patients in the high-GCIPS group had significantly worse recurrence-free survival (RFS) compared to those in the low-GCIPS group (log-rank P \u0026lt; 0.001). The HR for recurrence in the high-GCIPS group was 2.856 (95% CI: 1.986–4.106). Similarly, as illustrated in Fig.\u0026nbsp;4, OS was also significantly reduced in the high-GCIPS group (log-rank P \u0026lt; 0.001), with an HR of 3.222 (95% CI: 2.201–4.716). The number at risk tables confirm a consistent decline in survival probability over time in both groups, with more pronounced event occurrences in the high-GCIPS cohort. These results strongly indicate that elevated GCIPS is associated with unfavorable survival outcomes in gastric cancer.\u003c/p\u003e\u003cp\u003e\u003cem\u003eCOX regression analysis of 5-year RFS in patients with GC.\u003c/em\u003e Univariate and multivariate Cox regression analyses were performed to identify prognostic factors for RFS in GC patients (Table\u0026nbsp;3). Univariate analysis revealed that several factors were significantly associated with worse RFS, including poor pathological pattern (poor vs. well differentiated: HR = 7.88, 95% CI: 3.58–17.31, P \u0026lt; 0.001), advanced TNM stage (III vs. I: HR = 8.71, 95% CI: 4.03–18.86, P \u0026lt; 0.001), elevated CEA level (\u0026gt; 5 vs. ≤5 ng/mL: HR = 1.76, 95% CI: 1.18–2.63, P = 0.005), high GCIPS (high vs. low: HR = 2.86, 95% CI: 1.99–4.11, P \u0026lt; 0.001), as well as transfusion (yes vs. no: HR = 1.65, 95% CI: 1.04–2.62, P = 0.033), and adjuvant therapy (yes vs. no: HR = 1.80, 95% CI: 1.14–2.83, P = 0.011).\u003c/p\u003e\u003cp\u003eMultivariate analysis confirmed that high GCIPS remained an independent predictor of poor RFS (HR = 2.43, 95% CI: 1.49–3.97, P \u0026lt; 0.001), along with poor pathological differentiation (moderate vs. well: HR = 2.54, 95% CI: 1.12–5.75, P = 0.025; poor vs. well: HR = 4.80, 95% CI: 1.86–12.41, P = 0.001) and advanced TNM stage (III vs. I: HR = 14.95, 95% CI: 1.92–116.64, P = 0.010). However, factors such as transfusion, N stage, adjuvant therapy, and elevated CEA, which were significant in univariate analysis, lost statistical significance in the multivariate model.\u003c/p\u003e\u003cp\u003e\u003cem\u003eCOX regression analysis of 5-year OS in patients with GC.\u003c/em\u003e Univariate and multivariate Cox regression analyses were performed to identify prognostic factors for OS in GC patients (Table\u0026nbsp;4). Univariate Cox regression analysis identified several factors significantly associated with worse OS in GC patients, including poor pathological differentiation (poor vs. well differentiated: HR = 9.78, 95% CI: 4.05–23.64, P \u0026lt; 0.001), advanced TNM stage (stage III vs. I: HR = 7.45, 95% CI: 3.44–16.15, P \u0026lt; 0.001), elevated CEA level (\u0026gt; 5 vs. ≤5 ng/mL: HR = 1.67, 95% CI: 1.10–2.53, P = 0.016), high GCIPS (high vs. low: HR = 3.22, 95% CI: 2.20–4.72, P \u0026lt; 0.001), and receipt of adjuvant therapy (yes vs. no: HR = 1.71, 95% CI: 1.07–2.72, P = 0.024).\u003c/p\u003e\u003cp\u003eMultivariate analysis demonstrated that GCIPS remained a strong independent prognostic factor for OS (HR = 2.43, 95% CI: 1.49–3.97, P = 0.001). Additionally, pathological differentiation (moderate vs. well: HR = 3.18, 95% CI: 1.25–8.05, P = 0.015; poor vs. well: HR = 6.34, 95% CI: 2.21–18.20, P \u0026lt; 0.001) and advanced TNM stage (III vs. I: HR = 15.45, 95% CI: 1.74–137.42, P = 0.014) retained their independent prognostic significance. Notably, elevated CEA level and adjuvant therapy, which showed significance in univariate analysis, were not independent predictors in the multivariate model.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present study demonstrates that the GCIPS serves as a robust and independent prognostic biomarker for predicting both RFS and OS in patients with GC following radical resection. Our results indicate that a preoperative GCIPS value ≥ 2.840 is significantly associated with aggressive tumor biology, including poorer histological differentiation and unfavorable survival outcomes.\u003c/p\u003e\u003cp\u003eAccumulating evidence indicates that systemic inflammation and immune dysfunction play pivotal roles in cancer progression and metastasis (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Inflammatory and nutritional indicators such as the NLR, PLR, MLR, PNI, and SII (\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e–\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). However, most of these conventional markers reflect only isolated aspects of the host's immune or nutritional status. In contrast, the innovative significance of the GCIPS lies in its integration of WBC count, LYM count, and INR, thereby providing a more comprehensive multidimensional reflection of the host’s inflammatory status, immune competence, and coagulation function (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). From a mechanistic perspective, each component of GCIPS carries significant biological implications: An elevated white blood cell count not only indicates a systemic inflammatory response—associated with the release of proinflammatory cytokines (e.g., IL-6, TNF-α) and expansion of myeloid-derived suppressor cells (MDSCs) (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)—but also contributes to a tumor-promoting milieu by inducing genomic instability and stimulating angiogenesis (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).Lymphopenia reflects immune exhaustion and failure of cancer immunoediting (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), whereas intact lymphocyte function—particularly that of cytotoxic T cells and NK cells—is essential for antitumor immunity (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). An abnormal international normalized ratio (INR) not only signals an increased risk of thrombosis but also promotes tumor growth, angiogenesis, and premetastatic niche formation through tissue factor-mediated activation of the coagulation cascade in the tumor microenvironment and protease-activated receptor (PAR) signaling pathways activated by coagulation proteases (such as factor \u003cem\u003eXa\u003c/em\u003e and thrombin) (\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e–\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Importantly, these three pathways—inflammation, immunity, and coagulation—do not act in isolation. Instead, they form a closely interconnected network wherein inflammatory cytokines modulate both coagulation activity and immune cell function (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). This tripartite interaction constitutes a complex biological network of tumor–host interactions [26]. By integrating these three key pathways, the GCIPS comprehensively captures the global state of this network, which fully accounts for its exceptional prognostic value and clinical superiority (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eThe findings of the present study are highly consistent with those reported by Zuo et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) and further extend the applicability of the GCIPS from patients with advanced disease ineligible for immune checkpoint inhibitor therapy to GC patients undergoing radical resection. Notably, in the multivariate analysis of this study, GCIPS remained an independent predictor of prognosis (RFS: HR = 2.43; OS: HR = 2.43) even after adjusting for traditional strong prognostic factors such as TNM stage and tumor differentiation. This result strongly suggests that the systemic inflammatory, immune, and coagulation status captured by GCIPS provides unique biological insights beyond traditional anatomic staging systems, aiding in the identification of patient subgroups with vastly different actual prognoses who are classified into the same stage by conventional criteria (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe clinical utility of GCIPS is also reflected in the simplicity of its components. All indicators (WBC, LYM, INR) are derived from routine preoperative blood tests, requiring no additional costs and are easily standardized across healthcare institutions at various levels (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). This makes GCIPS a highly cost-effective prognostic stratification tool, particularly suitable for regions with limited medical resources. Obtaining the GCIPS score preoperatively can provide crucial reference for clinicians in perioperative decision-making. For instance, for patients with a high GCIPS score, even those at a relatively early stage, more aggressive adjuvant therapy strategies and closer follow-up monitoring could be considered to facilitate early detection and intervention of recurrence and metastasis (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCertainly, this study has several limitations. First, as a single-center retrospective study, it carries inherent selection bias. Second, the study population was sourced from a single region in China, and the generalizability of its conclusions to external populations (different ethnicities, regions) requires further validation. Third, the optimal cutoff value for GCIPS (2.840) was derived from ROC analysis of this study's data; its universality and stability need external validation in prospective, multi-center, large-sample cohorts. Finally, molecular subtypes of tumors (such as EBV status, microsatellite instability status, HER2 expression status) have been confirmed to be closely related to the prognosis and treatment response of gastric cancer (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). However, this study could not obtain and analyze these data. Future research should explore the combined application value of GCIPS and molecular subtyping to build more accurate prognostic prediction models.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, this study confirms that the preoperative GCIPS is a robust and independent predictive factor for survival outcomes in patients with GC undergoing radical resection. By integrating the three biological pathways of systemic inflammatory response, immune status, and coagulation function, it provides a comprehensive prognostic assessment tool that surpasses traditional clinicopathological indicators. GCIPS is simple to calculate, low-cost, and easy to implement clinically, showing promise as a novel and practical biomarker for the individualized assessment of postoperative risk and for guiding adjuvant therapy and follow-up strategies in gastric cancer patients. Future prospective multi-center studies are warranted to validate its cutoff value and explore its potential for combined application with tumor molecular characteristics.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\u003cp\u003e The present study was conducted in accordance with the Declaration of Helsinki and was approved by the Research Ethics Committee at The Jingdezhen First People’s Hospital (Jingdezhen, China; approval no. jdzyy202537). The requirement for informed consent was waived due to the retrospective nature of the study and data anonymization.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003ePatient consent for publication\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eNo funding was received.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eXH and SZ contributed to the study conceptualization and methodology. They were also responsible for validation, investigation, and resource acquisition. Data curation—including data collection, cleaning, organization, preparation for analysis, and quality assurance—was carried out by XH and JL. XH attests to the authenticity of all raw data. The original draft of the manuscript was written by XH, and SZ reviewed and edited it. Visualization was conducted by XH and JL. SZ supervised the study, while project administration was handled by XH, JL, and SZ. All authors read and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data generated in the present study may be requested from the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209\u0026ndash;49. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3322/caac.21660\u003c/span\u003e\u003cspan address=\"10.3322/caac.21660\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSmyth EC, Nilsson M, Grabsch HI, van Grieken NC, Lordick F. Gastric cancer. Lancet. 2020;396(10251):635\u0026ndash;48. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(20)31288-5\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(20)31288-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJoshi SS, Badgwell BD. Current treatment and recent progress in gastric cancer. CA Cancer J Clin. 2021;71(3):264\u0026ndash;79. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3322/caac.21657\u003c/span\u003e\u003cspan address=\"10.3322/caac.21657\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJanjigian YY, Shitara K, Moehler M, et al. First-line nivolumab plus chemotherapy versus chemotherapy alone for advanced gastric, gastro-oesophageal junction, and oesophageal adenocarcinoma (CheckMate 649): a randomised, open-label, phase 3 trial. Lancet. 2021;398(10294):27\u0026ndash;40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(21)00797-2\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(21)00797-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKang YK, Boku N, Satoh T, et al. Nivolumab in patients with advanced gastric or gastro-oesophageal junction cancer refractory to, or intolerant of, at least two previous chemotherapy regimens (ONO-4538-12, ATTRACTION-2): a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet. 2017;390(10111):2461\u0026ndash;71. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0140-6736(17)31827-5\u003c/span\u003e\u003cspan address=\"10.1016/S0140-6736(17)31827-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSmyth EC, Verheij M, Allum W, et al. Gastric cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2016;27(suppl 5):v38\u0026ndash;49. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/annonc/mdw350\u003c/span\u003e\u003cspan address=\"10.1093/annonc/mdw350\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLauren P. The two histological main types of gastric carcinoma: diffuse and so-called intestinal-type carcinoma. An attempt at a histo-clinical classification. Acta Pathol Microbiol Scand. 1965;64:31\u0026ndash;49. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/apm.1965.64.1.31\u003c/span\u003e\u003cspan address=\"10.1111/apm.1965.64.1.31\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSundararaju U, Rajakumar HK. Prognostic value of neutrophil-to-lymphocyte ratio in gastric cancer: Enhancing clinical relevance. World J Gastrointest Oncol. 2025;17(4):103128. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4251/wjgo.v17.i4.103128\u003c/span\u003e\u003cspan address=\"10.4251/wjgo.v17.i4.103128\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang S, Li Z, Wang T et al. Correlation between neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio with prognosis in gastric cancer patients undergoing neoadjuvant chemotherapy: a meta-analysis. \u003cem\u003eBMC Gastroenterol\u003c/em\u003e. 2025;25(1):445. Published 2025 Jul 1. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12876-025-04056-x\u003c/span\u003e\u003cspan address=\"10.1186/s12876-025-04056-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAn S, Eo W, Lee S, Lee YJ. Monocyte-to-lymphocyte ratio as a determinant of survival in patients with gastric cancer undergoing gastrectomy: A cohort study. Med (Baltim). 2023;102(22):e33930. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/MD.0000000000033930\u003c/span\u003e\u003cspan address=\"10.1097/MD.0000000000033930\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKanbur B, Unek IT, Uzun M, Ozturk C, Yarol RC, Balci A. Association of Systemic Inflammatory Response Index and Prognostic Nutritional Index Scores with Sarcopenia in Patients with Metastatic Gastric Cancer. Med (Kaunas). 2025;61(5):785. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/medicina61050785\u003c/span\u003e\u003cspan address=\"10.3390/medicina61050785\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Published 2025 Apr 23.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZuo Y, Sun H, Pan H, Zhao R, Xue Y, Song H. The Gastric Cancer Immune Prognostic Score (GCIPS) Shows Potential in Predicting an Unfavorable Prognosis for Gastric Cancer Patients Undergoing Immune Checkpoint Inhibitor Treatment. Biomedicines. 2024;12(3):491. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/biomedicines12030491\u003c/span\u003e\u003cspan address=\"10.3390/biomedicines12030491\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Published 2024 Feb 22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAmin MB, Edge SB, Greene FL. AJCC Cancer Staging Manual. 8th ed. New York: Springer; 2016.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMantovani A, Allavena P, Sica A, Balkwill F. Cancer-related inflammation. Nature. 2008;454(7203):436\u0026ndash;44. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nature07205\u003c/span\u003e\u003cspan address=\"10.1038/nature07205\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGabrilovich DI, Nagaraj S. Myeloid-derived suppressor cells as regulators of the immune system. Nat Rev Immunol. 2009;9(3):162\u0026ndash;74. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nri2506\u003c/span\u003e\u003cspan address=\"10.1038/nri2506\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGrivennikov SI, Greten FR, Karin M. Immunity, inflammation, and cancer. Cell. 2010;140(6):883\u0026ndash;99. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cell.2010.01.025\u003c/span\u003e\u003cspan address=\"10.1016/j.cell.2010.01.025\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePalumbo JS, Talmage KE, Massari JV, et al. Tumor cell-associated tissue factor and circulating hemostatic factors cooperate to increase metastatic potential through natural killer cell-dependent and-independent mechanisms. Blood. 2007;110(1):133\u0026ndash;41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1182/blood-2007-01-065995\u003c/span\u003e\u003cspan address=\"10.1182/blood-2007-01-065995\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144(5):646\u0026ndash;74. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cell.2011.02.013\u003c/span\u003e\u003cspan address=\"10.1016/j.cell.2011.02.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eColotta F, Allavena P, Sica A, Garlanda C, Mantovani A. Cancer-related inflammation, the seventh hallmark of cancer: links to genetic instability. Carcinogenesis. 2009;30(7):1073\u0026ndash;81. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/carcin/bgp127\u003c/span\u003e\u003cspan address=\"10.1093/carcin/bgp127\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJiang X, Wang J, Deng X, et al. The role of microenvironment in tumor angiogenesis. J Exp Clin Cancer Res. 2020;39(1):204. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13046-020-01709-5\u003c/span\u003e\u003cspan address=\"10.1186/s13046-020-01709-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Published 2020 Sep 30.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFridman WH, Zitvogel L, Saut\u0026egrave;s-Fridman C, Kroemer G. The immune contexture in cancer prognosis and treatment. Nat Rev Clin Oncol. 2017;14(12):717\u0026ndash;34. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nrclinonc.2017.101\u003c/span\u003e\u003cspan address=\"10.1038/nrclinonc.2017.101\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFridman WH, Pag\u0026egrave;s F, Saut\u0026egrave;s-Fridman C, Galon J. The immune contexture in human tumours: impact on clinical outcome. Nat Rev Cancer. 2012;12(4):298\u0026ndash;306. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nrc3245\u003c/span\u003e\u003cspan address=\"10.1038/nrc3245\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Published 2012 Mar 15.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEsmon CT. The interactions between inflammation and coagulation. Br J Haematol. 2005;131(4):417\u0026ndash;30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.1365-2141.2005.05753.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-2141.2005.05753.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHisada Y, Mackman N. Cancer-associated pathways and biomarkers of venous thrombosis. Blood. 2017;130(13):1499\u0026ndash;506. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1182/blood-2017-03-743211\u003c/span\u003e\u003cspan address=\"10.1182/blood-2017-03-743211\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcMillan DC. The systemic inflammation-based Glasgow Prognostic Score: a decade of experience in patients with cancer. Cancer Treat Rev. 2013;39(5):534\u0026ndash;40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ctrv.2012.08.003\u003c/span\u003e\u003cspan address=\"10.1016/j.ctrv.2012.08.003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCoussens LM, Werb Z. Inflammation and cancer. Nature. 2002;420(6917):860\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nature01322\u003c/span\u003e\u003cspan address=\"10.1038/nature01322\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRuf W, Yokota N, Schaffner F. Tissue factor in cancer progression and angiogenesis. Thromb Res. 2010;125(0 2):S36\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0049-3848(10)70010-4\u003c/span\u003e\u003cspan address=\"10.1016/S0049-3848(10)70010-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRoxburgh CS, McMillan DC. Role of systemic inflammatory response in predicting survival in patients with primary operable cancer. Future Oncol. 2010;6(1):149\u0026ndash;63. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2217/fon.09.136\u003c/span\u003e\u003cspan address=\"10.2217/fon.09.136\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJiang AG, Chen HL, Lu HY. The relationship between Glasgow Prognostic Score and serum tumor markers in patients with advanced non-small cell lung cancer. \u003cem\u003eBMC Cancer\u003c/em\u003e. 2015;15:386. Published 2015 May 10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12885-015-1403-x\u003c/span\u003e\u003cspan address=\"10.1186/s12885-015-1403-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDolan RD, McSorley ST, Horgan PG, Laird B, McMillan DC. The role of the systemic inflammatory response in predicting outcomes in patients with advanced inoperable cancer: Systematic review and meta-analysis. Crit Rev Oncol Hematol. 2017;116:134\u0026ndash;46. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.critrevonc.2017.06.002\u003c/span\u003e\u003cspan address=\"10.1016/j.critrevonc.2017.06.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHirahara N, Tajima Y, Fujii Y, et al. Preoperative Prognostic Nutritional Index Predicts Long-term Outcome in Gastric Cancer: A Propensity Score-matched Analysis. Anticancer Res. 2018;38(8):4735\u0026ndash;46. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.21873/anticanres.12781\u003c/span\u003e\u003cspan address=\"10.21873/anticanres.12781\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCancer Genome Atlas Research Network. Comprehensive molecular characterization of gastric adenocarcinoma. Nature. 2014;513(7517):202\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nature13480\u003c/span\u003e\u003cspan address=\"10.1038/nature13480\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Gastric Cancer Immune Prognostic Score, GCIPS, gastric cancer, prognosis","lastPublishedDoi":"10.21203/rs.3.rs-7650071/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7650071/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e This study aimed to validate the preoperative Gastric Cancer Immune Prognostic Score (GCIPS) as a prognostic biomarker in resectable gastric cancer (GC).\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e We retrospectively analyzed 226 GC patients undergoing radical resection. The optimal cutoff value of the CALLY index was determined by ROC curve analysis, and patients were stratified accordingly to assess its prognostic value for RFS and OS.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e The GCIPS was calculated from preoperative blood parameters. Using ROC-derived cutoff (2.840), patients were stratified into high- and low-GCIPS groups. The high-GCIPS group showed significantly poorer tumor differentiation (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Kaplan-Meier analysis revealed that high GCIPS was associated with worse 5-year recurrence-free survival (HR\u0026thinsp;=\u0026thinsp;2.856, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and overall survival (HR\u0026thinsp;=\u0026thinsp;3.222, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Multivariate analysis confirmed GCIPS as an independent predictor for both outcomes after adjusting for TNM stage and differentiation.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e The GCIPS is a robust, independent prognostic biomarker derived from routine blood tests, offering a practical tool for risk stratification and guiding individualized management in GC after radical resection.\u003c/p\u003e","manuscriptTitle":"Preoperative Gastric Cancer Immune Prognostic Score (GCIPS) as a Novel Biomarker for Predicting Survival in Gastric Cancer Patients After Radical Resection ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-06 11:42:54","doi":"10.21203/rs.3.rs-7650071/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":"e354bb9e-d88d-40a7-b7c8-dda3d6d10fa7","owner":[],"postedDate":"October 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-30T09:53:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-06 11:42:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7650071","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7650071","identity":"rs-7650071","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}