Published online Jun 15, 2026. doi: 10.4251/wjgo.v18.i6.117582
Revised: January 5, 2026
Accepted: January 27, 2026
Published online: June 15, 2026
Processing time: 181 Days and 1.7 Hours
Patients with advanced gastric cancer typically exhibit a dismal prognosis. Although combined immunotherapy and chemotherapy have been shown to improve survival in a subset of patients, substantial interindividual heterogeneity in therapeutic response persists, and reliable, easily obtainable prognostic bio
To assess baseline CAR’s prognostic value and develop a model for guiding personalized immunochemotherapy in advanced gastric cancer.
Retrospectively, we collected clinical data from 200 advanced gastric cancer patients treated with first-line immunochemotherapy at the Affiliated Hospital of Xuzhou Medical University. The optimal CAR cutoff was determined by receiver operating characteristic curve (ROC) analysis, stratifying patients into high and low CAR groups. Survival was assessed using the Kaplan-Meier method and compared with the log-rank test. Cox regression evaluated CAR’s association with progression-free survival (PFS) and overall survival (OS). A prognostic nomogram was then constructed based on significant factors.
ROC curve analysis showed that the baseline CAR had an area under the curve of 0.80 [95% confidence intervals (CI): 0.76-0.85] for predicting OS, with an optimal cutoff value of 0.21. Based on this threshold, patients were stratified into a low CAR group (CAR < 0.21; n = 120) and a high CAR group (CAR ≥ 0.21; n = 80). The low CAR group exhibited a significantly longer median PFS of 12.3 months (95%CI: 9.5-21.0) compared with 7.2 months (95%CI: 6.2-9.8) in the high CAR group (P = 0.0023). Similarly, the median OS was markedly prolonged in the low CAR group (20.4 months; 95%CI: 18.5-25.1) relative to the high CAR group (14.3 months; 95%CI: 13.4-16.1; P < 0.001). The low CAR group also demonstrated superior objective response rate (ORR) and disease control rate (DCR), at 59.17% and 79.17%, respectively, compared with 45.00% and 66.25% in the high CAR group (P = 0.049; P = 0.041). Multivariate Cox regression analysis identified baseline CAR, programmed death-ligand 1 expression, mismatch repair status, tumor stage, and peritoneal metastasis as independent prognostic factors for both PFS and OS. A prognostic column-plot model integrating these variables exhibited robust discriminative ability and calibration in both the training and validation cohorts, with C-indices of 0.80 and 0.82 for PFS, and 0.78 and 0.73 for OS, respectively.
The baseline CAR index is an independent prognostic factor for long-term outcomes in advanced gastric cancer patients treated with combination immunotherapy and chemotherapy. Lower CAR levels correlate with improved PFS, OS, and higher ORR and DCR. The calibration curve model, incorporating CAR and clinical-pathological factors, demonstrates strong predictive performance, providing valuable insights for prognosis and personalized treatment.
Core Tip: The baseline C-reactive protein/albumin ratio (CAR) is a significant independent prognostic factor for advanced gastric cancer patients receiving combination immunotherapy and chemotherapy, with lower CAR levels associated with improved progression-free survival, overall survival, and higher objective response and disease control rates. By in
- Citation: Mei S, Ye ML, Hao QL, Li GC, Yao ZY, Wang HY. Prognostic significance of baseline C-reactive protein/albumin ratio for long-term outcomes in advanced gastric cancer under immunochemotherapy. World J Gastrointest Oncol 2026; 18(6): 117582
- URL: https://www.wjgnet.com/1948-5204/full/v18/i6/117582.htm
- DOI: https://dx.doi.org/10.4251/wjgo.v18.i6.117582
Gastric cancer, one of the most prevalent malignancies of the digestive system globally, remains a leading cause of cancer-related mortality[1,2]. Its incidence and mortality rates are particularly high in East Asia[1,3]. Due to the fact that, most patients are diagnosed at locally advanced or metastatic stages, the limited number of candidates for curative surgery results in a generally poor prognosis for advanced gastric cancer[2,4]. Recent advancements in first-line treatment, combining immune checkpoint inhibitors with chemotherapy, have improved survival outcomes for certain patients[5-7]. However, significant interindividual variations in treatment response persist, underscoring the urgent need for accessible and reliable biomarkers to predict therapeutic efficacy and assess prognosis[8-10].
The C-reactive protein (CRP)/albumin (ALB) ratio (CAR) is a comprehensive biomarker that reflects systemic inflammatory responses and nutritional status[11-13]. As an acute-phase reactant, CRP indicates the severity of systemic inflammation, while serum ALB levels are associated with nutritional status, immune function, and chronic inflammation[11,14]. CAR integrates both inflammatory and nutritional signals, and has been shown to be a strong prognostic indicator in various solid tumors, including hepatocellular carcinoma, lung cancer, and colorectal cancer[13,15-18]. Emerging evidence suggests that elevated CAR levels are linked to tumor progression, an immune-suppressive microenvironment, and poor survival outcomes, positioning CAR as a promising biomarker for predicting the efficacy of immunotherapy[19-22].
Although existing studies have explored the prognostic value of CAR in gastric cancer, evidence regarding its pre
Against this background, the aim of this study is to evaluate the long-term prognostic value of baseline CAR index in advanced gastric cancer patients undergoing combination immunotherapy and chemotherapy. Additionally, we aim to develop a nomogram-based predictive model centered on CAR to provide a more reliable and accessible reference for prognosis assessment and personalized treatment strategies in advanced gastric cancer.
This study is a single-center, retrospective analysis aimed at evaluating the efficacy and prognosis of sintilimab combined with chemotherapy in human epidermal growth factor receptor 2-negative (HER-2-negative), unresectable locally advanced or metastatic gastric cancer and gastroesophageal junction adenocarcinoma. The study was conducted at the Affiliated Hospital of Xuzhou Medical University, spanning from January 2021 to December 2024. The inclusion criteria were as follows: (1) Histologically or cytologically confirmed unresectable or metastatic gastric cancer or gastroesophageal junction adenocarcinoma; (2) Age ≥ 18 years; (3) HER-2 negative status; (4) An Eastern Cooperative Oncology Group (ECOG) performance status of 0-1; (5) Presence of measurable lesions according to response evaluation criteria in solid tumors version (RECIST v1.1) criteria; (6) Receipt of at least two cycles of sintilimab combined with chemotherapy; and (7) Availability of complete follow-up data. The exclusion criteria included: (1) Patients who had previously received systemic therapy; (2) Those with severe dysfunction of the heart, liver, kidneys, or other vital organs; (3) Hemoglobin < 80 g/L or platelets < 75 × 109/L; (4) Presence of autoimmune diseases; (5) Known allergy to the study drug; (6) Patients with other advanced malignancies; (7) Patients with evidence of active infection at baseline; (8) Patients with active inflammatory diseases or chronic inflammatory conditions; and (9) Patients with known liver cirrhosis or clinically significant chronic liver disease. The study was approved by the Ethics Committee of the Affiliated Hospital of Xuzhou Medical University (Ethical approval number: No. XYFY2025-KL385-01). Given the retrospective nature of the study, the Ethics Committee waived the requirement for informed consent, and the use of data complied with relevant legal and regulatory requirements.
The CAR index is calculated using the following formula: CAR = CRP concentration (mg/L)/serum ALB concentration (g/L). Both CRP and ALB concentrations are baseline hematological parameters, collected within one week prior to the initiation of combination immunotherapy and chemotherapy. CRP levels were measured using chemiluminescent immunoassay, a method known for its high sensitivity and accuracy in quantifying CRP levels in serum. ALB concentration was determined using an automated biochemical analyzer, ensuring stable and standardized results in accordance with clinical laboratory testing requirements.
In this study, overall survival (OS) in advanced gastric cancer patients receiving combination immunotherapy and chemotherapy was the primary endpoint. A receiver operating characteristic (ROC) curve was constructed based on the baseline CAR values obtained prior to treatment. The area under the curve (AUC) was 0.80 [95% confidence intervals (CI): 0.76-0.85], and the optimal cutoff value was determined to be 0.21 by maximizing the product of sensitivity (75.20%) and specificity (84.25%).
Data were collected through the electronic medical record system, which included patients’ baseline demographic information (e.g., gender, age, body mass index, ECOG performance status) as well as baseline hematological parameters obtained within one week prior to initiation of combination immunotherapy and chemotherapy [e.g., neutrophil and lymphocyte counts, CRP, ALB levels, carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA19-9)]. Addi
All enrolled patients received sintilimab combined with chemotherapy. Sintilimab was administered as a 200 mg intravenous infusion every 3 weeks. The chemotherapy regimen was selected based on the patient’s condition and included one of the following two options: Capecitabine was administered orally at a dose of 1000 mg/m2 twice daily after meals for 14 consecutive days (Days 1-14), with oxaliplatin given as a 130 mg/m2 intravenous infusion on Day 1. This regimen was repeated every 3 weeks for a total of 6 cycles. Alternatively, Tegafur-Uracil was administered orally at a dose based on body surface area (BSA): 40 mg for BSA < 1.25 m2, 50 mg for BSA ≥ 1.25 m2 but < 1.5 m2, and 60 mg for BSA ≥ 1.5 m2, twice daily after meals for Days 1-14, with oxaliplatin administered as a 130 mg/m2 intravenous infusion on Day 1. This regimen was also repeated every 3 weeks for 6 cycles. For patients without disease progression, maintenance therapy consisted of sintilimab combined with either Tegafur-Uracil or capecitabine, continued until disease progression, patient death, or intolerable adverse events. All patients were followed up until death or the data cutoff date (December 31, 2024).
The efficacy of this study was assessed according to the RECIST v1.1, focusing on both recent treatment outcomes and survival endpoints. Recent treatment outcomes were classified into four categories: Complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD). CR was defined as the complete disappearance of all measurable lesions, sustained for at least 4 weeks; PR was defined as a reduction of at least 30% in the maximum diameter of at least one measurable lesion, sustained for at least 4 weeks; SD referred to cases where tumor size did not meet the criteria for PR or PD, sustained for at least 4 weeks; and PD was defined as an increase of more than 20% in the size of the tumor or the appearance of new lesions. Based on these criteria, the objective response rate (ORR) and disease control rate (DCR) were calculated, with ORR representing the proportion of patients with CR or PR, and DCR representing the proportion of patients with CR, PR, or SD.
Survival outcomes included progression-free survival (PFS) and OS. PFS was defined as the time from the initiation of treatment until the first occurrence of disease progression or death, used to assess the effectiveness of disease control post-treatment. OS was defined as the time from the initiation of treatment until death from any cause. All efficacy assessments were conducted through regular imaging examinations, evaluated by independent radiologists, with follow-up imaging performed at weeks 6 and 12 post-treatment, and every three months thereafter, until disease progression or patient death. The final follow-up occurred in December 2024, with a median overall follow-up duration of 20 months.
All statistical analyses were performed using R software (version 4.5.1). Continuous variables were described using median values, and intergroup comparisons were conducted using the Wilcoxon rank-sum test. Categorical variables were compared using the χ2 test. Survival curves were plotted using the Kaplan-Meier method and compared with the Log-rank test. Cox proportional hazards models were used to identify independent prognostic factors affecting PFS and OS, with hazard ratios (HR) and 95%CI reported. To assess the collinearity among the biological upstream variables of CRP and ALB, the variance inflation factor (VIF) was calculated. All VIF values were found to be below 5, indicating that there was no significant collinearity among the selected variables. A nomogram model was constructed based on the results of multivariate analysis, and its predictive performance was evaluated using Harrell’s C-index, ROC curve analysis, AUC, and bootstrap calibration (1000 iterations). All statistical tests were two-sided, and a P value < 0.05 was considered statistically significant.
This study is a single-center retrospective analysis that included 200 advanced gastric cancer patients who received first-line combination immunotherapy and chemotherapy at the Affiliated Hospital of Xuzhou Medical University. Based on the optimal cutoff value derived from the ROC curve, patients were stratified into two groups: A low CAR index group (n = 120) and a high CAR index group (n = 80). Table 1 summarizes the baseline characteristics of the patients, including general demographic information (e.g., gender, age, body mass index, ECOG performance status) and hematological parameters obtained within one week prior to the initiation of immunochemotherapy (e.g., neutrophil and lymphocyte counts, CRP concentration, ALB concentration, CEA, CA199). Additionally, tumor characteristics were collected, including tumor location, tumor staging, tumor differentiation, metastatic sites, PD-L1 expression status, MMR status, and EBV infection status. Significant differences between the two groups were observed in ALB concentration, CRP levels, and CAR index (P < 0.001).
| Variables | Total (n = 200) | High CAR (n = 80) | Low CAR (n = 120) | Statistic | P value |
| Neutrophil | 3.91 ± 2.32 | 4.05 ± 2.44 | 3.81 ± 2.24 | t = 0.72 | 0.475 |
| Lymphocyte | 1.38 ± 0.59 | 1.36 ± 0.67 | 1.39 ± 0.53 | t = -0.37 | 0.713 |
| Albumin | 39.24 ± 5.73 | 36.59 ± 6.29 | 41.01 ± 4.55 | t = -5.42 | < 0.001 |
| CRP | 17.33 ± 27.09 | 39.74 ± 31.52 | 2.38 ± 2.28 | t = 10.58 | < 0.001 |
| CAR | 0.49 ± 0.79 | 1.13 ± 0.94 | 0.06 ± 0.06 | t = 10.14 | < 0.001 |
| Sex | χ2 = 1.42 | 0.233 | |||
| Female | 75 (37.50) | 26 (32.50) | 49 (40.83) | ||
| Male | 125 (62.50) | 54 (67.50) | 71 (59.17) | ||
| Age | χ2 = 0.42 | 0.515 | |||
| < 60 years | 78 (39.00) | 29 (36.25) | 49 (40.83) | ||
| ≥ 60 years | 122 (61.00) | 51 (63.75) | 71 (59.17) | ||
| BMI | χ2 = 1.00 | 0.318 | |||
| < 25 | 166 (83.00) | 69 (86.25) | 97 (80.83) | ||
| ≥ 25 | 34 (17.00) | 11 (13.75) | 23 (19.17) | ||
| CEA | χ2 = 2.54 | 0.111 | |||
| < 3 | 79 (39.50) | 37 (46.25) | 42 (35.00) | ||
| ≥ 3 | 121 (60.50) | 43 (53.75) | 78 (65.00) | ||
| CA199 | χ2 = 1.03 | 0.309 | |||
| < 37 | 126 (63.00) | 47 (58.75) | 79 (65.83) | ||
| ≥ 37 | 74 (37.00) | 33 (41.25) | 41 (34.17) | ||
| ECOG PS | χ2 = 2.08 | 0.149 | |||
| 0 | 72 (36.00) | 24 (30.00) | 48 (40.00) | ||
| 1 | 128 (64.00) | 56 (70.00) | 72 (60.00) | ||
| Staging | χ2 = 0.48 | 0.487 | |||
| III | 91 (45.50) | 34 (42.50) | 57 (47.50) | ||
| IV | 109 (54.50) | 46 (57.50) | 63 (52.50) | ||
| Differentiation | χ2 = 2.08 | 0.149 | |||
| Poorly/undifferentiated | 180 (90.00) | 75 (93.75) | 105 (87.50) | ||
| Medium/high | 20 (10.00) | 5 (6.25) | 15 (12.50) | ||
| Site | χ2 = 0.00 | 0.949 | |||
| Gastric and esophageal binding | 58 (29.00) | 23 (28.75) | 35 (29.17) | ||
| Stomach | 142 (71.00) | 57 (71.25) | 85 (70.83) | ||
| Peritoneal metastasis | χ2 = 2.84 | 0.092 | |||
| No | 157 (78.50) | 58 (72.50) | 99 (82.50) | ||
| Yes | 43 (21.50) | 22 (27.50) | 21 (17.50) | ||
| Liver metastasis | χ2 = 3.54 | 0.060 | |||
| No | 156 (78.00) | 57 (71.25) | 99 (82.50) | ||
| Yes | 44 (22.00) | 23 (28.75) | 21 (17.50) | ||
| EBV status | χ2 = 1.66 | 0.198 | |||
| No-infect | 187 (93.50) | 77 (96.25) | 110 (91.67) | ||
| Infect | 13 (6.50) | 3 (3.75) | 10 (8.33) | ||
| PD-L1 expression | χ2 = 2.29 | 0.130 | |||
| CPS < 5 | 87 (43.50) | 40 (50.00) | 47 (39.17) | ||
| CPS ≥ 5 | 113 (56.50) | 40 (50.00) | 73 (60.83) | ||
| MMR status | χ2 = 2.24 | 0.135 | |||
| pMMR | 168 (84.00) | 71 (88.75) | 97 (80.83) | ||
| dMMR | 32 (16.00) | 9 (11.25) | 23 (19.17) |
Table 2 presents the treatment response data for the two groups of patients. In the low CAR index group, 7 patients achieved CR (5.83%), 64 patients achieved PR (53.33%), 24 patients had SD (20.00%), and 25 patients experienced PD (20.83%). In the high CAR index group, 4 patients achieved CR (5.00%), 32 patients achieved PR (40.00%), 17 patients had SD (21.25%), and 27 patients experienced PD (33.75%). The ORR in the low CAR index group was modestly but significantly higher than that in the high CAR index group (59.17% vs 45.00%, P = 0.049), and the DCR was also modestly but significantly higher (79.17% vs 66.25%, P = 0.041).
| Variables | Total (n = 200) | High (n = 80) | Low (n = 120) | Statistic | P value |
| CR | |||||
| No | 189 (94.50) | 76 (95.00) | 113 (94.17) | ||
| Yes | 11 (5.50) | 4 (5.00) | 7 (5.83) | ||
| PR | |||||
| No | 104 (52.00) | 48 (60.00) | 56 (46.67) | ||
| Yes | 96 (48.00) | 32 (40.00) | 64 (53.33) | ||
| SD | |||||
| No | 159 (79.50) | 63 (78.75) | 96 (80.00) | ||
| Yes | 41 (20.50) | 17 (21.25) | 24 (20.00) | ||
| PD | |||||
| No | 148 (74.00) | 53 (66.25) | 95 (79.17) | ||
| Yes | 52 (26.00) | 27 (33.75) | 25 (20.83) | ||
| ORR | χ2 = 3.87 | 0.049 | |||
| No | 93 (46.50) | 44 (55.00) | 49 (40.83) | ||
| Yes | 107 (53.50) | 36 (45.00) | 71 (59.17) | ||
| DCR | χ2 = 4.16 | 0.041 | |||
| No | 52 (26.00) | 27 (33.75) | 25 (20.83) | ||
| Yes | 148 (74.00) | 53 (66.25) | 95 (79.17) |
Figure 1A shows that the median PFS in the low CAR index group was 12.3 months (95%CI: 9.5-21.0), significantly longer than the 7.2 months (95%CI: 6.2-9.8) observed in the high CAR index group (P = 0.0023). Regarding OS, the median OS in the low CAR index group was 20.4 months (95%CI: 18.5-25.1), significantly longer than the 14.3 months (95%CI: 13.4-16.1) in the high CAR index group (P < 0.0001) (Figure 1B).
Univariate Cox regression analysis for PFS revealed that the CAR index, MMR status, PD-L1 expression, tumor stage, tumor differentiation, and peritoneal metastasis were significantly associated with PFS. Further multivariate Cox regression analysis identified the following independent prognostic factors for PFS: Low CAR index (HR = 0.67, 95%CI: 0.46-0.97, P = 0.034), high PD-L1 expression (HR = 0.42, 95%CI: 0.28-0.62, P < 0.001), deficient MMR (dMMR) (HR = 0.34, 95%CI: 0.18-0.65, P = 0.001), advanced tumor stage (HR = 1.67, 95%CI: 1.10-2.52, P = 0.015), and peritoneal metastasis (HR = 1.95, 95%CI: 1.31-2.88, P < 0.001) (Table 3).
| Variables | Univariate | Multivariate | ||
| P value | HR (95%CI) | P value | HR (95%CI) | |
| PD-L1 expression (CPS < 5 vs CPS ≥ 5) | < 0.001 | 0.34 (0.24-0.50) | < 0.001 | 0.42 (0.28-0.62) |
| MMR status (pMMR vs dMMR) | < 0.001 | 0.25 (0.13-0.48) | 0.001 | 0.34 (0.18-0.65) |
| CAR (high vs low) | 0.003 | 0.57 (0.40-0.82) | 0.034 | 0.67 (0.46-0.97) |
| Sex (male vs female) | 0.908 | 1.02 (0.70-1.49) | ||
| Age (≥ 60 vs < 60), years | 0.114 | 1.36 (0.93-2.00) | ||
| BMI (< 25 vs ≥ 25), kg/m2 | 0.813 | 1.06 (0.66-1.70) | ||
| CEA (< 3 vs ≥ 3), ng/mL | 0.853 | 1.04 (0.72-1.50) | ||
| CA199 (< 37 vs ≥ 37), ng/mL | 0.365 | 1.19 (0.82-1.73) | ||
| ECOG (0 vs 1) | 0.821 | 1.04 (0.72-1.52) | ||
| Staging (III vs IV) | < 0.001 | 2.55 (1.74-3.75) | 0.015 | 1.67 (1.10-2.52) |
| Differentiation (poorly/undifferentiated vs medium/high) | 0.041 | 0.45 (0.21-0.97) | 0.235 | 0.62 (0.29-1.36) |
| Site (gastric and esophageal binding vs stomach) | 0.772 | 0.94 (0.63-1.40) | ||
| Peritoneal metastasis (no vs yes) | < 0.001 | 2.08 (1.44-3.00) | < 0.001 | 1.95 (1.31-2.88) |
| Liver metastasis (no vs yes) | 0.072 | 1.47 (0.97-2.25) | ||
| EBV status (infect vs no-infect) | 0.099 | 0.47 (0.19-1.15) | ||
| Neutrophil | 0.280 | 1.04 (0.97-1.11) | ||
| Lymphocyte | 0.971 | 1.01 (0.75-1.36) | ||
Univariate Cox regression analysis for OS showed that the CAR index, MMR status, PD-L1 expression, tumor stage and peritoneal metastasis were significantly associated with OS. Further multivariate Cox regression analysis identified the following independent prognostic factors for OS: Low CAR index (HR = 0.51, 95%CI: 0.35-0.73, P < 0.001), high PD-L1 expression (HR = 0.45, 95%CI: 0.30-0.66, P < 0.001), dMMR (HR = 0.34, 95%CI: 0.18-0.64, P = 0.001), advanced tumor stage (HR = 1.70, 95%CI: 1.12-2.58, P = 0.012), and peritoneal metastasis (HR = 1.73, 95%CI: 1.17-2.54, P = 0.005) (Table 4).
| Variables | Univariate | Multivariate | ||
| P value | HR (95%CI) | P value | HR (95%CI) | |
| PD-L1 expression (CPS < 5 vs CPS ≥ 5) | < 0.001 | 0.34 (0.23-0.49) | < 0.001 | 0.45 (0.30-0.66) |
| MMR status (pMMR vs dMMR) | < 0.001 | 0.22 (0.12-0.42) | 0.001 | 0.34 (0.18-0.64) |
| CAR (high vs low) | < 0.001 | 0.45 (0.31-0.64) | < 0.001 | 0.51 (0.35-0.73) |
| Sex (male vs female) | 0.595 | 0.91 (0.63-1.31) | ||
| Age (≥ 60 vs < 60), years | 0.702 | 1.07 (0.74-1.55) | ||
| BMI (< 25 vs ≥ 25), kg/m2 | 0.408 | 0.82 (0.51-1.31) | ||
| CEA (< 3 vs ≥ 3), ng/mL | 0.644 | 1.09 (0.76-1.57) | ||
| CA199 (< 37 vs ≥ 37), ng/mL | 0.544 | 1.12 (0.78-1.62) | ||
| ECOG (0 vs 1) | 0.326 | 1.21 (0.83-1.76) | ||
| Staging (III vs IV) | < 0.001 | 2.69 (1.83-3.94) | 0.012 | 1.70 (1.12-2.58) |
| Differentiation (poorly/undifferentiated vs medium/high) | 0.131 | 0.59 (0.30-1.17) | ||
| Site (gastric and esophageal binding vs stomach) | 0.823 | 1.05 (0.70-1.57) | ||
| Peritoneal metastasis (no vs yes) | < 0.001 | 2.02 (1.41-2.91) | 0.005 | 1.73 (1.17-2.54) |
| Liver metastasis (no vs yes) | 0.064 | 1.50 (0.98-2.29) | ||
| EBV status (infect vs no-infect) | 0.196 | 0.60 (0.28-1.30) | ||
| Neutrophil | 0.178 | 1.05 (0.98-1.12) | ||
| Lymphocyte | 0.368 | 0.87 (0.65-1.17) | ||
Adverse events (AEs) were assessed according to the Common Terminology Criteria for Adverse Events (CTCAE 5.0) by the National Cancer Institute, with grading from 1 to 4. Common adverse events included leukopenia, anemia, neutropenia, nausea, and fever (Table 5). All patients received standard supportive care during treatment, including infection control, antiemetics, and blood transfusions. Although no patient permanently discontinued treatment due to adverse events, a proportion of patients experienced grade ≥ 3 adverse events, with some patients requiring temporary treatment delays or dose adjustments according to protocol specified management guidelines to ensure safety and tolerability. The incidence of adverse events at each grade did not differ significantly between the two groups (P > 0.05).
| Variables | Total (n = 200) | High (n = 80) | Low (n = 120) | χ2 value | P value |
| Leukopenia | 79 (39.50) | 34 (42.50) | 45 (37.50) | 0.50 | 0.479 |
| Anemia | 83 (41.50) | 32 (40.00) | 51 (42.50) | 0.12 | 0.725 |
| Neutropenia | 80 (40.00) | 32 (40.00) | 48 (40.00) | 0.00 | 1.000 |
| Thrombocytopenia | 72 (36.00) | 33 (41.25) | 39 (32.50) | 1.60 | 0.207 |
| Nausea | 61 (30.50) | 26 (32.50) | 35 (29.17) | 0.25 | 0.616 |
| Pyrexia | 51 (25.50) | 23 (28.75) | 28 (23.33) | 0.74 | 0.389 |
| Elevated ALT | 37 (18.50) | 18 (22.50) | 19 (15.83) | 1.41 | 0.234 |
| Elevated AST | 41 (20.50) | 17 (21.25) | 24 (20.00) | 0.05 | 0.830 |
| Hypothyroidism | 37 (18.50) | 15 (18.75) | 22 (18.33) | 0.01 | 0.941 |
| Diarrhea | 39 (19.50) | 19 (23.75) | 20 (16.67) | 1.53 | 0.215 |
| Hypertension | 26 (13.00) | 13 (16.25) | 13 (10.83) | 1.25 | 0.264 |
| Pneumonitis | 22 (11.00) | 10 (12.50) | 12 (10.00) | 0.31 | 0.580 |
| Proteinuria | 23 (11.50) | 10 (12.50) | 13 (10.83) | 0.13 | 0.717 |
| Hyperbilirubinemi | 18 (9.00) | 7 (8.75) | 11 (9.17) | 0.01 | 0.920 |
| Fatigue | 26 (13.00) | 12 (15.00) | 14 (11.67) | 0.47 | 0.492 |
| ≥ 3 grades: Thrombocytopenia | 35 (17.50) | 16 (20.00) | 19 (15.83) | 0.58 | 0.447 |
| ≥ 3 grades: Neutropenia | 25 (12.50) | 12 (15.00) | 13 (10.83) | 0.76 | 0.383 |
| ≥ 3 grades: Leukopenia | 20 (10.00) | 7 (8.75) | 13 (10.83) | 0.23 | 0.630 |
| ≥ 3 grades: Anemia | 15 (7.50) | 5 (6.25) | 10 (8.33) | 0.30 | 0.584 |
| ≥ 3 grades: Diarrhea | 6 (3.00) | 3 (3.75) | 3 (2.50) | 0.01 | 0.933 |
| ≥ 3 grades: Nausea | 9 (4.50) | 4 (5.00) | 5 (4.17) | 0.00 | 1.000 |
| ≥ 3 grades: Elevated ALT | 7 (3.50) | 3 (3.75) | 4 (3.33) | 0.00 | 1.000 |
| ≥ 3 grades: Elevated AST | 6 (3.00) | 2 (2.50) | 4 (3.33) | 0.00 | 1.000 |
| ≥ 3 grades: Hypothyroidism | 5 (2.50) | 2 (2.50) | 3 (2.50) | 0.00 | 1.000 |
| ≥ 3 grades: Hypertension | 4 (2.00) | 2 (2.50) | 2 (1.67) | 0.00 | 1.000 |
| ≥ 3 grades: Pneumonitis | 3 (1.50) | 2 (2.50) | 1 (0.83) | 0.13 | 0.722 |
| ≥ 3 grades: Hyperbilirubinemia | 4 (2.00) | 2 (2.50) | 2 (1.67) | 0.00 | 1.000 |
Through multivariate Cox regression analysis, we identified variables significantly associated with patient survival (OS or PFS) (P < 0.05) and calculated the regression coefficients (β values) for each variable. Using the “rms” package in R software (version 4.5.1), the regression coefficients were converted into corresponding points, with each variable’s score being proportional to its risk effect. The higher the risk effect, the higher the score. By summing the scores of all variables, we obtained the total score for each patient. Specifically, independent prognostic factors for PFS and OS included CAR index, MMR status, PD-L1 expression, tumor stage, and peritoneal metastasis. These factors were used to construct prognostic models for predicting PFS at 9, 12, and 15 months (Figure 2). Similarly, based on the independent prognostic factors for OS, we constructed prognostic models to predict survival at 12, 15, and 18 months (Figure 3).
To evaluate the predictive performance of the nomogram model, all patients were randomly divided into a training set (140 cases) and a validation set (60 cases) in a 7:3 ratio. The baseline characteristics of the two groups are presented in Table 6. The nomogram model was initially constructed using the training set, followed by internal validation to assess its efficacy. Internal validation was performed using the Bootstrap resampling method (1000 iterations), and the model’s performance was evaluated using the C-index, ROC curve, and calibration curve.
| Variables | Total (n = 200) | Test (n = 60) | Train (n = 140) | Statistic | P value |
| Neutrophil | 3.91 ± 2.32 | 4.21 ± 3.06 | 3.78 ± 1.92 | t = 1.00 | 0.320 |
| Lymphocyte | 1.38 ± 0.59 | 1.40 ± 0.69 | 1.37 ± 0.54 | t = 0.35 | 0.724 |
| PD-L1 expression | χ2 = 0.12 | 0.732 | |||
| CPS < 5 | 87 (43.50) | 25 (41.67) | 62 (44.29) | ||
| CPS ≥ 5 | 113 (56.50) | 35 (58.33) | 78 (55.71) | ||
| MMR status | χ2 = 1.20 | 0.274 | |||
| pMMR | 168 (84.00) | 53 (88.33) | 115 (82.14) | ||
| dMMR | 32 (16.00) | 7 (11.67) | 25 (17.86) | ||
| CAR | χ2 = 0.00 | 1.000 | |||
| High | 80 (40.00) | 24 (40.00) | 56 (40.00) | ||
| Low | 120 (60.00) | 36 (60.00) | 84 (60.00) | ||
| Sex | χ2 = 0.63 | 0.426 | |||
| Female | 75 (37.50) | 20 (33.33) | 55 (39.29) | ||
| Male | 125 (62.50) | 40 (66.67) | 85 (60.71) | ||
| Age | χ2 = 0.58 | 0.448 | |||
| < 60 years | 78 (39.00) | 21 (35.00) | 57 (40.71) | ||
| ≥ 60 years | 122 (61.00) | 39 (65.00) | 83 (59.29) | ||
| BMI | χ2 = 1.73 | 0.189 | |||
| < 25 | 166 (83.00) | 53 (88.33) | 113 (80.71) | ||
| ≥ 25 | 34 (17.00) | 7 (11.67) | 27 (19.29) | ||
| CEA | χ2 = 0.01 | 0.925 | |||
| < 3 | 79 (39.50) | 24 (40.00) | 55 (39.29) | ||
| ≥ 3 | 121 (60.50) | 36 (60.00) | 85 (60.71) | ||
| CA199 | χ2 = 2.76 | 0.097 | |||
| < 37 | 126 (63.00) | 43 (71.67) | 83 (59.29) | ||
| ≥ 37 | 74 (37.00) | 17 (28.33) | 57 (40.71) | ||
| ECOG PS | χ2 = 0.70 | 0.403 | |||
| 0 | 72 (36.00) | 19 (31.67) | 53 (37.86) | ||
| 1 | 128 (64.00) | 41 (68.33) | 87 (62.14) | ||
| Staging | χ2 = 0.16 | 0.687 | |||
| III | 91 (45.50) | 26 (43.33) | 65 (46.43) | ||
| IV | 109 (54.50) | 34 (56.67) | 75 (53.57) | ||
| Differentiation | χ2 = 0.26 | 0.607 | |||
| Poorly/undifferentiated | 180 (90.00) | 55 (91.67) | 125 (89.29) | ||
| Medium/high | 20 (10.00) | 5 (8.33) | 15 (10.71) | ||
| Site | χ2 = 0.04 | 0.838 | |||
| Gastric and esophageal binding | 58 (29.00) | 18 (30.00) | 40 (28.57) | ||
| Stomach | 142 (71.00) | 42 (70.00) | 100 (71.43) | ||
| Peritoneal metastasis | χ2 = 0.17 | 0.679 | |||
| No | 157 (78.50) | 46 (76.67) | 111 (79.29) | ||
| Yes | 43 (21.50) | 14 (23.33) | 29 (20.71) | ||
| Liver metastasis | χ2 = 0.20 | 0.655 | |||
| No | 156 (78.00) | 48 (80.00) | 108 (77.14) | ||
| Yes | 44 (22.00) | 12 (20.00) | 32 (22.86) | ||
| EBV status | χ2 = 0.06 | 0.802 | |||
| No-infect | 187 (93.50) | 57 (95.00) | 130 (92.86) | ||
| infect | 13 (6.50) | 3 (5.00) | 10 (7.14) |
For PFS prediction, the C-index was 0.80 (95%CI: 0.74-0.87) in the training set and 0.82 (95%CI: 0.79-0.86) in the validation set, demonstrating good discriminative ability. ROC curve analysis further confirmed the model’s predictive accuracy, with AUC values in the training set of 0.85 (95%CI: 0.79-0.92) at 9 months, 0.87 (95%CI: 0.81-0.94) at 12 months, and 0.86 (95%CI: 0.78-0.94) at 15 months. For the validation set, the AUC values were 0.77 (95%CI: 0.64-0.90) at 9 months, 0.76 (95%CI: 0.61-0.91) at 12 months, and 0.77 (95%CI: 0.58-0.97) at 15 months (Figure 4A and B).
For OS prediction, the C-index was 0.78 (95%CI: 0.74-0.83) in the training set and 0.73 (95%CI: 0.68-0.79) in the validation set. ROC curve analysis showed AUC values of 0.80 (95%CI: 0.71-0.89) at 12 months, 0.82 (95%CI: 0.74-0.90) at 15 months, and 0.83 (95%CI: 0.75-0.92) at 18 months in the training set. In the validation set, the AUC values were 0.75 (95%CI: 0.60-0.90) at 12 months, 0.80 (95%CI: 0.67-0.93) at 15 months, and 0.78 (95%CI: 0.65-0.92) at 18 months (Figure 4C and D).
The calibration curve demonstrated excellent agreement between the predicted survival probabilities and the actual observed outcomes, further validating the high predictive accuracy of the nomogram model in clinical application (Figure 5). These results suggest that this model possesses strong prognostic predictive ability and can be effectively applied for prognostic assessment in clinical patients.
This study evaluated the prognostic value of the baseline CAR index in predicting the long-term outcomes of advanced gastric cancer patients receiving combination immunotherapy and chemotherapy. The results showed that patients with a low CAR index had better PFS, OS, DCR, and ORR compared to those with a high CAR index. Further Cox regression analysis confirmed that the CAR index was significantly associated with both PFS and OS, and that it interacted with other independent prognostic factors, such as PD-L1 expression and MMR protein expression, to build a prognostic model with strong clinical predictive performance. These findings suggest that the baseline CAR index can serve as an independent predictor for long-term prognosis in advanced gastric cancer patients undergoing combination immunotherapy and chemotherapy.
The immune microenvironment in advanced gastric cancer is typically characterized by immune evasion and systemic inflammation[8,28]. Low ALB levels often indicate malnutrition and impaired immune function, while elevated CRP levels reflect widespread inflammatory responses[14]. A high CAR index typically reflects an enhanced immunosuppressive state, which may be associated with poor responses to immunotherapy[20,21]. Previous studies have de
Additionally, a high CAR index is often accompanied by systemic inflammation. By activating pro-inflammatory signaling pathways, such as nuclear factor kappa-B and Janus kinase/signal transducer and activator of transcription, it promotes the release of immunosuppressive factors like interleukin-6 and tumor necrosis factor-alpha, which not only support tumor cell growth but also inhibit immune cell function, further diminishing the effectiveness of immunotherapy[28,29]. Elevated CAR levels, which correlate with decreased ALB levels, suggest an exacerbation of immune tolerance, posing an additional challenge to immunotherapy. Therefore, the CAR index significantly impacts the immune microenvironment, activates pro-inflammatory pathways, and facilitates immune evasion mechanisms, making it an important biomarker for predicting the response to combination immunotherapy and chemotherapy[30].
Compared to previous studies, the CAR index offers significant advantages as a predictive marker for immunotherapy. Traditional inflammatory biomarkers, such as CRP or ALB, generally reflect only a single aspect of the immune microenvironment and fail to comprehensively reveal the complex interactions between immune evasion, immune tolerance, and systemic inflammation[11,13]. The CAR index, by integrating CRP and ALB levels, provides a more precise reflection of the immune status and systemic inflammatory response in cancer patients. It reveals the dual effects of immune suppression and inflammation, offering more sensitive and specific prognostic information[15,17]. Furthermore, the CAR index can help identify the efficacy differences between various immunotherapeutic approaches (e.g., immune checkpoint inhibitors and cell therapies), thereby providing critical insights for selecting individualized treatment strategies in clinical practice[20,22]. Compared to individual biomarkers, the CAR index offers a more comprehensive assessment of the immune microenvironment, making it an effective biomarker for predicting immunotherapy response and possessing higher clinical applicability.
In clinical practice, using this predictive model, clinicians can rapidly assess the immune microenvironment of patients based on their baseline CAR index and predict their likely response to immunotherapy. Specifically, a low CAR index is generally associated with a better immune response and higher treatment efficacy, while a high CAR index indicates poorer treatment responses and prognosis. Therefore, for patients with a high CAR index, clinicians may consider more aggressive supportive therapies (e.g., infection management, ALB supplementation) and combination treatment strategies (e.g., chemotherapy or targeted therapy) to optimize treatment outcomes and improve patient quality of life.
However, it is important to clarify that the CAR index, as examined in this study, remains primarily a prognostic indicator rather than a strictly predictive biomarker of immunotherapy benefit. Unlike biomarkers that directly reflect tumor intrinsic sensitivity to immune checkpoint inhibition, such as PD-L1 or MMR status, CAR captures systemic host factors including inflammation and nutritional status that are associated with overall disease trajectory and survival rather than specific therapeutic mechanisms[31]. Consequently, while CAR stratifies patients by overall risk and may flag those at risk for poorer outcomes, its use as a definitive determinant of treatment selection should be approached cautiously until prospective validation is achieved[31].
Furthermore, the dual nature of CAR as a marker of both inflammation and nutrition suggests potential utility beyond prognosis[32]. Elevated CAR reflects a state of systemic inflammation and relative nutritional depletion, both of which are potentially modifiable clinical features. This raises the possibility that CAR could be leveraged in treatment stratification and preintervention optimization, for example by identifying patients who may benefit from nutritional support, anti-inflammatory interventions, or tailored supportive care prior to or alongside immunotherapy[32]. Such strategies might not only improve general patient status but also potentially enhance immunotherapy effectiveness by mitigating inflammation related immune suppression. Future studies should therefore explore whether interventions targeting inflammation and nutritional status before or during treatment can modulate CAR levels and translate into improved clinical outcomes[31].
Although this study provides clinically significant results, several limitations remain. First, as a single-center re
In conclusion, this study demonstrates that the baseline CAR index is an independent prognostic indicator for advanced gastric cancer patients undergoing combination immunotherapy and chemotherapy. It can aid in identifying patients with poorer prognoses. The nomogram model based on CAR demonstrates strong predictive performance and provides valuable reference for the development of individualized treatment strategies in clinical settings.
This study confirms that the CAR index is an independent prognostic factor for advanced gastric cancer patients un
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