Published online Oct 15, 2026. doi: 10.4251/wjgo.122724
Revised: May 27, 2026
Accepted: June 29, 2026
Published online: October 15, 2026
Processing time: 167 Days and 0.9 Hours
Reliable and readily available prognostic biomarkers for advanced gastric cancer (AGC) treated with immunochemotherapy remain limited.
To evaluate the prognostic value of the prognostic immune-inflammatory-nutritional (PIIN) score in patients with AGC receiving first-line sintilimab plus chemotherapy and to develop clinically applicable prognostic models.
In this single-center retrospective study, 200 patients with human epidermal growth factor receptor 2-negative unresectable locally AGC treated at the Affi
The optimal PIIN cutoff for OS prediction was 25.69, with an area under the curve of 0.71 (95%CI: 0.64-0.78). Compared with the high-PIIN group, the low-PIIN group had significantly longer median PFS (14.5 months vs 7.5 months, P = 0.032) and OS (27.7 months vs 15.0 months, P < 0.001), as well as a higher disease control rate (77.94% vs 62.12%, P = 0.024), whereas the objective response rate did not differ significantly. Multivariable analysis identified PIIN, programmed death-ligand 1 expression, mismatch repair status, TNM stage, and Eastern Co
PIIN is an independent prognostic biomarker for survival outcomes in patients with AGC receiving first-line sintilimab plus chemotherapy. A lower PIIN is associated with improved PFS, OS, and disease control, and the PIIN-based nomograms may facilitate individualized prognostic assessment and treatment decision-making.
Core Tip: Advanced gastric cancer shows poor prognosis and heterogeneous response to first-line immunochemotherapy. The prognostic immune-inflammatory-nutritional (PIIN) score, combining neutrophil-to-lymphocyte ratio, systemic immune-inflammation index, fibrinogen, albumin-bilirubin, and prognostic nutritional index, independently predicts survival in advanced gastric cancer patients treated with sintilimab plus chemotherapy. Lower PIIN scores correlate with longer progression-free survival, overall survival, and higher disease control. Nomograms integrating PIIN, TNM stage, pro
- Citation: Hao QL, Li ZY, Yao ZY, Shen YM, Sun DS, Gao C, Jiang RR. Prognostic value of the prognostic immune-inflammatory-nutritional score in patients with advanced gastric cancer treated with immunochemotherapy. World J Gastrointest Oncol 2026; 18(10): 122724
- URL: https://www.wjgnet.com/1948-5204/full/v18/i10/122724.htm
- DOI: https://dx.doi.org/10.4251/wjgo.122724
Globally, gastric cancer ranks high among frequent malignant diseases and constitutes the third primary trigger of cancer-induced fatalities[1]. In China, it ranks fifth in incidence and third in mortality among all cancers[2]. Because early-stage gastric cancer lacks specific clinical manifestations, most patients are diagnosed at an advanced stage and consequently have a poor prognosis[3]. Therefore, the identification of reliable prognostic biomarkers and the deve
Previous studies have demonstrated that immune status, nutritional status, and systemic inflammation play important roles in tumor progression[7,8]. Consequently, biomarkers reflecting these factors have gained increasing attention in prognostic evaluation. The prognostic immune-inflammatory-nutritional (PIIN) score, a composite index integrating major biologic parameters used in routine clinical practice, has been reported as an independent prognostic factor in several malignancies, including intrahepatic cholangiocarcinoma, pancreatic cancer, and non-small cell lung cancer[9-11]. With the increasing application of immunotherapy combined with chemotherapy as first-line treatment for AGC, conventional prognostic indicators based solely on tumor burden or histopathological features may be insufficient to fully reflect response heterogeneity, especially with respect to immune-related effects[6]. By contrast, readily available clinical indicators that simultaneously capture systemic inflammation and immune status may have greater clinical relevance[12]. However, the prognostic value of the PIIN score in patients with AGC receiving first-line immunochemotherapy remains unclear. Therefore, this study aimed to evaluate the association between the PIIN score and survival outcomes in this population and to establish PIIN-based nomograms for individualized prognostic prediction.
This single-center, retrospective study evaluated the efficacy and prognostic value of sintilimab combined with che
Inclusion criteria: (1) Histologically or cytologically confirmed unresectable locally advanced or metastatic gastric cancer or gastroesophageal junction adenocarcinoma; (2) Age ≥ 18 years; (3) Human epidermal growth factor receptor 2-negative status; (4) Eastern Cooperative Oncology Group (ECOG) performance status 0-1; (5) At least one measurable lesion according to Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST 1.1); (6) Receipt of at least two cycles of sintilimab plus chemotherapy; and (7) Complete follow-up data.
Exclusion criteria: (1) Previous systemic antitumor therapy; (2) Severe dysfunction of major organs; (3) Hemoglobin < 80 g/L or platelet count < 75 × 109/L; (4) Autoimmune disease; (5) Known allergy to the study drugs; (6) Other concomitant malignancies; (7) Active infection at baseline; (8) Active or chronic inflammatory disease; and (9) Cirrhosis or clinically significant chronic liver disease.
This study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Affiliated Hospital of Xuzhou Medical University (approval No. XYFY2023-KL277-01). The requirement for written informed consent was waived due to the retrospective nature of the study.
The PIIN score is a composite scoring system incorporating five parameters: (1) Neutrophil-to-lymphocyte ratio (NLR); (2) Systemic immune-inflammation index (SII); (3) Fibrinogen (FIB); (4) Albumin-bilirubin (ALBI); and (5) Prognostic nutritional index (PNI). It was calculated as follows: PIIN = NLR × 0.876 + SII × 0.0174 + FIB × 14.355 + ALBI × 2.209 - PNI × 0.386[9]. Hematologic and biochemical parameters were measured using an automated hematology analyzer (Mindray BC-5390CRP) and an automated biochemical analyzer (Olympus AU2700), respectively. Overall survival (OS) was defined as the primary endpoint. Based on pretreatment PIIN values, receiver operating characteristic (ROC) curve analysis yielded an area under the curve (AUC) of 0.71 (95%CI: 0.64-0.78) (Figure 1). The optimal cutoff value for the PIIN score was 25.69, as determined by maximizing the product of sensitivity and specificity.
Relevant clinical information was retrospectively retrieved via the electronic health records at the Affiliated Hospital of Xuzhou Medical University. Variables collected included baseline demographic and clinical characteristics, including age, sex, body mass index, and ECOG performance status, as well as hematologic parameters assessed within 1 week before initiation of immunotherapy plus chemotherapy. Tumor-related variables included stage, differentiation grade, programmed death-ligand 1 (PD-L1) expression, mismatch repair (MMR) status, and the presence of peritoneal or liver metastases. All data were reviewed, verified, and entered by trained personnel to ensure accuracy and reliability.
All enrolled patients received sintilimab plus chemotherapy as first-line treatment. Sintilimab was administered intravenously at a fixed dose of 200 mg on day 1 of each 21-day cycle. Patients received conventional oxaliplatin-centric chemotherapy, specifically the SOX or XELOX protocols. Both regimens utilized an intravenous infusion of oxaliplatin (130 mg/m2) on the first day. For those assigned to the SOX group, oral S-1 was administered twice daily from day 1 to days 14 of each cycle, with the daily dosage calibrated by body surface area (BSA): (1) 80 mg (BSA < 1.25 m2); (2) 100 mg (BSA 1.25-1.5 m2); and (3) 120 mg (BSA ≥ 1.5 m2). Alternatively, patients in the XELOX group were treated with oral capecitabine at a dose of 1000 mg/m2 twice a day throughout the cycle.
Sintilimab was selected as the immunotherapeutic agent because of its regional availability and its routine use at the participating institution. As a domestically developed programmed cell death protein 1 (PD-1) inhibitor in China, sintilimab has demonstrated favorable efficacy and safety in Chinese patients across multiple clinical trials involving various malignancies. During the study period, other PD-1 inhibitors, including nivolumab and pembrolizumab, were not routinely available in the study region. Therefore, all patients uniformly received sintilimab as part of the com
In accordance with the RECIST 1.1, radiologic evaluations were conducted to determine treatment efficacy in all included patients. Baseline imaging was performed before treatment initiation using contrast-enhanced computed tomography or magnetic resonance imaging to determine pretreatment tumor status. Thereafter, imaging assessments were repeated every two treatment cycles (approximately every 6 weeks) to monitor dynamic changes in tumor burden and evaluate treatment response. Imaging scans were independently evaluated by two oncologic radiologists (each with > 3 years of experience). Any interpretation disagreements were resolved through consensus by a third senior radiologist. Based on RECIST 1.1 guidelines, tumor outcomes were categorized as complete response (CR), partial response (PR), stable disease (SD), or progressive disease (PD). These categories were utilized to calculate the objective response rate (ORR) (the combined proportion of CR and PR) and the disease control rate (DCR) (the combined proportion of CR, PR, and SD). The main survival endpoints included progression-free survival (PFS) and OS. PFS denoted the interval from the start of therapy until either disease progression or all-cause mortality, whereas OS measured the time from therapy initiation to death from any cause. Patient monitoring, conducted via phone consultations and electronic health record reviews, concluded in December 2025, yielding a median observation period of 16 months.
All statistical analyses were performed using R software (version 4.5.0). Continuous variables were expressed as medians and compared using the Wilcoxon rank-sum test, while categorical variables were compared using the χ2 test. Survival curves were estimated using the Kaplan-Meier method and compared with the log-rank test. Subsequently, 200 patients were randomly split into a training set and a validation set in a 7:3 ratio. Univariate and multivariable Cox proportional hazards (PH) regression analyses were performed to identify independent prognostic factors for PFS and OS, with hazard ratios and 95%CIs reported. Variance inflation factors were calculated to assess potential collinearity between the PIIN score and other included variables. All variables had variance inflation factor values < 5, indicating no significant multicollinearity. Variables with a P value < 0.05 in univariate analysis were included in the multivariable Cox regression model. Nomograms were then constructed based on the identified significant prognostic factors. Internal validation was performed using 1000 bootstrap resamples. Use C-index, ROC curve, and draw calibration curve and decision curve analysis (DCA) to evaluate models performance. All tests were two-sided, and a P value < 0.05 was considered statistically significant.
This single-center retrospective study included 200 patients with AGC who received first-line immunotherapy combined with chemotherapy at the Affiliated Hospital of Xuzhou Medical University. The enrolled patients were dichotomized into high-PIIN (n = 132) and low-PIIN (n = 68) cohorts, utilizing the optimal cutoff value derived from the ROC evaluation. The AUC value of PIIN is greater than NLR [0.64 (95%CI: 0.56-0.72)], SII [0.57 (95%CI: 0.49-0.65)], FIB [0.65 (95%CI: 0.58-0.73)], ALBI [0.53 (95%CI: 0.45-0.61)] and PNI [0.55 (95%CI: 0.47-0.64)]. The baseline clinical characteristics of the two groups are summarized in Table 1, including demographic variables (sex, age, and body mass index), ECOG performance status, and hematologic parameters measured within 1 week before treatment initiation. Tumor-related characteristics were also collected and analyzed, including TNM stage, tumor differentiation, peritoneal metastasis, liver metastasis, PD-L1 expression status, and MMR status. Comparisons between the two groups showed significant differences in NLR, SII, PNI, FIB level, and tumor differentiation (P < 0.05).
| Variables | Total (n = 200) | Low (n = 68) | High (n = 132) | t /χ2 | P value |
| ALBI | -2.38 ± 0.54 | -2.48 ± 0.49 | -2.33 ± 0.56 | -1.88 | 0.061 |
| NLR | 3.21 ± 2.01 | 2.01 ± 0.91 | 3.83 ± 2.14 | -8.38 | < 0.001 |
| SII | 748.17 ± 528.33 | 433.34 ± 234.34 | 910.35 ± 563.89 | -8.41 | < 0.001 |
| PNI | 42.49 ± 6.61 | 44.21 ± 5.64 | 41.61 ± 6.92 | 2.68 | 0.008 |
| FIB | 2.85 ± 0.82 | 2.36 ± 0.46 | 3.10 ± 0.86 | -7.89 | < 0.001 |
| Gender | 0.21 | 0.644 | |||
| Female | 75 (37.50) | 24 (35.29) | 51 (38.64) | ||
| Male | 125 (62.50) | 44 (64.71) | 81 (61.36) | ||
| Age (years) | 0.06 | 0.801 | |||
| < 60 years | 78 (39.00) | 28 (41.18) | 50 (37.88) | ||
| ≥ 60 years | 122 (61.00) | 40 (58.82) | 82 (62.12) | ||
| BMI (kg/m2) | 3.11 | 0.078 | |||
| < 25 | 166 (83.00) | 52 (76.47) | 114 (86.36) | ||
| ≥ 25 | 34 (17.00) | 16 (23.53) | 18 (13.64) | ||
| CEA (ng/mL) | 0.76 | 0.382 | |||
| ≥ 3 | 121 (60.50) | 44 (64.71) | 77 (58.33) | ||
| < 3 | 79 (39.50) | 24 (35.29) | 55 (41.67) | ||
| CA199 (ng/mL) | 0.76 | 0.382 | |||
| < 37 | 127 (63.50) | 46 (67.65) | 81 (61.36) | ||
| ≥ 37 | 73 (36.50) | 22 (32.35) | 51 (38.64) | ||
| ECOG | 3.34 | 0.068 | |||
| 0 | 71 (35.50) | 30 (44.12) | 41 (31.06) | ||
| 1 | 129 (64.50) | 38 (55.88) | 91 (68.94) | ||
| TNM staging | 0.14 | 0.705 | |||
| III | 89 (44.50) | 29 (42.65) | 60 (45.45) | ||
| IV | 111 (55.50) | 39 (57.35) | 72 (54.55) | ||
| Differentiation | 4.37 | 0.037 | |||
| Poorly/undifferentiated | 180 (90.00) | 57 (83.82) | 123 (93.18) | ||
| Medium/high | 20 (10.00) | 11 (16.18) | 9 (6.82) | ||
| Peritoneal metastasis | 3.54 | 0.060 | |||
| No | 113 (56.50) | 45 (66.18) | 68 (51.52) | ||
| Yes | 87 (43.50) | 23 (33.82) | 64 (48.48) | ||
| Liver metastasis | 2.82 | 0.093 | |||
| No | 157 (78.50) | 58 (85.29) | 99 (75.00) | ||
| Yes | 43 (21.50) | 10 (14.71) | 33 (25.00) | ||
| PD-L1 expression | 3.55 | 0.060 | |||
| CPS < 5 | 95 (47.50) | 26 (38.24) | 69 (52.27) | ||
| CPS ≥ 5 | 105 (52.50) | 42 (61.76) | 63 (47.73) | ||
| MMR status | 0.99 | 0.319 | |||
| pMMR | 158 (79.00) | 51 (75.00) | 107 (81.06) | ||
| dMMR | 42 (21.00) | 17 (25.00) | 25 (18.94) |
All enrolled patients were randomly split to a training cohort (n = 140) and an internal validation cohort (n = 60) at a ratio of 7:3. As summarized in Table 2, the baseline clinical profiles were well-balanced between the training and validation sets (all P > 0.05), confirming a robust comparability between the two groups.
| Variables | Total (n = 200) | Test (n = 60) | Train (n = 140) | χ2 | P value |
| Gender | 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 (years) | 3.14 | 0.076 | |||
| < 60 years | 78 (39.00) | 29 (48.33) | 49 (35.00) | ||
| ≥ 60 years | 122 (61.00) | 31 (51.67) | 91 (65.00) | ||
| BMI (kg/m2) | 0.55 | 0.46 | |||
| < 25 | 166 (83.00) | 48 (80.00) | 118 (84.29) | ||
| ≥ 25 | 34 (17.00) | 12 (20.00) | 22 (15.71) | ||
| CEA (ng/mL) | 0.17 | 0.682 | |||
| ≥ 3 | 121 (60.50) | 35 (58.33) | 86 (61.43) | ||
| < 3 | 79 (39.50) | 25 (41.67) | 54 (38.57) | ||
| CA199 (ng/mL) | 0.08 | 0.773 | |||
| < 37 | 127 (63.50) | 39 (65.00) | 88 (62.86) | ||
| ≥ 37 | 73 (36.50) | 21 (35.00) | 52 (37.14) | ||
| PIIN score | 0.72 | 0.397 | |||
| Low | 68 (34.00) | 23 (38.33) | 45 (32.14) | ||
| High | 132 (66.00) | 37 (61.67) | 95 (67.86) | ||
| ECOG | 0.76 | 0.384 | |||
| 0 | 71 (35.50) | 24 (40.00) | 47 (33.57) | ||
| 1 | 129 (64.50) | 36 (60.00) | 93 (66.43) | ||
| TNM staging | 0.16 | 0.686 | |||
| III | 89 (44.50) | 28 (46.67) | 61 (43.57) | ||
| IV | 111 (55.50) | 32 (53.33) | 79 (56.43) | ||
| Peritoneal metastasis | 0 | 0.975 | |||
| No | 113 (56.50) | 34 (56.67) | 79 (56.43) | ||
| Yes | 87 (43.50) | 26 (43.33) | 61 (43.57) | ||
| Liver metastasis | 2.15 | 0.143 | |||
| No | 157 (78.50) | 51 (85.00) | 106 (75.71) | ||
| Yes | 43 (21.50) | 9 (15.00) | 34 (24.29) | ||
| PD-L1 expression | 0.19 | 0.663 | |||
| CPS < 5 | 88 (44.00) | 25 (41.67) | 63 (45.00) | ||
| CPS ≥ 5 | 112 (56.00) | 35 (58.33) | 77 (55.00) | ||
| MMR status | 0.28 | 0.596 | |||
| pMMR | 158 (79.00) | 46 (76.67) | 112 (80.00) | ||
| dMMR | 42 (21.00) | 14 (23.33) | 28 (20.00) |
Table 3 summarizes the treatment responses in the low-PIIN and high-PIIN groups. In the low-PIIN group (n = 68), 5 patients achieved CR (7.35%), 34 achieved PR (50.00%), 14 had SD (20.59%), and 15 experienced PD (22.06%). In the high-PIIN group (n = 132), 6 patients achieved CR (4.55%), 55 achieved PR (41.67%), 21 had SD (15.91%), and 50 had PD (37.88%). While the low-PIIN cohort exhibited a numerically superior but statistically non-significant ORR compared to the high-PIIN cohort (57.35% vs 48.48%, P > 0.05), it demonstrated a markedly higher DCR (77.94% vs 62.12%, P = 0.024).
| Variables | Total (n = 200) | Low (n = 68) | High (n = 132) | χ2 | P value |
| CR | - | - | |||
| No | 189 (94.50) | 63 (92.65) | 126 (95.45) | ||
| Yes | 11 (5.50) | 5 (7.35) | 6 (4.55) | ||
| PR | - | - | |||
| No | 108 (54.00) | 31 (45.59) | 77 (58.33) | ||
| Yes | 92 (46.00) | 37 (54.41) | 55 (41.67) | ||
| SD | - | - | |||
| No | 168 (84.00) | 57 (83.82) | 111 (84.09) | ||
| Yes | 32 (16.00) | 11 (16.18) | 21 (15.91) | ||
| PD | - | - | |||
| No | 135 (67.50) | 53 (77.94) | 82 (62.12) | ||
| Yes | 65 (32.50) | 15 (22.06) | 50 (37.88) | ||
| ORR | 1.41 | 0.235 | |||
| No | 97 (48.50) | 29 (42.65) | 68 (51.52) | ||
| Yes | 103 (51.50) | 39 (57.35) | 64 (48.48) | ||
| DCR | 5.12 | 0.024 | |||
| No | 65 (32.50) | 15 (22.06) | 50 (37.88) | ||
| Yes | 135 (67.50) | 53 (77.94) | 82 (62.12) |
Kaplan-Meier survival analysis showed significant differences in both PFS and OS between the low-PIIN and high-PIIN groups. As shown in Figure 2A, the median PFS was 14.5 months (95%CI: 9.0-20.0) in the low-PIIN group, which was significantly longer than that in the high-PIIN group [7.5 months (95%CI: 6.5-9.0); P = 0.032]. Similarly, the median OS was 27.7 months (95%CI: 20.3-36.0) in the low-PIIN group, significantly longer than 15.0 months (95%CI: 13.0-17.0) in the high-PIIN group (P < 0.001; Figure 2B).
Univariate Cox regression analysis for PFS identified PIIN, ECOG performance status, TNM stage, peritoneal metastasis, PD-L1 expression status, and MMR status as factors significantly associated with PFS (all P <0.05). These variables were subsequently entered into the multivariable Cox regression model, followed by backward stepwise selection. The results showed that PIIN, TNM stage, PD-L1 expression status, and MMR status were independent prognostic factors for PFS (all P < 0.05; Table 4).
| Variables | Univariate | Multivariate | ||
| P value | HR (95%CI) | P value | HR (95%CI) | |
| Gender | ||||
| Male vs female | 0.334 | 0.83 (0.56-1.22) | 0.242 | 1.27 (0.85-1.91) |
| Age (years) | ||||
| ≥ 60 vs < 60 | 0.18 | 1.30 (0.89-1.91) | 0.884 | 0.97 (0.66-1.44) |
| BMI (kg/m2) | ||||
| < 25 vs ≥ 25 | 0.171 | 1.38 (0.87-2.19) | ||
| CEA (ng/mL) | ||||
| < 3 vs ≥ 3 | 0.3 | 1.22 (0.83-1.80) | ||
| CA199 (ng/mL) | ||||
| < 37 vs ≥ 37 | 0.103 | 1.37 (0.94-2.01) | ||
| PIIN score | ||||
| < 25.69 vs ≥ 25.69 | 0.004 | 1.82 (1.20-2.76) | 0.004 | 1.88 (1.23-2.87) |
| ECOG | ||||
| 0 vs 1 | 0.005 | 1.80 (1.19-2.73) | ||
| TNM staging | ||||
| III vs IV | < 0.001 | 2.74 (1.82-4.15) | < 0.001 | 2.24 (1.44-3.47) |
| Peritoneal metastasis | ||||
| No vs yes | 0.005 | 1.74 (1.19-2.56) | ||
| Liver metastasis | ||||
| No vs yes | 0.103 | 1.49 (0.92-2.40) | ||
| PD-L1 expression | ||||
| CPS < 5 vs CPS ≥ 5 | < 0.001 | 0.27 (0.17-0.42) | < 0.001 | 0.35 (0.22-0.55) |
| MMR status | ||||
| pMMR vs dMMR | < 0.001 | 0.33 (0.19-0.56) | < 0.001 | 0.35 (0.20-0.63) |
For OS, univariate Cox regression analysis showed that PIIN, ECOG performance status, TNM stage, liver metastasis, PD-L1 expression status, and MMR status were significantly associated with OS (all P < 0.05). These variables were then included in the multivariable Cox regression model and further screened using backward stepwise selection. Final multivariable analysis demonstrated that PIIN, ECOG performance status, TNM stage, PD-L1 expression status, and MMR status were independent prognostic factors for OS (all P < 0.05; Table 5).
| Variables | Univariate | Multivariate | ||
| P value | HR (95%CI) | P value | HR (95%CI) | |
| Gender | ||||
| Male vs female | 0.941 | 0.98 (0.63-1.54) | 0.182 | 1.38 (0.86-2.21) |
| Age (years) | ||||
| ≥ 60 vs < 60 | 0.295 | 1.29 (0.80-2.06) | 0.781 | 1.07 (0.67-1.70) |
| BMI (kg/m2) | ||||
| < 25 vs ≥ 25 | 0.348 | 0.73 (0.38-1.41) | ||
| CEA (ng/mL) | ||||
| < 3 vs ≥ 3 | 0.231 | 1.31 (0.84-2.02) | ||
| CA199 (ng/mL) | ||||
| < 37 vs ≥ 37 | 0.200 | 1.33 (0.86-2.06) | ||
| PIIN score | ||||
| < 25.69 vs ≥ 25.69 | < 0.001 | 2.75 (1.57-4.83) | 0.013 | 2.09 (1.17-3.75) |
| ECOG | ||||
| 0 vs 1 | 0.002 | 2.20 (1.33-3.61) | 0.033 | 1.76 (1.05-2.95) |
| TNM staging | ||||
| III vs IV | < 0.001 | 3.20 (1.98-5.16) | < 0.001 | 2.77 (1.62-4.75) |
| Peritoneal metastasis | ||||
| No vs yes | 0.173 | 1.35 (0.88-2.09) | ||
| Liver metastasis | ||||
| No vs yes | 0.008 | 1.90 (1.18-3.05) | ||
| PD-L1 expression | ||||
| CPS < 5 vs CPS ≥ 5 | < 0.001 | 0.25 (0.16-0.41) | < 0.001 | 0.41 (0.25-0.69) |
| MMR status | ||||
| pMMR vs dMMR | < 0.001 | 0.30 (0.16-0.58) | 0.003 | 0.37 (0.19-0.72) |
The Common Terminology Criteria for Adverse Events served as the standard for monitoring and evaluating all adverse events (AEs). The most common treatment-related AEs included leukopenia, anemia, neutropenia, nausea, and pyrexia, and their detailed incidence rates are presented in Table 6. All enrolled patients received standardized supportive care during treatment, including infection control, antiemetic therapy, and symptomatic supportive measures, such as agents to increase white blood cell and platelet counts, to ensure treatment continuity. Safety analysis showed that no patient permanently discontinued treatment because of AEs. However, a small proportion of patients experienced grade ≥ 3 AEs and underwent temporary treatment delay or dose adjustment in accordance with clinical guidelines to ensure treatment safety and tolerability. No statistically significant difference in the incidence of AEs was observed between the low-PIIN and high-PIIN groups (P > 0.05).
| Variables | Total (n = 200) | Low (n = 68) | High (n = 132) | χ2 | P value |
| Leukopenia | 82 (41.00) | 30 (44.12) | 52 (39.39) | 0.41 | 0.520 |
| Anemia | 88 (44.00) | 32 (47.06) | 56 (42.42) | 0.39 | 0.532 |
| Neutropenia | 85 (42.50) | 35 (51.47) | 50 (37.88) | 3.39 | 0.065 |
| Thrombocytopenia | 66 (33.00) | 27 (39.71) | 39 (29.55) | 2.10 | 0.148 |
| Nausea | 54 (27.00) | 22 (32.35) | 32 (24.24) | 1.50 | 0.221 |
| Pyrexia | 54 (27.00) | 24 (35.29) | 30 (22.73) | 3.60 | 0.058 |
| Elevated ALT | 31 (15.50) | 15 (22.06) | 16 (12.12) | 3.38 | 0.066 |
| Elevated AST | 38 (19.00) | 18 (26.47) | 20 (15.15) | 3.74 | 0.053 |
| Hypothyroidism | 34 (17.00) | 16 (23.53) | 18 (13.64) | 3.11 | 0.078 |
| Hypertension | 28 (14.00) | 10 (14.71) | 18 (13.64) | 0.04 | 0.836 |
| Pneumonitis | 23 (11.50) | 9 (13.24) | 14 (10.61) | 0.30 | 0.581 |
| Hyperbilirubinemia | 17 (8.50) | 6 (8.82) | 11 (8.33) | 0.01 | 0.906 |
| Fatigue | 31 (15.50) | 13 (19.12) | 18 (13.64) | 1.03 | 0.310 |
| > 3 grades: Leukopenia | 20 (10.00) | 9 (13.24) | 11 (8.33) | 1.20 | 0.274 |
| > 3 grades: Anemia | 17 (8.50) | 6 (8.82) | 11 (8.33) | 0.01 | 0.906 |
| > 3 grades: Neutropenia | 26 (13.00) | 12 (17.65) | 14 (10.61) | 1.97 | 0.161 |
| > 3 grades: Thrombocytopenia | 36 (18.00) | 16 (23.53) | 20 (15.15) | 2.13 | 0.144 |
| > 3 grades: Nausea | 9 (4.50) | 4 (5.88) | 5 (3.79) | 0.10 | 0.751 |
| > 3 grades: Elevated ALT | 7 (3.50) | 4 (5.88) | 3 (2.27) | 0.83 | 0.363 |
| > 3 grades: Elevated AST | 9 (4.50) | 3 (4.41) | 6 (4.55) | 0.00 | 1.000 |
| > 3 grades: Hypothyroidism | 6 (3.00) | 3 (4.41) | 3 (2.27) | 0.16 | 0.687 |
| > 3 grades: Hypertension | 8 (4.00) | 3 (4.41) | 5 (3.79) | 0.00 | 1.000 |
| > 3 grades: Pneumonitis | 3 (1.50) | 1 (1.47) | 2 (1.52) | 0.00 | 1.000 |
| > 3 grades: Hyperbilirubinemia | 6 (3.00) | 2 (2.94) | 4 (3.03) | 0.00 | 1.000 |
Based on the multivariable Cox regression analysis for PFS, PIIN, TNM stage, PD-L1 expression status, and MMR status were identified as independent prognostic factors for PFS. Accordingly, a nomogram incorporating these four variables was constructed to predict the probabilities of 6-month and 9-month PFS (Figure 3A). Likewise, based on the multivariable Cox regression analysis for OS, PIIN, ECOG performance status, TNM stage, PD-L1 expression status, and MMR status were identified as independent prognostic factors for OS. An OS nomogram was therefore established to predict the probabilities of 12-month, 15-month, and 18-month survival (Figure 3B).
To evaluate the predictive performance of the nomogram models, several validation approaches were applied. Specifically, the C-index was calculated, ROC curves were generated at each prespecified time point with the corresponding AUCs, and use 1000 self-lifting weight samples to construct calibration curves and DCA to systematically evaluate the model's discriminability, accuracy, and stability.
Validation of the PFS nomogram showed that the C-index was 0.776 in the training cohort and 0.787 in the internal validation cohort. In the training cohort, the AUCs for predicting 6-month and 9-month PFS were 0.776 (95%CI: 0.658-0.894) and 0.890 (95%CI: 0.812-0.968), respectively. In the internal validation cohort, the corresponding AUCs were 0.803 (95%CI: 0.712-0.894) and 0.868 (95%CI: 0.816-0.920), respectively (Figure 4A and B), indicating good predictive perfor
Regarding the OS nomogram's validation, concordance indices (C-indices) reached 0.753 for the training group and 0.730 for the internal validation set. When estimating 12-month, 15-month, and 18-month OS, the training set yielded AUCs of 0.717 (95%CI: 0.579-0.856), 0.755 (95%CI: 0.617-0.893), and 0.774 (95%CI: 0.644-0.904). Correspondingly, the internal validation set produced AUCs of 0.764 (95%CI: 0.666-0.861), 0.805 (95%CI: 0.720-0.891), and 0.834 (95%CI: 0.758-0.909) (Figure 4C-E). These findings underscore the model’s robust predictive capacity and reliability for medium-term to long-term survival. Furthermore, calibration plots for both cohorts displayed excellent consistency between the actual and nomogram-predicted probabilities for PFS and OS, confirming the models were well-calibrated (Figure 5).
DCA shows that the models have significant net benefits at different threshold probabilities, consistently outperforming the “treat all” and “treat none” strategies over a wide range of threshold probabilities. Further validated the clinical application potential and value of the models (Figure 6).
A single biomarker is insufficient to comprehensively characterize the prognostic risk of patients with AGC. In the present study, the PIIN score was identified as an independent prognostic factor in patients with AGC receiving first-line sintilimab plus chemotherapy. Specifically, PIIN, TNM stage, PD-L1 expression status, and MMR status were inde
It is noteworthy that MMR status and PD-L1 expression may also influence the efficacy and survival outcomes of sintilimab plus chemotherapy in AGC[23]. Previous studies have consistently shown that tumors with deficient MMR are characterized by a higher tumor mutational burden and neoantigen load, as well as increased immune cell infiltration within the tumor microenvironment, thereby exhibiting greater sensitivity to PD-1 inhibitors[24,25]. In contrast, patients with proficient MMR generally derive more limited benefit from immunotherapy. The analytical framework of the present study is broadly consistent with these observations, suggesting that MMR status may serve as an important biological basis for stratifying the benefit of immunochemotherapy in AGC. Meanwhile, multiple previous studies have shown that patients with high PD-L1 expression tend to achieve higher ORRs and longer survival with immune checkpoint inhibitor therapy[26]. Consistent with this trend, our findings indicate that patients with high PD-L1 expression and deficient MMR status are more likely to benefit from sintilimab combined with chemotherapy. Survival analyses demonstrated statistically significant differences, yielding a log-rank P value of 0.032 PFS and P < 0.001 for OS. However, the two KM curves for PFS intersected during the early follow-up period (approximately the first 4 months). To evaluate this, we tested the PH assumption using Schoenfeld residuals, which confirmed a slight violation of the PH assumption during this initial 4-month period. This phenomenon may be attributed to the delayed immune responses commonly observed in chemoimmunotherapy, such as pseudoprogression or hyperprogression, and does not com
ECOG performance status may also serve as an important prognostic factor in patients with AGC receiving sintilimab plus chemotherapy[30]. As a direct indicator of general condition, organ functional reserve, and tolerance to systemic treatment, ECOG performance status has been widely recognized as an independent prognostic factor in AGC[31]. In the present study, patients with lower ECOG scores appeared more likely to have better treatment tolerance and more preserved immune function, and therefore may derive more durable benefit from sintilimab plus chemotherapy. In contrast, patients with higher ECOG scores are often characterized by nutritional depletion, increased inflammatory burden, and impaired multiorgan function, which may compromise treatment intensity and efficacy while increasing the risk of treatment-related AEs, ultimately leading to worse PFS and OS[32]. This observation is also consistent with previous immunotherapy studies showing that patients with better performance status are more likely to benefit from treatment[12,32]. However, it must be acknowledged that the inclusion criteria requiring patients to have an ECOG PS of 0-1 and to have completed at least two cycles of treatment inevitably introduced a certain degree of selection bias. These criteria inherently excluded patients with hyperprogressive disease or an extremely poor baseline performance status who were unable to tolerate or continue first-line therapy, thereby potentially limiting the generalizability of our findings to a broader, unselected population of AGC. Despite this limitation, compared with earlier studies that primarily focused on tumor molecular biomarkers, the present study further demonstrates that ECOG PS and the PIIN score may provide complementary prognostic information. While ECOG PS predominantly reflects the patient's general physical condition and treatment tolerance, the PIIN score captures their inflammatory, immunological, and nutritional status. A comprehensive assessment integrating these variables, rather than relying solely on PD-L1 expression, MMR status, or any single clinical indicator, offers a more holistic framework for identifying patients most likely to benefit from sintilimab combined with chemotherapy. This integrated approach can provide a more practical basis for individualized treatment and prognostic stratification in AGC.
TNM stage was also identified as an independent prognostic factor in this study, consistent with previous reports and further supporting the robustness of the model. The PIIN-based nomogram demonstrated favorable predictive performance for both PFS and OS. In the internal validation cohort, the AUCs for predicting 6-month and 9-month PFS were 0.803 and 0.868, respectively, whereas those for predicting 12-month, 15-month, and 18-month OS were 0.764, 0.805, and 0.834, respectively. In addition, the calibration curves showed good agreement between predicted and observed outcomes, indicating potential clinical utility of the model.
The core value of PIIN score as an optimized composite indicator lies in integrating these interrelated but biologically diverse indicators (inflammation, nutrition, and coagulation) to minimize the limitations of relying on a single related variable. Due to the retrospective nature of this study and the limitation of single center sample size, we adopted ROC to obtain the optimal cutoff value as a practical grouping method. Nevertheless, several limitations should be noted. This was a single-center retrospective study without external validation, and selection bias may therefore be unavoidable. Therefore, this study should be considered as an exploratory study. In addition, the sample size was relatively small and all patients were recruited from a single center, which may limit the generalizability of the findings. Finally, although we used internal validation (random splitting and Bootstrap) to test the effectiveness of the model, the generalizability and robustness of the nomogram models have not been fully established. In the future, multi center, large-scale, prospective studies will be needed, or further external validation will be conducted in independent cohorts. Further incorporation of molecular biomarkers, such as MMR status and PD-L1 expression, may also help validate and optimize the model.
In conclusion, the PIIN score was closely associated with prognosis in patients with AGC receiving first-line sintilimab plus chemotherapy. The nomogram integrating the PIIN score with clinicopathological factors showed favorable predictive performance and may provide a useful tool for individualized prognostic assessment and treatment decision-making.
The PIIN score serves as an independent predictor of survival in patients with AGC treated with first-line sintilimab plus chemotherapy. Lower PIIN scores are associated with prolonged PFS, OS, and higher DCRs. Nomograms integrating PIIN with TNM stage, PD-L1 expression, MMR status, and ECOG performance status provide a practical framework for individualized prognostic assessment and treatment stratification. However, this single-center retrospective study with a limited sample size lacks external validation, and future multicenter prospective studies are needed to confirm and generalize these findings.
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