Yao ZY, Liu J, Ma X, Li WT, Shen Y, Cui YZ, Fang Y, Han ZX, Yang CH. Impact of fibrinogen-to-albumin ratio on the long-term prognosis of patients with advanced HER2-negative gastric cancer receiving immunochemotherapy. World J Gastrointest Oncol 2025; 17(6): 107980 [DOI: 10.4251/wjgo.v17.i6.107980]
Corresponding Author of This Article
Chun-Hua Yang, MD, PhD, Professor, Department of Radiotherapy, The Affiliated Hospital of Xuzhou Medical University, No. 99 Huaihai West Road, Xuzhou 221000, Jiangsu Province, China. 13063518075@126.com
Research Domain of This Article
Oncology
Article-Type of This Article
Retrospective Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Zhi-Yuan Yao, Zheng-Xiang Han, Department of Oncology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou 221000, Jiangsu Province, China
Jie Liu, Wan-Ting Li, Yu Shen, Yong-Zheng Cui, Chun-Hua Yang, Department of Radiotherapy, The Affiliated Hospital of Xuzhou Medical University, Xuzhou 221000, Jiangsu Province, China
Xiao Ma, Department of Oncology, The Second Affiliated Hospital of Nanjing Medical University, Nanjing 210008, Jiangsu Province, China
Yan Fang, Department of Gastroenterology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou 215000, Jiangsu Province, China
Co-corresponding authors: Zheng-Xiang Han and Chun-Hua Yang.
Author contributions: Yao ZY, Liu J, Han ZX, and Yang CH contributed to the conceptualization, writing-review and editing of this manuscript; Yao ZY and Yang CH were responsible for the methodology of this study; Yao ZY contributed to the formal analysis of this manuscript and the visualization of this article; Yao ZY, Liu J, Ma X, Li WT and Shen Y took part in the writing-original draft and investigation of this manuscript; Yao ZY, Han ZX and Yang CH contributed to the project administration and the supervision of this manuscript; Liu J, Ma X, Li WT, Shen Y and Fang Y took part in the data curation of this study; Yao ZY and Liu J were responsible for the validation of this manuscript; Shen Y and Cui YZ took part in the resources; Han ZX and Yang CH were involved in the supervision of this study; Yao ZY and Liu J contributed equally to the manuscript, they are co-first authors of this manuscript. Han ZX and Yang CH contributed to this manuscript equally, they are co-corresponding authors of this study.
Institutional review board statement: This research was carried out following the Declaration of Helsinki and received approval from the Ethics Committee at the Affiliated Hospital of Xuzhou Medical University (approval No. XYFY2023-KL277-01).
Informed consent statement: Given the retrospective design of this investigation, the Ethics Committee of the Affiliated Hospital of Xuzhou Medical University granted us an exemption from obtaining written informed consent.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: The data included in this study can be obtained from the corresponding author at 13063518075@126.com.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Chun-Hua Yang, MD, PhD, Professor, Department of Radiotherapy, The Affiliated Hospital of Xuzhou Medical University, No. 99 Huaihai West Road, Xuzhou 221000, Jiangsu Province, China. 13063518075@126.com
Received: April 2, 2025 Revised: April 17, 2025 Accepted: May 13, 2025 Published online: June 15, 2025 Processing time: 72 Days and 23.9 Hours
Abstract
BACKGROUND
There is currently no effective targeted therapy for advanced HER2-negative gastric cancer (GC). While immunotherapy combined with chemotherapy is the first-line treatment for GC, patient survival outcomes remain highly heterogeneous, highlighting the urgent need for reliable predictive biomarkers. The fibrinogen-to-albumin ratio (FAR) integrates both inflammation (elevated fibrinogen levels) and nutritional status (reduced albumin levels). Although FAR has been associated with immunotherapy resistance in various solid tumors, its prognostic value in GC patients receiving immunochemotherapy remains unclear.
AIM
To assess the predictive value of the FAR in the long-term prognosis of advanced HER2-negative GC patients receiving sintilimab-based immunotherapy combined with chemotherapy.
METHODS
This retrospective study included 260 patients with unresectable or metastatic HER2-negative GC who received sintilimab plus chemotherapy from 2021 to 2024. Pre-treatment FAR values were calculated, and the optimal cutoff value was determined using receiver operating characteristic curve analysis. The association between the FAR and overall survival (OS) and progression-free survival (PFS) was analyzed using Kaplan-Meier survival curves and Cox proportional hazards models. Independent prognostic factors were identified by multivariate Cox regression analysis based on OS, and a nomogram model was constructed incorporating FAR. The concordance index (C-index) and calibration curves were used to assess the predictive performance and calibration of the model.
RESULTS
Patients with high FAR (≥ 0.08) had significantly shorter median PFS [7.80 months (6.40-8.30) vs 10.00 months (9.30-11.20), P < 0.001] and OS [14.20 months (12.20-16.60) vs 19.50 months (18.80-22.00), P < 0.001] compared to the group with low FAR (< 0.08). Moreover, the group with high FAR had a significantly lower objective response rate (10.22% vs 19.51%, P = 0.034) and disease control rate (34.31% vs 49.59%, P = 0.013). The incidence of adverse events did not significantly differ between the two groups (P > 0.05). Multivariate analysis confirmed the FAR as an independent prognostic factor for OS (HR = 2.33, 95%CI: 1.59-3.43, P < 0.001). The nomogram model, incorporating FAR, Eastern Cooperative Oncology Group performance status, programmed cell death ligand 1 expression, tumor stage, and body mass index, demonstrated strong predictive accuracy, with an internal validation C-index of 0.73 (95%CI: 0.66-0.79). The calibration curve showed a high consistency between predicted and actual survival rates.
CONCLUSION
Patients with low FAR had significantly better prognostic outcomes than those with high FAR when receiving immunochemotherapy. Thus, FAR may serve as a valuable prognostic biomarker for predicting survival outcomes in patients with advanced HER2-negative GC.
Core Tip: This study is the first to validate the prognostic value of the fibrinogen-to-albumin ratio (FAR) in advanced HER2-negative gastric cancer patients receiving immunochemotherapy. High FAR was significantly associated with shorter progression-free survival and overall survival, establishing FAR as an independent prognostic factor. The predictive model incorporating FAR allows for personalized survival predictions, offering a valuable and cost-effective tool for clinical decision-making. These findings underscore FAR's potential as a practical biomarker for guiding treatment strategies in advanced gastric cancer.
Citation: Yao ZY, Liu J, Ma X, Li WT, Shen Y, Cui YZ, Fang Y, Han ZX, Yang CH. Impact of fibrinogen-to-albumin ratio on the long-term prognosis of patients with advanced HER2-negative gastric cancer receiving immunochemotherapy. World J Gastrointest Oncol 2025; 17(6): 107980
Gastric cancer (GC) is one of the leading causes of cancer-related deaths worldwide, ranking fifth in incidence and fifth in mortality in 2022[1]. Among patients with advanced GC, approximately 80% have the HER2-negative subtype, for which there is no effective targeted therapy and their treatment still relies heavily on conventional chemotherapy. However, the median overall survival (OS) of these patients remains less than 12 months[2]. In recent years, immune checkpoint inhibitors (ICIs), particularly programmed death-1 (PD-1) inhibitors such as sintilimab, combined with chemotherapy, have significantly improved survival outcomes by enhancing antitumor immunity and directly inducing tumor cell death[3,4]. For instance, the ORIENT-16 phase III clinical trial demonstrated that sintilimab plus chemotherapy extended the median OS to 15.2 months in patients with advanced GC[5]. However, 30%-40% of patients failed to benefit from immunochemotherapy, highlighting the substantial heterogeneity in treatment response. Identifying a simple, cost-effective biomarker to predict treatment efficacy remains a major challenge in optimizing therapeutic strategies.
Currently, the combined positive score (CPS) for programmed cell death ligand 1 (PD-L1) expression and tumor mutational burden (TMB) are the most commonly used predictive biomarkers for immunotherapy. However, both have significant limitations[6,7]. PD-L1 expression is highly spatiotemporally heterogeneous, with substantial variations observed between primary and metastatic tumors and before and after treatment[8]. TMB measurement requires whole-exome sequencing (WES) or large next-generation sequencing (NGS) panels, which are expensive, time-consuming, and lack standardization[9]. More importantly, these biomarkers primarily reflect intrinsic tumor characteristics, failing to account for the patients’ systemic inflammatory and nutritional status, both of which play key roles in modulating the tumor immune microenvironment.
Recent studies have shown that systemic inflammation can promote the infiltration of immunosuppressive cells [e.g., myeloid-derived suppressor cells (MDSCs) and regulatory T cells (Tregs)] and upregulate immune checkpoint molecules (e.g., PD-L1 and CTLA4), thereby driving immune evasion[10,11]. Moreover, malnutrition (e.g., hypoalbuminemia) is strongly associated with T-cell dysfunction and resistance to immunotherapy[12,13]. Therefore, integrating inflammatory and nutritional parameters into a composite biomarker may provide a more comprehensive predictive tool for immunotherapy outcomes.
The fibrinogen-to-albumin ratio (FAR) has emerged as a novel inflammation-nutrition composite index, showing significant prognostic value in various solid tumors. Fibrinogen, an acute-phase protein, is upregulated in response to tumor-associated inflammation and can promote angiogenesis by activation of the coagulation cascade, while simultaneously inhibiting the tumor infiltration of cytotoxic T cells and natural killer (NK) cells[14,15]. Conversely, albumin is a key marker of nutritional status, and low albumin levels indicate chronic inflammation-induced hypermetabolism, reduced drug-binding capacity, and exacerbated oxidative stress, ultimately reducing treatment efficacy[16,17]. By integrating these bidirectional pathophysiological processes, FAR has shown superior prognostic stratification compared to individual inflammatory or nutritional markers in certain cancers such as breast cancer and prostate cancer[18,19]. However, its prognostic value in GC, particularly in HER2-negative GC patients undergoing immunochemotherapy, remains largely unexplored.
Therefore, the aim of this study was to validate the prognostic significance of FAR in a homogeneous cohort of advanced HER2-negative GC patients receiving sintilimab-based immunotherapy combined with chemotherapy. In addition, we sought to develop a nomogram model incorporating FAR for personalized survival prediction. By elucidating the interplay between systemic inflammation and immunotherapy resistance, our findings may provide a rationale for host immunity-nutrition-based individualized treatment strategies for advanced GC.
MATERIALS AND METHODS
Study design and patients
A retrospective analysis was performed on clinical data from patients with advanced GC who received treatment at the Affiliated Hospital of Xuzhou Medical University (Xuzhou, China) between January 2021 and December 2024.
Inclusion criteria: (1) Histopathologically confirmed diagnosis of HER2-negative GC; (2) Clinical stage III (unresectable locally advanced) or stage IV (metastatic) disease, classified according to the 8th edition of the American Joint Committee on Cancer staging system; (3) Received first-line treatment with sintilimab-based immunotherapy combined with platinum-based chemotherapy regimens for ≥ 4 cycles; (4) Had fasting serum fibrinogen and albumin levels measured within one week prior to treatment initiation; (5) Had baseline computed tomography/magnetic resonance imaging (CT) showing measurable lesions as defined by the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1; and (6) Had an Eastern Cooperative Oncology Group (ECOG) performance status of ≤ 1.
The exclusion criteria: (1) Presence of other primary malignancies; (2) Active infections, uncontrolled autoimmune diseases, or long-term use of immunosuppressive agents; (3) Severe hepatic or renal dysfunction; (4) Prior treatment with PD-1/PD-L1 inhibitors or other forms of immunotherapy; (5) Discontinuation of immunochemotherapy due to unacceptable toxicity or treatment modification during the study period; (6) Missing key clinical data; or (7) Loss to follow-up or follow-up duration < 3 months.
This study was reviewed and approved by the Ethics Committee of the Affiliated Hospital of Xuzhou Medical University (Approval No. XYFY2023-KL227-01). Given the retrospective nature of this study, all data were obtained from anonymized medical records, with no additional interventions or risk of privacy disclosure to patients. As a result, the requirement for informed consent was waived by the Ethics Committee. The study was conducted in full compliance with the Declaration of Helsinki.
Definition
The FAR value was calculated using the following formula: FAR = fibrinogen concentration (g/L)/albumin concentration (g/L). Fibrinogen and albumin levels were measured in fasting venous blood samples collected within one week prior to treatment initiation. Fibrinogen levels were determined using the Clauss method (thrombin time assay) on a Sysmex CS-5100™ automated coagulation analyzer (Sysmex Corporation, Kobe, Japan), and albumin levels were measured by the bromocresol green method using a Beckman Coulter AU5800 biochemical analyzer (Beckman Coulter Inc., Brea, CA, United States). All laboratory procedures strictly adhered to international standard protocols. To determine the optimal FAR cutoff value, receiver operating characteristic (ROC) curve analysis was performed using OS as the endpoint in patients receiving sintilimab-based immunotherapy combined with chemotherapy. The optimal cutoff value for FAR was determined, which maximized the product of sensitivity and specificity.
Grouping and treatment protocol
Based on the FAR cutoff value, patients were divided into two groups: The low-FAR (< 0.08) group and the high-FAR (≥ 0.08) group. Sintilimab (200 mg) was administered by intravenous infusion every 3 weeks (q3w). The SOX regimen consisted of oxaliplatin (130 mg/m²) intravenously on Day 1, and S-1 (40-60 mg) orally twice daily based on body surface area, administered from Day 1 to Day 14 every 3 weeks (q3w). The XELOX regimen included oxaliplatin (130 mg/m²) intravenously on Day 1, and capecitabine (1000 mg/m²) orally twice daily from Day 1 to Day 14, repeated every 3 weeks (q3w). The FOLFOX regimen consisted of oxaliplatin (85 mg/m²) intravenously on Day 1, leucovorin (400 mg/m²) intravenously on Day 1, and 5-fluorouracil (5-FU, 400 mg/m²) intravenously on Day 1, followed by continuous infusion of 2400 mg/m² for 46 hours, every 2 weeks.
Evaluation
According to the RECIST (version 1.1) criteria, all patients underwent baseline imaging assessments within 14 days prior to treatment initiation, including contrast-enhanced CT scans of the chest, abdomen, and pelvis. The first efficacy assessment was performed after two treatment cycles, followed by abdominal CT scans every two months until disease progression or treatment discontinuation. The OS was defined as the time from treatment initiation to death from any cause, while progression-free survival (PFS) was defined as the time from treatment initiation to radiologically confirmed disease progression or death. Short-term efficacy was evaluated using the modified RECIST criteria, classifying tumor response into complete response (CR), partial response (PR), stable disease (SD), and progressive disease (PD). Image evaluations were independently performed by two radiologists, and in cases of discrepancy, a third senior radiologist reviewed the scans to minimize observational bias. All adverse events (AEs) were documented and graded according to the Common Terminology Criteria for Adverse Events version 5.0, issued by the National Cancer Institute of the National Institute of Health (NIH, Bethesda, MD, United States).
Statistical analysis
All statistical analyses were performed using the R software (version 4.2.0; R Foundation for Statistical Computing, Vienna, Austria; http://www.r-project.org), with a significance level set at two-sided P < 0.05. The Kolmogorov-Smirnov test was used to assess normality prior to hypothesis testing for continuous variables. Continuous variables are presented as the mean ± SD, and categorical variables are described using frequencies (percentages). The baseline characteristics between groups were compared using the Fisher's exact test, χ2 test, and t-test. The Kaplan-Meier method was used to estimate OS and PFS, and group differences were assessed using the Log-rank test. Cox proportional hazards models were used for univariate and multivariate regression analysis of PFS and OS. Variables with P < 0.05 in the univariate analysis were included in the multivariate analysis to identify independent prognostic factors for PFS and OS. A nomogram model was constructed based on the independent prognostic factors identified by multivariate Cox regression analysis for OS. The Bootstrap method (1000 resamples) was used to calculate the concordance index (C-index) and generate calibration curves to evaluate the predictive accuracy of the model.
RESULTS
Patient characteristics
This study retrospectively analyzed 455 patients with advanced GC who received sintilimab-based immunotherapy combined with chemotherapy at the Affiliated Hospital of Xuzhou Medical University from January 2021 to January 2024. Ultimately, 260 patients were included in the analysis (Figure 1). The optimal cutoff value for FAR was determined to be 0.08, which maximized the product of sensitivity (68.65%) and specificity (72.13%). The area under the ROC curve (AUC) was 0.71 (95%CI: 0.63-0.78) (Figure 2). Based on the optimal FAR cutoff value, patients were divided into a low-FAR (< 0.08) group (n = 123) and a high-FAR (≥ 0.08) group (n = 137). The demographic characteristics (including age, sex, etc.), tumor characteristics (stage, pathology, presence of peritoneal or liver metastasis), and various laboratory parameters (such as carcinoembryonic antigen, fibrinogen concentration, albumin concentration) of the patients are summarized in Table 1. Statistically significant differences were observed between the two groups in terms of FAR index, albumin concentration, and fibrinogen concentration (all P < 0.001).
Figure 1 Flow chart of the screening process for advanced gastric cancer patients receiving immunotherapy combined with chemotherapy.
PD-1: Programmed cell death protein 1; PD-L1: Programmed cell death ligand 1; FAR: Fibrinogen-to-albumin ratio.
Figure 2 Receiver operating characteristics curve analysis based on fibrinogen to albumin ratio for overall survival.
AUC: Area under the curve.
Table 1 Baseline characteristics of fibrinogen to albumin ratio < 0.08 and fibrinogen to albumin ratio ≥ 0.08, n (%).
Variables
Overall, mean ± SD/n (%), n = 260
Low FAR, mean ± SD/n (%), n = 123
High FAR, mean ± SD/n (%), n = 137
P value
Fibrinogen, g/L
3.63 ± 0.91
2.91 ± 0.55
4.27 ± 0.66
< 0.001
Albumin, g/L
40.24 ± 4.86
42.30 ± 4.48
38.40 ± 4.43
< 0.001
FAR
0.09 ± 0.03
0.07 ± 0.01
0.11 ± 0.02
< 0.001
Sex
0.799
Female
129 (49.62)
60 (48.78)
69 (50.36)
Male
131 (50.38)
63 (51.22)
68 (49.64)
Age, years
0.861
< 60
119 (45.77)
57 (46.34)
62 (45.26)
≥ 60
141 (54.23)
66 (53.66)
75 (54.74)
Drinking history
0.264
No
180 (69.23)
81 (65.85)
99 (72.26)
Yes
80 (30.77)
42 (34.15)
38 (27.74)
Smoking history
0.932
No
179 (68.85)
85 (69.11)
94 (68.61)
Yes
81 (31.15)
38 (30.89)
43 (31.39)
Body mass index, kg/m2
0.286
< 25
200 (76.92)
91 (73.98)
109 (79.56)
≥ 25
60 (23.08)
32 (26.02)
28 (20.44)
ECOG
0.669
0
172 (66.15)
83 (67.48)
89 (64.96)
1
88 (33.85)
40 (32.52)
48 (35.04)
Site
Stomach
128 (49.23)
68 (55.28)
60 (43.80)
0.062
Gastric and esophageal binding
132 (50.77)
55 (44.72)
77 (56.20)
Histological
0.306
Adenocarcinoma
191 (73.46)
94 (76.42)
97 (70.80)
Others
69 (26.54)
29 (23.58)
40 (29.20)
Staging
0.098
3
80 (30.77)
44 (35.77)
36 (26.28)
4
180 (69.23)
79 (64.23)
101 (73.72)
CEA, ng/mL
0.683
< 3
107 (41.15)
49 (39.84)
58 (42.34)
≥ 3
153 (58.85)
74 (60.16)
79 (57.66)
Peritoneal metastasis
0.172
No
213 (81.92)
105 (85.37)
108 (78.83)
Yes
47 (18.08)
18 (14.63)
29 (21.17)
Liver metastasis
0.815
No
171 (65.77)
80 (65.04)
91 (66.42)
Yes
89 (34.23)
43 (34.96)
46 (33.58)
EBV status
0.826
No-infect
217 (83.46)
102 (82.93)
115 (83.94)
Infect
43 (16.54)
21 (17.07)
22 (16.06)
PD-L1 expression
0.619
CPS < 5
52 (20.00)
23 (18.70)
29 (21.17)
CPS ≥ 5
208 (80.00)
100 (81.30)
108 (78.83)
MMR status
0.113
pMMR
248 (95.38)
120 (97.50)
128 (93.43)
dMMR
12 (4.62)
3 (2.44)
9 (6.57)
Tumor response
The patients’ tumor response outcomes are shown in Table 2. In the low-FAR group, 2 patients achieved CR, 22 patients had PR, and 37 patients had SD. In contrast, in the high-FAR group, 1 patient achieved CR, 13 patients had PR, and 33 patients had SD. The objective response rate (ORR) was 19.51% in the low-FAR group and 10.22% in the high-FAR group (P = 0.034), while the disease control rate (DCR) was 49.59% in the low-FAR group and 34.31% in the high-FAR group (P = 0.013).
Table 2 Tumor responses of fibrinogen to albumin ratio < 0.08 and fibrinogen to albumin ratio ≥ 0.08, n (%).
Variables
Low FAR (n = 123)
High FAR (n = 137)
χ2
P value
CR
0
121 (98.37)
136 (99.27)
1
2 (1.63)
1 (0.73)
PR
0
101 (82.11)
124 (90.51)
1
22 (17.89)
13 (9.49)
SD
0
86 (69.92)
104 (75.91)
1
37 (30.08)
33 (24.09)
PD
0
61 (49.59)
47 (34.31)
1
62 (50.41)
90 (65.69)
ORR
4.49
0.034
0
99 (80.49)
123 (89.78)
1
24 (19.51)
14 (10.22)
DCR
6.24
0.013
0
62 (50.41)
90 (65.69)
1
61 (49.59)
47 (34.31)
PFS and OS
The low-FAR group had a median PFS of 10.00 months (95%CI: 9.30-11.20), whereas the high-FAR group had a significantly shorter median PFS of 7.80 months (95%CI: 6.40-8.30), with a statistically significant difference between the two groups (P < 0.001, HR = 1.953, 95%CI: 1.421-2.684, Figure 3A). Similarly, the low-FAR group had a median OS of 19.50 months (95%CI: 18.80-22.00), which was significantly longer than the 14.20 months (95%CI: 12.20-16.60) of the high-FAR group (P < 0.001, HR = 2.235, 95%CI: 1.632-3.062, Figure 3B).
Figure 3 Effects of different fibrinogen to albumin on the long-term prognosis of gastric cancer patients.
A: Kaplan-Meier plot of the fibrinogen-to-albumin ratio (FAR) < 0.08 and FAR ≥ 0.08 groups; B: Kaplan-Meier plot of overall survival in the FAR < 0.08 and FAR ≥ 0.08 groups. FAR: Fibrinogen-to-albumin ratio; HR: Hazard ratio.
Univariate and multifactorial analyses of PFS and OS
In the multivariate Cox regression analysis for PFS, the following independent prognostic factors were identified: ECOG performance status (HR = 1.55, 95%CI: 1.05-2.28), FAR (HR = 1.96, 95%CI: 1.33-2.88), PD-L1 expression (HR = 0.46, 95%CI: 0.29-0.73), and body mass index (BMI) (HR = 0.51, 95%CI: 0.31-0.82) (Table 3). Similarly, the independent prognostic factors for OS, including ECOG performance status (HR = 1.78, 95%CI: 1.21-2.61), FAR (HR = 2.33, 95%CI: 1.59-3.43), tumor stage (HR = 1.93, 95%CI: 1.23-3.03), PD-L1 expression (HR = 0.45, 95%CI: 0.29-0.71), and BMI (HR = 0.61, 95%CI: 0.39-0.96) are summarized in Table 4.
Table 3 Univariate and multivariate analyses of prognostic factors for progression-free survival.
Factors
Univariate
Multivariate
HR (95%CI)
P value
HR (95%CI)
P value
Age (< 60 vs ≥ 60), years
1.12 (0.77-1.63)
0.562
Sex (male vs female)
1.07 (0.74-1.55)
0.725
Site (stomach vs gastric and esophageal binding)
1.22 (0.85-1.53)
0.305
ECOG (0 vs 1)
1.55 (1.05-2.29)
0.027
1.55 (1.05-2.28)
0.028
Histological (others vs adenocarcinoma)
0.74 (0.49-1.14)
0.173
FAR (low vs high)
2.18 (1.49-3.20)
< 0.001
1.96 (1.33-2.88)
< 0.001
Staging (3 vs 4)
1.55 (1.01-2.38)
0.045
1.49 (0.97-2.29)
0.072
CEA (< 3 vs ≥ 3), ng/mL
1.09 (0.75-1.59)
0.655
Liver metastasis (no vs yes)
1.03 (0.70- 1.51)
0.896
Peritoneal metastasis (no vs yes)
1.12 (0.68-1.83)
0.664
EBV status (infect vs no-infect)
0.85 (0.51- 1.40)
0.517
PD-L1 expression (CPS < 5 vs CPS ≥ 5)
0.48 (0.31-0.75)
0.001
0.46 (0.29-0.73)
< 0.001
MMR status (dMMR vs pMMR)
2.11 (0.77-5.74)
0.144
BMI (< 25 vs ≥ 25), kg/m2
0.47 (0.29-0.76)
0.002
0.51 (0.31-0.82)
0.006
Table 4 Univariate and multivariate analyses of prognostic factors for overall survival.
Factors
Univariate
Multivariate
HR (95%CI)
P value
HR (95%CI)
P value
Age (< 60 vs ≥ 60), years
1.12 (0.77-1.62)
0.563
Sex (male vs female)
0.97 (0.67-1.40)
0.856
Site (stomach vs gastric and esophageal binding)
1.11 (0.76-1.61)
0.595
ECOG (0 vs 1)
1.81 (1.23-2.65)
0.002
1.78 (1.21-2.61)
0.003
Histological (others vs adenocarcinoma)
0.77 (0.50-1.18)
0.234
FAR (low vs high)
2.56 (1.75-3.76)
< 0.001
2.33 (1.59-3.43)
< 0.001
Staging (3 vs 4)
1.95 (1.25-3.05)
0.004
1.93 (1.23-3.03)
0.005
CEA (< 3 vs ≥ 3), ng/mL
1.08 (0.74-1.57)
0.705
Liver metastasis (no vs yes)
1.20 (0.82- 1.75)
0.354
Peritoneal metastasis (no vs yes)
1.21 (0.73-1.98)
0.459
EBV status (infect vs no-infect)
0.71 (0.42- 1.20)
0.200
PD-L1 expression (CPS < 5 vs CPS ≥ 5)
0.49 (0.31-0.77)
0.002
0.45 (0.29-0.71)
< 0.001
MMR status (dMMR vs pMMR)
1.38 (0.61-3.16)
0.441
BMI (< 25 vs ≥ 25), kg/ m2
0.59 (0.36-0.89)
0.013
0.61 (0.39-0.96)
0.033
Validation of prognostic models
The 260 patients were divided into a training set (n = 182) and an internal validation set (n = 78) in a 7:3 ratio. The baseline characteristics of both groups are shown in Table 5. Based on the independent prognostic factors identified by the Cox multivariate regression analysis for OS, we developed prognostic models for 12, 15, and 18 months (Figure 4). The Bootstrap method (1000 resamples) was used to determine the C-index and generate calibration curves to evaluate the predictive accuracy of the models. The C-index for the training set was 0.74 (95%CI: 0.69-0.78), while the C-index for the internal validation set was 0.73 (95%CI: 0.66-0.79). For the 12-month model, the AUC for the training set was 0.882 (95%CI: 0.828-0.936), and for the internal validation set 0.855 (95%CI: 0.771-0.940) (Figure 5A). The 15-month AUC for the training set was 0.821 (95%CI: 0.756-0.886), and for the internal validation set 0.779 (95%CI: 0.668-0.890) (Figure 5B). Similarly, the 18-month AUC for the training set was 0.821 (95%CI: 0.752-0.889), and for the internal validation set 0.773 (95%CI: 0.659-0.887) (Figure 5C). In addition, the validation of the reliability of the model, by plotting calibration curves for the 12, 15, and 18-month time points, revealed that the predicted risk closely matched the actual outcomes, demonstrating the good accuracy of the model (Figure 6).
Figure 5 Graph depicting the operating characteristic evaluation plot for a prognostic model.
A: Graph showing the training set and validation set receiver operating characteristic (ROC) evaluation plots for 12-month prognostic prediction model; B: Graph showing the training set and validation set ROC evaluation plots for 15-month prognostic prediction model; C: Graph showing the training set and validation set ROC evaluation plots for 18-month prognostic prediction model. AUC: Area under the curve.
Figure 6 Graph illustrating the calibration plots for a prognostic model.
A: Calibration plots for the training set 12-month overall survival (OS); B: Calibration plots for the validation set 12-month OS; C: Calibration plots for the training set 15-month OS; D: Calibration plots for the validation set 15-month OS; E: Calibration plots for the training set 18-month OS; F: Calibration plots for the validation set 18-month OS.
Table 5 Comparison of features between the training and validation sets, n (%).
Characteristic
Test (n = 78)
Train (n = 182)
χ2
P value
Age, years
0.80
0.370
< 60
39 (50.00)
80 (43.96)
≥ 60
39 (50.00)
102 (56.04)
Sex
0.21
0.645
Female
37 (47.44)
92 (50.55)
Male
41 (52.56)
90 (49.45)
BMI, kg/m2
0.21
0.054
< 25
66 (84.62)
134 (73.63)
≥ 25
12 (15.38)
48 (26.37)
FAR
0.62
0.432
Low
34 (43.59)
89 (48.90)
High
44 (56.41)
93 (51.10)
ECOG
0.21
0.647
0
50 (64.10)
122 (67.03)
1
28 (35.90)
60 (32.97)
Site
0.87
0.350
Stomach
42 (53.85)
86 (47.25)
Gastric and esophageal binding
36 (46.15)
96 (52.75)
Histological
1.74
0.188
Adenocarcinoma
25 (32.05)
44 (24.18)
Other
53 (67.95)
138 (75.82)
Staging
0.77
0.379
3
27 (36.62)
53 (29.12)
4
51 (65.38)
129 (70.88)
CEA, ng/mL
3.60
0.058
< 3
39 (50.00)
68 (37.36)
≥ 3
39 (50.00)
114 (62.64)
Liver metastasis
0.59
0.441
No
54 (69.23)
114 (64.29)
Yes
24 (30.77)
68 (37.36)
Peritoneal metastasis
1.04
0.308
No
61 (78.21)
152 (83.52)
Yes
17 (21.97)
30 (16.48)
EBV status
0.00
0.971
No-infect
65 (83.33)
152 (83.52)
Infect
13 (16.67)
30 (16.48)
PD-L1 expression
0.29
0.588
CPS < 5
14 (17.95)
38 (4.40)
CPS ≥ 5
64 (82.05)
174 (95.60)
MMR status
0.00
1.000
pMMR
4 (5.13)
8 (4.40)
dMMR
74 (94.87)
174 (95.60)
AEs
The most common AEs in both groups were anemia, leukopenia, neutropenia, thrombocytopenia, fatigue, nausea, and hand-foot syndrome. No patients discontinued treatment due to AEs, and there was no significant difference in the incidence of all-grade AEs between the two groups (Table 6).
Table 6 Adverse events associated with sintilimab plus chemotherapy in gastric cancer patients, n (%).
Variables
Low FAR (n = 123)
High FAR (n = 137)
χ2
P value
All grades: Anemia
72 (58.54)
84 (61.31)
0.21
0.648
All grades: Leukopenia
68 (55.28)
80 (58.39)
0.26
0.613
All grades: Neutropenia
48 (39.02)
55 (40.15)
0.03
0.854
All grades: Thrombocytopenia
35 (28.46)
43 (31.39)
0.27
0.607
All grades: Fatigue
35 (28.46)
45 (32.85)
0.59
0.444
All grades: Nausea
33 (26.83)
42 (30.66)
0.46
0.496
All grades: Hand-foot syndrome
32 (26.02)
38 (27.74)
0.10
0.755
All grades: Elevated ALT
30 (24.39)
34 (24.82)
0.01
0.936
All grades: Elevated AST
30 (24.39)
32 (23.36)
0.04
0.845
All grades: Pyrexia
28 (22.76)
32 (23.36)
0.01
0.910
All grades: Diarrhea
27 (21.95)
33 (24.09)
0.17
0.683
All grades: Hypertension
20 (16.26)
27 (19.71)
0.52
0.471
All grades: Pneumonitis
18 (14.63)
22 (16.06)
0.10
0.751
All grades: Proteinuria
12 (9.76)
18 (13.14)
0.73
0.394
All grades: Mucositis
10 (8.13)
15 (10.95)
0.59
0.441
≥ 3 grades: Anemia
18 (14.63)
22 (16.06)
0.10
0.751
≥ 3 grades: Leukopenia
15 (12.20)
20 (14.60)
0.32
0.571
≥ 3 grades: Neutropenia
15 (12.20)
18 (13.14)
0.02
0.880
≥ 3 grades: Thrombocytopenia
12 (9.76)
16 (11.68)
0.05
0.576
≥ 3 grades: Fatigue
12 (9.76)
20 (14.60)
0.05
0.820
≥ 3 grades: Diarrhea
8 (6.50)
12 (8.76)
0.46
0.496
≥ 3 grades: Nausea
8 (6.50)
10 (7.30)
0.06
0.801
≥ 3 grades: Elevated ALT
5 (4.07)
7 (5.11)
0.16
0.689
≥ 3 grades: Elevated AST
4 (3.25)
7 (5.11)
0.55
0.458
≥ 3 grades: Hand-foot syndrome
4 (3.25)
5 (3.65)
0.03
0.861
≥ 3 grades: Hypertension
3 (2.45)
4 (2.92)
0.06
0.811
≥ 3 grades: Pneumonitis
3 (2.45)
3 (2.19)
0.02
0.894
≥ 3 grades: Mucositis
0 (0.00)
0 (0.00)
-
-
≥ 3 grades: Pyrexia
0 (0.00)
0 (0.00)
-
-
≥ 3 grades: Proteinuria
0 (0.00)
0 (0.00)
-
-
DISCUSSION
This study is the first to demonstrate the dual predictive value of the FAR in patients with advanced HER2-negative GC undergoing sintilimab-based immunotherapy combined with chemotherapy. A high FAR was significantly associated with lower ORR (10.22% vs 19.51%, P = 0.034), lower DCR (34.31% vs 49.59%, P = 0.013), shorter PFS (7.8 months vs 10.0 months, P < 0.001, 95%CI: 1.421-2.684), and shorter OS (14.2 months vs 19.5 months, P < 0.001, 95%CI: 1.632-3.062). In multivariate analysis, FAR was identified as an independent prognostic factor for both OS and PFS, a finding that is consistent with previous studies on breast and prostate cancers. However, its underlying mechanisms in GC may be tumor-specific.
Increased fibrinogen levels not only promote angiogenesis by activating the coagulation cascade but also create a physical barrier that blocks cytotoxic T-cell infiltration[20,21]. Conversely, hypoalbuminemia indicates chronic inflammation-induced T cell exhaustion and reduced drug metabolism efficiency[22-24]. A high FAR may modulate the tumor immune microenvironment through various mechanisms. First, the pro-inflammatory and pro-tumorigenic effects of fibrinogen: As an acute-phase protein, fibrinogen binds to integrin receptors (e.g., αvβ3), activating the NF-κB signaling pathway, which induces vascular endothelial growth factor and interleukin (IL)-8 secretion, thereby promoting tumor angiogenesis and recruiting immunosuppressive cells such as MDSCs[25-28]. Additionally, fibrinogen deposition leads to the formation of an extracellular matrix network, physically blocking CD8+ T-cell infiltration into the tumor core while upregulating PD-L1 expression, ultimately resulting in T-cell exhaustion[29-31].
Second, the metabolic and immunosuppressive effects of hypoalbuminemia: Low albumin levels not only indicate malnutrition but also exacerbate tissue edema by reducing colloid osmotic pressure[32], thereby limiting chemotherapy drug penetration and infiltration of immune cells into the tumor. The loss of the antioxidant function of albumin may further increase reactive oxygen species accumulation[33], thereby activating DNA damage repair pathways (e.g., PARP activation), which in turn enhances chemoresistance[34,35].
Third, the synergistic effect of the inflammation-nutrition imbalance: A high FAR may activate the JAK-STAT3 signaling pathway, amplifying the IL-6/IL-6R signaling cascade, leading to a vicious cycle of systemic inflammation and formation of a local immunosuppressive microenvironment[36,37]. This dynamic imbalance may reduce the efficacy of PD-1 inhibitors and promote the expansion of Tregs[38,39], ultimately enhancing tumor immune evasion[40].
The nomogram model, integrating PD-L1 CPS, FAR, BMI, ECOG performance status, and tumor stage, provides a multidimensional prognostic framework, bridging local tumor immune phenotypes with the host systemic status. PD-L1 CPS and FAR, as core markers of the tumor microenvironment and systemic inflammation-metabolism, respectively, create a complementary prognostic system. BMI and ECOG performance status, on the other hand, address metabolic reserves and functional heterogeneity in treatment tolerance. The model demonstrated high predictive accuracy in both the training and validation cohorts, offering dynamic survival probability predictions at 12, 15, and 18 months, thereby facilitating a more refined approach to risk stratification and clinical decision-making.
Although patients in the high-FAR group had poorer prognosis, there was no significant difference in the incidence of all-grade treatment-related AEs between the two groups, and no patients discontinued treatment due to toxicity. This suggests that for patients with high FAR, treatment strategies should focus on adjunct anticoagulation therapy or nutritional support rather than simply reducing treatment intensity.
It is important to note that this study has certain limitations. First, as a single-center retrospective study, potential selection bias cannot be ruled out. Although bootstrap resampling and a random splitting ratio of 7:3 were used for validation, future prospective, multicenter, and larger randomized controlled trials are needed to validate these findings. Second, we focused on FAR as the main biomarker, and did not include in our analysis other common inflammatory biomarkers such as C-reactive protein (CRP) and the neutrophil-to-lymphocyte ratio (NLR). Given the scope and primary objectives of our study, we made a conscious decision to focus on FAR to maintain the clarity and authenticity of the research. Including additional biomarkers would have extended the study beyond its original purpose, potentially introducing complexity and confounding factors that could affect the interpretation of results. Future studies may consider comparing FAR with CRP and NLR to assess the relative prognostic value of these biomarkers. Moreover, FAR is a non-specific marker that reflects both inflammation and nutritional status. As such, it may be elevated in patients with various serious conditions, not just in advanced HER2-negative GC. Therefore, increased FAR should be interpreted within the broader context of systemic inflammation and malnutrition. Lastly, the generalizability of the model needs further validation in diverse populations and alternative immunotherapy regimens (e.g., PD-1/CTLA-4 dual blockade). Future studies should leverage single-cell sequencing and spatial transcriptomics to elucidate the spatial interactions between FAR and the immune microenvironment, as well as investigate the clinical translational potential of targeting the inflammation-nutrition axis.
As an integrated marker of inflammation and nutritional status, the FAR provides a cost-effective and accessible solution for prognostic stratification in patients with advanced HER2-negative GC undergoing immunochemotherapy. The nomogram model further advances precision medicine by enabling personalized treatment decisions. These findings expand the research dimensions of prognostic biomarkers in GC and provide new insights into overcoming immune resistance in cancer immunotherapy through novel combination strategies.
CONCLUSION
This study confirms that the FAR is an independent prognostic indicator for patients with advanced HER2-negative GC undergoing sintilimab-based immunotherapy combined with chemotherapy, with high FAR significantly associated with poorer survival outcomes.
Footnotes
Provenance and peer review: Unsolicited article; Externally peer reviewed.
Peer-review model: Single blind
Specialty type: Oncology
Country of origin: China
Peer-review report’s classification
Scientific Quality: Grade B, Grade C
Novelty: Grade B, Grade C
Creativity or Innovation: Grade B, Grade C
Scientific Significance: Grade C, Grade C
P-Reviewer: Batta A S-Editor: Li L L-Editor: A P-Editor: Zheng XM
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