A nomogram for predicting progression-free survival in stage III–IV gastric cancer based on post-treatment cholinesterase and C-reactive protein

Introduction

Gastric cancer is still one of the most frequent cancers worldwide, ranking seventh in both incidence and mortality in 2022 (1). Approximately 30–40% of patients are diagnosed in an advanced stage (stage III–IV) (2), and despite the availability of first-line systemic therapy, individual prognoses vary greatly (3). Traditional prognostic markers such as tumor-node-metastasis (TNM) staging and performance status (PS) are insufficient to properly represent the heterogeneity of treatment outcomes in this cohort (4).

Systemic inflammation is a well-known cause of tumor development. C-reactive protein (CRP), a sensitive inflammatory measure, has been repeatedly linked to a poor prognosis in a variety of malignancies, including gastric cancer, via immunosuppressive microenvironment modification and angiogenesis promotion (5-8), a finding further supported by a recent metaanalysis (9,10). In parallel, serum cholinesterase (CHE) is a sign of nutritional status and liver synthetic function; low CHE levels indicate malnutrition and have been related to poor outcomes in cancer patients (11,12). Emerging evidence suggests that CHE may also play a role in immuno-inflammatory control through the cholinergic anti-inflammatory pathway (13). Although CRP and CHE are considered a “nutrition-inflammation” phenotype, their combined predictive significance in gastric cancer remains largely understudied, especially when assessed at specific on-treatment time points during therapy.

Recent research has demonstrated the potential of nomograms that incorporate inflammatory and immunological indicators to improve prognosis in gastric cancer (14,15), and a contemporary example is the Controlling Nutritional Status (CONUT)based nomogram for resectable gastric cancer (10). However, most available models are based on baseline measures and do not account for the host response at posttreatment time points. While individual studies have proven the independent prognostic relevance of CHE and CRP in gastric cancer (5,11,12), few have studied their utility at specific time points during first-line treatment, such as after two cycles of therapy (16,17). Furthermore, the biological mechanisms that drive changes in these indicators are poorly understood.

To overcome this gap, we expected that combining post-treatment serum CHE and CRP levels evaluated after two cycles of first-line therapy would provide a superior predictive model than models based on either marker alone or static baseline values. This study aims to design and internally test a nomogram including post-therapy CHE, CRP, and other clinical factors to predict progression-free survival (PFS) in patients with stage III–IV gastric adenocarcinoma undergoing first-line treatment. We investigated biological pathways linked to the “nutrition-inflammation” phenotype using publicly accessible transcriptome data. We present this article in accordance with the TRIPOD reporting checklist (available at https://jgo.amegroups.com/article/view/10.21037/jgo-2026-0231/rc) (18).

Methods

Study design and population

Between January 2018 and January 2025, this single-center retrospective cohort study enrolled patients with stage III–IV gastric adenocarcinoma who had been diagnosed and were scheduled to receive first-line systemic therapy at Affiliated Huai’an Hospital of Xuzhou Medical University. The inclusion criteria were as follows: (I) age ≥18 years; (II) histopathologically confirmed gastric adenocarcinoma; (III) initial clinical diagnosis of stage III or IV disease according to the 8^th edition of the American Joint Committee on Cancer (AJCC) staging manual; and (IV) complete records of serum CHE and CRP levels before and after two cycles of therapy. Of note, all included stage III patients had locally advanced unresectable disease as determined by multidisciplinary team evaluation. Thus, both stage III and IV patients received first‑line systemic therapy with palliative or definitive intent, rather than neoadjuvant therapy followed by surgery. This common treatment framework justified their combined analysis for predicting PFS. Patients were excluded if they had: (I) concurrent active malignancies; (II) severe hepatic, renal, or cardiac dysfunction as determined by clinical records; (III) acute infection or active inflammatory disease at baseline; or (IV) insufficient clinical data or complex treatment courses that precluded efficacy evaluation. The final analysis comprised a total of 104 patients.

The study was authorized by the Ethics Committee of Affiliated Huai’an Hospital of Xuzhou Medical University (approval No. HEYLL2025133) and was carried out in accordance with the Declaration of Helsinki and its subsequent amendments. The requirement for informed consent was waived due to the study’s retrospective nature. A convenience sample was used; all patients meeting the eligibility criteria during the study period (January 2018 to January 2025) were included. No a priori sample size calculation was performed for this retrospective exploratory study. The sample size of 104 patients was determined by the availability of complete data meeting the inclusion criteria.

Variables and endpoints

Data were extracted from electronic medical records, including demographic characteristics [age, sex, body mass index (BMI), Eastern Cooperative Oncology Group performance status (ECOG PS)], tumor characteristics [primary tumor location, histological differentiation grade, human epidermal growth factor receptor 2 (HER2) status, clinical stage], metastasis status (presence of liver metastasis and peritoneal metastasis at baseline; the number of metastatic sites was recorded. The predictive markers of interest were serum CHE (U/L) and CRP (mg/L) levels after two cycles of treatment. Pre-treatment carcinoembryonic antigen (CEA) levels were also measured.

Blood samples were taken 1 week before therapy began. Post-treatment samples were acquired during regular follow-up following the conclusion of the second cycle and before the commencement of the third cycle (about weeks 4–6), in order to capture treatment-induced host systemic responses and correspond with the initial efficacy assessment window. The two‑cycle time point was specifically chosen because it coincides with the first routine radiographic tumor assessment [Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST 1.1)] in clinical practice. Therefore, integrating biomarker measurements at this time can directly inform clinical decisions, such as whether to continue, switch, or intensify therapy after the initial efficacy evaluation.

First‑line systemic therapy regimens were not uniform but were selected by treating physicians based on tumor characteristics (e.g., HER2 status) and patient performance status. The backbone chemotherapy for the majority of patients consisted of a fluoropyrimidine (capecitabine or S‑1) combined with a platinum agent (oxaliplatin or cisplatin). Specific combinations included chemotherapy alone, chemotherapy plus targeted therapy (anti‑HER2 or anti‑CLDN18.2), chemotherapy plus immunotherapy [programmed cell death-1 (PD-1)/programmed cell death-ligand 1 (PD-L1) inhibitors], or combinations of the above; some patients also received local radiotherapy as clinically indicated. Details of treatment allocation are summarized in Table 1.

The primary outcome was PFS, which was defined as the period from the start of first-line therapy to the first occurrence of disease progression (as measured by RECIST 1.1) or death from any cause. The secondary goal was overall survival (OS), defined as the time between therapy beginning and death from any cause.

Laboratory personnel measuring serum CHE and CRP levels were blinded to patient outcomes, as the biomarker measurements were performed prior to the occurrence of progression events.

Due to the retrospective nature of the study, formal blinding of outcome assessors was not prospectively implemented. However, disease progression was determined based on imaging findings (RECIST 1.1 criteria) by radiologists and oncologists who were unaware of the patients’ post-treatment CHE and CRP levels at the time of assessment.

Statistical analysis

Patients with missing data for key variables (including CHE, CRP, or essential clinical follow-up) were excluded from the analysis. Therefore, no missing data imputation was required for the final cohort of 104 patients (complete-case analysis).

All statistical analyses were carried out using R software (version 4.0.2; R Foundation for Statistical Computing, Vienna, Austria). Continuous variables were represented as medians (ranges), and categorical variables as frequencies (percentages). All tests were two-sided, and P<0.05 indicated statistical significance. P values were reported following the journal’s guidelines: P<0.001 was stated as “P<0.001”; for P values between 0.001 and 0.01, three decimal places were used; for P≥0.01, two decimal places were used; and exact P values were provided for values close to 0.05.

Determination and validation of prognostic cut-off values

The optimally selected rank statistics approach (survminer software) was used to find the best prognostic cut-off values for post-treatment CHE and CRP based on their correlation with PFS across the complete population (n=104). These continuous variables were then converted to binary categorical variables. To evaluate the durability of these data-driven cut-offs, bootstrap internal validation with 1,000 resampling iterations was undertaken, and the original cut-off values, together with their bootstrap 95% confidence intervals (CIs), were published.

Prognostic factor screening and model construction

The first step was to run a univariate Cox proportional hazards regression. To build a strong model, a pre-specified variable pool was defined, including variables with P<0.05 in univariate analysis and key variables chosen based on gastric cancer prognostic consensus and clinical guidelines (ECOG PS, number of metastatic sites, and HER2 status). All variables in this pool were added to an initial multivariate Cox model, and a backward stepwise selection technique (exclusion criterion: likelihood ratio test P>0.05) was performed to identify independent prognostic factors and create the final multivariate model.

The risk function for the Cox model was represented as:

h(t|X)=ho(t)exp(β1X1+β2X2+⋯+βpXp)[1]

Nomogram construction

Using the rms program and the regression coefficients from the final multivariate Cox model, a nomogram predicting 6-, 12-, and 18-month PFS probability was created. Each patient’s linear predictor (LP) was computed as:

LP=β_CHE∗CHE_group+β_CRP∗CRP_group+β_PM∗Peritoneal_Metastasis+β_MC∗Metastasis_Count[2]

The nomogram translated each variable’s LP into a 0–100 point scale on the “Points” axis, with the total points being the aggregate of scores for all variables. Survival probabilities were calculated from total points using the relationship:

S(t|X)=S_0(t)∧{\exp(LP)}[3]

Model performance evaluation and validation

Model performance was evaluated based on discrimination, calibration, prediction accuracy, clinical utility, and overfitting.

• Discrimination: the concordance index (C-index) and its 95% CI were calculated.
• Calibration curves were constructed using bootstrap resampling (1,000 iterations) to assess the agreement between anticipated and measured PFS probabilities.
• Predictive accuracy was determined by generating time-dependent receiver operating characteristic (ROC) curves and computing the area under the curve (AUC) at 6, 12, and 18 months.
• Clinical utility: a decision curve analysis (DCA) was used to determine the model’s net clinical benefit across a range of threshold probabilities.
• Overfitting: to quantify optimism, 100 repetitions of 5-fold cross-validation were performed; the average C-index from cross-validation and optimism (original C-index minus average cross-validated C-index) were reported, providing an optimism-corrected C-index.

Risk stratification and survival analysis

Patients were divided into low- and high-risk groups based on their median total nomogram score. PFS and OS Kaplan-Meier curves were produced, and the log-rank test was used to assess differences between groups.

Bioinformatics analysis

To investigate potential underlying mechanisms, RNA-sequencing data from 93 stage III–IV gastric adenocarcinoma samples were retrieved from The Cancer Genome Atlas (TCGA) database. Gene expression values were converted using log₂[fragments per kilobase of transcript per million (FPKM) +1]. The Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used to predefine 21 nutrition and inflammation-related pathways (Table S1). Each pathway’s enrichment score was calculated using gene set variation analysis (GSVA) (Figure S1). Using OS as the endpoint, univariate Cox regression was performed on pathway scores to find important pathways (P<0.05). These pathways were subsequently validated in an independent Asian Cancer Research Group (ACRG). Spearman correlation coefficients were determined between the GSVA scores of key pathways and the expression of M2-type macrophage marker genes to assess their relationship with the tumor immune microenvironment.

Results

Patient baseline characteristics

The study included 104 eligible patients with treatment-naïve stage III–IV gastric cancer (see Figure 1). The baseline clinicopathological features are summarized in Table 1. The median age was 70 years (range, 36–91 years), and 72.1% (75/104) were men. The vast majority of patients (76.9%, 80/104) had stage IV illness. The median post-treatment CHE level was 5,738.5 U/L (range, 1,865–10,734 U/L), while the median post-treatment CRP level was 7.9 mg/L (range, 0.2–88.42 mg/L). Baseline median CHE was 6,402.5 U/L (range, 1,865–10,734 U/L), and baseline median CRP was 3.6 mg/L (range, 0.2–88.4 mg/L).

Figure 1 Flowchart of patient selection process. A total of 104 patients with stage III–IV gastric adenocarcinoma who received first-line therapy were included after applying the inclusion and exclusion criteria.

Determination and validation of optimal cut-off values for prognostic markers

Maximally determined rank statistics revealed the best cut-off values for post-treatment CHE and CRP to be 4,865 U/L and 2.58 mg/L, respectively. Bootstrap resampling (1,000 iterations) validated the stability of these cut-offs. The median bootstrap cut-off for CHE was 4,865 U/L (95% CI: 4,560–7,619 U/L), and for CRP was 2.58 mg/L (95% CI: 0.7–8.88 mg/L). Both original cut-off values were within their respective bootstrap confidence ranges, demonstrating robustness (Figure S2).

Univariate and multivariate Cox regression analyses of prognostic factors

Univariate Cox regression of baseline markers showed that baseline CHE was not significantly associated with PFS (HR =1.00; 95% CI: 1.00–1.00; P=0.22), and baseline CRP showed a trend toward increased risk without reaching statistical significance (HR =1.008; 95% CI: 0.999–1.018; P=0.09). During the follow-up period, disease progression or death occurred in 88 of the 104 patients (84.6%). Univariate Cox regression revealed peritoneal metastasis (HR =2.17; 95% CI: 1.36–3.46; P=0.001), ≥3 metastatic sites (HR =1.74; 95% CI: 1.04–2.88; P=0.03), positive HER2 status (HR =0.42; 95% CI: 0.18–0.97; P=0.041), low post-treatment CHE (≤4,865 U/L; HR =0.27; 95% CI: 0.16–0.45; P<0.001), and high post-treatment CRP (>2.58; HR =2.33; 95% CI: 1.50–3.61; P<0.001).

Variables with P<0.05 in univariate analysis were combined with pre-specified critical factors (ECOG PS, number of metastatic locations, HER2 status) for multivariate Cox regression with backward stepwise selection. The final model found that decreased post-treatment CHE was an independent protective factor for PFS (HR =0.36; 95% CI: 0.20–0.63; P<0.001). High post-treatment CRP (HR =1.67; 95% CI: 0.99–2.84; P=0.056) and peritoneal metastasis (HR =1.66; 95% CI: 0.93–2.95; P=0.09) indicated an increased risk (Figure 2, Table 2).

Figure 2 Univariate and multivariate Cox regression analyses for PFS. (A) Forest plot of univariate analysis. (B) Multivariate analysis confirming low post-treatment CHE (≤4,865 U/L) as an independent protective factor (HR =0.36, 95% CI: 0.20–0.63, P<0.001). High post-treatment CRP (>2.58 mg/L) and peritoneal metastasis showed trends toward increased risk (P=0.056 and P=0.09, respectively). CEA, carcinoembryonic antigen; CHE, cholinesterase; CI, confidence interval; CRP, C-reactive protein; ECOG, Eastern Cooperative Oncology Group; HER2, human epidermal growth factor receptor 2; HR, hazard ratio; PFS, progression-free survival.

Nomogram construction, performance evaluation, risk stratification, and survival analysis

A nomogram with the independent predictors was created to estimate the 6-, 12-, and 18-month PFS probability (Figure 3). The model performed well in discrimination, with a C-index of 0.709 (95% CI: 0.652–0.766). To assess overfitting, 100 five-fold cross-validation runs were undertaken, providing a mean C-index of 0.667 [standard deviation (SD) =0.0188] and an optimism of 0.042, equating to an optimism-corrected C-index of 0.751 (Figure S3).

Figure 3 Nomogram for predicting 6-, 12-, and 18-month PFS. The nomogram integrates post-treatment CHE level, post-treatment CRP level, peritoneal metastasis status, and number of metastatic sites. To use the nomogram, locate each variable on its corresponding axis, draw a vertical line to the “Points” axis to obtain the score for each variable, sum the scores, and locate the total score on the “Total points” axis. Draw a vertical line from the total score to the survival axes to estimate the probability of 6-, 12-, and 18-month PFS. ECOG: 1, ambulatory; 2, capable of self-care. Metastasis count: 0, ≤2; 1, ≥3. HER2: 0, negative; 1, positive. Peritoneal metastasis: 0, no; 1, yes. CHE, cholinesterase; CRP, C-reactive protein; ECOG, Eastern Cooperative Oncology Group; HER2, human epidermal growth factor receptor 2; PFS, progression-free survival.

Time-dependent ROC analysis yielded AUCs of 0.789, 0.836, and 0.760 for 6-, 12-, and 18-month PFS, respectively (Figure 4A). Calibration curves showed a high match between expected and observed PFS rates (Figure 4B-4D). DCA demonstrated that the nomogram produced net clinical benefit across a wide variety of threshold probabilities, beating the ‘treat-all’ and ‘treat-none’ strategies, as well as a model based exclusively on peritoneal metastasis (Figure 4E).

Figure 4 Performance evaluation of the nomogram. (A) Time-dependent ROC curves for predicting 6-, 12-, and 18-month PFS, with AUC values of 0.789, 0.836, and 0.760, respectively. (B-D) Calibration curves for 6- (B), 12- (C), and 18-month (D) PFS, showing good agreement between nomogram-predicted probabilities and observed outcomes. (E) DCA demonstrating the clinical net benefit of the nomogram across a range of threshold probabilities, compared with a treat-all strategy, a treat-none strategy, and a model based solely on peritoneal metastasis. AUC, area under the curve; DCA, decision curve analysis; PFS, progression-free survival; ROC, receiver operating characteristic.

The median nomogram total score (1.01 points) was used to divide patients into low- and high-risk groups. Kaplan-Meier analysis found that the high-risk group had significantly lower PFS and OS compared to the low-risk group (both log-rank P<0.001; see Figure S4A,S4B). OS had a hazard ratio (HR) of 2.73 (95% CI: 1.70–4.38; P<0.001). In addition, post-treatment CHE levels were positively connected with PFS (Spearman’s r=0.47), whereas CRP levels were negatively correlated (Spearman’s r=−0.22; Figure S4C).

Bioinformatics analysis: exploring the underlying mechanisms of the “nutrition-inflammation” phenotype

Transcriptomic study of 93 stage III–IV gastric adenocarcinoma samples from TCGA revealed that the “complement and coagulation cascades” pathway was substantially linked with OS (HR =3.38; 95% CI: 1.03–11.17; P=0.045; Figure 5A,5B). This conclusion was confirmed in the independent ACRG cohort, where increased pathway activity was associated with a lower OS (P=0.045; Figure 5C,5D). Multivariate analysis revealed an independent predictive tendency for this pathway (HR =1.77; 95% CI: 0.98–3.18; P=0.057; Figure S5). Furthermore, pathway activity was positively linked with M2-type macrophage marker expression (MRC1, MS4A4A, and VSIG4) and varied among AJCC stages (Figure 5E,5F).

Figure 5 Bioinformatics analysis of the “complement and coagulation cascades” pathway in gastric adenocarcinoma. (A) Heatmap showing GSVA enrichment scores of 21 predefined KEGG nutrition- and inflammation-related pathways across stage III–IV gastric adenocarcinoma samples from the TCGA database, stratified by AJCC stage and vital status. (B) Forest plot of univariate Cox regression analyses for the 21 KEGG pathways, with the “complement and coagulation cascades” pathway showing a significant association with OS (HR =3.38, 95% CI: 1.03–11.17, P=0.045). (C) Kaplan-Meier survival curves for OS stratified by high vs. low activity of the complement and coagulation cascades pathway in the TCGA cohort (P=0.02). (D) Kaplan-Meier curves confirming the prognostic significance of this pathway in the ACRG cohort (P=0.045). (E) Spearman correlation analysis showing positive correlations between pathway activity and expression of M2-type macrophage markers (MRC1, MS4A4A, VSIG4). (F) Distribution of pathway enrichment scores across different AJCC stages. ACRG, Asian Cancer Research Group; AJCC, American Joint Committee on Cancer; CI, confidence interval; GSVA, gene set variation analysis; HR, hazard ratio; KEGG, Kyoto Encyclopedia of Genes and Genomes; OS, overall survival; STAD, stomach adenocarcinoma; TCGA, The Cancer Genome Atlas.

Discussion

In this study, we created and internally validated a nomogram integrating post-treatment blood CHE and CRP levels evaluated after two cycles of first-line therapy to predict PFS in patients with stage III–IV gastric adenocarcinoma. The model performed well in discrimination, with a concordance score of 0.709 (95% CI: 0.652–0.766) and time-dependent AUCs of 0.789, 0.836, and 0.760 for 6-, 12-, and 18-month PFS. Calibration analysis and DCA validated its dependability and clinical usefulness. Recently, An et al. also demonstrated the prognostic value of an immunenutritional index in gastric cancer patients undergoing curative resection (19), supporting the broader relevance of nutrition‑inflammation‑based risk stratification. Low post-treatment CHE (≤4,865 U/L) was found to be an independent protective factor (HR =0.36; 95% CI: 0.20–0.63; P<0.001). However, excessive CRP (>2.58 mg/L) and peritoneal metastasis were associated with increased risk (P=0.056 and P=0.09, respectively).

Our findings are consistent with prior studies demonstrating the predictive relevance of inflammatory and nutritional indicators in gastric cancer (5,11,12). Unlike models based exclusively on pre-treatment static measures (17,20), our nomogram incorporates post‑treatment levels after two cycles of therapy to capture the developing host response during treatment. This strategy is in line with the rising emphasis on posttreatment biomarker assessment (21). We used optimally selected rank statistics to construct appropriate cut-offs, as previously published in colon cancer (22). Model stability was rigorously verified using bootstrap resampling and repeated cross-validation. Adherence to the TRIPOD criteria (18,23) improves the transparency and reproducibility of our work. Our model outperformed previous nomograms for advanced gastric cancer (24,25) by including post-treatment levels, resulting in comparable or superior discrimination.

In our multivariable analysis, peritoneal metastasis and the number of metastatic sites were preserved as independent predictors, which is consistent with recognized prognostic variables in advanced gastric cancer (2,4). Although CEA is a widely used tumor marker, it was not included in the final model. This is most likely due to its collinearity with other variables indicating tumor load (e.g., peritoneal metastasis) and static nature (23). This discovery supports the findings of Sun et al. (24) and emphasizes the necessity of preferring dynamic and functionally relevant markers over static ones.

Identifying low post-treatment CHE as a protective factor requires additional discussion. CHE is a measure of nutritional status and liver function. It also contributes to the cholinergic anti-inflammatory system by hydrolyzing acetylcholine (13). In our group, low CHE levels, which are frequently associated with malnutrition and inflammation, were found to be associated with longer PFS. This could indicate a “decompensated” condition in advanced disease, in which persistent inflammation and caloric depletion overpower the cholinergic regulating ability, making low CHE a surrogate for severe systemic stress (26). The concurrent rise of CRP, a canonical inflammatory marker, supports the “decompensated dystrophic inflammatory” phenotype, which may contribute to treatment tolerance and rapid progression.

To investigate the biological foundations of this clinical phenotype, we examined public transcriptome data and discovered that the “complement and coagulation cascades” pathway was considerably elevated in advanced gastric cancer and linked with lower survival. This route connects innate immunity, inflammation, and coagulation (27). Its activity has a favorable correlation with M2-type macrophage markers, indicating a potential function in building an immunosuppressive tumor microenvironment. We suggest that systemic inflammation (as indicated by high CRP) may drive local activation of this pathway, boosting M2 polarization and tumor growth (28), whereas low CHE may signal hepatic dysfunction and metabolic imbalance, perpetuating the vicious cycle. These hypotheses require experimental validation.

The clinical utility of our nomogram stems from its capacity to stratify patients by risk after two cycles of therapy—an important decision point for prospective treatment adjustments. Our methodology, unlike classic TNM staging or static inflammation scores (4,17,20), considers posttreatment host-response indications to generate personalized PFS predictions. The net advantage of DCA was validated across several threshold probabilities, indicating its potential to lead clinical discussions on treatment intensification or de-escalation strategies. Future efforts should concentrate on external validation in multicenter cohorts and the investigation of additional time points to capture trajectory changes. Integration with imaging features (29) or circulating biomarkers may improve prediction accuracy.

Several limitations should be acknowledged. First, this was a single‑center retrospective study with a relatively small sample size (n=104), which may introduce selection bias and limit generalizability. External validation in larger, prospective multicenter cohorts is necessary. Second, the optimal cut‑off values for CHE and CRP were data‑driven and the variables were dichotomized, which carries risks of overfitting and information loss; our bootstrap and cross‑validation analyses partially mitigated this, but caution is warranted. Third, we only measured CHE and CRP at a single post‑treatment time point (after two cycles). We did not calculate the change from baseline (delta) or incorporate serial measurements; therefore, the term “dynamic” in earlier versions of this manuscript was imprecise—this study captures only one on‑treatment time point, not true longitudinal change. Fourth, first‑line regimens were heterogeneous, and because of the limited sample size we could not include treatment type as a covariate, which may introduce unmeasured confounding. Fifth, although all stage III patients in our cohort were deemed unresectable and thus received first‑line systemic therapy similar to stage IV patients, we acknowledge potential residual heterogeneity between the two stages. Due to the small sample size, we did not stratify by stage; future larger studies should validate the nomogram separately in stage III and IV populations. Sixth, metastatic count was used as a proxy for tumor burden; a more detailed assessment (e.g., imaging‑based body composition) would improve accuracy. Finally, the bioinformatics findings are hypothesis‑generating and require functional validation. Despite these limitations, our work demonstrates that post‑treatment CHE and CRP levels are promising prognostic indicators in advanced gastric cancer.

Conclusions

In conclusion, this study created and internally validated a nomogram incorporating post-treatment serum CHE and CRP levels after two cycles of first-line therapy, which shows potential to predict PFS and allows for risk stratification in patients with stage III–IV gastric adenocarcinomas. Additional bioinformatics research found that the “complement and coagulation cascades” pathway could explain the observed nutrition-inflammation phenotype and its connection with an immunosuppressive tumor microenvironment. These findings provide a clinically accessible tool for individualized risk assessment, as well as preliminary mechanistic insights that urge for additional exploration. External validation in multicenter prospective cohorts is required to ensure the model’s generalizability.

Acknowledgments

The authors thank the Medical Records Department of Affiliated Huai’an Hospital of Xuzhou Medical University for their assistance in data extraction.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2026-0231/rc

Data Sharing Statement: Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2026-0231/dss

Peer Review File: Available at https://jgo.amegroups.com/article/view/10.21037/jgo-2026-0231/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jgo.amegroups.com/article/view/10.21037/jgo-2026-0231/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was authorized by the Ethics Committee of Affiliated Huai’an Hospital of Xuzhou Medical University (approval No. HEYLL2025133) and was carried out in accordance with the Declaration of Helsinki and its subsequent amendments. The requirement for informed consent was waived due to the study’s retrospective nature.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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