Synergistic value of systemic inflammatory and tumor markers in predicting pretreatment distant metastasis of gastric cancer

Abstract

BACKGROUND

Early identification of distant metastasis before treatment is crucial for optimizing therapeutic strategies in patients with gastric cancer. However, conventional imaging modalities are limited in detecting occult metastasis, highlighting the need for reliable, accessible biomarkers to improve pre-treatment risk stratification.

AIM

To develop and validate a predictive model based on the integration of inflammatory and tumor markers for pre-treatment prediction of distant metastasis in gastric cancer.

METHODS

A total of 279 patients with newly diagnosed gastric adenocarcinoma at the Affiliated Hospital of Xuzhou Medical University from January 2020 to December 2024 were retrospectively enrolled and randomly divided into a training set (n = 152) and a validation set (n = 127). Clinical characteristics, peripheral blood inflammatory parameters [neutrophils (NE), lymphocytes (LY), monocytes, platelets], derived inflammatory indices [neutrophil-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, platelet-to-lymphocyte ratio, systemic immune-inflammation index (SII), and systemic inflammation response index (SIRI), and tumor markers (carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA19-9)] were collected. The calculation formulas for the indices were as follows: SII = (NE × platelets)/LY, and SIRI = (NE × monocytes)/LY. Variables associated with distant metastasis were screened using univariate analysis and least absolute shrinkage and selection operator regression. A multivariate logistic regression model was then constructed and internally validated. Model performance was evaluated using the receiver operating characteristic curve, calibration curve, Brier score, and decision curve analysis.

RESULTS

Tumor location, CEA, CA19-9, neutrophil-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, platelet-to-lymphocyte ratio, SII and SIRI were significantly associated with distant metastasis in univariate analysis (P < 0.05). Least absolute shrinkage and selection operator regression identified tumor location, CEA, CA19-9, NE, and LY as candidate predictors. Multivariate analysis revealed that CA19-9 [odds ratio (OR) = 1.019, P = 0.022] and NE (OR = 1.604, P = 0.005) were independent risk factors, whereas LY (OR = 0.244, P = 0.017) and tumor location in the gastric antrum (OR = 0.362, P = 0.036) were protective factors. The combined model achieved an area under the curve of 0.888 (95% confidence interval: 0.832-0.937) in the training set and 0.896 (95% confidence interval: 0.835-0.945) in the validation set. Calibration analysis demonstrated good agreement between predicted and observed outcomes, and decision curve analysis indicated favorable clinical net benefit across a wide range of threshold probabilities.

CONCLUSION

A predictive model incorporating CA19-9, NE, LY, and tumor location demonstrates good discrimination and calibration for predicting distant metastasis before treatment in gastric cancer. This practical and cost-effective model may assist clinicians in early risk stratification and individualized decision-making.

Keywords: Gastric cancer; Distant metastasis; Inflammatory indicators; Tumor markers; Predictive model; Calibration curve

Core Tip: Early and accurate identification of distant metastasis before treatment remains a clinical challenge in gastric cancer. This study developed and internally validated a practical predictive model integrating routinely available inflammatory markers and tumor markers. By combining carbohydrate antigen 19-9, neutrophil count, lymphocyte count, and tumor location, the model demonstrated good discrimination, calibration, and clinical net benefit. The proposed model provides a convenient and cost-effective tool for pre-treatment risk stratification, which may assist clinicians in optimizing diagnostic strategies and individualized management for patients with gastric cancer.