Epidemiological shifts, clinicopathological features, and integrative nomograms of gastric cancer metastasis: a large-scale retrospective cohort study

Background: China continues to face a substantial burden of gastric cancer (GC), particularly with respect to metastasis-related mortality. However, population-based analyses of distant metastasis patterns in Chinese GC patients remain unavailable. Methods: Global Burden of Disease (GBD) data on GC from the 1990 to 2021 period was obtained through the Global Health Data Exchange (GHDx) query tool and integrative data of 18 919 patients who underwent surgery were obtained from our hospital. Univariate and multivariate logistic regression identified independent risk factors for metastases, and survival analysis utilized univariate and multivariate Cox regression, Kaplan-Meier method, and log-rank test. Predictive nomograms were assessed using metrics such as the area under the curve (AUC), calibration curves, and decision curve analysis. Results: According to the GBD database, GC demonstrates declining global trends in both incidence and mortality. Nevertheless, China continues to face a substantial GC burden, with progressive annual rises in distant metastasis prevalence and metastasis-related mortality. Clinical characteristics and temporal patterns vary significantly across metastatic types. Furthermore, metastatic profiles exhibit sex-, age-, and stage-specific variations. Univariate and multivariate regression analyses identified independent risk factors for overall GC metastasis and site-specific metastases. The resulting prediction models demonstrated excellent predictive accuracy for metastatic progression. The prognostic nomogram was developed to predict 1-, 5-, and 10-year overall survival (OS) in GC patients, with AUCs of 0.86 (0.84–0.88), 0.87 (0.85–0.89), and 0.80 (0.74–0.85) in the training set, respectively, which showed good discriminative ability. Conclusions: In this study, metastatic spectrums across diverse patient subgroups and temporal patterns of metastasis in GC were investigated. Furthermore, we developed clinical predictive nomograms for various metastatic patterns and OS in GC, which enhance the understanding of metastatic behavior and provide a robust tool for personalized risk assessment and prognosis prediction.