ArticleFrontiers in immunology2025
Prognostic modeling of early-onset nondistal gastric cancer identifies
Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Despite a global decline in gastric cancer (GC) incidence, nondistal GC (NDGC) is increasingly prevalent among younger patients, necessitating targeted investigation of early-onset NDGC (EONDGC) to identify prognostic determinants for enhanced risk stratification. Methods: EONDGC patients were identified from multiple datasets, including the Surveillance, Epidemiology, and End Results (SEER) database, the Cancer Genome Atlas (TCGA) Stomach Adenocarcinoma cohort, and the Affiliated Hospitals of Sun Yat-sen University (SYSU) as an external validation cohort. Propensity score matching was performed to reduce baseline differences between groups. A prognostic model was developed using univariate and multivariate Cox regression and LASSO analysis in a 7:3 training-validation split. The prognostic model was applied to TCGA patients to generate risk scores, and high-risk patients were selected for differentially expressed genes (DEGs) analysis. The identified genes were then analyzed using Cox regression and Kaplan-Meier methods to determine prognostic relevance. In parallel, MGC-803 and AGS cells were transiently transfected to overexpress Results: A total of 535 EONDGC patients from SEER and 171 from SYSU were included. The prognostic model, incorporating seven clinical variables (race, pathological grade, T, N, and M stage, lymph node ratio, and chemotherapy), achieved robust performance with concordance index values of 0.758 (training), 0.718 (validation), and 0.762 (SYSU), with all AUCs > 0.75. In the TCGA patients, 73 upregulated genes were identified from high-risk patients through DEGs analysis. Among these, Conclusion: This study developed a clinical prognostic model for EONDGC and therefore identified
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