ITPG: an immune-related transcriptomic predictive model for gastric cancer prognosis

Although the global incidence of gastric cancer (GC) has declined over the past 5 years, it remains the fourth leading cause of cancer-related mortality worldwide. Given the molecular heterogeneity of GC, survival outcomes can vary significantly among patients receiving the same treatment at the same stage. Therefore, this study aimed to develop and validate a robust prognostic model for GC that complements the current staging system, to ultimately facilitate better clinical decision-making.

Utilizing gene expression data from four independent cohorts comprising 1,305 GC patients, we developed and validated the immune-related transcriptomic predictive model for gastric cancer prognosis (ITPG), which incorporates transcriptomic biomarkers and explores gene-gene interactions. Specifically, the ITPG model integrates two genes with main effects (KCNQ1, FLRT2) and two pairs of genes with gene-gene interactions (ATP4B×CD84, NPY×ITGBL1), in addition to clinical variables including age and pathological stage. Prognostic biomarkers were identified in The Cancer Genome Atlas (TCGA) cohort. The model's risk stratification ability, predictive performance, and clinical utility were subsequently evaluated in three external cohorts: GSE66229, GSE15459, and GSE84437.

The ITPG demonstrated strong risk stratification potential in identifying high-risk patients. Compared to those in the lowest 25^th percentile of ITPG scores, patients in the top 90^th percentile had significantly shorter overall survival [hazard ratio (HR) =9.79, 95% confidence interval (CI): 7.25-13.21, P=2.78×10^-50]. Furthermore, ITPG exhibited robust predictive performance across four cohorts, with pooled area under the curve (AUC) values for 1-year of 0.769 (95% CI: 0.735-0.803), 3-year of 0.762 (95% CI: 0.723-0.802), and 5-year of 0.765 (95% CI: 0.704-0.826) survival, and a C-index of 0.704 (95% CI: 0.678-0.729). Additionally, the model displayed substantial clinical utility in identifying GC patients at high risk of mortality [net benefit (NB) at 1-year =1.8%, NB_3-year =15.8%, NB_5-year =23.7%; net reduction (NR) at 1-year =58.6%, NR_3-year =20.4%, NR_5-year =11.7%]. Subgroup analyses confirmed the model's robustness across different population stratifications.

The ITPG model is an efficient and clinically relevant tool for prognostic prediction in GC.