CGRS

CGRS integrates genetic and clinical risk factors to predict prognosis in gastric cancer (GC) patients by combining a nine-gene Genetic Risk Score derived from Asian Cancer Research Group (ACRG) gene expression profiles with a Clinical Risk Score from the Surveillance, Epidemiology, and End Results (SEER) database.

Key Features:

• Integration of scores: CGRS combines a Genetic Risk Score (GRS) and a Clinical Risk Score (CRS) to generate a composite prognostic score for GC patients.
• Genetic Risk Score (GRS): GRS was derived from analysis of ACRG gene expression profiles using LASSO-Cox regression.
• Nine-gene signature: The GRS comprises APOD, CCDC92, CYS1, GSDME, ST8SIA5, STARD3NL, TIMEM245, TSPYL5, and VAT1.
• Clinical Risk Score (CRS): CRS was derived from the SEER database clinical data.
• Cross-platform validation: GRS and CGRS were validated across independent GC cohorts using microarray, RNA sequencing, and qRT-PCR data.
• Risk stratification: Patients were stratified into high-risk and low-risk groups with hazard ratios > 1 and P < 0.001.
• Independence from AJCC staging: Multivariable Cox regression confirmed GRS and CGRS as independent prognostic signatures beyond the AJCC staging system.
• Comparative performance: Receiver operating characteristic (ROC) analysis showed CGRS outperformed AJCC staging in most studied cohorts.
• Nomogram: A nomogram based on CGRS was developed for quantitative prognostic estimation.

Scientific Applications:

• Prognosis prediction: Predicts overall prognosis of gastric cancer patients by integrating molecular and clinical risk factors.
• Risk stratification: Stratifies patients into high- and low-risk groups for survival analyses across multiple cohorts and platforms.
• Staging augmentation: Provides an independent prognostic metric to complement and improve accuracy beyond the AJCC staging system.

Methodology:

Gene expression profiles from the ACRG cohort were analyzed with LASSO-Cox regression to identify a nine-gene GRS (APOD, CCDC92, CYS1, GSDME, ST8SIA5, STARD3NL, TIMEM245, TSPYL5, VAT1); a CRS was derived from SEER clinical data; the GRS and CRS were integrated to form CGRS, and performance was evaluated by multivariable Cox regression and ROC analysis.