Multidimensional integration: A novel breakthrough in prognostic prediction for immunochemotherapy in human epidermal growth factor receptor-2-negative advanced gastric cancer

Table 1 Representative biomarkers and modeling dimensions for prognostic prediction in human epidermal growth factor receptor-2-negative advanced gastric cancer

Dimension	Representative biomarkers/features	Biological/clinical relevance	Current limitations
Molecular	PD-L1, MSI, TMB, EBV, ctDNA	Reflect tumor immunogenicity and predict response to immune checkpoint inhibitors	Heterogeneity of assays; lack of standardized thresholds
Clinicopathological	TNM stage, histological differentiation, ECOG performance status	Indicate tumor burden, disease stage, and patient functional status	Static variables; limited ability to capture dynamic tumor evolution
Inflammatory	NLR, PLR, LMR, SII	Reflect systemic inflammatory response associated with tumor progression	Variability in cutoff values; influenced by non-cancer conditions
Nutritional	Albumin, PNI, CALLY index	Reflect host nutritional and immunological status	Susceptible to comorbidities and acute clinical conditions
Computational modeling	Nomogram, machine learning, radiomics	Enable individualized risk prediction through integration of multidimensional data	Limited external validation; risk of overfitting; lack of clinical interpretability

PD-L1: Programmed cell death ligand 1; MSI: Microsatellite instability; TMB: Tumor mutational burden; EBV: Epstein-Barr virus; ctDNA: Circulating tumor DNA; TNM: Tumor-node-metastasis; ECOG: Eastern Cooperative Oncology Group; NLR: Neutrophil-to-lymphocyte ratio; PLR: Platelet-to-lymphocyte ratio; LMR: Lymphocyte-to-monocyte ratio; SII: Systemic Immune-Inflammation Index; PNI: Prognostic nutritional index; CALLY: C-reactive protein-albumin-lymphocyte.