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Article Abstract
Background: The increasing incidence of early-stage T1 gastric cancer (GC) underscores the need for accurate preoperative risk stratification of lymph node metastasis (LNM). Current pathological assessments often misclassify patients, leading to unnecessary radical surgeries.
Methods: Through analysis of transcriptomic data from public databases and T1 GC tissues, we identified a 4-mRNA panel (SDS, TESMIN, NEB, and GRB14). We developed and validated a Risk Stratification Assessment (RSA) model combining this panel with clinical features using surgical specimens (training cohort: n = 218; validation cohort: n = 186), gastroscopic biopsies (n = 122), and liquid biopsies (training cohort: n = 147; validation cohort: n = 168).
Results: The RSA model demonstrated excellent predictive accuracy for LNM in surgical specimens (training AUC = 0.890, validation AUC = 0.878), gastroscopic biopsies (AUC = 0.928), and liquid biopsies (training AUC = 0.873, validation AUC = 0.852). This model significantly reduced overtreatment rates from 83.9 to 44.1% in tissue specimens and from 84.4 to 56.0% in liquid biopsies. The 4-mRNA panel showed specificity for T1 GC compared to other gastrointestinal cancers (P < 0.001).
Conclusions: We developed and validated a novel liquid biopsy-based RSA model that accurately predicts LNM in T1 GC patients. This non-invasive approach could significantly reduce unnecessary surgical interventions and optimize treatment strategies for high-risk T1 GC patients.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11804050 | PMC |
| http://dx.doi.org/10.1186/s13046-025-03305-x | DOI Listing |
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