Nomogram for predicting gastric cancer recurrence using biomarker gene expression.

Area of Science:

• Oncology
• Molecular Biology
• Biostatistics

Background:

• Predicting patient prognosis in cancer is crucial for treatment planning.
• Biomarker expression analysis offers a promising avenue for prognosis prediction.
• Gastric cancer prognosis prediction requires refined tools for better patient outcomes.

Purpose of the Study:

• To develop a nomogram for predicting the 5-year recurrence-free probability (RFP) in gastric cancer patients.
• To utilize prognostic biomarker gene expression as a key predictive factor.
• To provide a tool that assists in therapeutic decisions for gastric cancer management.

Main Methods:

• A predictive model and nomogram were developed using a training dataset of 360 gastric cancer patients.
• General patient variables and gene expression levels of 10 biomarker candidates were analyzed.
• External validation was performed on a separate dataset of 420 patients.

Main Results:

• The final nomogram incorporated age, sex, and expression levels of specific genes (CAPZA, PPase, OCT-1, PRDX4, gamma-enolase, c-Myc).
• The nomogram stratified patients into risk groups with distinct 5-year RFPs in both training and validation cohorts.
• Model performance showed significant predictive capability, with AUCs of 0.718 (training) and 0.640 (validation).

Conclusions:

• A novel nomogram integrating gene expression can predict 5-year RFP in gastric cancer patients post-surgery.
• This tool has the potential to guide therapeutic choices between endoscopic treatment and gastrectomy.
• The nomogram offers a valuable resource for personalized treatment strategies in gastric cancer care.