Nomogram model including LATS2 expression was constructed to predict the prognosis of advanced gastric cancer after surgery

ARTICLE HIGHLIGHTS

Research background

Gastric cancer is a significant global health concern, ranking fifth in incidence and fourth in mortality among all cancers. The prognosis is often poor due to late-stage diagnosis. Molecular signaling pathways and gene mutations, like those involving the LATS gene, play a crucial role in the pathogenesis of gastric cancer, affecting prognosis and treatment options.

Research motivation

There is a need for more accurate prognostic models for advanced gastric cancer that can incorporate molecular biomarkers like LATS2 expression. This would improve individual prognosis assessments and aid in personalizing treatment strategies.

Research objectives

The objective of this research is to construct a nomogram model based on LATS2 expression and evaluate its predictive accuracy for the survival prognosis of patients with advanced gastric cancer post-surgery.

Research methods

The study retrospectively analyzed clinical data of 245 advanced gastric cancer patients, dividing them into a training group and a validation group. Univariate and multivariate Cox regression analyses were used to assess the prognostic value of LATS2 expression. The model's performance was analyzed through various statistical methods including C-index, receiver operating characteristic curves, calibration curves, and decision curves.

Research results

The nomogram model demonstrated high C-indexes and area under curve values, indicating strong predictive accuracy. Calibration plots showed high agreement between predicted and actual survival, and decision curves indicated the model's superior net benefit over tumor-node-metastasis (TNM) staging alone.

Research conclusions

The nomogram model incorporating LATS2 expression provided significant clinical value in predicting the postoperative prognosis of advanced gastric cancer patients. It showed superior discrimination and net clinical benefit compared to TNM staging alone.

Research perspectives

The study suggests that the developed model can assist in clinical decision-making, but acknowledges limitations such as the small, single-center sample size. Future research should aim at external validation and include more comprehensive clinical and molecular data to optimize prognostic accuracy.