Combining biomarkers to construct a novel predictive model for predicting preoperative lymph node metastasis in early gastric cancer.

BACKGROUND

Accurately identifying the status of lymph node metastasis (LNM) is crucial for determining the appropriate treatment strategy for early gastric cancer (EGC) patients.

METHODS

Univariate and multivariate logistic regression analyses were used to explore the association between clinicopathological factors and LNM in EGC patients, leading to the development of a nomogram. Differential expression analysis was conducted to identify biomarkers associated with LNM, and their expression was evaluated through immunohistochemistry. The biomarker was integrated into the conventional model to create a new model, which was then assessed for reclassification and discrimination abilities.

RESULTS

Multivariate logistic regression analysis revealed that tumor size, histological type, and the presence of ulcers are independent risk factors for LNM in EGC patients. The nomogram demonstrated good clinical performance. Incorporating immunohistochemical expression into the new model further improved its performance, reclassification, and discrimination abilities.

CONCLUSION

The novel nomogram predictive model, based on preoperative clinicopathological factors such as tumor size, histological type, presence of ulcers, and expression, provides valuable guidance for selecting treatment strategies for EGC patients.