Abstract: This study examines the association between intestinal metaplasia (IM) and cuproptosis, and develops a predictive model. By analyzing the GSE2669 dataset, gene clusters and immune characteristics associated with cuproptosis in 22 IM samples were characterized, with differential gene clusters identified. The performance of four machine learning models: random forest (RF), support vector machine (SVM), generalized linear model (GLM), and XGBoost (XGB), was evaluated with the RF model showing superior performance. A RF model based on four genes (DMBT1, HPN, SLC13A3, and YWHAZ) was constructed, and their expression was found to be significantly correlated with CDX2 levels. Expression levels of these genes in normal gastric and intestinal metaplastic tissues were confirmed by qRT-PCR and Western blot, aligning with the model’s predictions. This research preliminarily identifies the role and potential mechanisms of cuproptosis in IM and establishes a predictive model, offering new directions for future clinical applications and research.
Key words: cuproptosis; intestinal metaplasia; machine learning models; immune infiltration; prediction model
（Acta Laser Biology Sinica, 2025, 34(1): 063-076）