Background: Gastric cancer (GC) is one of the most common and fatal cancers worldwide. Effective biomarkers to aid the early diagnosis of GC, as well as predict the course of disease, are urgently needed. Hence, we explored the role and function of cadherin-6 (CDH6) in the diagnosis and prognosis of gastric cancer. Methods: The expression levels of CDH6 in cancerous and normal gastric tissue were analyzed using multiple public databases. Gene set enrichment analysis (GSEA) was performed using The Cancer Genome Atlas (TCGA) dataset. The diagnostic efficiency of CDH6 expression in GC patients was determined through receiver operating characteristic (ROC) curve analysis. The associations between clinical variables and CDH6 expression were evaluated statistically, and the prognostic factors for overall survival were analyzed by univariate and multivariate Cox regression. 44 GC tissue samples, 20 donor-matched adjacent normal tissue samples, and associated detailed clinical information, were collected from the Tianjin Medical University General Hospital. CDH6 expression levels were determined for further validation. Results: CDH6 was upregulated in GC samples compared to normal gastric tissue. Furthermore, GSEA identified the tricarboxylic acid (TCA) cycle, extracellular matrix (ECM) receptor interaction, glyoxylate and dicarboxylate metabolism, oxidative phosphorylation, and the pentose phosphate pathway as differentially enriched in GC tissue samples. According to the area under the ROC curve (AUC) values (AUC = 0.829 in the TCGA and 0.966 in the GSE54129 dataset), CDH6 expression was associated with high diagnostic efficacy. Patients with high CDH6 levels in their GC tissues had a higher T number (according to the TNM classification) and a worse prognosis than those with low CDH6 expression. Univariate and multivariate Cox regression analysis showed that CDH6 was an independent risk factor for overall survival (univariate: HR = 1.305, P = 0.002, multivariate: HR = 1.481, P < 0.001). Conclusion: CDH6 was upregulated in GC, and high CDH6 expression was indicative of a higher T number and a worse prognosis. Therefore, CDH6 represents a potentially independent molecular biomarker for the diagnostic and prognostic prediction of GC.

A core challenge in the diagnosis and treatment of esophageal cancer (EC) lies in accurately identifying patients who will benefit from neoadjuvant therapy (NAT). Yang et al reported a predictive model for NAT response in EC, constructed using radiomics from T2-weighted magnetic resonance imaging (MRI) and machine learning. The model achieved an area under the curve of 0.932 in the training cohort and 0.900 in the validation cohort. While encouragingly, we urge caution with limitation. First, the study’s single-center, retrospective design with an insufficient sample size limits the model’s generalizability and significantly increases the risk of overfitting. Second, the study only extracted features from the T2-weighted MRI sequence, failing to integrate data from other functional MRI sequences such as diffusion-weighted imaging and dynamic contrast-enhanced MRI. Third, the model suffers from a "black box" issue regarding its extracted features—its low interpretability hinders clinicians’ trust in and acceptance of the model. This editorial reviews the study by Yang et al , identifies its limitations, and puts forward in-depth suggestions to further optimize the model.

BACKGROUND Gastrointestinal stromal tumors are the most common mesenchymal tumors of the gastrointestinal tract, and gastric gastrointestinal stromal tumors (gGISTs) account for the majority of these tumors. Currently, endoscopic removal (ER) is increasingly adopted as a minimally invasive treatment. However, postoperative perforation remains a critical complication, necessitating robust prediction tools. AIM To identify the risk factors and develop a validated nomogram for predicting perforation after ER of gGISTs. METHODS This retrospective study analyzed the patients undergoing ER at Fuyang People’s Hospital from 2019 to 2024. Clinical data, including tumor size, location, and procedural details, were collected and analyzed. The risk factors were identified via univariate and multivariate logistic regression, and a nomogram was developed. Both internal and external validations were performed, and the model performance was assessed by receiver operating characteristic curves and calibration plots. RESULTS Among 301 patients, the perforation rate was 6.3%. Multivariate analysis identified tumor size (odds ratio = 4.699, 95% confidence interval: 2.382-9.267, P = 0.001) and cardia/fundus location (odds ratio = 3.492, 95% confidence interval: 1.121-10.875, P = 0.031) as independent predictors. A nomogram was constructed and achieved good predictive performance in both the training (area under the curve = 0.881) and validation sets (area under the curve = 0.878). CONCLUSION This study identified that tumor size and location were independent risk factors, and provides a clinically actionable nomogram for evaluating and predicting postoperative perforation risk in gGISTs treated with ER, facilitating preprocedural planning and risk monitoring.