Background: Our previous study found that bone marrow-derived mesenchymal stem cells (BMSCs) promote Helicobacter pylori (H pylori)-associated gastric cancer (GC) progression by secreting thrombospondin-2 (THBS2). Extracellular vesicles (EVs) are important carriers for intercellular communication, and EVs secreted by BMSCs have been shown to be closely related to tumor development. The aim of this study was to investigate whether BMSC-derived microvesicles (MVs, a main type of EV) play a role in H. pylori-associated GC by transferring THBS2. Methods: BMSCs and THBS2-deficient BMSCs were treated with or without the supernatant of H. pylori for 12 h at a multiplicity of infection of 50, and their EVs were collected. Then, the effects of BMSC-derived MVs and THBS2-deficient BMSC-derived MVs on the GC cell line MGC-803 were assessed by in vitro proliferation, migration, and invasion assays. In addition, a subcutaneous xenograft tumor model, a nude mouse intraperitoneal metastasis model, and a tail vein injection metastasis model were constructed to evaluate the effects of BMSC-derived MVs and THBS2-deficient BMSC-derived MVs on GC development and metastasis in vivo. Results: BMSC-derived MVs could be readily internalized by MGC-803 cells. BMSC-derived MVs after H. pylori treatment significantly promoted their proliferation, migration and invasion in vitro (all P < 0.05) and promoted tumor development and metastasis in a subcutaneous xenograft tumor model, a nude mouse intraperitoneal metastasis model, and a tail vein injection metastasis model in vivo (all P < 0.05). The protein expression of THBS2 was significantly upregulated after H. pylori treatment in BMSC-derived MVs (P < 0.05). Depletion of the THBS2 gene reduces the tumor-promoting ability of BMSC-MVs in an H. pylori infection microenvironment both in vitro and in vivo. Conclusion: Overall, these findings indicate that MVs derived from BMSCs can promote H. pylori-associated GC development and metastasis by delivering the THBS2 protein.

A functional polymorphism of SSBP1 gene predicts prognosis and response to chemotherapy in resected gastric cancer patients A functional polymorphism of SSBP1 gene predicts prognosis and response to chemotherapy in resected gastric cancer patients

Objective: The objective is to evaluate the value of EUS in the determination of infiltration depth of early carcinoma and precancerous lesions in the upper gastrointestinal tract and to analyze the various factors affecting the accuracy of EUS. Methods: One hundred and sixty-three patients diagnosed with early gastric cancer or early esophageal cancer, and associated precancerous lesions, who were seen in our hospital in the recent 10 years were selected. These patients received EUS before endoscopic submucosal dissection or surgery. With a pathological diagnosis as the gold standard, the accuracy, sensitivity, specificity, and misjudgment rate of EUS in determining the invasion depth were evaluated using the pathological stratification (mucosa, M1/2; muscularis mucosa, M3; submucosa, [SM]; and muscularis propria) or TN stratification (mucosa, T1a; SM, T1b), and the possible causes of miscalculation were analyzed. Results: Based on the pathological stratification, the overall accuracy of EUS was 78.5%, and the overestimation and underestimation rates were 17.8% and 3.7%, respectively. Based on the TN stratification, the overall accuracy of EUS was 81%, and the overestimation and underestimation rates were 16.6% and 2.5%, respectively. There was a significant difference between the groups in terms of overestimation and underestimation rates (P < 0.05), indicating that EUS was more likely to overestimate the depth. Univariate analysis showed that the factors affecting accuracy included lesion size, macroscopic features, sunken mucosa, mucosa with granular and nodular changes, and ulceration. Multivariate logistic regression analysis revealed that larger lesions, mucosa with granular and nodular changes, and ulceration were independent risk factors for the overestimation of infiltration depth by EUS. Conclusion: EUS is highly accurate in determining the infiltration depth of early cancer and precancerous lesions in the upper gastrointestinal tract. It also has a good reference value for treatment selection and prognostication. However, attention should be paid to its overestimation, especially accompanied by the aforementioned factors.

Background: There are challenges for beginners to identify standard biliopancreatic system anatomical sites on endoscopic ultrasonography (EUS) images. Therefore, the authors aimed to develop a convolutional neural network (CNN)-based model to identify standard biliopancreatic system anatomical sites on EUS images. Methods: The standard anatomical structures of the gastric and duodenal regions observed by EUS was divided into 14 sites. The authors used 6230 EUS images with standard anatomical sites selected from 1812 patients to train the CNN model, and then tested its diagnostic performance both in internal and external validations. Internal validation set tests were performed on 1569 EUS images of 47 patients from two centers. Externally validated datasets were retrospectively collected from 16 centers, and finally 131 patients with 85 322 EUS images were included. In the external validation, all EUS images were read by CNN model, beginners, and experts, respectively. The final decision made by the experts was considered as the gold standard, and the diagnostic performance between CNN model and beginners were compared. Results: In the internal test cohort, the accuracy of CNN model was 92.1–100.0% for 14 standard anatomical sites. In the external test cohort, the sensitivity and specificity of CNN model were 89.45–99.92% and 93.35–99.79%, respectively. Compared with beginners, CNN model had higher sensitivity and specificity for 11 sites, and was in good agreement with the experts (Kappa values 0.84–0.98). Conclusions: The authors developed a CNN-based model to automatically identify standard anatomical sites on EUS images with excellent diagnostic performance, which may serve as a potentially powerful auxiliary tool in future clinical practice.

A pancreatic pseudocyst (PPC) is a frequent complication of pancreatitis, often stemming from alcohol, gallstones, or hyperlipidemia. Endoscopic treatment of PPC has become the mainstream treatment. A case of one giant and multiple small PPCs was observed, manifesting as repeated abdominal bloating, abdominal pain, nausea, and vomiting after meals. Initial computed tomography scans revealed the presence of multiple PPCs. Despite ineffective medical treatment, the pseudocysts progressively increased. In response, we conducted a combined endoscopic intervention, involving Hot AXIOS (Boston Scientific, Marlborough, MA) stenting through endoscopic ultrasound-guided transmural drainage (EUS-TMD) and the placement of the endoscopic nasopancreatic drainage (ENPD) mimic stent through endoscopic retrograde pancreatography (ERP). Remarkably, after nine months of postoperative follow-up, the patient had no discomfort symptoms and the cyst disappeared. We conducted a literature review on endoscopic combined drainage for PPCs, which is still controversial. Our presented case serves as a demonstration that endoscopic combined drainage can effectively and successfully manage giant and multiple PPCs.

Background: Esophageal squamous cell carcinoma (ESCC) is a major cause of cancer-related mortality worldwide, with a high prevalence and poor prognosis in specific regions. Despite advancements in clinical care, the identification of reliable biomarkers for accurate survival prediction remains a significant challenge, hindering personalized treatment. Methods: This study utilized genomic and clinical data from TCGA and GEO databases, applying ten machine learning algorithms to develop a prognostic model based on lipid metabolism-related genes. The Random Survival Forest (RSF) algorithm was identified as the optimal framework, supplemented by immune infiltration analysis and in vitro functional validation of the key gene ACOT9. Results: The resulting 33-gene signature achieved superior performance, with a C-index of 0.708 and survival prediction area under the curve (AUC) values approaching 1.0 in the training cohort. High-risk scores were significantly correlated with advanced histological grade, tumor stage, and increased regulatory T-cell infiltration. Furthermore, silencing of ACOT9 markedly suppressed the proliferation, migration, and invasion of ESCC cells. Conclusions: This study provides a robust framework for ESCC risk stratification and identifies ACOT9 as a critical oncogenic driver, offering a novel scientific basis for optimizing clinical management strategies.