Chunfeng Li

Gastric cardia adenocarcinoma is biologically distinct from distal disease, but deployable molecular markers remain scarce. We investigated whether explainable deep learning applied to public transcriptomes could nominate diagnostic and survival‐associated candidates. We developed and externally evaluated an explainable transcriptome‐based classifier. The Asian Cancer Research Group SuperSeries GSE66229 (300 tumors and 100 patient‐matched non‐tumor tissues) underwent robust multi‐array average preprocessing and quality control. An attention‐based deep neural network was evaluated by stratified fivefold cross‐validation for tumor‐versus‐non‐tumor classification. Inputs were restricted before training to genes available in all cohorts and ordered identically; no missing model inputs were imputed. Shapley additive explanations nominated 20 genes, and univariable Cox models evaluated overall survival associations. External testing without refitting used GSE29272 and The Cancer Genome Atlas stomach adenocarcinoma cohort. Pathway analyses provided biological context. Cross‐validated receiver operating characteristic and precision‐recall areas under the curve were both 1.00. External values were 0.85/0.93 for cardia and 0.50/0.67 for non‐cardia in GSE29272, and 0.78/0.80 and 0.71/0.50, respectively, in The Cancer Genome Atlas cohort. Six genes showed nominal survival associations in GSE66229. None replicated statistically in the strict external cardia subset; LVRN and WISP2 were nominally concordant in the full stomach adenocarcinoma cohort, but neither survived six‐test correction. Enrichment implicated immune and lipid‐related processes. Explainable deep learning prioritized candidates with stronger external performance in cardia‐versus‐non‐tumor contrasts. These exploratory diagnostic and survival findings require clinically adjusted, prospective validation before clinical use.

Accurate diagnosis and prognostic assessment of gastric cancer are critical for improving patient outcomes. The application of advanced machine learning methods, particularly the XGBoost algorithm, offers a promising approach for enhancing prognostic evaluations. In this study, data from 2,270 patients with gastric cancer were analysed to develop a predictive model for prognosis using the XGBoost algorithm and 20 key clinical features. Comprehensive data collection, preprocessing, and feature selection were conducted to ensure robust model construction and validation. The model demonstrated strong predictive performance in the test cohort, achieving an area under the curve (AUC) of 0.855, and it effectively differentiated patients at high-risk from those at low-risk. Feature importance analysis revealed that pTNM stage and CA125 level were the most influential prognostic factors. This study successfully implemented a machine learning-based model integrating the XGBoost algorithm and critical clinical indicators to predict the five-year survival rate of patients with gastric cancer. The findings highlight the potential of such approaches in supporting personalised treatment strategies and advancing cancer prognosis assessment methodologies.

This study aimed to establish the G-Risk scoring system, a prognostic model based on inflammatory and tumor biomarkers, to enhance survival predictions for gastric cancer patients without lymphovascular invasion (LVI) and guide more tailored treatment strategies. The key biomarkers associated with survival outcomes were identified using univariate and multivariate Cox regression analyses. These biomarkers were selected from a range of inflammatory and tumor markers to construct the G-Risk scoring system, which was specifically developed to improve prognostic accuracy in patients without LVI. The G-Risk score effectively stratified patients into high-risk and low-risk groups, achieving an AUC of 0.660, demonstrating strong predictive performance. Further multivariate analysis validated the G-Risk score as an independent prognostic factor with significant implications for patient survival. The G-Risk score was successfully developed and validated as a reliable prognostic tool for gastric cancer patients without LVI. Its clinical implementation offers enhanced precision in assessing prognostic risk, facilitating personalized treatment planning, improving therapeutic outcomes, and reducing unnecessary medical interventions.

Gastric cancer is one of the most common malignant tumors worldwide, with high incidence and mortality rates, and it has a complex etiology and complex pathological features. Depending on the tumor type, gastric cancer can be classified as intestinal-type and diffuse-type gastric cancer, each with distinct pathogenic mechanisms and clinical presentations. In recent years, machine learning techniques have been widely applied in the medical field, offering new perspectives for the diagnosis, treatment, and prognosis of gastric cancer patients. This study recruited 2158 gastric cancer patients and constructed prognostic prediction models for both intestinal-type and diffuse-type gastric cancer. Clinical pathological data were collected from patients, and machine learning algorithms were used for feature selection and model construction. The performance of the models was validated with training and testing datasets. The Shapley additive explanations (SHAP) values were used to interpret the model predictions and identify the main factors that influence patient survival. In the prognostic model for intestinal-type gastric cancer, the gradient boosting decision tree (GBDT) model demonstrated the best performance, with key features including pTNM, CA125, tumor size, CA199, and PALB. Similarly, in the prognostic model for diffuse-type gastric cancer, the GBDT model was utilized, with key features comprising pTNM, Borrmann type IV disease, lymphocyte (LYM), lactate dehydrogenase (LDH), potassium (K), perineural invasion (PNI), tumor size, and whole stomach location. Risk stratification analysis revealed that the prognosis of high-risk patients was significantly worse than that of low-risk patients. Machine learning shows great potential in predicting survival outcomes of gastric cancer patients, providing strong support for the development of personalized treatment plans.

Objective: To evaluate the anticancer effect of ellagic acid on gastric cancer cells. Methods: MTT assay was used to evaluate the effect of ellagic acid at different concentrations (0.5-100 μg/mL) on gastric cancer AGS cells. RT-qPCR and Western blot analyses were applied to assess apoptosis (BCL-2, CASP-3, and BAX) and autophagy (LC3, ATG5, and BECN1) in AGS cells treated with ellagic acid. The expression of invasion-related markers including TP53, CDKN2A, and PTEN was determined. In addition, cell cycle markers including cyclin A, B, D, and E were measured by ELISA. Oxidative stress markers were evaluated using spectrophotometry. Results: Ellagic acid inhibited the proliferation of AGS cells in a concentration-and time-dependent manner. The expression of BCL-2 was significantly decreased (PP0.05). Moreover, the oxidative stress markers including SOD, TAC, and MDA were increased by ellagic acid (Pin vivo and molecular studies are needed to verify its anticancer efficacy.

The present study was conducted to develop a green process that provides access to the development of Schiff base derivatives of chitosan with the heterocyclic moiety as a novel class of anti-gastric cancer agent. In the present study, we have synthesized these derivatives by reacting various pyrazoles with chitosan using CAN in PEG400. The compounds were synthesized in 20 min in excellent yield by using CAN at 5% in PEG400 at 80°C in the shortest reaction time of 20 min. The PEG400 could be efficiently recycled for the three consecutive runs. The developed compounds were tested for EGFR-TK inhibition using a Kinase-Glo Plus luminescence kinase assay kit where they exhibited significant activity revealing compound 2d as the most potent analog, while other compounds showed mild to moderate inhibitory activity. MTT assay was conducted to determine the effect of the three most potent EGFR inhibitors (2b, 2c, and 2d) on the proliferation of gastric cancer cells (SGC-7901). The results showed compound 2d as the most potent anticancer agent against SGC7901 cells. The effect of compound 2d was also quantified on the apoptosis and cell phase of SGC7901 cells using flow cytometry assay at various concentrations ranging from 0, 10, 20, and 30 µM. Results suggest that compound 2d showed significant inhibition of SGC-7901 by inducing apoptosis and arresting G0/G1 cell phase. The western blot analysis also revealed that compound 2d significantly inhibited the overexpression of EGFR in SGC-7901 cells. The study successfully demonstrated the development of N‑pyrazole amino chitosan as a novel class of agent against gastric cancer via inhibition of EGFR.