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OBJECTIVE: Precancerous lesions of gastric cancer (PLGC) are key pathological stages in the transformation of gastric "inflammation-cancer", and timely and effective intervention at this stage is of great importance in the prevention and treatment of gastric cancer. Zhiwei Fuwei Pills (ZWFW), as a traditional Chinese medicine formulation, has been proven to have good clinical efficacy in the treatment of PLGC, but its specific mechanism of action has not been fully explained. Thus, this study validated the efficacy and explored the potential mechanisms of ZWFW in treating PLGC by integrating network pharmacology analyses and experimental verification. METHODS: The TCMSP database was used to obtain the active ingredients of ZWFW and their corresponding targets, and the GeneCards database was used to retrieve PLGC-related targets. The intersecting targets between ZWFW and PLGC were obtained through mapping, and protein-protein interaction (PPI) networks and "drug-active ingredient-target" networks were constructed by using Cytoscape software. The DAVID database was used for GO functional enrichment analysis and KEGG pathway enrichment analysis. AutoDockTools software was used for molecular docking of key active ingredients and key targets. In order to verify the analysis results of network pharmacology, TEM and H&E were used to observe the effects of different dosage groups of ZWFW on gastric mucosal microvasculature in PLGC rats. Subsequently, the ELISA, IF, IHC, RT-PCR and western blot were used to detected the expression levels of relevant targets in the tissues, so as to verify the potential mechanism of ZWFW in intervening PLGC. RESULTS: After the screening, 258 effective active ingredients and 325 targets were obtained, and 1294 disease-related targets were determined, resulting in 139 intersection targets through mapping. The KEGG enrichment results showed that PI3K/Akt and HIF-1 signaling pathway might play important roles in the treatment mechanism of PLGC. The molecular docking results showed that active ingredients of ZWFW all had a strong affinity and stable structure with key targets, including AKT1 and VEGF. In vivo experiments confirmed that ZWFW could improve gastric mucosal microvascular abnormalities in PLGC, effectively intervene in gastric mucosal pathological grading. Meanwhile, compared with the model group, this formulation could reduce the expression levels of PI3K, Akt, mTOR, HIF-1α, and VEGF in gastric mucosa, showing a dose-effect relationship. CONCLUSION: ZWFW can intervene in the neovascularization and pathological evolution of PLGC, and this mechanism of action may be achieved by inhibiting abnormal activation of the PI3K/Akt/mTOR/HIF-1α/VEGF signaling pathway.

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OBJECTIVE: Patients with gastric atrophy and intestinal metaplasia (IM) were at risk for gastric cancer, necessitating an accurate risk assessment. We aimed to establish and validate a diagnostic approach for gastric biopsy specimens using deep learning and OLGA/OLGIM for individual gastric cancer risk classification. METHODS: In this study, we prospectively enrolled 545 patients suspected of atrophic gastritis during endoscopy from 13 tertiary hospitals between December 22, 2017, to September 25, 2020, with a total of 2725 whole-slide images (WSIs). Patients were randomly divided into a training set (n = 349), an internal validation set (n = 87), and an external validation set (n = 109). Sixty patients from the external validation set were randomly selected and divided into two groups for an observer study, one with the assistance of algorithm results and the other without. We proposed a semi-supervised deep learning algorithm to diagnose and grade IM and atrophy, and we compared it with the assessments of 10 pathologists. The model's performance was evaluated based on the area under the curve (AUC), sensitivity, specificity, and weighted kappa value. RESULTS: The algorithm, named GasMIL, was established and demonstrated encouraging performance in diagnosing IM (AUC 0.884, 95% CI 0.862-0.902) and atrophy (AUC 0.877, 95% CI 0.855-0.897) in the external test set. In the observer study, GasMIL achieved an 80% sensitivity, 85% specificity, a weighted kappa value of 0.61, and an AUC of 0.953, surpassing the performance of all ten pathologists in diagnosing atrophy. Among the 10 pathologists, GasMIL's AUC ranked second in OLGA (0.729, 95% CI 0.625-0.833) and fifth in OLGIM (0.792, 95% CI 0.688-0.896). With the assistance of GasMIL, pathologists demonstrated improved AUC (p = 0.013), sensitivity (p = 0.014), and weighted kappa (p = 0.016) in diagnosing IM, and improved specificity (p = 0.007) in diagnosing atrophy compared to pathologists working alone. CONCLUSION: GasMIL shows the best overall performance in diagnosing IM and atrophy when compared to pathologists, significantly enhancing their diagnostic capabilities.

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