Objectives: In this study, we investigated the potential of attenuated Salmonella carrying the co-expressing plasmid siPD-L1-Endo to effectively inhibit PD-L1 and VEGF expres-sion, thereby enhancing the anti-tumor effects of radiation therapy in GBM-bearing mice.

Methods: The regulatory mechanisms responsible for the treatment effect were detected by Flow cytometry, Immunohistochemistry, TUNEL, Immunofluorescence, H&E staining, and Western blot assays.

Results and Discussion: Upon administration of attenuated Salmonella carrying siRNA-PD-L1 and co-expressing endostatin plasmids, the results exhibited significant suppression of tumor growth and tumor cell proliferation, as well as a concurrent decrease in PD-L1 and VEGF expression in tumor tissues. Moreover, the treatment led to reduced expression levels of tumor-related proteins p-Stat3, MMP2, Cyclin D1, and PCNA, an increase in the expression of the apoptosis-related protein cleaved-caspase3, facilitated infiltration of CD4+ and CD8+ T cells within tumor tissues, and an elevation of the ratios of CD4+, CD8+ T cells, and NK cells in the spleen of tumor-bearing mice.

Conclusion: These findings highlight the ability of attenuated Salmonella carrying siR-NA-PD-L1 and co-expressing endostatin plasmids to effectively modulate PD-L1 and VEGF expression, thus strengthening the anti-tumor immune response in GBM-bearing mice subjected to radiation therapy. This combination therapy approach holds promise as a potential avenue for improving the efficacy of radiation therapy in the treatment of glioblastoma.

Methods: We analyzed the sequencing data of GSE14208 patients from the GEO database us-ing differential and enrichment analyses. Gastric cancer cells with high ADH1B expression and low ADH1B expression were selected by qPCR and WB to construct ADH1B overexpress and silence cells. The optimal cisplatin concentrations for treatment were determined via CCK-8 detection and WB. Furthermore, we assessed drug resistance, cellular activity, and in-vasion and migration capacities using IC50, CCK-8 assays, Transwell, and migration tests. Apoptosis was evaluated using flow cytometry and EDU staining. The pathway influenced by ADH1B was examined through WB and immunofluorescence. The impact of gene expression on the tumorigenic potential of gastric cancer cells was assessed by analyzing tumor size, mass, and histology in HE-stained tumor sections and Ki67 and protein pathway immuno-histochemistry in a mouse tumorigenesis model.

Results and Discussion: This study revealed that ADH1B may exhibit a cancer-promoting ef-fect, according to our database analysis, and is associated with drug resistance. Silencing ADH1B in AGS cells led to a reduced IC50, as well as decreased viability, invasion, and mi-gration capabilities while increasing apoptosis rates. Conversely, overexpressing ADH1B in MKN-45 cells reversed these effects. Western blot and immunofluorescence results indicated that the expression levels of proteins involved in the EMT, PI3K, and MAPK pathways were altered following the silencing and overexpression of ADH1B. Moreover, silencing ADH1B not only reduced tumor volume and weight but also enhanced the tumor-reducing effects of cisplatin, as evidenced by changes in tumor structure; overexpression had the opposite effects. The altera-tions in pathway protein expression in tumor sections mirrored those observed in cells.

Conclusion: This study identified the gene ADH1B as a critical factor in the incidence and drug resistance of gastric cancer. It was demonstrated through cellular and tumorigenic assays that ADH1B promotes carcinogenesis and enhances cisplatin resistance in gastric cancer cells via the EMT signaling pathways.]]> https://www.benthamscience.com/article/1518422026-04-06 Background: Molecular subtyping guides bladder cancer (BCa) care but typically requires RNA profiling. This study aimed to develop pathology-based subtypes of BCa using pathology deep learning features derived from routinely obtained hematoxylin and eosin (H&E)-stained whole-slide images (WSIs).

Methods: We developed a pathology-based subtype of BCa based on deep learning features extracted from H&E-stained WSIs. A modified Resnet50 model was trained to distinguish between tumor and normal regions and extract patch-level deep learning features. These features were aggregated at the WSI level, followed by weighted gene co-expression net-work analysis (WGCNA), Cox regression, and unsupervised K-means clustering to define pathology-based subtypes. External validation was performed using WSIs from four inde-pendent centers and transcriptomic data from IMvigor210 and GSE32894 cohorts. Interpret-ability used Grad-CAM on tumor patches.

Results and Discussion: The Resnet50 model achieved high and consistent performance in distinguishing tumor and normal patches. The analysis identified four BCa subtypes, with distinct clinical outcomes. Clusters 2/3 were predominantly luminal-like (cluster 3 was en-riched for FGFR3 and had the lowest TMB), whereas clusters 0/1 exhibited mixed lu-minal/basal features with higher immune/stromal scores; regulon and pathway profiles di-verged (e.g., EGFR/FOXM1/STAT3 in cluster 1 vs. FGFR3/FOXA1/PPARG in cluster 3). Grad-CAM highlighted distinct nuclear morphologies supporting interpretability.

Conclusion: We present a pathology-based subtype of BCa that is portable, prognostically informative, and mechanistically concordant with RNA-defined biology. The approach of-fers a cost-effective and accessible alternative to traditional molecular profiling methods, with potential applications in personalized treatment and improved patient stratification.]]>