Identification of anti-gastric cancer effects and molecular mechanisms of resveratrol: From network pharmacology and bioinformatics to experimental validation.

BACKGROUND: Gastric cancer (GC) is one of the most aggressive malignancies with limited therapeutic options and a poor prognosis. Resveratrol, a non-flavonoid polyphenolic compound found in a variety of Chinese medicinal materials, has shown excellent anti-GC effect. However, its exact mechanisms of action in GC have not been clarified. AIM: To identify the effects of resveratrol on GC progression and explore the related molecular mechanisms. METHODS: Action targets of resveratrol and GC-related targets were screened from public databases. The overlapping targets between the two were confirmed using a Venn diagram, and a "Resveratrol-Target-GC" network was constructed using Cytoscape software version 3.9.1. The protein-protein interaction (PPI) network was constructed using STRING database and core targets were identified by PPI network analysis. The Database for Annotation, Visualization and Integrated Discovery database was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. A "Target-Pathway" network was created by using Cytoscape 3.9.1. The RNA and protein expression levels of core target genes were observed using the Cancer Genome Atlas and the Human Protein Atlas databases. DriverDBv3 and Timer2.0 databases were used for survival and immune infiltration analysis. Subsequently, the findings were further verified by molecular docking technology and in vitro experiments. RESULTS: A total of 378 resveratrol action targets and 2154 GC disease targets were obtained from public databases, and 181 intersection targets between the two were screened by Venn diagram. The top 20 core targets were identified by PPI network analysis of the overlapping targets. GO function analysis mainly involved protein binding, identical protein binding, cytoplasm, nucleus, negative regulation of apoptotic process and response to xenobiotic stimulus. KEGG enrichment analysis suggested that the involved signaling pathways mainly included PI3K-AKT signaling pathway, MAPK signaling pathway, IL-17 signaling pathway, TNF signaling pathway, ErbB signaling pathway, etc. FBJ murine osteosarcoma viral oncogene homolog (FOS) and matrix metallopeptidase 9 (MMP9) were selected by differential expression analysis, and they were closely associated with immune infiltration. Molecular docking results showed that resveratrol docked well with these two targets. Resveratrol treatment arrested the cell cycle at the S phase, induced apoptosis, and weakened viability, migration and invasion in a dose-dependent manner. Furthermore, resveratrol could exhibit anti-GC effect by regulating FOS and MMP9 expression. CONCLUSION: The anti-GC effects of resveratrol are related to the inhibition of cell proliferation, migration, invasion and induction of cell cycle arrest and apoptosis by targeting FOS and MMP9.

The predictive value of miR-377 and phospholipase A2 in the early diagnosis of diabetic kidney disease and their relationship with inflammatory factors.

OBJECTIVE: The value of novel biomarkers for DKD has received increasing attention, and there is an urgent need for novel biomarkers with sensitivity, specificity and ability to detect kidney damage.miR-377 regulates many basic biological processes, plays a key role in tumor cell proliferation, migration and inflammation, and can also increase the expression of matrix proteins and fibronectin, leading to renal tubulointerstitial inflammation and renal fibrosis. Lipoprotein-associated phospholipase A2, as an inflammatory marker, is involved in the pathological process of microalbuminuria production and renal function decline, and is a predictive factor of microalbuminuria production and renal function decline, and can be used as an indicator to evaluate the progression of DKD.The aim of this study was to investigate the effects of miR-377 and phospholipase A2 on the development of diabetic kidney disease through regulation of inflammatory factors and the mechanism of action. METHODS: 80 diabetic patients were divided into two groups according to urinary albumin-to-creatinine ratio (UACR): diabetic normal proteinuria group (n = 42) and diabetic proteinuria group (n = 38). Forty-three healthy people were selected as the normal control group. The serum levels of TGF-ß, IL-6, and IL-18 were measured by ELISA, miR-377 was detected by qPCR, and the serum levels of phospholipase A2 were detected by electrochemiluminescence. Analyze the correlation of study group indicators, ROC curve was used to evaluate the diagnostic efficacy of miR-377 and phospholipase A2 in diabetic kidney disease. RESULTS: The average levels of serum TGF-ß, IL-6, IL-18, miR-377 and phospholipase A2 in diabetic proteinuria group were significantly higher than those in normal control group and diabetic proteinuria normal group(P < 0.05). miR-377, phospholipase A2 were significantly correlated with inflammatory factors such as glomerular filtration rate and TGF-ß. miR-377 and phospholipase A2 are independent predictors of diabetic kidney disease. The area under the curve of miR-377 and phospholipase A2 in the normal diabetic proteinuria group and the diabetic proteinuria group were 0.731 and 0.744, respectively. CONCLUSION: miR-377 and phospholipase A2 have good diagnostic efficiency for the early diagnosis of diabetic kidney disease. They can be used as early biomarkers.miR-377 and phospholipase A2 were positively correlated with inflammatory factors and involved in the occurrence and development of diabetic kidney disease.

Molecular Mechanism Analysis of the Effect of Hederagenin Combined with L-OHP on Chemosensitivity of AGS/L-OHP Based on Network Pharmacology.

AIMS AND OBJECTIVES: This study aimed to evaluate the pharmacological mechanism of Hederagenin (HD) combined with oxaliplatin (L-OHP) in treating gastric cancer (GC) through network pharmacology combined with experimental verification. MATERIAL AND METHODS: Network pharmacology methods were used to screen potential targets for HD, L-OHP, and GC-related targets from public databases, and the intersection of the three gene sets was taken. Cross genes were analyzed through protein-protein interaction (PPI) networks to predict core targets, and related pathways were predicted through GO and KEGG enrichment analysis. The experimental results were verified by the in vitro experiments. HD was applied on AGS/L-OHP cells, and then cellular chemosensitivity and the expressions of P-gp, Survivin, Bcl-2, p-Akt, and p-PI3K genes were detected. Wound assay and Transwell Chamber assay were employed to detect the effect of HD on AGS/L-OHP cells. Nude mice xenograft models transfected using AGS/L-OHP cells were also treated with HD in order to verify the results. The size and weight of the tumor, as well as the expressions of P-gp, Survivin, Bcl-2, p- Akt and p-PI3K genes, were also measured. RESULTS: KEGG analysis showed that the anti-gastric cancer effect of HD was mediated mainly by PI3K-Akt signaling pathways. The PI3K-Akt signaling pathway containing more enriched genes may play a greater role in anti-gastric cancer. It was observed that for AGS/L-OHP cells jointly treated with HD and L-OHP, their activity, migration and invasion were significantly lower than those treated only using HD or L-OHP group. Moreover, expressions of p-Akt, p- PI3K, Bcl-2, P-gp, and Survivin for the HD+L-OHP group decreased significantly. Results of the in vivo experiments showed that the sizes and weights of tumors in the HD+L-OHP group were the lowest compared to the HD group and L-OHP group. CONCLUSION: Our findings suggest that HD may reduce the resistance of AGS/L-OHP cells to LOHP by regulating the PI3K/Akt signaling pathway.

Research on Multicamera Photography Image Art in BERT Motion Based on Deep Learning Mode.

In order to improve the artistic expression effect of photographic images, this article combines the deep learning model to conduct multicamera photographic image art research in BERT motion. Moreover, this article analyzes the external parameter errors caused in the calibration process and uses the checkerboard in the common field of view to calibrate the spatial coordinates of the corners of the board in multiple camera coordinate systems. In addition, this article aims to match the spatial coordinates of the corresponding points to each other and solve the rotation and translation matrix in the transformation process. Finally, this article uses the LM algorithm to optimize the calibration parameters of the camera and combines the deep learning algorithm to perform image processing. The experimental research results show that the research method of multicamera photography image art in BERT motion based on the deep learning mode proposed in this article can effectively improve the expression effect of image art.