Objective: To study the myocardial benefit effect and mechanism of paeoniflorin on myocardial ischemia reperfusion injury (MIRI) in rats. Methods: Hundred SD rats were randomly divided into 5 groups: sham group, model group, Paeoniflorin (15 mg/kg) group, Paeoniflorin (30 mg/kg) group, and Paeoniflorin (60 mg/kg) group. Myocardial ischemia reperfusion model was established in each group except the sham group. The myocardial infarction and morphological changes were measured by the TTC staining and HE staining respectively. Myocardial caspase-3 was detected by immunohistochemistry. In addition, the protein levels of Bcl-2 and Bax and the expression ratio of p-erk, p-jnk, and p-p38 were detected by Western blot. Myocardial superoxide dismutase (SOD) activity and malondialdehyde (MDA) level were measured by the assay kit. Results: Paeoniflorin (30 mg/kg) and Paeoniflorin (60 mg/kg) can obviously alleviate myocardial infarction caused by MIRI (p < 0.05). HE staining showed that the myocardial morphology in the treatment group was obviously better than that in the model group. WB and immunohistochemistry showed that Paeoniflorin (30 mg/kg) andPaeoniflorin (60 mg/kg) can significantly increase the reduced protein level of bcl-2 (p < 0.05) and reduce the increased protein level of caspase3, bax p-erk, p-jnk, and p-p38 caused by MIRI (p < 0.05). The activity of SOD was increased and the level of MDA was decreased after Paeoniflorin treatment. Conclusion: Paeoniflorin preconditioning has a protective effect on MIRI in rats. Its mechanism is related to reducing oxidative stress and apoptosis by inhibiting the expression of apoptosis-related signaling pathway.

Background: Gastric cancer (GC) is the most prevailing digestive tract malignant tumor worldwide with high mortality and recurrence rates. However, its potential molecular mechanism and prognostic biomarkers are still not fully understood. We aim to screen novel prognostic biomarkers related to GC prognosis using comprehensive bioinformatic tools. Methods: Four gene expression microarray data were downloaded from the Gene Expression Omnibus (GEO) database (GSE26942, GSE33335, GSE63089, and GSE79973). Differentially expressed genes (DEGs) between gastric carcinoma and normal gastric tissue samples were identified by an integrated bioinformatic analysis. Gene Ontology (GO) term enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed using statistical software R. STRING and Cytoscape software were employed to construct protein–protein interaction (PPI) networks. Hub genes with a high score of connectivity identified from the PPI network were identified. Prognostic values of hub genes were evaluated in GSE15459 dataset. Hub genes related to GC overall survival were further validated in GEPIA (Gene Expression Profiling Interactive Analysis) online tool. Results: A total of 12 upregulated DEGs and 59 downregulated DEGs were identified when the 4 microarray data overlapped. Among them, 10 hub genes with a high score of connectivity were identified. High expression of ghrelin and obestatin prepropeptide (GHRL), BGN, TIMP metallopeptidase inhibitor 1, thrombospondin 2, secreted phosphoprotein 1, and low expression of CHGA were associated with a poor overall survival of gastric cancer (all log rank P < .05). After validation in GEPIA database, only GHRL was confirmed associated with a poor overall survival of gastric cancer (log rank P = .04). Conclusions: GHRL could be used as a novel biomarker for the prediction of a poor overall survival of gastric cancer, and could be a novel therapeutic target for gastric cancer treatment. However, future experimental studies are still required to validate these findings.