Background. Liver fibrosis is a serious human health problem, and there is a need for specific antifibrosis drugs in the clinic. Tanshinone IIA has recently been reported to have a role in the treatment of liver fibrosis. However, the evidence supporting its antifibrotic effect is not sufficient, and the underlying mechanism is not clear. We thus performed this meta-analysis of animal research to assess the therapeutic effect of tanshinone IIA on liver fibrosis and analyzed the possible associated mechanism to provide a reference for further clinical drug preparation and clinical research. Methods. We collect related articles from the databases PubMed, Web of Science, Embase, Wanfang, VIP, and CNKI. The quality of the included studies was evaluated according to the SYRCLE risk of bias tool for animal studies. Data were analyzed using RavMan 5.3 and Stata 12.0 software. Results. A total of 404 articles were retrieved from the databases. After screening, 11 articles were included in the analysis. The included studies’ methodological quality was generally low, and an obvious publication bias was found. The results showed that tanshinone IIA significantly improved liver function in experimental animals and reduced the level of liver fibrosis by reducing inflammation and inhibiting immunity, antiapoptotic processes, and HSC activation. Conclusion. Tanshinone IIA can effectively improve liver fibrosis and liver function in animal models and is worthy of future higher quality animal studies and clinical drug trials.
Gastric cancer remains one of the five major malignant tumors in the world, posing a great threat to public life and health. As gene sequencing technology develops, it is urgent to find out specific molecular markers for cancer therapy. In this study, datasets of GSE13911, GSE30727, GSE63089 and GSE118916 were investigated by bioinformatics analysis, and differentially expressed genes (DEGs) between GC tissues and normal tissues were screened for potential cancer therapeutic targets. Furthermore, the GSE63089 dataset was analyzed by Weighted Gene Co-expression Network Analysis (WGCNA), and the highly related genes were clustered. Then, the hub genes were searched using co-expression network and Molecular Complex Detection (MCODE) plug-in from Cytoscape software. Finally, ASPM, COL11A1 and CDC20 were obtained by intersection of hub genes and DEGs. The expressions of ASPM, COL11A1 and CDC20 gene in gastric cancer tissues and normal tissues from TCGA database were detected. For these genes, the least absolute shrink and selection operator (LASSO) Cox expression analysis was used to establish the prognostic risk model. COL11A1 and CDC20 genes were identified as candidate prognostic risk markers for GC. Analysis using qRT-PCR has shown that COL11A1 and CDC20 were significant differentially expressed between gastric cancer tissues and normal gastric tissues (P < 0.01). In conclusion, our study identifies specific DEGs involved in ECM process and metabolism by cytochrome P450 process, and these DEGs may be potential targets for GC therapy. The model constructed by COL11A1 and CDC20 genes can predict the prognosis risk of GC patients. Taken together, these findings provide reference for further analyses of key alterations during GC progression.
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