Endoscopic microvascular decompression versus microscopic microvascular decompression for trigeminal neuralgia: A systematic review and meta-analysis.

BACKGROUND: To compare the efficacy and safety of full endoscopic or endoscope-assisted microvascular decompression (E-MVD) and microscopic microvascular decompression (M-MVD) for primary trigeminal neuralgia (TN). METHODS: We systematically searched the online database, including PubMed, Embase and Cochrane Library. The search terms used included, but were not limited to, "Trigeminal Neuralgia", "Microvascular Decompression Surgery" and "Endoscope". Postoperative facial pain relief and postoperative complications were considered for meta-analysis. All the outcomes were calculated as odds ratios (ORs) with 95% confidence intervals using R language. RESULTS: A total of three studies involving 442 (E-MVD [218] versus M-MVD [224]) patients were included for analysis in our study. Postoperative facial pain relief (very much improved or much improved) was no difference between the two groups (OR, 0.95;95% CI, 0.57-1.58; I2 = 0%; p = 0.83). In addition, the occurrence of some postoperative complications was not statistically different between the two groups, including CSFleak (OR, 1.35;95% CI, 0.16-11.13; I2 = 0%; p = 0.94), facial paralysis (OR, 0.26;95% CI, 0.03-2.54; I2 = 0%; p = 0.67), hearing loss (OR, 0.87;95% CI, 0.30-2.55; I2 = 32%; p = 0.22), facial numbness (OR, 1.03;95% CI, 0.56-1.87; I2 = 62%; p = 0.10). CONCLUSIONS: Both endoscopic microvascular decompression and microscopic microvascular decompression for trigeminal neuralgia appear to provide patients with equivalent facial pain relief outcomes. Complication rates were also similar between the groups.

Proteomics identifies differentially expressed proteins in glioblastoma U87 cells treated with hederagenin.

OBJECTIVE: We investigated differentially expressed proteins (DEPs) in human glioblastoma U87 cells after treatment with hederagenin as a therapeutic screening mechanism and provided a theoretical basis for hederagenin in treating glioblastoma. METHODS: The Cell Counting Kit 8 assay was used to analyze the inhibitory effect of hederagenin on the proliferation of U87 cells. Protein was identified by tandem mass tags and LC-MS/MS analysis techniques. Annotation of DEPs, Gene Ontology enrichment and function, and Kyoto Encyclopedia of Genes and Genomes pathways and domains were all examined by bioinformatics. According to the TMT results, hub protein was selected from DEPs for WB verification. RESULTS: Protein quantitative analysis found 6522 proteins in total. Compared with the control group, 43 DEPs (P < 0.05) were involved in the highly enriched signaling pathway in the hederagenin group, among which 20 proteins were upregulated, and 23 proteins were downregulated. These different proteins are mainly involved in the longness regulating pathway-WORM, the hedgehog signaling pathway, Staphylococcus aureus infection, complement, coagulation cascades, and mineral absorption. KIF7 and ATAD2B expression were significantly down-regulated and PHEX and TIMM9 expression were significantly upregulated, according to WB analysis, supporting the TMT findings. CONCLUSION: Hederagenin inhibition of GBM U87 cells may be related to KIF7, which is mainly involved in the hedgehog signaling pathway. Our findings lay a foundation for additional study of the therapeutic mechanism of hederagenin.

Screening seven hub genes associated with prognosis and immune infiltration in glioblastoma.

Glioblastoma (GBM) is the most common and deadly primary brain tumor in adults. Diagnostic and therapeutic challenges have been raised because of poor prognosis. Gene expression profiles of GBM and normal brain tissue samples from GSE68848, GSE16011, GSE7696, and The Cancer Genome Atlas (TCGA) were downloaded. We identified differentially expressed genes (DEGs) by differential expression analysis and obtained 3,800 intersected DEGs from all datasets. Enrichment analysis revealed that the intersected DEGs were involved in the MAPK and cAMP signaling pathways. We identified seven different modules and 2,856 module genes based on the co-expression analysis. Module genes were used to perform Cox and Kaplan-Meier analysis in TCGA to obtain 91 prognosis-related genes. Subsequently, we constructed a random survival forest model and a multivariate Cox model to identify seven hub genes (KDELR2, DLEU1, PTPRN, SRBD1, CRNDE, HPCAL1, and POLR1E). The seven hub genes were subjected to the risk score and survival analyses. Among these, CRNDE may be a key gene in GBM. A network of prognosis-related genes and the top three differentially expressed microRNAs with the largest fold-change was constructed. Moreover, we found a high infiltration of plasmacytoid dendritic cells and T helper 17 cells in GBM. In conclusion, the seven hub genes were speculated to be potential prognostic biomarkers for guiding immunotherapy and may have significant implications for the diagnosis and treatment of GBM.