Chemotherapy is an important part of retinoblastoma (RB) treatment. However, the development of drug resistance increases the likelihood of treatment failure. Therefore, increasing the sensitivity of chemotherapeutic drugs is very important. Recent research has explored the relationship between the expression level of gasdermin E (GSDME) and drug sensitivity in RB. Our study found that GSDME expression was significantly reduced in human RB tissues and cell lines. Downregulation of GSDME expression reduced the sensitivity of cells to chemotherapeutic drugs. Decitabine treatment and transfection with GSDME-overexpressing lentivirus (LV-GSDME) upregulated GSDME expression in Y79 and WERI-RB-1 cell lines. The half maximal inhibitory concentrations (IC50) for carboplatin-induced cell death were significantly reduced. Low-dose carboplatin could achieve the IC50, and no significant difference was found in the production of prodeath-activating proteins, but the mode of cell death changed from apoptosis to pyroptosis. Increased GSDME expression can reduce the required dose of chemotherapeutic drugs. After inhibition of caspase-3 activation, the IC50 of carboplatin-induced cell death was significantly increased in cells with high GSDME expression, and the method of cell death switched from pyroptosis to apoptosis, which increased the concentration of chemotherapeutic drugs. Furthermore, the sensitivity of cells to carboplatin was reduced. The in vivo xenograft tumor model further confirmed that GSDME upregulation could promote carboplatin-induced tumor cell death. Therefore, the sensitivity of cells to chemotherapeutic drugs can be predicted by detecting the GSDME expression level, and we used pyroptosis induction as a new method for promoting tumor death.

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Introduction: Uveal melanoma (UM) is the most common primary intraocular malignancy among adults. Altered metabolism has been shown to contributes to the development of cancer closely, but the prognostic role of metabolism in UM remains to be explored. This study aimed to construct a metabolic-related signature for UM. Method: We collected the mRNA sequencing data and corresponding clinical information from The Cancer Genome Atlas and Gene Expression Omnibus databases. A univariate Cox regression analysis, the Lasso-penalized Cox regression analysis and multivariate Cox regression analyzes were used to construct a metabolic signature based on TCGA. The time-dependent ROC and Kaplan-Meier survival curves were calculated to validate the prognostic ability of the signature. The immune-related features and mutation profile were characterized by CIBERSORT and maftools between high- and low risk groups. Result： A novel metabolic-related signature (risk score= -0.246*SLC25A38-0.50186*ABCA12 +0.032*CA12 +0.086*SYNJ2) was constructed to predict the prognosis of UM patients. In TCGA and GSE22138, the signature had high sensitivity and specificity in predicting the prognosis of UM patients (survival probability; P＜0.0001,P=0.012）. GO pathway enrichment analysis and GSEA were used to discriminate several significantly enriched metabolism-related pathways, including channel activity and passive transmembrane transporter activity, which may reveal the underlying mechanisms. The high-risk group had more immune cell infiltration and greater distribution of BAP1 mutations. Conclusion： Our study developed a robust metabolic-gene signature based on TCGA to predict the prognosis of UM patients. The signature indicates a dysregulated metabolic microenvironment and provides new metabolic biomarkers and therapeutic targets for UM patients.

Retinoblastoma (RB) is the most common type of intraocular malignant tumor that lowers the quality of life among children worldwide. Long noncoding RNAs (lncRNAs) are reported to play a dual role in tumorigenesis and development of RB. Autophagy is also reported to be involved in RB occurrence. Although several studies of autophagy-related lncRNAs in RB have been explored before, there are still unknown potential mechanisms in RB. In the present study, we mined dataset GSE110811 from the Gene Expression Omnibus database and downloaded autophagy-related genes from the Human Autophagy Database for further bioinformatic analysis. By implementing the differential expression analysis and Pearson correlation analysis on the lncRNA expression matrix and autophagy-related genes expression matrix, we identified four autophagy-related lncRNAs (namely, N4BP2L2-IT2, SH3BP5-AS1, CDKN2B-AS1, and LINC-PINT) associated with RB. We then performed differential expression analysis on microRNA (miRNA) from dataset GSE39105 for further analyses of lncRNA–miRNA–mRNA regulatory mechanisms. With the miRNA–lncRNA module on the StarBase 3.0 website, we predicted the differentially expressed miRNAs that could target the autophagy-related lncRNAs and constructed a potential lncRNA–miRNA–mRNA regulatory network. Furthermore, the functional annotations of these target genes in regulatory networks were presented using the Cytoscape and the Metascape annotation tool. Finally, the expression pattern of the four autophagy-related lncRNAs was evaluated via qRT-PCR. In conclusion, our findings suggest that the four autophagy-related lncRNAs could be critical molecules associated with the development of RB and affect the occurrence and development of RB through the lncRNA–miRNA–mRNA regulatory network. Genes (GRP13B, IFT88, EPHA3, GABARAPL1, and EIF4EBP1) may serve as potential novel therapeutic targets and biomarkers in RB.

BackgroundBehcet’s disease (BD) is a chronic immune disease that involves multiple systems. As the pathogenesis of BD is not clear, and new treatments are needed, we used bioinformatics to identify potential drugs and validated them in mouse models.MethodsBehcet’s disease-related target genes and proteins were screened in the PubMed and UVEOGENE databases. The biological functions and pathways of the target genes were analyzed in detail by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. A protein-protein interaction (PPI) network was constructed by the STRING database, and hub genes were identified by the Cytoscape plug-in CytoHubba. Gene-drug interactions were identified from the DGIdb database. Experimental autoimmune uveitis (EAU) mice were used as an animal model for drug validation.ResultsA total of 249 target genes and proteins with significant differences in BD were screened, and the results of functional enrichment analysis suggested that these genes and proteins were more located on the cell membrane, involved in regulating the production of cytokines and affecting the activity of cytokines. They mainly regulated “Cytokine- Cytokine receptor interaction”, “Inflammatory bowel disease (IBD)” and “IL-17 signaling Pathway”. In addition, 10 hub genes were obtained through PPI network construction and CytoHubba analysis, among which the top 3 hub genes were closely related to BD. The DGIdb analysis enriched seven drugs acting together on the top 3 hub genes, four of which were confirmed for the treatment of BD or its complications. There is no evidence in the research to support the results in omeprazole, rabeprazole, and celastrol. However, animal experiments showed that rabeprazole and celastrol reduced anterior chamber inflammation and retinal inflammation in EAU mice.ConclusionsThe functional analysis of genes and proteins related to BD, identification of hub genes, and validation of potential drugs provide new insights into the disease mechanism and potential for the treatment of BD.