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Background: Diabetic retinopathy is a diabetic microvascular complication. Pyroptosis, as a way of inflammatory death, plays an important role in the occurrence and development of diabetic retinopathy, but its underlying mechanism has not been fully elucidated. The purpose of this study is to identify the potential pyroptosis-related genes in diabetic retinopathy by bioinformatics analysis and validation in a diabetic retinopathy model and predict the microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) interacting with them. Subsequently, the competing endogenous RNA (ceRNA) regulatory network is structured to explore their potential molecular mechanism. Methods: We obtained mRNA expression profile dataset GSE60436 from the Gene Expression Omnibus (GEO) database and collected 51 pyroptosis-related genes from the PubMmed database. The differentially expressed pyroptosis-related genes were obtained by bioinformatics analysis with R software, and then eight key genes of interest were identified by correlation analysis, Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and protein-protein interaction (PPI) network analysis. Then, the expression levels of these key pyroptosis-related genes were validated with quantitative real-time polymerase chain reaction (qRT-PCR) in human retinal endothelial cells with high glucose incubation, which was used as an in vitro model of diabetic retinopathy. Western blot was performed to measure the protein levels of gasdermin D (GSDMD), dasdermin E (GSDME) and cleaved caspase-3 in the cells. Moreover, the aforementioned genes were further confirmed with the validation set. Finally, the ceRNA regulatory network was structured, and the miRNAs and lncRNAs which interacted with CASP3, TLR4, and GBP2 were predicted. Results: A total of 13 differentially expressed pyroptosis-related genes were screened from six proliferative diabetic retinopathy patients and three RNA samples from human retinas, including one downregulated gene and 12 upregulated genes. A correlation analysis showed that there was a correlation among these genes. Then, KEGG pathway and GO enrichment analyses were performed to explore the functional roles of these genes. The results showed that the mRNA of these genes was mainly related to inflammasome complex, interleukin-1 beta production, and NOD-like receptor signaling pathway. In addition, eight hub genes-CASP3, TLR4, NLRP3, GBP2, CASP1, CASP4, PYCARD, and GBP1-were identified by PPI network analysis using Cytoscape software. High glucose increased the protein level of GSDMD and GSDME, as critical effectors of pyroptosis, in retinal vascular endothelial cells. Verified by qRT-PCR, the expression of all these eight hub genes in the in vitro model of diabetic retinopathy was consistent with the results of the bioinformatics analysis of mRNA chip. Among them, CASP4, GBP1, CASP3, TLR4, and GBP2 were further validated in the GSE179568 dataset. Finally, 20 miRNAs were predicted to target three key genes-CASP3, GBP2, and TLR4, and 22 lncRNAs were predicted to potentially bind to these 20 miRNAs. Then, we constructed a key ceRNA network that is expected to mediate cellular pyroptosis in diabetic retinopathy. Conclusion: Through the data analysis of the GEO database by R software and verification by qRT-PCR and validation set, we successfully identified potential pyroptosis-related genes involved in the occurrence of diabetic retinopathy. The key ceRNA regulatory network associated with these genes was structured. These findings might improve the understanding of molecular mechanisms underlying pyroptosis in diabetic retinopathy.

Asunto(s)

Diabetes Mellitus , Retinopatía Diabética , MicroARNs , ARN Largo no Codificante , Caspasa 3/genética , Retinopatía Diabética/genética , Células Endoteliales/metabolismo , Redes Reguladoras de Genes , Glucosa , Humanos , Inflamación/genética , MicroARNs/genética , MicroARNs/metabolismo , Piroptosis/genética , ARN Largo no Codificante/genética , ARN Mensajero/genética , Receptor Toll-Like 4/genética

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Introduction: Recurrent implantation failure (RIF) is a distressing problem in assisted reproductive technology (ART). Immunity plays a vital role in recurrent implantation failure (RIF) occurrence and development, but its underlying mechanism still needs to be fully elucidated. Through bioinformatics analysis, this study aims to identify the RIF-associated immune cell types and immune-related genes. Methods: The differentially expressed genes (DEGs) were screened based on RIF-associated Gene Expression Omnibus (GEO) datasets. Then, the enrichment analysis and protein-protein interaction (PPI) analysis were conducted with the DEGs. The RIF-associated immune cell types were clarified by combining single sample gene set enrichment analysis (ssGSEA) and CIBERSORT. Differentially expressed immune cell types-related modules were identified by weighted gene co-expression network analysis (WGCNA) and local maximal quasi-clique merger (lmQCM) analysis. The overlapping genes between DEGs and genes contained by modules mentioned above were delineated as candidate hub genes and validated in another two external datasets. Finally, the microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) that interacted with hub genes were predicted, and the competing endogenous RNA (ceRNA) regulatory network was structured. Results: In the present study, we collected 324 DEGs between RIF and the control group, which functions were mainly enriched in immune-related signaling pathways. Regarding differential cell types, the RIF group had a higher proportion of activated memory CD4 T cells and a lower proportion of γδ T cells in the endometrial tissue. Finally, three immune-related hub genes (ALOX5AP, SLC7A7, and PTGS2) were identified and verified to effectively discriminate RIF from control individuals with a specificity rate of 90.8% and a sensitivity rate of 90.8%. In addition, we constructed a key ceRNA network that is expected to mediate molecular mechanisms in RIF. Conclusion: Our study identified the intricate correlation between immune cell types and RIF and provided new immune-related hub genes that offer promising diagnostic and therapeutic targets for RIF.

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Objective:To comprehensively analyze the expression profile of circular RNA (circRNA) and construct competing endogenous RNA (ceRNA) regulatory networks in tuberous sclerosis complex related renal angiomyolipoma (TSC-RAML).Methods:According to the diagnostic criteria of TSC determined by the international consensus group on tuberous sclerosis in 2012, tumor tissues and paired normal renal tissues of 3 patients with TSC-RAML who were diagnosed in our hospital from January 2017 to January 2019 were collected. The circRNA, miRNA and mRNA of 3 paired samples were detected by circRNA, miRNA chip technology and next generation sequencing respectively, and the differential molecules were determined. Gene Ontology(GO) functional enrichment analysis and Kyoto Encyclopedia of genes and genomes(KEGG) pathway enrichment analysis were performed based on differential mRNA molecules and host genes of circRNA. Based on differential circRNA, miRNA and mRNA, up-regulated and down-regulated ceRNA regulatory networks were established.Results:A total of 330 up-regulated and 336 down-regulated differential circRNA, 8 up-regulated and 7 down-regulated miRNA, 800 up-regulated and 1130 down-regulated mRNA were screened. Through GO and KEGG enrichment analysis, many pathways including lipid metabolism, focal adhesion and mineral absorption were abnormally altered. Finally, the up-regualted ceRNA network led by hsa_circ_0092022, hsa_circ_0076859 and hsa_circ_0033388 and down-regulated network led by hsa_circ_0000374, hsa_circ_0000141, hsa_circ_0072665, hsa_circ_0009503 and hsa_circ_0000009 were constructed.Conclusions:There were many differentially expressed circRNA between TSC-RAML and paired normal renal tissues. ceRNA regulatory networks may be involved in the occurrence and development of TSC-RAML.