Analysis of the expression profile of miRNAs related to skin photoaging in the GEO database

Background:Skin aging has recently gained significant attention in both society and skin care research.Understanding the biological processes of photoaging caused by long-term skin exposure to ultraviolet radiation is critical for preventing and treating skin aging.Therefore,it is important to identify genes related to skin photoaging and shed light on their functions.Methods:We used data from the Gene Expression Omnibus(GEO)database and conducted bioinformatics analyses to screen and extract microRNAs(miRNAs)and their downstream target genes related to skin photoaging,and to determine possible biological mechanisms of skin photoaging.Results:A total of 34 differentially expressed miRNAs and their downstream target genes potentially related to the biological process of skin photoaging were identified.Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis showed that these target genes were enriched in pathways related to human papillomavirus infection,extracellular matrix(ECM)-receptor signaling,estrogen receptor,skin development,epidermal development,epidermal cell differentiation,keratinocyte differentiation,structural components of the ECM,structural components of the skin epidermis,and others.Conclusion:Based on the GEO database-derived findings,we determined that target genes of two miRNAs,namely miR-4667-5P-KRT79 and miR-139-5P-FOS,play an important role in skin photoaging.These observations could provide theoretical support and guidance for further research on skin aging-related biological processes.

To analyze the differentially expressed genes in chronic rejection after renal transplantation by bioinformatics

Objective: To use bioinformatics technology to analyse differentially expressed genes in chronic rejection after renal transplantation, we can screen out potential pathogenic targets associated with the development of this disease, providing a theoretical basis for finding new therapeutic targets. Methods: Gene microarray data were downloaded from the Gene Expression Profiling Integrated Database (GEO) and cross-calculated to identify differentially expressed genes (DEGs). Analysis of differentially expressed genes (DEGs) with gene ontology (GO) is a method used to study the differences in gene expression under different conditions as well as their functions and interrelationships, while Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis is a tool used to explore the functions and pathways of genes in specific biological processes. By calculating the distribution of immune cell infiltration, the result of immune infiltration in the rejection group can be analysed as a trait in Weighted Gene Co-Expression Network Analysis (WGCNA) for genes associated with rejection. Then, protein-protein interaction networks (PPI) were constructed using the STRING database and Cytoscape software to identify hub gene markers. Results: A total of 60 integrated DEGs were obtained from 3 datasets (GSE7392, GSE181757, GSE222889). By GO and KEGG analysis, the GEDs were mainly concentrated in the regulation of immune response, defence response, regulation of immune system processes, and stimulation response. The pathways were mainly enriched in antigen processing and presentation, EBV infection, graft-versus-host, allograft rejection, and natural killer cell-mediated cytotoxicity. After further screening using WGCNA and PPI networks, HLA-A, HLA-B, HLA-F, and TYROBP were identified as hub genes (Hub genes). The data GSE21374 with clinical information was selected to construct the diagnostic efficacy and risk prediction model plots of the four hub genes, and the results concluded that all four Hub genes had good diagnostic value (area under the curve in the range of 0.794-0.819). From the inference, it can be concluded that the four genes, HLA-A, HLA-B, HLA-F and TYROBP, may have an important role in the development and progression of chronic rejection after renal transplantation. Conclusion: DEGs play an important role in the study of the pathogenesis of chronic rejection after renal transplantation, and can provide theoretical support for further research on the pathogenesis of chronic rejection after renal transplantation and the discovery of new therapeutic targets through enrichment analysis and pivotal gene screening, as well as inferential analyses of related diagnostic efficacy and disease risk prediction.

Network pharmacology-based approach to investigate the mechanism of Huang-Lian-Jie-Du-Decoction for treatment of type 2 diabetes mellitus

Background:Although the benefits of Huang-Lian-Jie-Du-Decoction(HLJDD)on type 2 diabetes mellitus are noted,the material base and action mechanism remain unknown.This paper aim is to reveal the material base and action mechanism of HLJDD against type 2 diabetes mellitus in a system pharmacology framework.Methods:The compounds in HLJDD were first retrieved from the Traditional Chinese Medicine Systems Pharmacology database and analysis platform.Once retrieved,they were fed into the SwissTargetPrediction database to predict the interacting targets.Meanwhile,a human expression profile dataset was analyzed in the Gene Expression Omnibus database,and subsequently,the differentially expressed genes were compared to the HLJDD-related targets.We conducted a protein-protein interaction analysis,Kyoto Encyclopedia of Genes and Genomes pathway analysis,and Gene Ontology analysis to identify the potential active compounds and targets.Lastly,to verify the binding affinities of those compounds and targets,we performed molecular docking.Results:We obtained 15 key compounds,such as quercetin,epiberberine,and berberine,and 10 hub genes,such as IκB kinase-βand phosphatidylinositol 3-kinase regulatory subunit alpha.The top 10 enriched pathways were also found to be tightly related to type 2 diabetes mellitus,including insulin resistance and FoxO signaling pathway.Moreover,all the key compounds were found to bind well to the hub genes.Particularly for the target of IκB kinase-β,11 out of 15 compounds bound to it with energies of<−9.0 kcal/mol.Conclusion:In summary,15 key compounds of HLJDD may affect type 2 diabetes mellitus development by multiple genes such as IκB kinase-βand phosphatidylinositol 3-kinase regulatory subunit alpha and signaling pathways such as insulin resistance and FoxO signaling pathway.