RESUMO
Purpose: Age?related macular degeneration (AMD) is the leading cause of irreversible blindness in older individuals. More studies focused on screening the genes, which may be correlated with the development of AMD. With advances in various technologies like multiple microarray datasets, researchers could identify differentially expressed genes (DEGs) more accurately. Exploring abnormal gene expression in disease status can help to understand pathophysiological changes in complex diseases. This study aims to identify the key genes and upstream regulators in AMD and reveal factors, especially genetic association, and the prognosis of the development of this disease. Methods: Data from expression profile GSE125564 and profile GSE29801 were obtained from the Gene Expression Omnibus (GEO) database. We analyzed DEGs using R software (version 3.6.3). Functional enrichment and PPI network analysis were performed using the R package and online database STRING (version 11.0). Results: We compared AMD with normal and found 68 up?regulated genes (URGs) and 25 down?regulated genes (DRGs). We also compared wet AMD with dry AMD and found 41 DRGs in dry AMD. Further work including PPI network analysis, GO classification, and KEGG analysis was done to find connections with AMD. The URGs were mainly enriched in the biological process such as DNA replication, nucleoplasm, extracellular exosome, and cadherin binding. Besides, DRGs were mainly enriched in these functions such as an integral component of membrane and formation of the blood?aqueous barrier (BAB). Conclusion: This study implied that core genes might involve in the process of AMD. Our findings may contribute to revealing the pathogenesis, developing new biomarkers, and raising strategies of treatment for AMD
RESUMO
OBJECTIVES@#To identify the differentially expressed genes (DEGs) during the pathogenesis of periodontitis by bioinformatics analysis.@*METHODS@#GEO2R was used to screen DEGs in GSE10334 and GSE16134. Then, the overlapped DEGs were used for further analysis. g:Profiler was used to perform Gene Ontology analysis and pathway analysis for upregulated and downregulated DEGs. The STRING database was used to construct the protein-protein interaction (PPI) network, which was further visua-lized and analyzed by Cytoscape software. Hub genes and key modules were identified by cytoHubba and MCODE plug-ins, respectively. Finally, transcription factors were predicted via iRegulon plug-in.@*RESULTS@#A total of 196 DEGs were identified, including 139 upregulated and 57 downregulated DEGs. Functional enrichment analysis showed that the upregulated DEGs were mainly enriched in immune-related pathways including immune system, viral protein interaction with cytokine and cytokine receptor, cytokine-cytokine receptor interaction, leukocyte transendothelial migration, and chemokine receptors bind chemokines. On the contrary, the downregulated DEGs were mainly related to the formation of the cornified envelope and keratinization. The identified hub genes in the PPI network were CXCL8, CXCL1, CXCR4, SEL, CD19, and IKZF1. The top three modules were involved in chemokine response, B cell receptor signaling pathway, and interleukin response, respectively. iRegulon analysis revealed that IRF4 scored the highest.@*CONCLUSIONS@#The pathogenesis of periodontitis was closely associated with the expression levels of the identified hub genes including CXCL8, CXCL1, CXCR4, SELL, CD19, and IKZF1. IRF4, the predicted transcription factor, might serve as a dominant upstream regulator.
Assuntos
Humanos , Biologia Computacional , Perfilação da Expressão Gênica , Análise em Microsséries , Periodontite , Mapas de Interação de ProteínasRESUMO
Objective To study the pathogenesis of nasopharyngeal carcinoma and identify potential biomarkers or therapeutic targets. Methods Microarray data (GSE12452 and GSE13597) were downloaded from Gene Expression Omnibus. Processing of original microarray data and screening of differentially expressed genes were performed through bioinformatics analysis. Then, GO and KEGG pathway enrichment analysis was performed for these genes using DAVID database. Real time-PCR and Western blot assay were used to detect the expression levels of the identified genes. Results A total of 260 overlap DEGs were obtained including 16 GO entries and 4 signal pathways. Eighteen potential therapeutic targets that relative to cell cycle were identified by gene enrichment analysis. Expression levels of 12 selected genes were confirmed by real-time PCR. Finally, 4 selected genes were confirmed by Western blot assay. Conclusion By bioinformatics analysis of two sets of microarray data and molecular biology research, four genes were found including CDC6, CDK1,MCM2 and CCNB1, which might be potential key genes that can be developed for therapy targets of NPC in the future.
RESUMO
Human tongue cancer (TC) is an aggressive malignancy with a very poor prognosis.There is an urgent need to elucidate the underlying molecular mechanisms involved in TC progression.mRNA expression profiles play a vital role in the exploration of cancer-related genes.Therefore,the purpose of our study was to identify the progression associated candidate genes of TC by bioinformatics analysis.Five microarray datasets of TC samples were downloaded from the Gene Expression Omnibus (GEO) database and the data of 133 TC patients were screened from The Cancer Genome Atlas (TCGA) head and neck squamous cell carcinoma (HNSC) database.The integrated analysis of five microarray datasets and the RNA sequencing data of TC samples in TCGA-HNSC was performed to obtain 1023 overlapping differentially expressed genes (DEGs) in TC and adjacent normal tissue (ANT) samples.Next,Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was conducted to enrich the significant pathways of the 1023 DEGs and PI3K-Akt signaling pathway (P=0.011) was selected to be the candidate pathway.A total of 23 DEGs with |log2 fold change (FC)｜ ≥1.0 in phosphatidylinositol 3-kinase-serine/threonine kinase (PI3K-Akt) signaling pathway were subjected to survival analysis of 125 eligible TC samples in TCGA database,indicating increased integrin-α3 gene (ITGA3) expression was significantly associated with poorer prognosis.Taken together,our study suggested ITGA3 may facilitate the development of TC via activating PI3K-Akt signaling pathway.
RESUMO
Human tongue cancer (TC) is an aggressive malignancy with a very poor prognosis.There is an urgent need to elucidate the underlying molecular mechanisms involved in TC progression.mRNA expression profiles play a vital role in the exploration of cancer-related genes.Therefore,the purpose of our study was to identify the progression associated candidate genes of TC by bioinformatics analysis.Five microarray datasets of TC samples were downloaded from the Gene Expression Omnibus (GEO) database and the data of 133 TC patients were screened from The Cancer Genome Atlas (TCGA) head and neck squamous cell carcinoma (HNSC) database.The integrated analysis of five microarray datasets and the RNA sequencing data of TC samples in TCGA-HNSC was performed to obtain 1023 overlapping differentially expressed genes (DEGs) in TC and adjacent normal tissue (ANT) samples.Next,Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis was conducted to enrich the significant pathways of the 1023 DEGs and PI3K-Akt signaling pathway (P=0.011) was selected to be the candidate pathway.A total of 23 DEGs with |log2 fold change (FC)｜ ≥1.0 in phosphatidylinositol 3-kinase-serine/threonine kinase (PI3K-Akt) signaling pathway were subjected to survival analysis of 125 eligible TC samples in TCGA database,indicating increased integrin-α3 gene (ITGA3) expression was significantly associated with poorer prognosis.Taken together,our study suggested ITGA3 may facilitate the development of TC via activating PI3K-Akt signaling pathway.