BACKGROUND Helicobacter pylori(H.pylori)infection is related to various extragastric diseases including type 2 diabetes mellitus(T2DM).However,the possible mechanisms connecting H.pylori infection and T2DM remain unkn...BACKGROUND Helicobacter pylori(H.pylori)infection is related to various extragastric diseases including type 2 diabetes mellitus(T2DM).However,the possible mechanisms connecting H.pylori infection and T2DM remain unknown.AIM To explore potential molecular connections between H.pylori infection and T2DM.METHODS We extracted gene expression arrays from three online datasets(GSE60427,GSE27411 and GSE115601).Differentially expressed genes(DEGs)commonly present in patients with H.pylori infection and T2DM were identified.Hub genes were validated using human gastric biopsy samples.Correlations between hub genes and immune cell infiltration,miRNAs,and transcription factors(TFs)were further analyzed.RESULTS A total of 67 DEGs were commonly presented in patients with H.pylori infection and T2DM.Five significantly upregulated hub genes,including TLR4,ITGAM,C5AR1,FCER1G,and FCGR2A,were finally identified,all of which are closely related to immune cell infiltration.The gene-miRNA analysis detected 13 miRNAs with at least two gene cross-links.TF-gene interaction networks showed that TLR4 was coregulated by 26 TFs,the largest number of TFs among the 5 hub genes.CONCLUSION We identified five hub genes that may have molecular connections between H.pylori infection and T2DM.This study provides new insights into the pathogenesis of H.pylori-induced onset of T2DM.展开更多
Introduction:The difficulty in treating lung adenocarcinoma(LUAD)is caused by a shortage of knowledge about the biological mechanisms and a lack of treatment choices.Objectives:The aim of this study was to identify a ...Introduction:The difficulty in treating lung adenocarcinoma(LUAD)is caused by a shortage of knowledge about the biological mechanisms and a lack of treatment choices.Objectives:The aim of this study was to identify a valuable molecular target for the treatment of LUAD.Methods:Using multiple databases,we screened for hub genes in LUAD using Cytoscape and explored the expression and prognosis of DLG associated protein 5(DLGAP5)in LUAD.We investigated the genetic variation,functional enrichment,and epigenetic activity of DLGAP5.Furthermore,we evaluated the relationship between the tumor microenvironment(TME)and DLGAP5.Results:Our study identified 10 hub genes in LUAD:CDC45,KIAA0101,DLGAP5,CDT1,NCAPG,CCNB1,CDCA5,CDC20,KIF11,and AURKA.We discovered that DLGAP5 was overexpressed and associated with poor prognosis in LUAD.DLGAP5 exhibited an overall genetic variation frequency of 2%,and its DNA promoter was hypomethylated in LUAD(p<0.05).The expression of DLGAP5 in LUAD showed a positive correlation with the majority of N6-methyladenosine(m6A)-methylation genes.Additionally,DLGAP5 was primarily associated with the cell cycle in LUAD.Notably,there was a significant favorable association between DLGAP5 and CD274,CTLA4,HAVCR2,and LAG3 in LUAD.Conclusion:DLGAP5 may be a therapeutic target for LUAD,as it affects cancer cells proliferation and development through the regulation of cell-cycle checkpoints and modulation of immune cell infiltration and immune checkpoints in the TME.展开更多
AIM:To explore hub genes for glaucoma based on bioinformatics analysis and an experimental model verification.METHODS:In the Gene Expression Omnibus(GEO)database,the GSE25812 and GSE26299 datasets were selected to ana...AIM:To explore hub genes for glaucoma based on bioinformatics analysis and an experimental model verification.METHODS:In the Gene Expression Omnibus(GEO)database,the GSE25812 and GSE26299 datasets were selected to analyze differentially expressed genes(DEGs)by the GEO2R tool.Through bioinformatics analysis,9 hub genes were identified.Receiver operating characteristic(ROC)curves and principal component analysis(PCA)were performed to verify whether the hub gene can distinguish glaucoma from normal eyes.The mouse model of glaucoma was constructed,and the real-time reverse transcriptasepolymerase chain reaction(RT-q PCR)assay was performed to detect the expression levels of hub genes in glaucoma.RESULTS:There were 128 overlapping DEGs in the GSE25812 and GSE26299 datasets,mainly involved in intracellular signalling,cell adhesion molecules and the Ras signalling pathway.A total of 9 hub genes were screened out,including GNAL,BGN,ETS2,FCGP4,MAPK10,MMP15,STAT1,TSPAN8,and VCAM1.The area under the curve(AUC)values of 9 hub genes were greater than 0.8.The PC1 axle could provide a 70.5%interpretation rate to distinguish glaucoma from normal eyes.In the ocular tissues of glaucoma in the mice model,the expression of BGN,ETS2,FCGR4,STAT1,TSPAN8,and VCAM1 was increased,while the expression of GNAL,MAPK10,and MMP15 was decreased.CONCLUSION:Nine hub genes in glaucoma are identified,which may provide new biomarkers and therapeutic targets for glaucoma.展开更多
The exact mechanisms associated with secondary brain damage following traumatic brain injury(TBI)remain unclear;therefore,identifying the critical molecular mechanisms involved in TBI is essential.The m RNA expression...The exact mechanisms associated with secondary brain damage following traumatic brain injury(TBI)remain unclear;therefore,identifying the critical molecular mechanisms involved in TBI is essential.The m RNA expression microarray GSE2871 was downloaded from the Gene Expression Omnibus(GEO)repository.GSE2871 comprises a total of 31 cerebral cortex samples,including two post-TBI time points.The microarray features eight control and seven TBI samples,from 4 hours post-TBI,and eight control and eight TBI samples from 24 hours post-TBI.In this bioinformatics-based study,109 and 66 differentially expressed genes(DEGs)were identified in a Sprague-Dawley(SD)rat TBI model,4 and 24 hours post-TBI,respectively.Functional enrichment analysis showed that the identified DEGs were significantly enriched in several terms,such as positive regulation of nuclear factor-κB transcription factor activity,mitogen-activated protein kinase signaling pathway,negative regulation of apoptotic process,and tumor necrosis factor signaling pathway.Moreover,the hub genes with high connectivity degrees were primarily related to inflammatory mediators.To validate the top five hub genes,a rat model of TBI was established using the weight-drop method,and real-time quantitative polymerase chain reaction analysis of the cerebral cortex was performed.The results showed that compared with control rats,Tnf-α,c-Myc,Spp1,Cxcl10,Ptprc,Egf,Mmp9,and Lcn2 were upregulated,and Fn1 was downregulated in TBI rats.Among these hub genes,Fn1,c-Myc,and Ptprc may represent novel biomarkers or therapeutic targets for TBI.These identified pathways and key genes may provide insights into the molecular mechanisms of TBI and provide potential treatment targets for patients with TBI.This study was approved by the Experimental Animal Ethics Committee of the First Affiliated Hospital of Nanchang University,China(approval No.003)in January 2016.展开更多
Traumatic brain injury(TBI)is a common injury caused by external forces that lead to damaged brain function or pathological changes in the brain tissue.To explore the molecular mechanism and the hub genes of TBI,we do...Traumatic brain injury(TBI)is a common injury caused by external forces that lead to damaged brain function or pathological changes in the brain tissue.To explore the molecular mechanism and the hub genes of TBI,we downloaded gene expression profiles of the TBI model of rat and the sham control for the subsequent gene set enrichment analysis,pathway analysis and protein-protein interactions analysis.The results of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis indicated that multiple biological pathways,including immune response,inflammatory response and cellular response to interleukin-1,as well as signaling pathways,such as tumor necrosis factor signaling pathway,chcmokine signaling pathway,cytokine-cytokine receptor interaction,Toll-like receptor signaling pathway and nuclear factor kappa B signaling pathway were implicated in the TBI.In conclusion,this study provides insights into the molecular mechanism of TBI by screening the differentially expressed genes and hub genes that can be used as biomarkers and therapeutic targets.展开更多
Peripheral T-cell lymphoma(PTCL)is a very aggressive and heterogeneous hematological malignancy and has no effective targeted therapy.The molecular pathogenesis of PTCL remains unknown.In this study,we chose the gene ...Peripheral T-cell lymphoma(PTCL)is a very aggressive and heterogeneous hematological malignancy and has no effective targeted therapy.The molecular pathogenesis of PTCL remains unknown.In this study,we chose the gene expression profile of GSE6338 from the Gene Expression Omnibus(GEO)database to identify hub genes and key pathways and explore possible molecular pathogenesis of PTCL by bioinformatic analysis.Diferentially expressed gencs(DEGs)between PTCL and normal T cells were selected using GEO2R tool.Gene ontology(GO)analysis and Kyoto Encyclopedia of Gene and Genome(KEGG)pathway analysis were performed using Database for Annotation,Visualization and Integrated Discovery(DAVID).Moreover,the Search Tool for the Retrieval of Interacting Genes(STRING)and Molecular Complex Detection(MCODE)were utilized to construct protein-protein interaction(PPI)network and perform module analysis of these DEGs.A total of 518 DEGs were identifed,including 413 down-regulated and 105 up-regulated gencs.The down-regulated genes were enriched in osteoclast differentiation,Chagas disease and mitogen-activated protein kinase(MAPK)signaling pathway.The up-regulated genes were mainly associated with extracellular matrix(ECM)-receptor interaction,focal adhesion and pertussis.F our important modules were detected from the PPI network by using MCODE software.Fifteen hub genes with a high degree of connectivity were selected.Our study identifed DEGs,hub genes and pathways associated with PTCL by bioinformatic analysis.Results provide a basis for further study on the pathogenesis of PTCL.展开更多
BACKGROUND Breast cancer(BC)is the most common malignant tumor in women.AIM To investigate BC-associated hub genes to obtain a better understanding of BC tumorigenesis.METHODS In total,1203 BC samples were downloaded ...BACKGROUND Breast cancer(BC)is the most common malignant tumor in women.AIM To investigate BC-associated hub genes to obtain a better understanding of BC tumorigenesis.METHODS In total,1203 BC samples were downloaded from The Cancer Genome Atlas database,which included 113 normal samples and 1090 tumor samples.The limma package of R software was used to analyze the differentially expressed genes(DEGs)in tumor tissues compared with normal tissues.The cluster Profiler package was used to perform Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analysis of upregulated and downregulated genes.Univariate Cox regression was conducted to explore the DEGs with statistical significance.Protein-protein interaction(PPI)network analysis was employed to investigate the hub genes using the CytoHubba plug-in of Cytoscape software.Survival analyses of the hub genes were carried out using the Kaplan-Meier method.The expression level of these hub genes was validated in the Gene Expression Profiling Interactive Analysis database and Human Protein Atlas database.RESULTS A total of 1317 DEGs(fold change>2;P<0.01)were confirmed through bioinformatics analysis,which included 744 upregulated and 573 downregulated genes in BC samples.KEGG enrichment analysis indicated that the upregulated genes were mainly enriched in the cytokine-cytokine receptor interaction,cell cycle,and the p53 signaling pathway(P<0.01);and the downregulated genes were mainly enriched in the cytokine-cytokine receptor interaction,peroxisome proliferator-activated receptor signaling pathway,and AMP-activated protein kinase signaling pathway(P<0.01).CONCLUSION In view of the results of PPI analysis,which were verified by survival and expression analyses,we conclude that MAD2L1,PLK1,SAA1,CCNB1,SHCBP1,KIF4A,ANLN,and ERCC6L may act as biomarkers for the diagnosis and prognosis in BC patients.展开更多
<strong>Objective:</strong><span style="font-family:""><span style="font-family:Verdana;"> This study aimed to identify hub genes that are associated with hepatocellula...<strong>Objective:</strong><span style="font-family:""><span style="font-family:Verdana;"> This study aimed to identify hub genes that are associated with hepatocellular carcinoma (HCC) prognosis by bioinformatics analysis. </span><b><span style="font-family:Verdana;">Methods:</span></b><span style="font-family:Verdana;"> Data were collected from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) liver HCC datasets. </span><a name="_Hlk11768117"></a><span style="font-family:Verdana;">The robust rank ag</span><span style="font-family:Verdana;">gregation algorithm was used in integrating the data on differentially ex</span><span style="font-family:Verdana;">pressed genes (DEGs). Online databases DAVID 6.8 and REACTOME were used for </span><span style="font-family:Verdana;">gene ontology and pathway enrichment analysis. R software version 3.5.1, </span><span style="font-family:Verdana;">Cytoscape, and Kaplan-Meier plotter were used to identify hub genes. </span><b><span style="font-family:Verdana;">Results:</span></b><span style="font-family:Verdana;"> Six GEO datasets and the TCGA liver HCC dataset were included in this analysis. A total of 151 upregulated and 245 downregulated DEGs were iden</span><span style="font-family:Verdana;">tified. The upregulated DEGs most significantly enriched in the functional</span><span style="font-family:Verdana;"> categories of cell division, chromosomes, centromeric regions, and </span><span style="font-family:Verdana;">protein binding, whereas the downregulated DEGs most significantly</span><span style="font-family:Verdana;"> enriched in the </span><a name="_Hlk11059934"></a><span style="font-family:Verdana;">epoxygenase P450 pathway, extracellular region, and heme binding, with respect to biological process, cellular component, and molecular function analysis, respectively. Upregulated DEGS most significantly enriched the cell cycle pathway, whereas downregulated DEGs most significantly enriched </span><span style="font-family:Verdana;">the metabolism pathway. Finally, 88 upregulated and 40 downregulated genes were </span><span><span style="font-family:Verdana;">identified as hub genes. The top 10 upregulated hub DEGs were </span><i><span style="font-family:Verdana;">CDK</span></i><span style="font-family:Verdana;">1,</span></span><i><span style="font-family:Verdana;"> CCNB</span></i><span><span style="font-family:Verdana;">1,</span><i><span style="font-family:Verdana;"> CCNB</span></i><span style="font-family:Verdana;">2,</span><i><span style="font-family:Verdana;"> CDC</span></i><span style="font-family:Verdana;">20,</span><i><span style="font-family:Verdana;"> CCNA</span></i><span style="font-family:Verdana;">2,</span><i><span style="font-family:Verdana;"> AURKA</span></i><span style="font-family:Verdana;">,</span><i><span style="font-family:Verdana;"> MAD</span></i><span style="font-family:Verdana;">2</span><i><span style="font-family:Verdana;">L</span></i><span style="font-family:Verdana;">1,</span><i><span style="font-family:Verdana;"> TOP</span></i><span style="font-family:Verdana;">2</span><i><span style="font-family:Verdana;">A</span></i><span style="font-family:Verdana;">,</span><i><span style="font-family:Verdana;"> BUB</span></i><span style="font-family:Verdana;">1</span><i><span style="font-family:Verdana;">B </span></i><span style="font-family:Verdana;">and</span></span><i> <span style="font-family:Verdana;">BUB</span></i><span><span style="font-family:Verdana;">1. The top 10 downregulated hub DEGs were </span><i><span style="font-family:Verdana;">ESR</span></i><span style="font-family:Verdana;">1,</span><i><span style="font-family:Verdana;"> IGF</span></i><span style="font-family:Verdana;">1,</span><i><span style="font-family:Verdana;"> FTCD</span></i><span style="font-family:Verdana;">,</span></span><i><span style="font-family:Verdana;"> CYP</span></i><span style="font-family:Verdana;">3</span><i><span style="font-family:Verdana;">A</span></i><span style="font-family:Verdana;">4,</span><i><span style="font-family:Verdana;"> SPP</span></i><span style="font-family:Verdana;">2,</span><i> <span style="font-family:Verdana;">C</span></i><span><span style="font-family:Verdana;">8</span><i><span style="font-family:Verdana;">A</span></i><span style="font-family:Verdana;">,</span><i><span style="font-family:Verdana;"> CYP</span></i><span style="font-family:Verdana;">2</span><i><span style="font-family:Verdana;">E</span></i><span style="font-family:Verdana;">1,</span><i><span style="font-family:Verdana;"> TAT</span></i><span style="font-family:Verdana;">,</span><i><span style="font-family:Verdana;"> F</span></i><span style="font-family:Verdana;">9 and </span><i><span style="font-family:Verdana;">CYP</span></i><span style="font-family:Verdana;">2</span><i><span style="font-family:Verdana;">C</span></i><span style="font-family:Verdana;">9. </span><b><span style="font-family:Verdana;">Conclusions:</span></b><span style="font-family:Verdana;"> This study identified</span></span><span style="font-family:Verdana;"> several upregulated and downregulated hub genes that are associated with the prognosis of HCC patients. Verification of these results using </span><i><span style="font-family:Verdana;">in vitro</span></i><span style="font-family:Verdana;"> and </span><i><span style="font-family:Verdana;">in vivo</span></i><span style="font-family:Verdana;"> studies is warranted.</span></span>展开更多
Objective:to screen the genes differentially expressed in autism using bioinformatics methods,and to explore their functional enrichment,related signaling pathways and the tissue-specific expression of hub genes.Metho...Objective:to screen the genes differentially expressed in autism using bioinformatics methods,and to explore their functional enrichment,related signaling pathways and the tissue-specific expression of hub genes.Methods:the autism expression profile chip numbered GSE77103 in the Gene Expression Omnibus(GEO)database was selected for examination.R language and related R packages were used for the screening and visualization of the diflerentially expressed genes.Gene Ontology(GO)function enrichment and Kyoto Encyclopedia of Genes and Genomes(KEGG)signaling pathway analysis of the differential genes were carried out using the relevant R package of R language.The database STRING was used to construct the interaction network of the proteins encoded by the differentially expressed genes,and the software Cytoscape was used to screen the hub genes in the network.The selected hub genes were imported into the BioGPS database to analyze the tissue-specific expression of the hub genes.Results:six hundred and sixty diflerentially expressed genes were screened out.Three hundred and seventy-three up-regulated genes and 287 down-regulated genes in the peripheral blood mononuclear cells of autistic children were compared with the peripheral blood mononuclear cells of healthy children.GO functional enrichment results showed that biological processes(BP)were mainly involved in viral response,negative regulation of viral genome replication,negative regulation of multiple biological processes,negative reg ulation reg ulation of viral life cycle,and defense responses to viruses.Cell components(CC)were involved in vesicles,lysosomal membranes,lysosomal lumen,etc.;molecular functions(MF)were involved in regulating glutathione transferase activity peroxidase activity,oxidoreductase activity,Glutathione peroxidase and transferase activity,etc.The results of KEGG signaling pathway analysis showed that the differentially expressed genes were related to the lysosomal pathway,the glutathione metabolism pathway and the arachidonic acid metabolism pathway.The hub genes screened by cytoHubba were:interferon regulatory factor 7(IRF7),interferon stimulated exonuclease gene 15(ISG15),XIAP associated factor 1(XAF1),MX dynamin like GTPase 1(MX1),interferon induced protein with tetratricopeptide repeats 1(IFIT1),interferon induced protein with tetratricopeptide repeats 5(IFIT5),29-55-oligoadenylate synthetase 3(OAS3),interferon induced protein 44(IFI44),HECT and the RLD domain containing E3 ubiquitin protein ligase 5(HERC5),interferon stimulated exonuclease gene 20(ISG20).Conclusion:there are genes that are diflerentially expressed in the peripheral blood mononuclear cells of autistic toddlers and healthy toddlers.IRF7,ISG15,XAF1,MX],IFIT1,IFIT5,OAS3,IFI44,HERC5,ISG20 are the hub regulatory genes of autism.展开更多
Objective:To identify the thyroid cancer-related hub genes and pathways by bioinformatics initially in order to lay the foundation for further study.Methods:The expression profile chips and data of thyroid cancer were...Objective:To identify the thyroid cancer-related hub genes and pathways by bioinformatics initially in order to lay the foundation for further study.Methods:The expression profile chips and data of thyroid cancer were screened and downloaded from the gene expression omnibus(GEO).The GEO2R was applied to identify the differential expressed genes between thyroid cancer tissues and normal thyroid tissues.And the Metascape online website was used for pathway and function enrichment.With the usage of STRING and Cytoscape,the protein-protein interaction network was constructed,and the plug-in app cytoHubba in Cytoscape was applied to screen hub genes.Kaplan-Meier Plotter was implemented to conduct survival analysis of hub genes for further screening and discussion.Results:A total of 304 differential expressed genes were screened,and were mainly enriched in the biological processes of extracellular matrix,cell-substrate adhesion,response to wounding,muscle structure development and hormone metabolic process etc.by Metascape.Protein-protein interaction network visualized 284 nodes;the top ten scores of Maximal Clique Centrality algorithm were taken as the criteria to screen out the hub genes with high connectivity in the gene expression network.The KM plotter analysis confirmed that 5 of 9 hub genes were correlated with the prognosis of thyroid cancer patients.Conclusion:FN1,SPP1,TIMP1,VCAN,COL1A1,COL1A2,MMP1,DCN,COMP and FMOD may play a significant role in the development of thyroid cancer.Genes which have prognostic significance in survival analyses were found to be relevant to the composition and regulation of extracellular matrix.展开更多
BACKGROUND A growing number of clinical examples suggest that coronavirus disease 2019(COVID-19)appears to have an impact on the treatment of patients with liver cancer compared to the normal population,and the preval...BACKGROUND A growing number of clinical examples suggest that coronavirus disease 2019(COVID-19)appears to have an impact on the treatment of patients with liver cancer compared to the normal population,and the prevalence of COVID-19 is significantly higher in patients with liver cancer.However,this mechanism of action has not been clarified.Gene sets for COVID-19(GSE180226)and liver cancer(GSE87630)were obtained from the Gene Expression Omnibus database.After identifying the common differentially expressed genes(DEGs)of COVID-19 and liver cancer,functional enrichment analysis,protein-protein interaction network construction and scree-ning and analysis of hub genes were performed.Subsequently,the validation of the differential expression of hub genes in the disease was performed and the regulatory network of transcription factors and hub genes was constructed.RESULTS Of 518 common DEGs were obtained by screening for functional analysis.Fifteen hub genes including aurora kinase B,cyclin B2,cell division cycle 20,cell division cycle associated 8,nucleolar and spindle associated protein 1,etc.,were further identified from DEGs using the“cytoHubba”plugin.Functional enrichment analysis of hub genes showed that these hub genes are associated with P53 signalling pathway regulation,cell cycle and other functions,and they may serve as potential molecular markers for COVID-19 and liver cancer.Finally,we selected 10 of the hub genes for in vitro expression validation in liver cancer cells.CONCLUSION Our study reveals a common pathogenesis of liver cancer and COVID-19.These common pathways and key genes may provide new ideas for further mechanistic studies.展开更多
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...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.展开更多
Proanthocyanidin(PA)is an important bioactive compound with multiple physiological benefits in jujube(Ziziphus jujube Mill.).However,the molecular mechanisms underlying PA biosynthesis in jujube fruit have not been in...Proanthocyanidin(PA)is an important bioactive compound with multiple physiological benefits in jujube(Ziziphus jujube Mill.).However,the molecular mechanisms underlying PA biosynthesis in jujube fruit have not been investigated.Here,the profiling of PA,(+)-catechin and(–)-epicatechin and transcriptome sequencing of three jujube cultivars from Xinjiang Uyghur Autonomous Region of China at five developmental stages were analyzed.The levels of total PAs and catechin exhibited a decreased trend over jujube ripening,and epicatechin content of two jujube cultivars increased first and then declined.Transcriptome analysis revealed that the differentially expressed genes(DEGs)were mainly enriched in ribosome,glycolysis/gluconeogenesis,fructose and mannose metabolism.17 DEGs encoding PAL,CHS,CHI,CHS,F3'H,LAR,ANR,C4Hs,4CLs,FLSs,DFRs and UFGTs involved in PA biosynthesis were relatively abundant.The highly transcribed LAR gene may greatly contribute to epicatechin accumulation.A weighted gene co-expression network analysis(WGCNA)was performed,and a network module including 1620 genes highly correlated with content of Pas and catechin was established.We identified 58 genes including 9 structural genes and 49 regulatory genes related to PA biosynthesis and regulation in the WGCNA module.Sixteen genes encoding 9 families of transcriptional factors(i.e.,MYB,bHLH,ERF,bZIP,NAC,SBP,MIKC,HB,WRKY)were considered as hub genes.The results of qRT-PCR analysis validating 10 genes were well consistent with the transcriptome data.These findings provide valuable knowledge to facilitate its genetic studies and molecular breeding.展开更多
Cardiomyopathies represent the most common clinical and genetic heterogeneous group of diseases that affect the heart function.Though progress has been made to elucidate the process,molecular mechanisms of different c...Cardiomyopathies represent the most common clinical and genetic heterogeneous group of diseases that affect the heart function.Though progress has been made to elucidate the process,molecular mechanisms of different classes of cardiomyopathies remain elusive.This paper aims to describe the similarities and differences in molecular features of dilated cardiomyopathy(DCM)and ischemic cardiomyopathy(ICM).We firstly detected the co-expressed modules using the weighted gene co-expression network analysis(WGCNA).Significant modules associated with DCM/ICM were identified by the Pearson correlation coefficient(PCC)between the modules and the phenotype of DCM/ICM.The differentially expressed genes in the modules were selected to perform functional enrichment.The potential transcription factors(TFs)prediction was conducted for transcription regulation of hub genes.Apoptosis and cardiac conduction were perturbed in DCM and ICM,respectively.TFs demonstrated that the biomarkers and the transcription regulations in DCM and ICM were different,which helps make more accurate discrimination between them at molecular levels.In conclusion,comprehensive analyses of the molecular features may advance our understanding of DCM and ICM causes and progression.Thus,this understanding may promote the development of innovative diagnoses and treatments.展开更多
BACKGROUND The objectives of this study were to identify hub genes and biological pathways involved in lung adenocarcinoma(LUAD)via bioinformatics analysis,and investigate potential therapeutic targets.AIM To determin...BACKGROUND The objectives of this study were to identify hub genes and biological pathways involved in lung adenocarcinoma(LUAD)via bioinformatics analysis,and investigate potential therapeutic targets.AIM To determine reliable prognostic biomarkers for early diagnosis and treatment of LUAD.METHODS To identify potential therapeutic targets for LUAD,two microarray datasets derived from the Gene Expression Omnibus(GEO)database were analyzed,GSE3116959 and GSE118370.Differentially expressed genes(DEGs)in LUAD and normal tissues were identified using the GEO2R tool.The Hiplot database was then used to generate a volcanic map of the DEGs.Weighted gene co-expression network analysis was conducted to cluster the genes in GSE116959 and GSE-118370 into different modules,and identify immune genes shared between them.A protein-protein interaction network was established using the Search Tool for the Retrieval of Interacting Genes database,then the CytoNCA and CytoHubba components of Cytoscape software were used to visualize the genes.Hub genes with high scores and co-expression were identified,and the Database for Annotation,Visualization and Integrated Discovery was used to perform enrichment analysis of these genes.The diagnostic and prognostic values of the hub genes were calculated using receiver operating characteristic curves and Kaplan-Meier survival analysis,and gene-set enrichment analysis was conducted.The University of Alabama at Birmingham Cancer data analysis portal was used to analyze relationships between the hub genes and normal specimens,as well as their expression during tumor progression.Lastly,validation of protein expression was conducted on the identified hub genes via the Human Protein Atlas database.RESULTS Three hub genes with high connectivity were identified;cellular retinoic acid binding protein 2(CRABP2),matrix metallopeptidase 12(MMP12),and DNA topoisomerase II alpha(TOP2A).High expression of these genes was associated with a poor LUAD prognosis,and the genes exhibited high diagnostic value.CONCLUSION Expression levels of CRABP2,MMP12,and TOP2A in LUAD were higher than those in normal lung tissue.This observation has diagnostic value,and is linked to poor LUAD prognosis.These genes may be biomarkers and therapeutic targets in LUAD,but further research is warranted to investigate their usefulness in these respects.展开更多
基金Supported by National Natural Science Foundation of China,No.82100594.
文摘BACKGROUND Helicobacter pylori(H.pylori)infection is related to various extragastric diseases including type 2 diabetes mellitus(T2DM).However,the possible mechanisms connecting H.pylori infection and T2DM remain unknown.AIM To explore potential molecular connections between H.pylori infection and T2DM.METHODS We extracted gene expression arrays from three online datasets(GSE60427,GSE27411 and GSE115601).Differentially expressed genes(DEGs)commonly present in patients with H.pylori infection and T2DM were identified.Hub genes were validated using human gastric biopsy samples.Correlations between hub genes and immune cell infiltration,miRNAs,and transcription factors(TFs)were further analyzed.RESULTS A total of 67 DEGs were commonly presented in patients with H.pylori infection and T2DM.Five significantly upregulated hub genes,including TLR4,ITGAM,C5AR1,FCER1G,and FCGR2A,were finally identified,all of which are closely related to immune cell infiltration.The gene-miRNA analysis detected 13 miRNAs with at least two gene cross-links.TF-gene interaction networks showed that TLR4 was coregulated by 26 TFs,the largest number of TFs among the 5 hub genes.CONCLUSION We identified five hub genes that may have molecular connections between H.pylori infection and T2DM.This study provides new insights into the pathogenesis of H.pylori-induced onset of T2DM.
基金funded by the supporting funds for scientific research of the Sixth Affiliated Hospital,Sun Yat-sen University(P20200217202404781).
文摘Introduction:The difficulty in treating lung adenocarcinoma(LUAD)is caused by a shortage of knowledge about the biological mechanisms and a lack of treatment choices.Objectives:The aim of this study was to identify a valuable molecular target for the treatment of LUAD.Methods:Using multiple databases,we screened for hub genes in LUAD using Cytoscape and explored the expression and prognosis of DLG associated protein 5(DLGAP5)in LUAD.We investigated the genetic variation,functional enrichment,and epigenetic activity of DLGAP5.Furthermore,we evaluated the relationship between the tumor microenvironment(TME)and DLGAP5.Results:Our study identified 10 hub genes in LUAD:CDC45,KIAA0101,DLGAP5,CDT1,NCAPG,CCNB1,CDCA5,CDC20,KIF11,and AURKA.We discovered that DLGAP5 was overexpressed and associated with poor prognosis in LUAD.DLGAP5 exhibited an overall genetic variation frequency of 2%,and its DNA promoter was hypomethylated in LUAD(p<0.05).The expression of DLGAP5 in LUAD showed a positive correlation with the majority of N6-methyladenosine(m6A)-methylation genes.Additionally,DLGAP5 was primarily associated with the cell cycle in LUAD.Notably,there was a significant favorable association between DLGAP5 and CD274,CTLA4,HAVCR2,and LAG3 in LUAD.Conclusion:DLGAP5 may be a therapeutic target for LUAD,as it affects cancer cells proliferation and development through the regulation of cell-cycle checkpoints and modulation of immune cell infiltration and immune checkpoints in the TME.
基金Supported by Research Fund of Anhui Institute of Translational Medicine(No.2022zhyx-C73)。
文摘AIM:To explore hub genes for glaucoma based on bioinformatics analysis and an experimental model verification.METHODS:In the Gene Expression Omnibus(GEO)database,the GSE25812 and GSE26299 datasets were selected to analyze differentially expressed genes(DEGs)by the GEO2R tool.Through bioinformatics analysis,9 hub genes were identified.Receiver operating characteristic(ROC)curves and principal component analysis(PCA)were performed to verify whether the hub gene can distinguish glaucoma from normal eyes.The mouse model of glaucoma was constructed,and the real-time reverse transcriptasepolymerase chain reaction(RT-q PCR)assay was performed to detect the expression levels of hub genes in glaucoma.RESULTS:There were 128 overlapping DEGs in the GSE25812 and GSE26299 datasets,mainly involved in intracellular signalling,cell adhesion molecules and the Ras signalling pathway.A total of 9 hub genes were screened out,including GNAL,BGN,ETS2,FCGP4,MAPK10,MMP15,STAT1,TSPAN8,and VCAM1.The area under the curve(AUC)values of 9 hub genes were greater than 0.8.The PC1 axle could provide a 70.5%interpretation rate to distinguish glaucoma from normal eyes.In the ocular tissues of glaucoma in the mice model,the expression of BGN,ETS2,FCGR4,STAT1,TSPAN8,and VCAM1 was increased,while the expression of GNAL,MAPK10,and MMP15 was decreased.CONCLUSION:Nine hub genes in glaucoma are identified,which may provide new biomarkers and therapeutic targets for glaucoma.
基金supported by the National Natural Science Foundation of China,Nos.81860409(to ZF),81660382(to ZF)Graduate Students Innovation Fund Project in Jiangxi Province of China,No.YC2019-B036(to YLT)。
文摘The exact mechanisms associated with secondary brain damage following traumatic brain injury(TBI)remain unclear;therefore,identifying the critical molecular mechanisms involved in TBI is essential.The m RNA expression microarray GSE2871 was downloaded from the Gene Expression Omnibus(GEO)repository.GSE2871 comprises a total of 31 cerebral cortex samples,including two post-TBI time points.The microarray features eight control and seven TBI samples,from 4 hours post-TBI,and eight control and eight TBI samples from 24 hours post-TBI.In this bioinformatics-based study,109 and 66 differentially expressed genes(DEGs)were identified in a Sprague-Dawley(SD)rat TBI model,4 and 24 hours post-TBI,respectively.Functional enrichment analysis showed that the identified DEGs were significantly enriched in several terms,such as positive regulation of nuclear factor-κB transcription factor activity,mitogen-activated protein kinase signaling pathway,negative regulation of apoptotic process,and tumor necrosis factor signaling pathway.Moreover,the hub genes with high connectivity degrees were primarily related to inflammatory mediators.To validate the top five hub genes,a rat model of TBI was established using the weight-drop method,and real-time quantitative polymerase chain reaction analysis of the cerebral cortex was performed.The results showed that compared with control rats,Tnf-α,c-Myc,Spp1,Cxcl10,Ptprc,Egf,Mmp9,and Lcn2 were upregulated,and Fn1 was downregulated in TBI rats.Among these hub genes,Fn1,c-Myc,and Ptprc may represent novel biomarkers or therapeutic targets for TBI.These identified pathways and key genes may provide insights into the molecular mechanisms of TBI and provide potential treatment targets for patients with TBI.This study was approved by the Experimental Animal Ethics Committee of the First Affiliated Hospital of Nanchang University,China(approval No.003)in January 2016.
基金the Military Logistics Scientific Research Project(No.CLB20J027).
文摘Traumatic brain injury(TBI)is a common injury caused by external forces that lead to damaged brain function or pathological changes in the brain tissue.To explore the molecular mechanism and the hub genes of TBI,we downloaded gene expression profiles of the TBI model of rat and the sham control for the subsequent gene set enrichment analysis,pathway analysis and protein-protein interactions analysis.The results of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis indicated that multiple biological pathways,including immune response,inflammatory response and cellular response to interleukin-1,as well as signaling pathways,such as tumor necrosis factor signaling pathway,chcmokine signaling pathway,cytokine-cytokine receptor interaction,Toll-like receptor signaling pathway and nuclear factor kappa B signaling pathway were implicated in the TBI.In conclusion,this study provides insights into the molecular mechanism of TBI by screening the differentially expressed genes and hub genes that can be used as biomarkers and therapeutic targets.
基金The study was supported by grants from the National Natural Science Foundation of China(No.81660036)the Project of the Bingtuan Science and Technology(No.2019DB012).
文摘Peripheral T-cell lymphoma(PTCL)is a very aggressive and heterogeneous hematological malignancy and has no effective targeted therapy.The molecular pathogenesis of PTCL remains unknown.In this study,we chose the gene expression profile of GSE6338 from the Gene Expression Omnibus(GEO)database to identify hub genes and key pathways and explore possible molecular pathogenesis of PTCL by bioinformatic analysis.Diferentially expressed gencs(DEGs)between PTCL and normal T cells were selected using GEO2R tool.Gene ontology(GO)analysis and Kyoto Encyclopedia of Gene and Genome(KEGG)pathway analysis were performed using Database for Annotation,Visualization and Integrated Discovery(DAVID).Moreover,the Search Tool for the Retrieval of Interacting Genes(STRING)and Molecular Complex Detection(MCODE)were utilized to construct protein-protein interaction(PPI)network and perform module analysis of these DEGs.A total of 518 DEGs were identifed,including 413 down-regulated and 105 up-regulated gencs.The down-regulated genes were enriched in osteoclast differentiation,Chagas disease and mitogen-activated protein kinase(MAPK)signaling pathway.The up-regulated genes were mainly associated with extracellular matrix(ECM)-receptor interaction,focal adhesion and pertussis.F our important modules were detected from the PPI network by using MCODE software.Fifteen hub genes with a high degree of connectivity were selected.Our study identifed DEGs,hub genes and pathways associated with PTCL by bioinformatic analysis.Results provide a basis for further study on the pathogenesis of PTCL.
文摘BACKGROUND Breast cancer(BC)is the most common malignant tumor in women.AIM To investigate BC-associated hub genes to obtain a better understanding of BC tumorigenesis.METHODS In total,1203 BC samples were downloaded from The Cancer Genome Atlas database,which included 113 normal samples and 1090 tumor samples.The limma package of R software was used to analyze the differentially expressed genes(DEGs)in tumor tissues compared with normal tissues.The cluster Profiler package was used to perform Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analysis of upregulated and downregulated genes.Univariate Cox regression was conducted to explore the DEGs with statistical significance.Protein-protein interaction(PPI)network analysis was employed to investigate the hub genes using the CytoHubba plug-in of Cytoscape software.Survival analyses of the hub genes were carried out using the Kaplan-Meier method.The expression level of these hub genes was validated in the Gene Expression Profiling Interactive Analysis database and Human Protein Atlas database.RESULTS A total of 1317 DEGs(fold change>2;P<0.01)were confirmed through bioinformatics analysis,which included 744 upregulated and 573 downregulated genes in BC samples.KEGG enrichment analysis indicated that the upregulated genes were mainly enriched in the cytokine-cytokine receptor interaction,cell cycle,and the p53 signaling pathway(P<0.01);and the downregulated genes were mainly enriched in the cytokine-cytokine receptor interaction,peroxisome proliferator-activated receptor signaling pathway,and AMP-activated protein kinase signaling pathway(P<0.01).CONCLUSION In view of the results of PPI analysis,which were verified by survival and expression analyses,we conclude that MAD2L1,PLK1,SAA1,CCNB1,SHCBP1,KIF4A,ANLN,and ERCC6L may act as biomarkers for the diagnosis and prognosis in BC patients.
文摘<strong>Objective:</strong><span style="font-family:""><span style="font-family:Verdana;"> This study aimed to identify hub genes that are associated with hepatocellular carcinoma (HCC) prognosis by bioinformatics analysis. </span><b><span style="font-family:Verdana;">Methods:</span></b><span style="font-family:Verdana;"> Data were collected from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) liver HCC datasets. </span><a name="_Hlk11768117"></a><span style="font-family:Verdana;">The robust rank ag</span><span style="font-family:Verdana;">gregation algorithm was used in integrating the data on differentially ex</span><span style="font-family:Verdana;">pressed genes (DEGs). Online databases DAVID 6.8 and REACTOME were used for </span><span style="font-family:Verdana;">gene ontology and pathway enrichment analysis. R software version 3.5.1, </span><span style="font-family:Verdana;">Cytoscape, and Kaplan-Meier plotter were used to identify hub genes. </span><b><span style="font-family:Verdana;">Results:</span></b><span style="font-family:Verdana;"> Six GEO datasets and the TCGA liver HCC dataset were included in this analysis. A total of 151 upregulated and 245 downregulated DEGs were iden</span><span style="font-family:Verdana;">tified. The upregulated DEGs most significantly enriched in the functional</span><span style="font-family:Verdana;"> categories of cell division, chromosomes, centromeric regions, and </span><span style="font-family:Verdana;">protein binding, whereas the downregulated DEGs most significantly</span><span style="font-family:Verdana;"> enriched in the </span><a name="_Hlk11059934"></a><span style="font-family:Verdana;">epoxygenase P450 pathway, extracellular region, and heme binding, with respect to biological process, cellular component, and molecular function analysis, respectively. Upregulated DEGS most significantly enriched the cell cycle pathway, whereas downregulated DEGs most significantly enriched </span><span style="font-family:Verdana;">the metabolism pathway. Finally, 88 upregulated and 40 downregulated genes were </span><span><span style="font-family:Verdana;">identified as hub genes. The top 10 upregulated hub DEGs were </span><i><span style="font-family:Verdana;">CDK</span></i><span style="font-family:Verdana;">1,</span></span><i><span style="font-family:Verdana;"> CCNB</span></i><span><span style="font-family:Verdana;">1,</span><i><span style="font-family:Verdana;"> CCNB</span></i><span style="font-family:Verdana;">2,</span><i><span style="font-family:Verdana;"> CDC</span></i><span style="font-family:Verdana;">20,</span><i><span style="font-family:Verdana;"> CCNA</span></i><span style="font-family:Verdana;">2,</span><i><span style="font-family:Verdana;"> AURKA</span></i><span style="font-family:Verdana;">,</span><i><span style="font-family:Verdana;"> MAD</span></i><span style="font-family:Verdana;">2</span><i><span style="font-family:Verdana;">L</span></i><span style="font-family:Verdana;">1,</span><i><span style="font-family:Verdana;"> TOP</span></i><span style="font-family:Verdana;">2</span><i><span style="font-family:Verdana;">A</span></i><span style="font-family:Verdana;">,</span><i><span style="font-family:Verdana;"> BUB</span></i><span style="font-family:Verdana;">1</span><i><span style="font-family:Verdana;">B </span></i><span style="font-family:Verdana;">and</span></span><i> <span style="font-family:Verdana;">BUB</span></i><span><span style="font-family:Verdana;">1. The top 10 downregulated hub DEGs were </span><i><span style="font-family:Verdana;">ESR</span></i><span style="font-family:Verdana;">1,</span><i><span style="font-family:Verdana;"> IGF</span></i><span style="font-family:Verdana;">1,</span><i><span style="font-family:Verdana;"> FTCD</span></i><span style="font-family:Verdana;">,</span></span><i><span style="font-family:Verdana;"> CYP</span></i><span style="font-family:Verdana;">3</span><i><span style="font-family:Verdana;">A</span></i><span style="font-family:Verdana;">4,</span><i><span style="font-family:Verdana;"> SPP</span></i><span style="font-family:Verdana;">2,</span><i> <span style="font-family:Verdana;">C</span></i><span><span style="font-family:Verdana;">8</span><i><span style="font-family:Verdana;">A</span></i><span style="font-family:Verdana;">,</span><i><span style="font-family:Verdana;"> CYP</span></i><span style="font-family:Verdana;">2</span><i><span style="font-family:Verdana;">E</span></i><span style="font-family:Verdana;">1,</span><i><span style="font-family:Verdana;"> TAT</span></i><span style="font-family:Verdana;">,</span><i><span style="font-family:Verdana;"> F</span></i><span style="font-family:Verdana;">9 and </span><i><span style="font-family:Verdana;">CYP</span></i><span style="font-family:Verdana;">2</span><i><span style="font-family:Verdana;">C</span></i><span style="font-family:Verdana;">9. </span><b><span style="font-family:Verdana;">Conclusions:</span></b><span style="font-family:Verdana;"> This study identified</span></span><span style="font-family:Verdana;"> several upregulated and downregulated hub genes that are associated with the prognosis of HCC patients. Verification of these results using </span><i><span style="font-family:Verdana;">in vitro</span></i><span style="font-family:Verdana;"> and </span><i><span style="font-family:Verdana;">in vivo</span></i><span style="font-family:Verdana;"> studies is warranted.</span></span>
文摘Objective:to screen the genes differentially expressed in autism using bioinformatics methods,and to explore their functional enrichment,related signaling pathways and the tissue-specific expression of hub genes.Methods:the autism expression profile chip numbered GSE77103 in the Gene Expression Omnibus(GEO)database was selected for examination.R language and related R packages were used for the screening and visualization of the diflerentially expressed genes.Gene Ontology(GO)function enrichment and Kyoto Encyclopedia of Genes and Genomes(KEGG)signaling pathway analysis of the differential genes were carried out using the relevant R package of R language.The database STRING was used to construct the interaction network of the proteins encoded by the differentially expressed genes,and the software Cytoscape was used to screen the hub genes in the network.The selected hub genes were imported into the BioGPS database to analyze the tissue-specific expression of the hub genes.Results:six hundred and sixty diflerentially expressed genes were screened out.Three hundred and seventy-three up-regulated genes and 287 down-regulated genes in the peripheral blood mononuclear cells of autistic children were compared with the peripheral blood mononuclear cells of healthy children.GO functional enrichment results showed that biological processes(BP)were mainly involved in viral response,negative regulation of viral genome replication,negative regulation of multiple biological processes,negative reg ulation reg ulation of viral life cycle,and defense responses to viruses.Cell components(CC)were involved in vesicles,lysosomal membranes,lysosomal lumen,etc.;molecular functions(MF)were involved in regulating glutathione transferase activity peroxidase activity,oxidoreductase activity,Glutathione peroxidase and transferase activity,etc.The results of KEGG signaling pathway analysis showed that the differentially expressed genes were related to the lysosomal pathway,the glutathione metabolism pathway and the arachidonic acid metabolism pathway.The hub genes screened by cytoHubba were:interferon regulatory factor 7(IRF7),interferon stimulated exonuclease gene 15(ISG15),XIAP associated factor 1(XAF1),MX dynamin like GTPase 1(MX1),interferon induced protein with tetratricopeptide repeats 1(IFIT1),interferon induced protein with tetratricopeptide repeats 5(IFIT5),29-55-oligoadenylate synthetase 3(OAS3),interferon induced protein 44(IFI44),HECT and the RLD domain containing E3 ubiquitin protein ligase 5(HERC5),interferon stimulated exonuclease gene 20(ISG20).Conclusion:there are genes that are diflerentially expressed in the peripheral blood mononuclear cells of autistic toddlers and healthy toddlers.IRF7,ISG15,XAF1,MX],IFIT1,IFIT5,OAS3,IFI44,HERC5,ISG20 are the hub regulatory genes of autism.
文摘Objective:To identify the thyroid cancer-related hub genes and pathways by bioinformatics initially in order to lay the foundation for further study.Methods:The expression profile chips and data of thyroid cancer were screened and downloaded from the gene expression omnibus(GEO).The GEO2R was applied to identify the differential expressed genes between thyroid cancer tissues and normal thyroid tissues.And the Metascape online website was used for pathway and function enrichment.With the usage of STRING and Cytoscape,the protein-protein interaction network was constructed,and the plug-in app cytoHubba in Cytoscape was applied to screen hub genes.Kaplan-Meier Plotter was implemented to conduct survival analysis of hub genes for further screening and discussion.Results:A total of 304 differential expressed genes were screened,and were mainly enriched in the biological processes of extracellular matrix,cell-substrate adhesion,response to wounding,muscle structure development and hormone metabolic process etc.by Metascape.Protein-protein interaction network visualized 284 nodes;the top ten scores of Maximal Clique Centrality algorithm were taken as the criteria to screen out the hub genes with high connectivity in the gene expression network.The KM plotter analysis confirmed that 5 of 9 hub genes were correlated with the prognosis of thyroid cancer patients.Conclusion:FN1,SPP1,TIMP1,VCAN,COL1A1,COL1A2,MMP1,DCN,COMP and FMOD may play a significant role in the development of thyroid cancer.Genes which have prognostic significance in survival analyses were found to be relevant to the composition and regulation of extracellular matrix.
文摘BACKGROUND A growing number of clinical examples suggest that coronavirus disease 2019(COVID-19)appears to have an impact on the treatment of patients with liver cancer compared to the normal population,and the prevalence of COVID-19 is significantly higher in patients with liver cancer.However,this mechanism of action has not been clarified.Gene sets for COVID-19(GSE180226)and liver cancer(GSE87630)were obtained from the Gene Expression Omnibus database.After identifying the common differentially expressed genes(DEGs)of COVID-19 and liver cancer,functional enrichment analysis,protein-protein interaction network construction and scree-ning and analysis of hub genes were performed.Subsequently,the validation of the differential expression of hub genes in the disease was performed and the regulatory network of transcription factors and hub genes was constructed.RESULTS Of 518 common DEGs were obtained by screening for functional analysis.Fifteen hub genes including aurora kinase B,cyclin B2,cell division cycle 20,cell division cycle associated 8,nucleolar and spindle associated protein 1,etc.,were further identified from DEGs using the“cytoHubba”plugin.Functional enrichment analysis of hub genes showed that these hub genes are associated with P53 signalling pathway regulation,cell cycle and other functions,and they may serve as potential molecular markers for COVID-19 and liver cancer.Finally,we selected 10 of the hub genes for in vitro expression validation in liver cancer cells.CONCLUSION Our study reveals a common pathogenesis of liver cancer and COVID-19.These common pathways and key genes may provide new ideas for further mechanistic studies.
基金National Natural Science Foundation of China(No.82260154)。
文摘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.
基金supported by Major scientific and technological projects of Xinjiang Production and Construction Corps(2017DB006 and 2020KWZ-012)。
文摘Proanthocyanidin(PA)is an important bioactive compound with multiple physiological benefits in jujube(Ziziphus jujube Mill.).However,the molecular mechanisms underlying PA biosynthesis in jujube fruit have not been investigated.Here,the profiling of PA,(+)-catechin and(–)-epicatechin and transcriptome sequencing of three jujube cultivars from Xinjiang Uyghur Autonomous Region of China at five developmental stages were analyzed.The levels of total PAs and catechin exhibited a decreased trend over jujube ripening,and epicatechin content of two jujube cultivars increased first and then declined.Transcriptome analysis revealed that the differentially expressed genes(DEGs)were mainly enriched in ribosome,glycolysis/gluconeogenesis,fructose and mannose metabolism.17 DEGs encoding PAL,CHS,CHI,CHS,F3'H,LAR,ANR,C4Hs,4CLs,FLSs,DFRs and UFGTs involved in PA biosynthesis were relatively abundant.The highly transcribed LAR gene may greatly contribute to epicatechin accumulation.A weighted gene co-expression network analysis(WGCNA)was performed,and a network module including 1620 genes highly correlated with content of Pas and catechin was established.We identified 58 genes including 9 structural genes and 49 regulatory genes related to PA biosynthesis and regulation in the WGCNA module.Sixteen genes encoding 9 families of transcriptional factors(i.e.,MYB,bHLH,ERF,bZIP,NAC,SBP,MIKC,HB,WRKY)were considered as hub genes.The results of qRT-PCR analysis validating 10 genes were well consistent with the transcriptome data.These findings provide valuable knowledge to facilitate its genetic studies and molecular breeding.
基金supported by the National Natural Science Foundation of China under Grants No.61720106004 and No.61872405the Key R&D Project of Sichuan Province,China under Grants No.20ZDYF2772 and No.2020YFS0243.
文摘Cardiomyopathies represent the most common clinical and genetic heterogeneous group of diseases that affect the heart function.Though progress has been made to elucidate the process,molecular mechanisms of different classes of cardiomyopathies remain elusive.This paper aims to describe the similarities and differences in molecular features of dilated cardiomyopathy(DCM)and ischemic cardiomyopathy(ICM).We firstly detected the co-expressed modules using the weighted gene co-expression network analysis(WGCNA).Significant modules associated with DCM/ICM were identified by the Pearson correlation coefficient(PCC)between the modules and the phenotype of DCM/ICM.The differentially expressed genes in the modules were selected to perform functional enrichment.The potential transcription factors(TFs)prediction was conducted for transcription regulation of hub genes.Apoptosis and cardiac conduction were perturbed in DCM and ICM,respectively.TFs demonstrated that the biomarkers and the transcription regulations in DCM and ICM were different,which helps make more accurate discrimination between them at molecular levels.In conclusion,comprehensive analyses of the molecular features may advance our understanding of DCM and ICM causes and progression.Thus,this understanding may promote the development of innovative diagnoses and treatments.
文摘BACKGROUND The objectives of this study were to identify hub genes and biological pathways involved in lung adenocarcinoma(LUAD)via bioinformatics analysis,and investigate potential therapeutic targets.AIM To determine reliable prognostic biomarkers for early diagnosis and treatment of LUAD.METHODS To identify potential therapeutic targets for LUAD,two microarray datasets derived from the Gene Expression Omnibus(GEO)database were analyzed,GSE3116959 and GSE118370.Differentially expressed genes(DEGs)in LUAD and normal tissues were identified using the GEO2R tool.The Hiplot database was then used to generate a volcanic map of the DEGs.Weighted gene co-expression network analysis was conducted to cluster the genes in GSE116959 and GSE-118370 into different modules,and identify immune genes shared between them.A protein-protein interaction network was established using the Search Tool for the Retrieval of Interacting Genes database,then the CytoNCA and CytoHubba components of Cytoscape software were used to visualize the genes.Hub genes with high scores and co-expression were identified,and the Database for Annotation,Visualization and Integrated Discovery was used to perform enrichment analysis of these genes.The diagnostic and prognostic values of the hub genes were calculated using receiver operating characteristic curves and Kaplan-Meier survival analysis,and gene-set enrichment analysis was conducted.The University of Alabama at Birmingham Cancer data analysis portal was used to analyze relationships between the hub genes and normal specimens,as well as their expression during tumor progression.Lastly,validation of protein expression was conducted on the identified hub genes via the Human Protein Atlas database.RESULTS Three hub genes with high connectivity were identified;cellular retinoic acid binding protein 2(CRABP2),matrix metallopeptidase 12(MMP12),and DNA topoisomerase II alpha(TOP2A).High expression of these genes was associated with a poor LUAD prognosis,and the genes exhibited high diagnostic value.CONCLUSION Expression levels of CRABP2,MMP12,and TOP2A in LUAD were higher than those in normal lung tissue.This observation has diagnostic value,and is linked to poor LUAD prognosis.These genes may be biomarkers and therapeutic targets in LUAD,but further research is warranted to investigate their usefulness in these respects.
