Identification of hub genes associated with Helicobacter pylori infection and type 2 diabetes mellitus:A pilot bioinformatics study

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.

Functional investigation and two-sample Mendelian randomization study of primary biliary cholangitis hub genes

BACKGROUND The identification of specific gene expression patterns is crucial for understanding the mechanisms underlying primary biliary cholangitis(PBC)and finding relevant biomarkers for diagnosis and therapeutic evaluation.AIM To determine PBC-associated hub genes and assess their clinical utility for disease prediction.METHODS PBC expression data were obtained from the Gene Expression Omnibus database.Overlapping genes from differential expression analysis and weighted gene coexpression network analysis(WGCNA)were identified as key genes for PBC.Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analyses were performed to explore the potential roles of key genes.Hub genes were identified in protein-protein interaction(PPI)networks using the Degree algorithm in Cytoscape software.The relationship between hub genes and immune cells was investigated.Finally,a Mendelian randomization study was conducted to determine the causal effects of hub genes on PBC.RESULTS We identified 71 overlapping key genes using differential expression analysis and WGCNA.These genes were primarily enriched in pathways related to cytokinecytokine receptor interaction,and Th1,Th2,and Th17 cell differentiation.We utilized Cytoscape software and identified five hub genes(CD247,IL10,CCL5,CCL3,and STAT3)in PPI networks.These hub genes showed a strong correlation with immune cell infiltration in PBC.However,inverse variance weighting analysis did not indicate the causal effects of hub genes on PBC risk.CONCLUSION Hub genes can potentially serve as valuable biomarkers for PBC prediction and treatment,thereby offering significant clinical utility.

Hub genes and key pathways of traumatic brain injury: bioinformatics analysis and in vivo validation

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.

Identification of hub genes for glaucoma:a study based on bioinformatics analysis and experimental verification

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.

Identification of Hub Genes and Key Pathways Associated with Peripheral T-cell Lymphoma

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.

Analysis of functional hub genes indicates DLGAP5 is linked to lung adenocarcinoma prognosis

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.

Identification of Hub Genes Associated with Hepatocellular Carcinoma Prognosis by Bioinformatics Analysis

Objective: This study aimed to identify hub genes that are associated with hepatocellular carcinoma (HCC) prognosis by bioinformatics analysis. Methods: Data were collected from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) liver HCC datasets. The robust rank aggregation algorithm was used in integrating the data on differentially expressed genes (DEGs). Online databases DAVID 6.8 and REACTOME were used for gene ontology and pathway enrichment analysis. R software version 3.5.1, Cytoscape, and Kaplan-Meier plotter were used to identify hub genes. Results: Six GEO datasets and the TCGA liver HCC dataset were included in this analysis. A total of 151 upregulated and 245 downregulated DEGs were identified. The upregulated DEGs most significantly enriched in the functional categories of cell division, chromosomes, centromeric regions, and protein binding, whereas the downregulated DEGs most significantly enriched in the 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 the metabolism pathway. Finally, 88 upregulated and 40 downregulated genes were identified as hub genes. The top 10 upregulated hub DEGs were CDK1, CCNB1, CCNB2, CDC20, CCNA2, AURKA, MAD2L1, TOP2A, BUB1B and BUB1. The top 10 downregulated hub DEGs were ESR1, IGF1, FTCD, CYP3A4, SPP2, C8A, CYP2E1, TAT, F9 and CYP2C9. Conclusions: This study identified several upregulated and downregulated hub genes that are associated with the prognosis of HCC patients. Verification of these results using in vitro and in vivo studies is warranted.

Bioinformatics analysis of genes differentially expressed in autism and screening of hub genes in the occurrence and development of autism

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.

Screening and bioinformatics analysis of thyroid cancer-related hub genes

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.

Bioinformatics-based Identification of Key Pathways and Hub Genes of Traumatic Brain Injury in a Rat Model

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.

Hub Genes of Astrocyte Involved in Glaucoma with Ocular Hypertension by Integrated Bioinformatics Analysis

This study was conducted to elucidate the potential key candidate genes and pathways in role of astrocyte involved in glaucoma with ocular hypertension.Methods Expression profiles GSE2378 and GSE758 including 27 reactive optic nerve head astrocytes(ONHAs)by hypertensions and 26 normal controls,were integrated and deeply analyzed.Differentially expressed genes(DEGs)were sorted and candidate genes and pathways enrichment were analyzed.DEGs-associated protein-protein interaction network(PPI)was performed.Results A total of 119 consistently expressed genes were identified from 281 commonly changed DEGs,including 68 up-regulated genes and 51 down-regulated genes.PPI network complex filtered 75 DEGs(43 up-regulated and 32 down-regulated genes)of the 119 consistently altered DEGs and developed 117 edges,and 10 hub genes were identified.The most significant 3 modules were filtered from PPI,pathway enrichment analysis showed that module 1 was associated with extracellular exosome.Module 2 was mainly associated with antibody-dependent cellular cytotoxicity(ADCC)and module 3 was mainly associated with Hippo signaling pathway.Conclusion Taken above,using integrated bioinformatical analysis,we have identified DEGs candidate genes and pathways in role of astrocyte involved in glaucoma with ocular hypertension,which could improve our understanding of the cause and underlying molecular events,and these candidate genes and pathways could be therapeutic targets for glaucoma.

Eight hub genes as potential biomarkers for breast cancer diagnosis and prognosis:A TCGA-based study

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.

Key genes and regulatory networks for diabetic retinopathy based on hypoxia-related genes:a bioinformatics analysis

AIM:To prevent neovascularization in diabetic retinopathy(DR)patients and partially control disease progression.METHODS:Hypoxia-related differentially expressed genes(DEGs)were identified from the GSE60436 and GSE102485 datasets,followed by gene ontology(GO)functional annotation and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analysis.Potential candidate drugs were screened using the CMap database.Subsequently,a protein-protein interaction(PPI)network was constructed to identify hypoxia-related hub genes.A nomogram was generated using the rms R package,and the correlation of hub genes was analyzed using the Hmisc R package.The clinical significance of hub genes was validated by comparing their expression levels between disease and normal groups and constructing receiver operating characteristic curve(ROC)curves.Finally,a hypoxia-related miRNA-transcription factor(TF)-Hub gene network was constructed using the NetworkAnalyst online tool.RESULTS:Totally 48 hypoxia-related DEGs and screened 10 potential candidate drugs with interaction relationships to upregulated hypoxia-related genes were identified,such as ruxolitinib,meprylcaine,and deferiprone.In addition,8 hub genes were also identified:glycogen phosphorylase muscle associated(PYGM),glyceraldehyde-3-phosphate dehydrogenase spermatogenic(GAPDHS),enolase 3(ENO3),aldolase fructose-bisphosphate C(ALDOC),phosphoglucomutase 2(PGM2),enolase 2(ENO2),phosphoglycerate mutase 2(PGAM2),and 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3(PFKFB3).Based on hub gene predictions,the miRNA-TF-Hub gene network revealed complex interactions between 163 miRNAs,77 TFs,and hub genes.The results of ROC showed that the except for GAPDHS,the area under curve(AUC)values of the other 7 hub genes were greater than 0.758,indicating their favorable diagnostic performance.CONCLUSION:PYGM,GAPDHS,ENO3,ALDOC,PGM2,ENO2,PGAM2,and PFKFB3 are hub genes in DR,and hypoxia-related hub genes exhibited favorable diagnostic performance.

Comprehensive analysis of the potential pathogenesis of COVID-19 infection and liver cancer

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.

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.

Mining the key regulatory genes of chicken inosine 5'-monophosphate metabolism based on time series microarray data

IMP （inosine 5＇-monophosphate） is a compound that enhances the flavor of poultry meat. IMP has become a new breeding trait to improve poultry meat quality. We tried to identify several potential regulatory genes, and construct their predicted regulatory relationships. Time series gene expression profiles of thigh muscle tissues of Rugao chicken, a famous indigenous breed in China, were performed for analysis of genes that are co-expressed or correlated with the concentration of IMP. We found 15 crucial co-expression genes, which are Hspa2, Pten, Gabpa, Bpi, Mkll, Srf,, Cd34, Hspa4, EtvS, Bmpr2, Gdel, IgfbpS, Cd28, Pecam1 and Gja1, that may directly or indirectly regulate IMP metabolism. Eventually, we computed the correlation coefficient between 19 IMP Genes and 15 CGs （15 co-expression genes）, and we identified and constructed a predicted regulation network. In conclusion, variation of IMP concentration was primarily connected with the muscle development process. During this process, 15 CGs were identified that may have significant influence on IMP metabolism. In particular, Bmpr2, Pten and co-expression genes correlated with Entpd8 might play important roles in regulating IMP de novo synthesis, decomposition and salvage synthesis.

Identification of key genes involved in axon regeneration and Wallerian degeneration by weighted gene co-expression network analysis

Peripheral nerve injury repair requires a certain degree of cooperation between axon regeneration and Wallerian degeneration.Therefore,investigating how axon regeneration and degeneration work together to repair peripheral nerve injury may uncover the molecular mechanisms and signal cascades underlying peripheral nerve repair and provide potential strategies for improving the low axon regeneration capacity of the central nervous system.In this study,we applied weighted gene co-expression network analysis to identify differentially expressed genes in proximal and distal sciatic nerve segments from rats with sciatic nerve injury.We identified 31 and 15 co-expression modules from the proximal and distal sciatic nerve segments,respectively.Functional enrichment analysis revealed that the differentially expressed genes in proximal modules promoted regeneration,while the differentially expressed genes in distal modules promoted neurodegeneration.Next,we constructed hub gene networks for selected modules and identified a key hub gene,Kif22,which was up-regulated in both nerve segments.In vitro experiments confirmed that Kif22 knockdown inhibited proliferation and migration of Schwann cells by modulating the activity of the extracellular signal-regulated kinase signaling pathway.Collectively,our findings provide a comparative framework of gene modules that are co-expressed in injured proximal and distal sciatic nerve segments,and identify Kif22 as a potential therapeutic target for promoting peripheral nerve injury repair via Schwann cell proliferation and migration.All animal experiments were approved by the Institutional Animal Ethics Committee of Nantong University,China(approval No.S20210322-008)on March 22,2021.

Transcriptome and weighted gene co-expression network analysis of jujube(Ziziphus jujuba Mill.)fruit reveal putative genes involved in proanthocyanin biosynthesis and regulation

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.

An integrated analysis of mRNA-miRNA transcriptome data revealed hub regulatory networks in three genitourinary cancers

Bladder,kidney,prostate and testicular carcinoma are the top four genitourinary cancers in China.Here we analyzed mRNA and miRNA expression profiles of carcinomas of the bladder(TCC),kidney(ccRCC)and testis(TGCT)to uncover their specific regulatory mechanisms.The gene expression profiles of GSE31617 were downloaded from GEO database,which contained 27 samples,including 10 TCC,7 TGCT and 10 ccRCC.Specific up-and downregulated differentially expressed genes(DEGs)and differentially expressed microRNAs(DEmiRNAs)of each cancer were selected and target genes of DEmiRNAs were predicted.Gene interaction network of the shared genes and target genes of DEmiRNAs of each cancer was predicted by STRING and constructed by Cytoscape.In each cancer,we build regulatory networks of hub genes selected and conducted GO analysis of enriched genes.Furthermore,we chose four hub genes(SALL4,RHEB,CDC42 and TNN)for survival analysis in OncoLnc database,and they all had effects on the survival rate of another genitourinary cancer-kidney renal clear cell carcinoma(KIRC).In conclusion,the present study indicated that the identified hub genes promote our understanding of molecular mechanisms underlying the development of three genitourinary cancers,and might be used as molecular targets and diagnostic biomarkers for the treatment of them.

Identification of key genes and biological pathways in lung adenocarcinoma by integrated bioinformatics analysis

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.