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.

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.

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.

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 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.

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.

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.

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.

Single-cell transcriptomics reveals T-cell heterogeneity and immunomodulatory role of CD4^(+) T native cells in Candida albicans infection

Objective:Candida albicans is a common fungal pathogen that triggers complex host defense mechanisms,including coordinated innate and adaptive immune responses,to neutralize invading fungi effectively.Exploring the immune microenvironment has the potential to inform the development of therapeutic strategies for fungal infections.Methods:The study analyzed individual immune cell profiles in peripheral blood mononuclear cells from Candida albicans-infected mice and healthy control mice using single-cell transcriptomics,fluorescence quantitative PCR,and Western blotting.We investigated intergroup differences in the dynamics of immune cell subpopulation infiltration,pathway enrichment,and differentiation during Candida albicans infection.Results:Our findings indicate that infiltration of CD4^(+)naive cells,regulatory T(Treg)cells,and Microtubules(MT)-associated cells increased after infection,along with impaired T cell activity.Notably,CD4^(+) T cells and plasma cells were enhanced after infection,suggesting that antibody production is dependent on T cells.In addition,we screened 6 hub genes,transcription factor forkhead box protein 3(Foxp3),cytotoxic T-lymphocyte associated protein 4(CTLA4),Interleukin 2 Receptor Subunit Beta(Il2rb),Cd28,C-C Motif Chemokine Ligand 5(Ccl5),and Cd27 for alterations associated with CD4^(+) T cell differentiation.Conclusions:These results provide a comprehensive immunological landscape of the mechanisms of Candida albicans infection and greatly advance our understanding of adaptive immunity in fungal infections.

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.

基于加权基因共表达网络和癌症基因组图谱临床数据分析并鉴定肝细胞癌的Hub基因研究

背景 肝细胞癌(HCC)是全球常见的癌症相关死亡的第三大原因,约占所有原发性肝癌病例的90%,其复发率和死亡率较高,目前发生的分子机制仍不清楚。目的 探索HCC潜在的分子机制,发掘新的生物标志物。方法 从TCGA数据库下载RNA-seq表达数据和临床相关信息,通过差异基因表达分析正常肝脏组织与HCC组织的差异基因;对差异表达基因进行富集分析;基于TCGA中HCC的基因表达数据概况,使用WGCNA R包建立共表达网络,进行加权基因共表达网络分析(WGCNA),选择具有临床意义的模块,并筛选候选Hub基因;进一步分析候选Hub基因在HCC组织和正常肝脏组织显著差异表达、与HCC患者总体生存期和无病生存期是否显著相关,最终确定Hub基因;通过人类蛋白质图谱数据库对Hub基因蛋白表达进行验证。结果 本研究的基因表达数据来自50个正常肝脏组织样本和373个HCC组织样本。通过差异基因表达分析发现7 230个在HCC和正常肝脏组织之间差异表达的基因(HCC中3 691个上调基因和3 539个下调基因)。富集分析表明,上调的差异表达基因主要参与细胞周期调控和有丝分裂过程;下调的差异表达基因主要参与小分子代谢和有机酸代谢等过程。WGCNA确定了19个与HCC患者临床特征相关基因模块,通过分析模块与临床特征之间的关系,筛选出青色模块和紫色模块。青色模块基因中同时与患者总生存期和无病生存期强烈相关的前两个基因为VPS45和FAM189B;紫色模块基因中同时与患者总生存期和无病生存期强烈相关的前两个基因分别为CLEC1B和FCN3,因此将VPS45、FAM189B、CLEC1B和FCN3确定为最终的Hub基因。人类蛋白质图谱数据库免疫组织化学染色显示:VPS45和FAM189B在HCC组织中的表达高于正常肝脏组织,FCN3在HCC组织中的表达低于正常肝脏组织,CLEC1B在HCC组织和正常肝脏组织中表达差异不明显。结论 初步确定VPS45、FAM189B、CLEC1B和FCN3可能是HCC的新型潜在生物标志物,这些Hub基因可能为HCC的靶向治疗提供理论基础。

应用生物信息学筛选结直肠癌Hub基因及验证

目的通过运用生物信息学分析,筛选出与结直肠癌相关的差异表达基因(Differentially Expressed genes,DEGs),并验证其生物学功能。方法云浮市人民医院检验科从基因表达综合数据库(Gene Expression Omnibus,GEO)中下载结直肠癌芯片数据GSE 21815、GSE 31905、GSE 35279资料,应用GEO2R语言进行处理得出结直肠癌和正常结直肠组织之间的差异表达基因,并通过生物信息学工具DAVID、STRING、Cytoscape构建差异表达基因的蛋白互作网络,筛选Hub基因,应用基因本体论(Gene Ontology,GO)、基因百科全书(Kyoto Encyclopedia of Genes and Genomes,KEGG)分析筛选出的Hub基因的生物功能,并利用MiRDB工具找出可能调控Hub基因的miRNA,并于2017—2022年8月收集30例结直肠癌组织和30例正常结直肠组织样本,通过实时荧光定量PCR(Quantitative Real-time PCR,qPCR)验证。结果经生物学信息分析和蛋白质相互作用网络图分析催产素受体基因、基质金属蛋白酶11基因、间质上皮转化因子基因、基质金属蛋白酶7基因、激肽释放酶8基因、激肽释放酶10基因为结直肠癌组织发生发展的关键Hub基因。结直肠癌组织中基质金属蛋白酶11基因(4.38±1.58)、间质上皮转化因子基因(2.69±0.29)、基质金属蛋白酶7基因(0.88±0.14)、激肽释放酶8基因(11.09±3.90)、激肽释放酶10基因mRNA(7.88±2.20)的表达,显著高于正常结直肠组织组织,差异有统计学意义(t=9.605、25.339、26.376、9.541、3.726,P均<0.001)。结论结直肠癌组织中基质金属蛋白酶11基因、间质上皮转化因子基因、基质金属蛋白酶7基因、激肽释放酶8基因、激肽释放酶10基因异常表达,可能参与结直肠癌发生过程,有望为后续结直肠癌基础研究及临床诊疗提供依据。

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.

Weighted gene co-expression network analysis reveals similarities and differences of molecular features between dilated and ischemic cardiomyopathies

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.