Mining of Gene Modules and Identification of Key Genes for Early Diagnosis of Gastric Cancer

Gastric cancer (GC) is one of the most common malignant tumors with high incidence and mortality rates. Most patients with GC are not diagnosed until the advanced stage of cancer or during tumor screening, resulting in missing the best treatment time. This study identified key modules and hub genes associated with GC by weighted gene co-expression network analysis (WGCNA). The "limma" package in R was used to identify differentially expressed genes (DEGs) in GC samples from TCGA, and a total of 4892 DEGs were identified. GO enrichment and KEGG pathway enrichment analyses were conducted to detect the related pathways and functions of DEGs. These DEGs were primarily associated with extracellular matrix organization, DNA replication, cell cycle, and p53 signaling pathway. Gene modules associated with clinical characteristics were identified with WGCNA in tumor and normal samples. Six gene modules were obtained in the WGCNA network, of which two modules were significantly correlated with GC. Hub genes of key modules were identified using survival analysis and expression analysis. Finally, one-way ANOVA was used to explore the relationship between hub gene expression in normal tissues and different pathological stages of GC. Through survival and expression analysis, a total of 19 genes with good prognosis and significantly differential expressed were identified. The hub genes were significantly differential expressed in normal tissues and different pathological stages of GC, indicating that these genes have important diagnostic value for early GC and can be used as auxiliary indicators in the diagnosis of early GC.

Screening and bioinformatics analysis of key autophagy-related genes in alcoholic hepatitis / 器官移植

Objective To screen key autophagy-related genes in alcoholic hepatitis (AH) and investigate potential biomarkers and therapeutic targets for AH. Methods Two AH gene chips in Gene Expression Omnibus (GEO) and autophagy-related data sets obtained from MSigDB and GeneCards databases were used, and the key genes were verified and obtained by weighted gene co-expression network analysis (WGCNA). The screened key genes were subject to gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), protein-protein interaction (PPI) and immune infiltration analyses. Messenger RNA (mRNA)- microRNA (miRNA) network was constructed to analyze the expression differences of key autophagy-related genes during different stages of AH, which were further validated by real-time fluorescence quantitative polymerase chain reaction (RT-qPCR) in the liver tissues of AH patients and mice. Results Eleven autophagy-related genes were screened in AH (EEF1A2, CFTR, SOX4, TREM2, CTHRC1, HSPB8, TUBB3, PRKAA2, RNASE1, MTCL1 and HGF), all of which were up-regulated. In the liver tissues of AH patients and mice, the relative expression levels of SOX4, TREM2, HSPB8 and PRKAA2 in the AH group were higher than those in the control group. Conclusions SOX4, TREM2, HSPB8 and PRKAA2 may be potential biomarkers and therapeutic targets for AH.

Identification and Analysis of Prognosis-related Core Genes in Sepsis Based on Bioinformatics / 医学研究杂志

Objective To identify the potential core genes affecting the prognosis of sepsis based on bioinformatics.Methods The Gene Expression Omnibus(GEO)database was used to screen the gene expression datasets GSE54514 and GSE65682 from septic pa-tients.Key genes related to the prognosis of sepsis were screened by weighted gene co-expression network analysis(WGCNA)and Venn analysis.the Metascape database,the RcisTarget package,and the CIBERSORT algorithm were used to perform gene function enrichment analysis,transcription factor enrichment analysis,and immune infiltration analysis.The dataset GSE5772 was selected for validation to screen core genes associated with the prognosis of sepsis,and the survival analysis was performed by the Kaplan-Meier method.Results Co-expression network analysis was performed on the datasets GSE54514 and GSE65682,respectively,and the"green"and"brown"modules with the highest prognostic correlation to sepsis were selected.The intersection of genes in the two modules was taken,and 20 key genes were obtained by Venn analysis.These key genes were mainly enriched into the regulation of cell morphology,monocyte migration,and other pathways.Enrichment analysis of the transcription factor showed that the transcription factor ZNF148 might be one of the main regulators of the gene set.Further verification of data set GSE5772 revealed that the genes FGD3,MBP,MSN,RNF130 and SETD1B were significantly low expressed in septic patients(P＜0.05).Immune infiltration analysis showed that these five core genes were closely related to the content of immune cells.The expressions of FGD3,MSN and RNF130 were correlated with the survival rate of septic pa-tients(P＜0.05).Conclusion Five core genes associated with the prognosis of sepsis were screened via bioinformatics methods,which are closely related to immune cells.The genes FGD3,MSN and RNF130 may be important predictors for the prognosis of sepsis.

Identification and Analysis of Hub Genes of Basal-like Breast Cancer by Integrated Bioinformatics Methods / 医学研究杂志

Objective To mine and analyse the hub genes associated with the prognosis of basal-like breast cancer(BLBC)by bioinformatic methods.Methods We searched the GEO database to obtain an appropriate microarray dataset related to molecular subtyp-ing of breast cancer,and identified modules associated with BLBC by WGCNA.Then,the top 10％differential expressed genes in the module were screened as candidate genes using PPI and cytohubba.The candidate genes were subjected to survival analysis and expression analysis to obtain hub genes.Finally,we explored the correlation between the expressive level of hub genes and immune cell infiltration,chemokines,and immunomodulators by TIMER and TISIDB database.Furthermore,transcription factors(TFs)-hub gene network was constructed.Results A total of 891 genes in black modules related to BLBC were analyzed,and two hub genes,ESPL1 and CCNB2,were identified from the 80differential expressed genes.Two hub genes are associated with BLBC immune cell infiltration,mainly inclu-ding Th2 cells,CD8+T cells,endothelial cells,and tumor-associated fibroblasts.They were also related to chemokines,immunostimu-lators,immunosuppressive factors,and MHC molecules.The upstream transcriptional regulatory network of hub genes showed that 22 transcription factors simultaneously regulate two hub genes.Conclusion ESPL1 and CCNB2 are prognostic markers of BLBC and related to breast tumor immunity.

Development of a prognostic risk marker of colon adenocarcinoma based on a weighted gene co-expression network and its clinical significance / 中国比较医学杂志

Objective Because of the poor prognosis of colon adenocarcinoma(COAD),it is necessary to screen prognosis-related genes in COAD patients and establish a new prognostic risk assessment model.Methods COAD-related data from the cancer genome atlas(TCGA)and gene expression omnibus(GEO)were used as training and validation sets,respectively.Weighted gene co-expression network analysis(WGCNA),a Cox regression model and least absolute selection and shrinkage operator(LASSO)regression analysis were used to screen prognosis-related genes of COAD and establish a prognostic model.A receiver operating characteristic(ROC)curve was combined with a survival curve to verify the model accuracy,and a nomogram was constructed.Patients were divided into two groups by the median risk score.The immune cell proportion score(IPS)was used to evaluate the immunotherapy response of the two groups.Results A total of 15 feature genes were screened.The area under the ROC curve in the predictive model of COAD patients was＞0.6,and the survival rate of the high-risk group was significantly lower than that of the low-risk group(P＜0.05),suggesting a good distinguishing ability for high-and low-risk COAD patients.Patients in the low-risk group had a higher IPS(P=0.026),indicating a better response to immunotherapy.Conclusions The model developed for COAD in this study has a good ability to predict the survival of patients at high and low risk of COAD.

Identification of M1 macrophage-related genes in rejection after kidney transplantation based on weighted gene co-expression network analysis / 器官移植

Objective To identify M1 macrophage-related genes in rejection after kidney transplantation and construct a risk prediction model for renal allograft survival. Methods GSE36059 and GSE21374 datasets after kidney transplantation were downloaded from Gene Expression Omnibus (GEO) database. GSE36059 dataset included the samples from the recipients with rejection and stable allografts. Using this dataset, weighted gene co-expression network analysis (WGCNA) and differential analysis were conducted to screen the M1 macrophage-related differentially expressed gene (M1-DEG). Then, GSE21374 dataset (including the follow-up data of graft loss) was divided into the training set and validation set according to a ratio of 7∶3. In the training set, a multivariate Cox's model was constructed using the variables screened by least absolute shrinkage and selection operator (LASSO), and the ability of this model to predict allograft survival was evaluated. CIBERSORT was employed to analyze the differences of infiltrated immune cells between the high-risk group and low-risk group, and the distribution of human leukocyte antigen (HLA)-related genes was analyzed between two groups. Gene set enrichment analysis (GSEA) was used to further clarify the biological process and pathway enrichment in the high-risk group. Finally, the database was employed to predict the microRNA (miRNA) interacting with the prognostic genes. Results In the GSE36059 dataset, 14 M1-DEG were screened. In the GSE21374 dataset, Toll-like receptor 8 (TLR8), Fc gamma receptor 1B (FCGR1B), BCL2 related protein A1 (BCL2A1), cathepsin S (CTSS), guanylate binding protein 2(GBP2) and caspase recruitment domain family member 16 (CARD16) were screened by LASSO-Cox regression analysis, and a multivariate Cox's model was constructed based on these 6 M1-DEG. The area under curve (AUC) of receiver operating characteristic of this model for predicting the 1- and 3-year graft survival was 0.918 and 0.877 in the training set, and 0.765 and 0.736 in the validation set, respectively. Immune cell infiltration analysis showed that the infiltration of rest and activated CD4+ memory T cells, γδT cells and M1 macrophages were increased in the high-risk group (all P < 0.05). The expression level of HLA I gene was up-regulated in the high-risk group. GSEA analysis suggested that immune response and graft rejection were enriched in the high-risk group. CTSS interacted with 8 miRNA, BCL2A1 and GBP2 interacted with 3 miRNA, and FCGR1B interacted with 1 miRNA. Conclusions The prognostic risk model based on 6 M1-DEG has high performance in predicting graft survival, which may provide evidence for early interventions for high-risk recipients.

Identification of key immune related genes in diabetes retinopathy based on weighted gene co-expression network / 国际眼科杂志(Guoji Yanke Zazhi)

AIM: To explore the key genes related to immunity and immune cell infiltration levels in diabetes retinopathy(DR)using bioinformatics.METHODS: Differential expression genes(DEGs)were obtained by “limma” R from Gene Expression Omnibus(GEO)data from September to October 2022, Gene ontology(GO)and Kyoto encyclopedia of genes and genomes(KEGG)were analyzed, and the infiltration of immune cell types in each sample was calculated based on CIBERSORT algorithm. Weighted gene co-expression network analysis(WGCNA)was used to screen for DEGs in immune-related gene modules. The protein-protein interaction(PPI)network was established by STRING online database and Cytoscape, and the hub genes were screened by MCODE and cytoHubba plug-ins.RESULTS: The results showed that 1 426 up-regulated and 206 down-regulated differential genes were screened, where 7 immune cell types, including B cell naive, Plasma cells, CD4+T cells, T cells regulatory(Tregs), Macrophages M0, Macrophages M1 and Neutrophils were significantly overexpressed(P&#x003C;0.05), while others were low expressed(P&#x003C;0.05). After WGCNA, a total of 820 DEGs were found in the modules most related to immunity. After constructing the PPI network, 10 key genes were screened using plug-ins, and two key genes were further screened using the expression amount of each differential gene in PPI: DLGAP5 and AURKB.CONCLUSION: This study used bioinformatics to screen the infiltration of immune cells and key genes related to immunity in patients with DR. These findings may provide evidences for future research, diagnosis, and treatment of DR.

Identification of SULF1 as a Shared Gene in Idiopathic Pulmonary Fibrosis and Lung Adenocarcinoma / 中国肺癌杂志

BACKGROUND@#Idiopathic pulmonary fibrosis (IPF) is an idiopathic chronic, progressive interstitial lung disease with a diagnosed median survival of 3-5 years. IPF is associated with an increased risk of lung cancer. Therefore, exploring the shared pathogenic genes and molecular pathways between IPF and lung adenocarcinoma (LUAD) holds significant importance for the development of novel therapeutic approaches and personalized precision treatment strategies for IPF combined with lung cancer.@*METHODS@#Bioinformatics analysis was conducted using publicly available gene expression datasets of IPF and LUAD from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis was employed to identify common genes involved in the progression of both diseases, followed by functional enrichment analysis. Subsequently, additional datasets were used to pinpoint the core shared genes between the two diseases. The relationship between core shared genes and prognosis, as well as their expression patterns, clinical relevance, genetic characteristics, and immune-related functions in LUAD, were analyzed using The Cancer Genome Atlas (TCGA) database and single-cell RNA sequencing datasets. Finally, potential therapeutic drugs related to the identified genes were screened through drug databases.@*RESULTS@#A total of 529 shared genes between IPF and LUAD were identified. Among them, SULF1 emerged as a core shared gene associated with poor prognosis. It exhibited significantly elevated expression levels in LUAD tissues, concomitant with high mutation rates, genomic heterogeneity, and an immunosuppressive microenvironment. Subsequent single-cell RNA-seq analysis revealed that the high expression of SULF1 primarily originated from tumor-associated fibroblasts. This study further demonstrated an association between SULF1 expression and tumor drug sensitivity, and it identified potential small-molecule drugs targeting SULF1 highly expressed fibroblasts.@*CONCLUSIONS@#This study identified a set of shared molecular pathways and core genes between IPF and LUAD. Notably, SULF1 may serve as a potential immune-related biomarker and therapeutic target for both diseases.

Identification of biomarkers in laryngeal cancer by weighted gene co-expression network analysis / 中南大学学报(医学版)

OBJECTIVES@#Laryngeal cancer (LC) is a globally prevalent and highly lethal tumor. Despite extensive efforts, the underlying mechanisms of LC remain inadequately understood. This study aims to conduct an innovative bioinformatic analysis to identify hub genes that could potentially serve as biomarkers or therapeutic targets in LC.@*METHODS@#We acquired a dataset consisting of 117 LC patient samples, 16 746 LC gene RNA sequencing data points, and 9 clinical features from the Cancer Genome Atlas (TCGA) database in the United States. We employed weighted gene co-expression network analysis (WGCNA) to construct multiple co-expression gene modules. Subsequently, we assessed the correlations between these co-expression modules and clinical features to validate their associations. We also explored the interplay between modules to identify pivotal genes within disease pathways. Finally, we used the Kaplan-Meier plotter to validate the correlation between enriched genes and LC prognosis.@*RESULTS@#WGCNA analysis led to the creation of a total of 16 co-expression gene modules related to LC. Four of these modules (designated as the yellow, magenta, black, and brown modules) exhibited significant correlations with 3 clinical features: The age of initial pathological diagnosis, cancer status, and pathological N stage. Specifically, the yellow and magenta gene modules displayed negative correlations with the age of pathological diagnosis (r=-0.23, P<0.05; r=-0.33, P<0.05), while the black and brown gene modules demonstrated negative associations with cancer status (r=-0.39, P<0.05; r=-0.50, P<0.05). The brown gene module displayed a positive correlation with pathological N stage. Gene Ontology (GO) enrichment analysis identified 77 items, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis identified 30 related signaling pathways, including the calcium signaling pathway, cytokine-cytokine receptor interaction, neuro active ligand-receptor interaction, and regulation of lipolysis in adipocytes, etc. Consequently, central genes within these modules that were significantly linked to the overall survival rate of LC patients were identified. Central genes included CHRNB4, FOXL2, KCNG1, LOC440173, ADAMTS15, BMP2, FAP, and KIAA1644.@*CONCLUSIONS@#This study, utilizing WGCNA and subsequent validation, pinpointed 8 genes with potential as gene biomarkers for LC. These findings offer valuable references for the clinical diagnosis, prognosis, and treatment of LC.

Key Prediction Genes of Nasopharyngeal Carcinoma:Screening Based on Systematic Bioinformatics and Validation by Cell Experiments / 中国医学科学院学报

Objective To screen out the potential prediction genes for nasopharyngeal carcinoma(NPC)from the gene microarray data of NPC samples and then verify the genes by cell experiments.Methods The NPC dataset was downloaded from Gene Expression Omnibus,and limma package was employed to screen out the differentially expressed genes.Weighted correlation network analysis package was used for weighted gene co-expression network analysis,and Venn diagram was drawn to find the common genes.The gene ontology annotation and Kyoto encyclopedia of genes and genomes pathway enrichment were then performed for the common genes.The biomarkers for NPC were further explored by protein-protein interaction network,LASSO regression,and non-parametric tests.Real-time quantitative PCR and Western blotting were employed to determine the mRNA and protein levels of key predictors of NPC,so as to verify the screening results.Results There were 622 up-regulated genes and 351 down-regulated genes in the GSE12452 dataset.A total of 116 common genes were obtained by limma analysis and weighted gene co-expression network analysis.The common genes were mainly involved in the biological processes of cell proliferation and regulation and regulation of intercellular adhesion.They were mainly enriched in Rap1,Ras,and tumor necrosis factor signaling pathways.Six key genes were screened out,encoding angiopoietin-2(ANGPT2),dual oxidase 2(DUOX2),coagulation factor Ⅲ(F3),interleukin-15(IL-15),lipocalin-2,and retinoic acid receptor-related orphan receptor B(RORB).Real-time quantitative PCR and Western blotting showed that the NPC cells had up-regulated mRNA and protein levels of ANGPT2 and IL-15 and down-regulated mRNA and protein levels of DUOX2,F3,and RORB,which was consistent with the results predicted by bioinformatics.Conclusion ANGPT2,DUOX2,F3,IL-15 and RORB are potential predictive molecular markers and therapeutic targets for NPC,which may be involved in Rap1,Ras,tumor necrosis factor and other signaling pathways.

Keloid nomogram prediction model based on weighted gene co-expression network analysis and machine learning / 生物医学工程学杂志

Keloids are benign skin tumors resulting from the excessive proliferation of connective tissue in wound skin. Precise prediction of keloid risk in trauma patients and timely early diagnosis are of paramount importance for in-depth keloid management and control of its progression. This study analyzed four keloid datasets in the high-throughput gene expression omnibus (GEO) database, identified diagnostic markers for keloids, and established a nomogram prediction model. Initially, 37 core protein-encoding genes were selected through weighted gene co-expression network analysis (WGCNA), differential expression analysis, and the centrality algorithm of the protein-protein interaction network. Subsequently, two machine learning algorithms including the least absolute shrinkage and selection operator (LASSO) and the support vector machine-recursive feature elimination (SVM-RFE) were used to further screen out four diagnostic markers with the highest predictive power for keloids, which included hepatocyte growth factor (HGF), syndecan-4 (SDC4), ectonucleotide pyrophosphatase/phosphodiesterase 2 (ENPP2), and Rho family guanosine triphophatase 3 (RND3). Potential biological pathways involved were explored through gene set enrichment analysis (GSEA) of single-gene. Finally, univariate and multivariate logistic regression analyses of diagnostic markers were performed, and a nomogram prediction model was constructed. Internal and external validations revealed that the calibration curve of this model closely approximates the ideal curve, the decision curve is superior to other strategies, and the area under the receiver operating characteristic curve is higher than the control model (with optimal cutoff value of 0.588). This indicates that the model possesses high calibration, clinical benefit rate, and predictive power, and is promising to provide effective early means for clinical diagnosis.

Identification of hub genes in airway epithelial cells of asthma patients by WGCNA and PPI network

Bronchial asthma is a common chronic disease of airway inflammation, high mucus secretion and airway hyper responsiveness. The pathogenetic mechanisms of asthma remain unclear. In this study, we aimed at identifying genes playing an import role in disease-related pathways in airway epithelial cells of asthma patients. Microarray data GSE41861 of asthma airway epithelial cells was used to screen differentially expressed genes (DEGs) through GEO2R analysis. The weighted gene co-expression network analysis (WGCNA) was performed to identify gene co-expression network modules in bronchial asthma. The DAVID database was then used to perform functional and pathway enrichment analysis of these DEGs. In addition, we have conducted protein-protein interaction (PPI) network of DEGs by STRING, and eventually found key genes and significant modules. A total of 315 DEGs (111 up-regulated and 204 down-regulated) were identified between severe asthma and healthy individual, which were mainly involved in pathways of cilium assembly, cilium morphogenesis, axon guidance, positive regulation of fat cell differentiation, and positive regulation of cell substrate adhesion. A total of 60 genes in the black module and green module were considered to be correlated with the severity of asthma. Combining PPI network, several key genes were identified, such as BP2RY14, PTGS1, SLC18A2, SIGLEC6, RGS13, CPA3, and HPGDS. Our findings revealed several genes that may be involved in the process of development of bronchial asthma and potentially be candidate targets for diagnosis or therapy of bronchial asthma.

Comparative molecular analysis of oral submucous fibrosis and other organ fibrosis based on weighted gene co-expression network analysis / 中南大学学报(医学版)

OBJECTIVES@#There is currently a lack of economic and suitable animal models that can accurately recapitulate the oral submucous fibrosis (OSF) disease state for indepth study. This is one of the primary reasons for the limited therapeutic methods available for OSF. Based on the underlying logic of pan-cancer analysis, this study systematically compares OSF and the other four types of organ fibrosis from the aspects of molecules, signaling pathways, biological processes, etc. A comprehensive analysis of the similarities and differences between OSF and other organ fibrosis is helpful for researchers to discover some general rules of fibrosis disease and may provide new ideas for studying OSF.@*METHODS@#Microarray data of the GSE64216, GSE76882, GSE171294, GSE92592, and GSE90051 datasets were downloaded from GEO. Differentially expressed mRNAs (DEmRNAs) of each type of fibrosis were identified by Limma package. Weighted gene co-expression network analysis (WGCNA) was used to identify each type of fibrosis-related module. The similarities and differences of each fibrosis-related-module genes were analyzed by function and pathway enrichment analysis.@*RESULTS@#A total of 6 057, 10 910, 27 990, 10 480, and 4 801 DEmRNAs were identified in OSF, kidney intestinal fibrosis (KIF), liver fibrosis (LF), idiopathic pulmonary fibrosis (IPF), and skin fibrosis (SF), respectively. By using WGCNA, each type of fibrosis-related module was identified. The co-expression networks for each type of fibrosis were constructed respectively. Except that KIF and LF have 5 common hub genes, other fibrotic diseases have no common hub genes with each other. The common pathways of OSF, KIF, LF, IPF, and SF mainly focus on immune-related pathways.@*CONCLUSIONS@#OSF and the other 4 types of fibrotic diseases are tissue- and organ-specific at the molecular level, but they share many common signaling pathways and biological processes, mainly in inflammation and immunity.

Identification of core genes related to severity of respiratory syncytial virus bronchiolitis based on RNA sequencing data / 中华微生物学和免疫学杂志

Objective:To identify the core genes related to the disease severity of respiratory syncytial virus (RSV) bronchiolitis in children using RNA sequencing (RNA-seq) and weighted gene co-expression network analysis (WGCNA), aiming to provide reference for predicting the condition of RSV infection.Methods:Twenty-two patients admitted to the Second Affiliated Hospital of Wenzhou Medical University with RSV bronchiolitis from October 1, 2019 to February 29, 2020 were enrolled as the case group. They were divided into three groups based on the severity of the disease: mild group, moderate group and severe group. Twenty-two healthy children were selected as the control group. Total RNA was extracted from whole blood leukocytes and analyzed by RNA-seq to compare the differentially expressed genes (DEGs) between children with RSV bronchiolitis and healthy children. The gene co-expression modules related to disease severity and biological indicators for disease severity assessment were identified.Results:The median age of the 22 patients (19 males and 3 females) was 3 months. The median age of the 22 healthy children (14 males and 8 females) was 4 months. There was no significant difference in age or gender between the two groups. There were 8 cases in the mild group, 7 cases in the moderate group and 7 cases in the severe group. Through significance analysis, 416 DEGs were found in the mild group, 586 in the moderate group and 846 in the severe group. According to WGCNA analysis, 10 co-expression modules were found, among which brown module ( r=0.62, P<0.001) was significantly correlated with disease severity. The protein-protein interaction network of DEGs in brown module was constructed and the top 30 core genes were selected according to the connectivity of gene nodes, among which the genes with high correlation were RBX1 and PSMA7. The expression of RBX1 and PSMA7 genes was up-regulated in the severe group, but their expression in the mild and moderate groups was not significantly different from that in the control group. Conclusions:RBX1 and PSMA7 genes might be biological predictors of disease severity in RSV bronchiolitis.

Identification of key genes in nonarteritic anteriorischemic optic neuropathy through weighted gene co-expression network analysis / 国际眼科杂志(Guoji Yanke Zazhi)

AIM: We sought to identify key genes related to nonarteritic anterior ischemic optic neuropathy(NAION)and provide bioinformatics support for elucidating the pathogenesis of NAION.METHODS: Based on rat GSE43671 dataset, which was acquired from GEO, we identified modular genes with highly correlated clinical phenotype by WGCNA package in the R language. Then Gene Ontology(GO)and Kyoto encyclopedia of genes and genomes(KEGG)analysis were performed with ClusterProfiler package. In addition, Cytoscape was used to screen potential key genes and establish miRNA-key genes network.RESULTS: There were 22 modules identified from the GSE43671 dataset by the WGCNA method, among which the blue module has the highest correlation coefficient. GO enrichment analysis suggested that the genes in the module mainly manifest in the epithelial tube morphogenesis and other biological processes, receptor complex and other cell components, and structural constituent of eye lens and other molecular functions. KEGG suggested that the genes in the module mainly relate to signaling pathways including neuroactive ligand-receptor interaction, human papillomavirus, MAPK and PI3K/Akt. There were 10 key genes screened by PPI network and Cytoscape including Psmb9, Psma7, Map3k14, Psme1, Nfkb1, Rela, Psma5, Relb, Psmb4 and Nfkb2, and 6 miRNA were predicted as miR-383-5p, miR-9a-5p, miR-155-5p, miR-223-3p, miR-495 and miR-325-3p.CONCLUSION: Using the WGCNA method to screen out the relevant pathways, key genes, and microRNA for NAION, it provides a theoretical basis for exploring pathogenesis and treatment methods of NAION, however, more animal and cell experiments are needed to further validate.

Identification of hub genes in response to valproate treatment in patients with epilepsy / 中国药理学通报

Aim To investigate the hub genes associated with response to valproate treatment in patients with epilepsy by using weighted gene co-expression network analysis.Methods We downloaded data from the GEO database and constructed the gene co-expression network.Pearson correlation test was used to calculate the correlation between module genes and clinical traits, to screen gene modules significantly associated with response to valproate treatment, and to screen hub genes according to the connectivity within modules.GO functional enrichment analysis and KEGG pathway analysis were used to annotate the functions of the modules.Results A total of 12 gene co-expression modules were constructed from the correlations of gene expression, in which the yellow module was significantly correlated with the drug treatment(r=0.57, P<0.000 1)and the blue module was significantly correlated with the response to valproate(r=-0.53, P<0.000 1).We found that S1PR5, SARM1 and MAGED1, FBXO31 were in the hub of the co-expression network.The biological annotation function revealed that the genes in both modules were mainly enriched in immune response and MPAK pathways.Conclusions Our work delivers preliminary data that valproate treatment causes the changes of immune and metabolic pathways in patients, and the response to epilepsy may be related to the expression of MAGED1, FBXO31.

Analysis of sepsis-related genes through weighted gene co-expression network / 中华危重病急救医学

Objective:To identify the Key genes in the development of sepsis through weighted gene co-expression network analysis (WGCNA).Methods:The gene expression dataset GSE154918 was downloaded from the public database Gene Expression Omnibus (GEO) database, which containes data from 105 microarrays of 40 control cases, 12 cases of asymptomatic infection, 39 cases of sepsis, and 14 cases of follow-up sepsis. The R software was used to screen out differentially expressed genes (DEG) in sepsis, and the distributed access view integrated database (DAVID), search tool for retrieval of interacting neighbouring genes (STRING) and visualization software Cytoscape were used to perform gene function and pathway enrichment analysis, Protein-protein interaction (PPI) network analysis and key gene analysis to screen out the key genes in the development of sepsis.Results:Forty-six candidate genes were obtained by WGCNA and combined with DEG expression analysis, and these 46 genes were analyzed by gene ontology (GO) and Kyoto City Encyclopedia of Genes and Genomes (KEGG) pathway enrichment to obtain gene functions and involved signaling pathways. The PPI network was further constructed using the STRING database, and 5 key genes were selected by the PPI network visualization software Cytoscape, including the mast cell expressed membrane protein 1 gene (MCEMP1), the S100 calcium-binding protein A12 gene (S100A12), the adipokine resistance factor gene (RETN), the c-type lectin structural domain family 4 member gene (CLEC4D), and peroxisome proliferator-activated receptor gene (PPARG), and differential expression analysis of each of these 5 genes showed that the expression levels of the above 5 genes were significantly upregulated in sepsis patients compared with healthy controls.Conclusion:In this study, 5 key genes related to sepsis were screened by constructing WGCNA method, which may be potential candidate targets related to sepsis diagnosis and treatment.

Screening and identifying genes associated with malignant pleural mesothelioma based on public database / 中国职业医学

OBJECTIVE: To explore the related signaling pathways, biomarkers and prognostic genes of malignant pleural mesothelioma(MPM) based on the gene chip and second-generation sequencing datasets in public database by bioinformatics-related method. METHODS: MPM microarray expression datasets GSE51024 and GSE2549, with 82 and 49 MPM patients, respectively, were downloaded from the Gene Expression Omnibus database. The RNA sequencing data of 86 MPM patients were downloaded from the The Cancer Genome Atlas(TCGA). The weighted gene co-expression network analysis(WGCNA) and differentially expressed genes(DEGs) screening were used to screen and identify hub genes in the GSE51024 dataset by RStudio 4.0 software. The gene set enrichment analysis(GSEA) was used to explore relevant signaling pathways. Finally, a total of 135 MPM gene expression data from GSE2549 dataset and TCGA database were used to verify the hub genes. RESULTS: The green key gene module identified by the WGCNA was highly correlated with MPM, with a correlation coefficient of 0.83(P<0.01). A total of 3 245 DEGs were screened by DEGs analysis. Among them, 1 229 genes were up-regulated and 2 016 genes were down-regulated. GSEA results showed that the genes were significantly enriched in the areas of G2/M cell cycle checkpoint, epithelial-mesenchymal transition, E2 F target gene, and mitotic spindle pathways. Three hub genes were screened, including the proliferating cell nuclear antigen-associated factor(PCLAF), nucleolar and spindle-associated protein 1(NUSAP1) and topoisomerase Ⅱ-α(TOP2 A). Compared with para-cancerous tissues, normal pleural tissues or lung tissues, the relative expression of PCLAF, NUSAP1 and TOP2 A were increased in the MPM tissues(all P<0.05). Downregulation of these three genes was correlated with good prognosis, and upregulation of these three genes was correlated with poor prognosis in the patients. CONCLUSION: G2/M checkpoint, epithelial-mesenchymal transition, E2 F target gene and mitotic spindle pathway are the key signaling pathways in the occurrence and development of MPM. PCLAF, TOP2 A and NUSAP1 genes could be the biomarkers for the prognosis of MPM.

Identification and functional analysis of pathogenic genes and key transcription factors in prostate adenocarcinoma / 中国肿瘤生物治疗杂志

@#[Abstract] Objective: To investigate the pathogenesis of prostate cancer by analyzing the associated hub gene modules of prostate cancer and identifying key transcription factors and genes that affect these modules. Methods: WGCNA (weighted gene co-expressed network analysis) was used to identify hub gene modules associated with important clinicopathological features of prostate cancer, such as pathological staging, Gleason grading etc. The OPOSSUM online tool was used to analyze the transcription factors enriching and regulating those genes. Pathway enrichment analysis and protein-protein interaction network analysis were used to identify key genes in prostate cancer. Finally, the effects of these genes on clinical features and disease-free survival (DFS) of prostate cancer patients were analyzed. Results: Three hub modules were identified, and they were highly associated with pathologic T stage, pathologic N stage and Gleason grading of prostate cancer, respectively. Further screening revealed 13 key dysregulated transcription factors that participated in the regulation of these three hub modules. The differentially expressed genes regulated by the 13 key transcription factors were significantly enriched in Calcium signaling pathway, cGMP-PKG signaling pathway and cAMP signaling pathway. 14 key genes (PRKG1, PRKG2, CYSLTR2, GRPR, CHRM3, ADCY5, ADRA1D, EDNRA, EDNRB, CYSLTR2, AGTR1, GRPR, GRIA1 and OXT) were at important nodes in the gene network. Among them, the high expression of ADRA1A, PRKG2, CHRM3, ADRA1D and EDN3 significantly extended the DFS of patients with prostate cancer (all P<0.01). Conclusion: ADRA1A, PRKG2, CHRM3, ADRA1D and EDN3 are regulated by key dysregulated transcription factors and highly associated with clinical features of prostate cancer. Their high expressions will significantly prolong the DFS of prostate cancer patients, which may shed light to the discovery of mechanism in prostate adenocarcinoma.

Application of weighted gene co-expression network analysis technology in human tumor research / 肿瘤

Weighted gene co-expression network analysis (WGCNA) technology is a high-throughput gene expression data mining algorithm, which uses the idea of system biology to find the correlation of gene expression and to construct gene modules, so as to further discovery a high-throughput data mining algorithm with biological significance modules. In recent years, with the deep understanding of human diseases to gene level, WGCNA has been used increasingly in the researches of various diseases, especially in mining the highthroughput data about tumor-related genes. Moreover, with the continuous improvement of this technology, the research of this technology in disease pathogenesis, development and treatment etc has been developed from a single co-expression network analysis to the multiple technologies [such as genome-wide association study (GWAS) and support vector machine (SVM)] combined WGCNA or the innovative applicated WGCNA. As a high-throughput research method based on gene level, WGCNA is playing an important role.