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 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<0.05), while others were low expressed(P<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.

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 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.

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 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.

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.

Exploring biological connotation of blood stasis syndrome of rheumatoid arthritis and establishment of improved animal models based on syndrome-symptom mapping / 药学学报

Blood stasis syndrome is one of the core clinical syndrome of rheumatoid arthritis (RA), but the biological connotation of this syndrome is not clear, and there is a lack of disease improved animal models that match the characteristics of this disease and syndrome. The aim of this study was to screen the candidate biomarker gene set of blood stasis syndrome of RA, reveal the biological connotation of this syndrome, and explore and evaluate the preparation method of the improved animal model based on the characteristics of "disease-syndrome-symptom". The study was approved by the ethics committee of Guang'anmen Hospital, Chinese Academy of Traditional Chinese Medicine (No. 2019-073-KY-01) and the First Affiliated Hospital of Tianjin University of Traditional Chinese Medicine (No. TYLL2021[K]018), and the study subjects gave their informed consent. Animal welfare and experimental procedures followed the regulations of the Experimental Animal Ethics Committee of the Chinese Academy of Traditional Chinese Medicine (No. IBTCMCACMS21-2207-01). The whole blood samples were collected clinically from RA patients with blood stasis syndrome (3 cases) or other syndromes (7 types, 3 cases/type), and healthy volunteers (4 cases), and then transcriptome sequencing, KEGG, gene set enrichment analysis (GSEA) and weighted correlation network analysis (WGCNA) analysis were performed. 126 pivotal genes were screened, and their functional annotation results were significantly enriched in "immune-inflammation" related pathways and lipid metabolism regulation (sphingolipids, ether lipid metabolism and steroid biosynthesis). Syndrome-symptom mapping of hub gene set to the TCM primary and secondary symptoms, Western phenotypic symptoms and pathological links showed that joint tingling, abnormal joint morphology, petechiae and abnormal blood circulation are representative of blood stasis syndrome of RA. The results of the improved animal model showed that the rats in the collagen-induced arthritis + adrenaline hydrochloride (CIA+Adr) 3 model group had increased blood rheology, coagulation, platelet function and endothelial function abnormalities compared with the CIA-alone model group, suggesting that the rats with blood stasis syndrome of RA may be in a state of "blood stasis". The results of the study can help to advance the objective study of the evidence of blood stasis syndrome in RA, and provide new ideas for the establishment of an animal model that reflects the clinical characteristics of the disease and syndrome.

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.

Association of CDH11 with Autism Spectrum Disorder Revealed by Matched-gene Co-expression Analysis and Mouse Behavioral Studies / 神经科学通报·英文版

A large number of putative risk genes for autism spectrum disorder (ASD) have been reported. The functions of most of these susceptibility genes in developing brains remain unknown, and causal relationships between their variation and autism traits have not been established. The aim of this study was to predict putative risk genes at the whole-genome level based on the analysis of gene co-expression with a group of high-confidence ASD risk genes (hcASDs). The results showed that three gene features - gene size, mRNA abundance, and guanine-cytosine content - affect the genome-wide co-expression profiles of hcASDs. To circumvent the interference of these features in gene co-expression analysis, we developed a method to determine whether a gene is significantly co-expressed with hcASDs by statistically comparing the co-expression profile of this gene with hcASDs to that of this gene with permuted gene sets of feature-matched genes. This method is referred to as "matched-gene co-expression analysis" (MGCA). With MGCA, we demonstrated the convergence in developmental expression profiles of hcASDs and improved the efficacy of risk gene prediction. The results of analysis of two recently-reported ASD candidate genes, CDH11 and CDH9, suggested the involvement of CDH11, but not CDH9, in ASD. Consistent with this prediction, behavioral studies showed that Cdh11-null mice, but not Cdh9-null mice, have multiple autism-like behavioral alterations. This study highlights the power of MGCA in revealing ASD-associated genes and the potential role of CDH11 in ASD.

Effects of sodium iodide symporter co-expression on proliferation and cytotoxic activity of chimeric antigen receptor T cells in vitro / 南方医科大学学报

OBJECTIVE@#To investigate the effects of co-expression of sodium iodide symporter (NIS) reporter gene on the proliferation and cytotoxic activity of chimeric antigen receptor (CAR)-T cells in vitro.@*METHODS@#T cells expressing CD19 CAR (CAR-T cells), NIS reporter gene (NIS-T cells), and both (NIS-CAR-T cells) were prepared by lentiviral infection. The transfection rates of NIS and CAR were determined by flow cytometry, and the cell proliferation rate was assessed using CCK-8 assay at 24, 48 and 72 h of routine cell culture. The T cells were co-cultured with Nalm6 tumor cells at the effector-target ratios of 1∶2, 1∶1, 2∶1 and 4∶1 for 24, 48 and 72 h, and the cytotoxicity of CAR-T cells to the tumor cells was evaluated using lactate dehydrogenase (LDH) assay. ELISA was used to detect the release of IFN-γ and TNF-β in the co-culture supernatant, and the function of NIS was detected with iodine uptake test.@*RESULTS@#The CAR transfection rate was 91.91% in CAR-T cells and 99.41% in NIS-CAR-T cells; the NIS transfection rate was 47.83% in NIS-T cells and 50.24% in NIS- CAR-T cells. No significant difference in the proliferation rate was observed between CAR-T and NIS-CAR-T cells cultured for 24, 48 or 72 h (P> 0.05). In the co-cultures with different effector-target ratios, the tumor cell killing rate was significantly higher in CAR-T group than in NIS-CAR-T group at 24 h (P < 0.05), but no significant difference was observed between the two groups at 48 h or 72 h (P>0.05). Higher IFN-γ and TNF-β release levels were detected in both CAR-T and NIS-CAR-T groups than in the control group (P < 0.05). NIS-T cells and NIS-CAR-T cells showed similar capacity of specific iodine uptake (P>0.05), which was significantly higher than that in the control T cells (P < 0.05).@*CONCLUSION@#The co-expression of the NIS reporter gene does not affect CAR expression, proliferation or tumor cell-killing ability of CAR-T cells.

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.

Transcriptomic markers in pediatric septic shock prognosis: an integrative analysis of gene expression profiles

The goal of this study was to identify potential transcriptomic markers in pediatric septic shock prognosis by an integrative analysis of multiple public microarray datasets. Using the R software and bioconductor packages, we performed a statistical analysis to identify differentially expressed (DE) genes in pediatric septic shock non-survivors, and further performed functional interpretation (enrichment analysis and co-expression network construction) and classification quality evaluation of the DE genes identified. Four microarray datasets (3 training datasets and 1 testing dataset, 252 pediatric patients with septic shock in total) were collected for the integrative analysis. A total of 32 DE genes (18 upregulated genes; 14 downregulated genes) were identified in pediatric septic shock non-survivors. Enrichment analysis revealed that those DE genes were strongly associated with acute inflammatory response to antigenic stimulus, response to yeast, and defense response to bacterium. A support vector machine classifier (non-survivors vs survivors) was also trained based on DE genes. In conclusion, the DE genes identified in this study are suggested as candidate transcriptomic markers for pediatric septic shock prognosis and provide novel insights into the progression of pediatric septic shock.

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.

Dysregulation of MAD2L1/CAMK2A/PTTG1 Gene Cluster Maintains the Stemness Characteristics of Uterine Corpus Endometrial Carcinoma / 中国医学科学院学报

Objective To study the stemness characteristics of uterine corpus endometrial carcinoma(UCEC)and its potential regulatory mechanism.Methods Transcriptome sequencing data of UCEC was obtained from The Cancer Genome Atlas.Gene expression profile was normalized by edgeR package in R3.5.1.A one-class logistic regression machine learning algorithm was employed to calculated the mRNA stemness index(mRNAsi)of each UCEC sample.Then,the prognostic significance of mRNAsi and candidate genes was evaluated by survminer and survival packages.The high-frequency sub-pathways mining approach(HiFreSP)was used to identify the prognosis-related sub-pathways enriched with differentially expressed genes(DEGs).Subsequently,a gene co-expression network was constructed using WGCNA package,and the key gene modules were analyzed.The clusterProfiler package was adopted to the function annotation of the modules highly correlated with mRNAsi.Finally,the Human Protein Atlas(HPA)was retrieved for immunohistochemical validation.Results The mRNAsi of UCEC samples was significantly higher than that of normal tissues(

Integration-based co-expression network analysis to investigate tumor-associated modules across three cancer types / 生物工程学报

In case/control gene expression data, differential expression (DE) represents changes in gene expression levels across various biological conditions, whereas differential co-expression (DC) represents an alteration of correlation coefficients between gene pairs. Both DC and DE genes have been studied extensively in human diseases. However, effective approaches for integrating DC-DE analyses are lacking. Here, we report a novel analytical framework named DC&DEmodule for integrating DC and DE analyses and combining information from multiple case/control expression datasets to identify disease-related gene co-expression modules. This includes activated modules (gaining co-expression and up-regulated in disease) and dysfunctional modules (losing co-expression and down-regulated in disease). By applying this framework to microarray data associated with liver, gastric and colon cancer, we identified two, five and two activated modules and five, five and one dysfunctional module(s), respectively. Compared with the other methods, pathway enrichment analysis demonstrated the superior sensitivity of our method in detecting both known cancer-related pathways and those not previously reported. Moreover, we identified 17, 69, and 11 module hub genes that were activated in three cancers, which included 53 known and three novel cancer prognostic markers. Random forest classifiers trained by the hub genes showed an average of 93% accuracy in differentiating tumor and adjacent normal samples in the TCGA and GEO database. Comparison of the three cancers provided new insights into common and tissue-specific cancer mechanisms. A series of evaluations demonstrated the framework is capable of integrating the rapidly accumulated expression data and facilitating the discovery of dysregulated processes.

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.