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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.
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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.
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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.
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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.
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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.
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Animals , Oral Submucous Fibrosis/genetics , Gene Expression Profiling , Inflammation , Signal Transduction , FibrosisABSTRACT
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.
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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.
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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.
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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.
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@#[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.
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Objective: To explore the disease targets of breast cancer related to age at diagnosis and tumor stage by weighted gene co-expression network analysis (WGCNA) from public database The Cancer Genome Atlas (TCGA). Methods: We obtained the breast cancer gene chip expression data and corresponding clinical data of 53 Asians and 126 Africans from TCGA database. R software WGCNA package was used to construct the co-expression network of the two populations, and the significant modules related to age at diagnosis and cancer stage were obtained. Online website DAVID was used for function enrichment and online website UALCAN for survival analysis. Results: WGCNA yielded 11 modules significantly related to cancer stage and age at diagnosis. Forty-two candidate genes were obtained after 11 modules were intersected. Gene ontology (GO) enrichment analysis was carried out using online website DAVID and these genes were mainly involved in protein binding function. Nine of the 42 candidate genes were identified as hub genes by WGCNA, the 9 genes were used in UALCAN for differential analysis and survival analysis, and 2 candidate biomarkers (ERLIN2 and ASH2L) were screened out. The expression of the 2 genes in normal tissues and breast cancer tissues was significantly different (P<0.01), and the expression level significantly influenced the survival of breast cancer patients (P<0.05). Conclusion: Data mining from public databases for biomarkers or therapeutic targets is a cost-effective research method. In this study ERLIN2 and ASH2L have been found to be candidate biomarkers for breast cancer through data mining, which needs large sample study and mechanism exploration.
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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.
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Objective To screen the critical genes related to the development of esophageal squamous cell carcinoma ( ESCC ) by weighted gene co-expression network analysis ( WGCNA ) and to verify by experiments.Methods Gene expression data of ESCC were downloaded from gene expression omnibus (GEO) database based on gene chip platform ( GPL) 570, GPL571, GPL96/97 or GPL14613 platform, respectively. Meanwhile, the obtained differentially expressed genes together with gene expression data of 81 ESCC patients from the cancer genome atlas ( TCGA ) and clinical data were analyzed by WGCNA to set up co-expression networks including mRNA and microRNA ( miRNA ) . The expression of miRNA in ESCC tissues and paracancerous tissues was examined by quantitative real-time polymerase chain reaction ( RT-PCR ) .And the expression of target protein Kruppel like factor 4 ( KLF4 ) and desmocollin 2 ( DSC2 ) were detected by immunohistochemistry .After ESCC cell line ECA-109 cells were transfected with miRNA-92b-3p mimic, cell cycle was tested by flow cytometry ,the cell invasion and migration ability was measured by Transwell chamber assay and scratch-wound assay.The expression of KLF4 and DSC2 was observed by confocal laser scanning microscopy and Western blotting .The target genes were verified by luciferase assay .T-test, rank sum test, chi-square test and Pearson correlation analysis were performed for statistical analysis .Results A total of 4023 differential expression gene ( DEG) and 328 differential expression miRNA ( DEM) were screened and 11 gene modules were set up by WGCNA .Among them, the brown modules were negatively associated with tumor grade and T stage (r=-0.340 and -0.268, P=0.002 and 0.016).Meanwhile, has-miR-92b and the potential target genes KLF4 and DSC2 were all in the brown module .Furthermore, the results of RT-PCR showed the expression of miRNA-92b-3p in ESCC tissues was higher than that in paracancerous tissues (3.052(1.652, 5.371) vs.0.985(0.558, 2.032)), and the difference was statistically significant (Z=-4.021,P<0.01). The results of immunohistochemistry demonstrated that the positive rates of KLF 4 and DSC2 in ESCC tissues were 43.3％(13/30) and 20.0％(6/30), respectively, which were lower than those of paracancerous tissues (70.0％(21/30) and 63.3％(19/30)), and the differences were statistically significant (χ2 =4.344 and 1.589, both P<0.05).After ECA-109 cells were transfected with miRNA-92b-3p mimics, the percentage of cells at G0/G1 phase decreased ((63.71 ±2.83)％vs.(54.62 ±4.00)％) and the percentage of cells at the S phase and G2/M phase increased ((31.81 ±2.88)％vs.(41.20％±2.87)％, and (3.87 ±1.75)％vs. (8.10 ±1.71)％, respectively), and the differences were statistically significant (t =3.215, 4.000 and 2.998;P=0.032, 0.016 and 0.040).The invasion and migration ability of the cells were significantly improved (79.67 ±27.54 vs.280.33 ±46.18, (69.72 ±3.91)％ vs.(84.90 ±5.25)％), and the differences were statistically significant (t=6.465 and 4.019, P=0.003 and 0.016).The results of Western blotting indicated that, compared with control mimic group , the expression of KLF4 and DSC2 was both dramatically downregulated after transfected with miRNA-92b-3p mimics transfected (1.00 ±0.23 vs.0.42 ±0.03, 1.00 ±0.20 vs.0.55 ± 0.21), and differences were statistically significant (t=4.470 and 5.493, P=0.042 and 0.032).The results of luciferase assay demonstrated that miRNA-92b-3p could directly bind KLF4 and DSC2. Conclusion WGCNA is an efficient systemic biological approach by which miRNA-92b-3p is identified as a new cancer-promoting gene .
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Ulcerative colitis (UC) is a chronic inflammatory disease and its involvement area in colon is influenced by a complex network of gene interactions.We analyzed the weighted gene co-expression networks in microarray dataset from colonic mucosa of patients with UC and identified one gene co-expression module that was highly associated with the progression of involved area in UC colon (Pearson coefficient=0.81,P＜0.0001).In total,523 hub genes in this module were found to be involved in immune system process after enrichment analysis in Gene Ontology.By the STRING and Cytoscape analysis,we observed that interleukin-8 (IL-8) and matrix metalloproteinase-9 (MMP-9) were centered in the network of hub genes.We then detected the expression of IL-8 and MMP-9 in mucosa from left-sided colon of patients using quantitative PCR and immunofluorescence assay respectively.Both quantitative PCR and immunofluorescence assay revealed the expression levels of IL-8 and MMP-9 were significantly different among the healthy controls,left-sided colitis group and pancolitis group (P＜0.05).IL-8 and MMP-9 were detected with an enhanced expression in pancolitis as compared with left-sided colitis and healthy controls,respectively (P＜0.05).This study demonstrates that immune system process is indispensable in the progression of disease in colon,and identifies that IL-8 and MMP-9 play potential critical roles for the progression.
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Ulcerative colitis (UC) is a chronic inflammatory disease and its involvement area in colon is influenced by a complex network of gene interactions.We analyzed the weighted gene co-expression networks in microarray dataset from colonic mucosa of patients with UC and identified one gene co-expression module that was highly associated with the progression of involved area in UC colon (Pearson coefficient=0.81,P＜0.0001).In total,523 hub genes in this module were found to be involved in immune system process after enrichment analysis in Gene Ontology.By the STRING and Cytoscape analysis,we observed that interleukin-8 (IL-8) and matrix metalloproteinase-9 (MMP-9) were centered in the network of hub genes.We then detected the expression of IL-8 and MMP-9 in mucosa from left-sided colon of patients using quantitative PCR and immunofluorescence assay respectively.Both quantitative PCR and immunofluorescence assay revealed the expression levels of IL-8 and MMP-9 were significantly different among the healthy controls,left-sided colitis group and pancolitis group (P＜0.05).IL-8 and MMP-9 were detected with an enhanced expression in pancolitis as compared with left-sided colitis and healthy controls,respectively (P＜0.05).This study demonstrates that immune system process is indispensable in the progression of disease in colon,and identifies that IL-8 and MMP-9 play potential critical roles for the progression.
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High-throughput biological technologies are now widely applied in biology and medicine, allowing scientists to monitor thousands of parameters simultaneously in a specific sample. However, it is still an enormous challenge to mine useful information from high-throughput data. The emergence of network biology provides deeper insights into complex bio-system and reveals the modularity in tissue/cellular networks. Correlation networks are increasingly used in bioinformatics applications. Weighted gene co-expression network analysis (WGCNA) tool can detect clusters of highly correlated genes. Therefore, we systematically reviewed the application of WGCNA in the study of disease diagnosis, pathogenesis and other related fields. First, we introduced principle, workflow, advantages and disadvantages of WGCNA. Second, we presented the application of WGCNA in disease, physiology, drug, evolution and genome annotation. Then, we indicated the application of WGCNA in newly developed high-throughput methods. We hope this review will help to promote the application of WGCNA in biomedicine research.
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Esophageal cancer is a common malignant tumor,whose pathogenesis and prognosis factors are not fully understood.This study aimed to discover the gene clusters that have similar functions and can be used to predict the prognosis of esophageal cancer.The matched microarray and RNA sequencing data of 185 patients with esophageal cancer were downloaded from The Cancer Genome Atlas (TCGA),and gene co-expression networks were built without distinguishing between squamous carcinoma and adenocarcinoma.The result showed that 12 modules were associated with one or more survival data such as recurrence status,recurrence time,vital status or vital time.Furthemaore,survival analysis showed that 5 out of the 12 modules were related to progression-free survival (PFS) or overall survival (OS).As the most important module,the midnight blue module with 82 genes was related to PFS,apart from the patient age,tumor grade,primary treatment success,and duration of smoking and tumor histological type.Gene ontology enrichment analysis revealed that "glycoprotein binding" was the top enriched function of midnight blue module genes.Additionally,the blue module was the exclusive gene clusters related to OS.Platelet activating factor receptor (PTAFR) and feline Gardner-Rasheed (FGR) were the top hub genes in both modeling datasets and the STRING protein interaction database.In conclusion,our study provides novel insights into the prognosis-associated genes and screens out candidate biomarkers for esophageal cancer.