Gene coexpression analysis offers important modules and pathway of human lung adenocarcinomas

Lung adenocarcinomas injured greatly on the people worldwide. Although clinic experiments and gene profiling analyses had been well performed, to our knowledge, systemic coexpression analysis of human genes for this cancer is still limited to date. Here, using the published data GSE75037, we built the coexpression modules of genes by Weighted Gene Co-Expression Network Analysis (WGCNA), and investigated function and protein–protein interaction network of coexpression genes by Database for Annotation, visualization, and Integrated Discovery (DAVID) and String database, respectively. First, 11 coexpression modules were conducted for 5,000 genes in the 83 samples recently. Number of genes for each module ranged from 90 to 1,260, with the mean of 454. Second, interaction relationships of hub-genes between pairwise modules showed great differences, suggesting relatively high scale independence of the modules. Third, functional enrichment of the coexpression modules showed great differences. We found that genes in modules 8 significantly enriched in the biological process and/or pathways of cell adhesion, extracellular matrix (ECM)–receptor interaction, focal adhesion, and PI3K-Akt signaling pathway, and so forth. It was inferred as the key module underlying lung adenocarcinomas. Furthermore, PPI analysis revealed that the genes COL1A1, COL1A2, COL3A1, CTGF, and BGN owned the largest number of adjacency genes, unveiling that they may functioned importantly during the occurrence of lung adenocarcinomas. To summary, genes involved in cell adhesion, ECM–receptor interaction, focal adhesion, and PI3K-Akt signaling pathway play crucial roles in human lung adenocarcinomas.     

Identification of key gene modules and pathways of human glioma through coexpression network

Glioma causes great harm to people worldwide. Systemic coexpression analysis of this disease could be beneficial for the identification and development of new prognostic and predictive markers in the clinical management of glioma. In this study, we extracted data sets from the Gene Expression Omnibus data set by using “glioma” as the keyword. Then, a coexpression module was constructed with the help of Weighted Gene Coexpression Network Analysis software. Besides, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on the genes in these modules. As a result, the critical modules and target genes were identified. Eight coexpression modules were constructed using the 4,000 genes with a high expression value of the total 141 glioma samples. The result of the analysis of the interaction among these modules showed that there was a high scale independence degree among them. The GO and KEGG enrichment analyses showed that there was a significant difference in the enriched terms and degree among these eight modules, and module 5 was identified as the most important module. Besides, the pathways it was enriched in, hsa04510: Focal adhesion and hsa04610: Complement and coagulation cascades, were determined as the most important pathways. In summary, module 5 and the pathways it was enriched in, hsa04510: Focal adhesion and has 04610: Complement and coagulation cascades, have the potential to serve as biomarkers for patients with glioma.     

Key genes and functional coexpression modules involved in the pathogenesis of systemic lupus erythematosus

We performed a systematic review of genome‐wide gene expression datasets to identify key genes and functional modules involved in the pathogenesis of systemic lupus erythematosus (SLE) at a systems level. Genome‐wide gene expression datasets involving SLE patients were searched in Gene Expression Omnibus and ArrayExpress databases. Robust rank aggregation (RRA) analysis was used to integrate those public datasets and identify key genes associated with SLE. The weighted gene coexpression network analysis (WGCNA) was adapted to identify functional modules involved in SLE pathogenesis, and the gene ontology enrichment analysis was utilized to explore their functions. The aberrant expressions of several randomly selected key genes were further validated in SLE patients through quantitative real‐time polymerase chain reaction. Fifteen genome‐wide gene expression datasets were finally included, which involved a total of 1,778 SLE patients and 408 healthy controls. A large number of significantly upregulated or downregulated genes were identified through RRA analysis, and some of those genes were novel SLE gene signatures and their molecular roles in etiology of SLE remained vague. WGCNA further successfully identified six main functional modules involved in the pathogenesis of SLE. The most important functional module involved in SLE included 182 genes and mainly enriched in biological processes, including defense response to virus, interferon signaling pathway, and cytokine‐mediated signaling pathway. This study identifies a number of key genes and functional coexpression modules involved in SLE, which provides deepening insights into the molecular mechanism of SLE at a systems level and also provides some promising therapeutic targets.     

Identification of EPHX2 and RMI2 as two novel key genes in cervical squamous cell carcinoma by an integrated bioinformatic analysis

Cervical cancer is the fourth most common malignancy in women worldwide and cervical squamous cell carcinoma (CESC) is the most common histological type of cervical cancer. The dysregulation of genes plays a significant role in cancer. In the present study, we screened out differentially expressed genes (DEGs) of CESC in the GSE63514 data set from the Gene Expression Omnibus database. An integrated bioinformatics analysis was used to select hub genes, as well as to investigate their related prognostic signature, functional annotation, methylation mechanism, and candidate molecular drugs. As a result, a total of 1907 DEGs were identified (944 were upregulated and 963 were downregulated). In the protein–protein interaction network, three hub modules and 30 hub genes were identified. And two hub modules and 116 hub genes were screened out from four CESC-related modules by the weighted gene coexpression network analysis. The gene ontology term enrichment analysis and Kyoto encyclopedia of genes and genomes pathway analysis were performed to better understand functions and pathways. Genes with a significant prognostic value were found by prognostic signature analysis. And there were five genes (EPHX2, CHAF1B, KIAA1524, CDC45, and RMI2) identified as significant CESC-associated genes after expression validation and survival analysis. Among them, EPHX2 and RMI2 were noted as two novel key genes for the CESC-associated methylation and expression. In addition, four candidate small molecule drugs for CESC (camptothecin, resveratrol, vorinostat, and trichostatin A) were defined. Further studies are required to explore these significant CESC-associated genes for their potentiality in diagnosis, prognosis, and targeted therapy.     

Key modules and hub genes identified by coexpression network analysis for revealing novel biomarkers for larynx squamous cell carcinoma

Larynx squamous cell carcinoma (LSCC) is the second most aggressive head and neck squamous cell carcinoma. Numerous genes have been identified to be aberrantly expressed during the development of LSCC. However, currently, researchers focus more on the individual molecule and downstream genes, leaving the coexpression among genes and key upstream disease driver genes unexploited. In this study, we applied weighted gene coexpression analysis (WGCNA) to decipher potential hub genes driving the development of LSCC. After downloading of LSCC microarray profile from gene expression omnibus, different expression analysis was performed, which was used to conduct functional enrichment analysis. Then, we applied WGCNA to highlight the hub genes which were relevant to the carcinogenesis and progression. A total of 2858 differentially expressed genes were identified in LSCC samples compared with adjacent non-neoplastic tissues. WGCNA revealed three LSCC set-specific modules having significant Kyoto Encyclopedia of Genes and Genomes enrichment effect, including pink, cyan, and black module. Nine hub genes were identified to be crucial in LSCC onset and progression, which may assist clinical decisions and serve as potential targets for LSCC treatment.     

Identification of gene coexpression modules,hub genes,and pathways related to spinal cord injury using integrated bioinformatics methods

Spinal cord injury (SCI) is characterized by dramatic neurons loss and axonal regeneration suppression. The underlying mechanism associated with SCI-induced immune suppression is still unclear. Weighted gene coexpression network analysis (WGCNA) is now widely applied for the identification of the coexpressed modules, hub genes, and pathways associated with clinic traits of diseases. We performed this study to identify hub genes associated with SCI development. Gene Expression Omnibus (GEO) data sets GSE45006 and GSE20907 were downloaded and the significant correlativity and connectivity between them were detected using WGCNA. Three significant consensus modules, including 567 eigengenes, were identified from the master GSE45006 data following the preconditions of approximate scale-free topology for WGCNA. Further bioinformatics analysis showed these eigengenes were involved in inflammatory and immune responses in SCI. Three hub genes Rac2, Itgb2, and Tyrobp and one pathway “natural killer cell-mediated cytotoxicity” were identified following short time-series expression miner, protein-protein interaction network, and functional enrichment analysis. Gradually upregulated expression patterns of Rac2, Itgb2, and Tyrobp genes at 0, 3, 7, and 14 days after SCI were confirmed based on GSE45006 and GSE20907 data set. Finally, we found that Rac2, Itgb2, and Tyrobp genes might take crucial roles in SCI development through the “natural killer cell–mediated cytotoxicity” pathway.     

应用GSEA和WGCNA方法分析细菌性败血症宿主关键差异基因及意义

【目的】采用生物信息学方法分析公共数据库来源的细菌性败血症患者全血转录组学表达谱,探讨细菌败血症相关的宿主关键差异基因及意义。【方法】基于GEO数据库中GSE80496和GSE72829全血转录组基因数据集,采用GEO2R、基因集富集分析(GSEA)联用加权基因共表达网络分析(WGCNA)筛选细菌性败血症患者相比健康人群显著改变的差异基因,通过R软件对交集基因进行GO功能分析和KEGG富集分析。同时,通过String 11.0和Cytoscape分析枢纽基因,验证枢纽基因在数据集GSE72809(Health组52例,Definedsepsis组52例)全血标本中的表达情况,并探讨婴儿性别、月(胎)龄、出生体重、是否接触抗生素等因素与靶基因表达谱间的关系。【结果】分析GSE80496和GSE72829数据集分别筛选得到932个基因和319个基因,联合WGCNA枢纽模块交集得到与细菌性败血症发病相关的10个枢纽基因(MMP9、ITGAM、CSTD、GAPDH、PGLYRP1、FOLR3、OSCAR、TLR5、IL1RN和TIMP1);GSEA分析获得关键通路(氨基酸糖类-核糖代谢、PPAR信号通路、聚糖生物合成通路、自噬调控通路、补体、凝血因子级联反应、尼古丁和烟酰胺代谢、不饱和脂肪酸生物合成和阿尔兹海默症通路)及生物学过程(类固醇激素分泌、腺苷酸环化酶的激活、细胞外基质降解和金属离子运输)。【结论】本项研究通过GEO2R、GSEA联用WGCNA分析,筛选出与细菌性败血症发病相关的2个枢纽模块、10个枢纽基因以及一些关键信号通路和生物学过程,可为后续深入研究细菌性败血症致病机制奠定理论依据。     

Coexpression network analysis linked H2AFJ to chemoradiation resistance in colorectal cancer

Neoadjuvant chemoradiotherapy (CRT) resistance is a complex phenomenon and it remains a major problem for patients with a priori resistant tumor. Therefore, there is a strong need to investigate molecular biomarkers which may guide for treatment decision-making. In our study, weighted gene coexpression network analysis was applied to identify CRT-resistance hub modules in 12 colorectal cancer (CRC) cell lines with different CRT sensitivities from GSE20298 data set. The green module and purple module had the highest correlations with CRT resistance. Gene ontology enrichment analysis indicated that the function of these two modules focused on interferon-mediated signaling pathway, immune response, chromatin modulation, Rho GTPases activities, and regulation of apoptotic process. Then, 15 hub genes in both the coexpression and protein-protein interaction networks were selected. Among these hub genes, higher H2A histone family member J (H2AFJ) expression was independently validated in patient cohorts from two testing data sets of GSE46862 and GSE68204 to be related to CRT resistance. The receiver operating characteristic curve showed that H2AFJ could efficiently distinguish CRT-resistance cases from CRT-sensitive cases in another two testing data sets. Furthermore, meta-analysis of 12 Gene Expression Omnibus–sourced data sets showed that H2AFJ messenger RNA levels were significantly higher in CRC tissues than in normal colon tissues. High H2AFJ expression was correlated with a significant worse event- and relapse-free survival by analyzing the data from the R2: Genomics Analysis and Visualization Platform. Gene set enrichment analysis determined that the mechanism of H2AFJ-mediated CRT resistance might involve the ERK5 (MAPK7), human immunodeficiency virus Nef (HIV Nef), and inflammatory pathways. This study is the first, to the best of our knowledge, to implicate and verify H2AFJ as an effective new marker for CRT response prediction.     

Systematic prediction of key genes for ovarian cancer by co-expression network analysis

Ovarian cancer (OC) is the most lethal gynaecological malignancy, characterized by high recurrence and mortality. However, the mechanisms of its pathogenesis remain largely unknown, hindering the investigation of the functional roles. This study sought to identify key hub genes that may serve as biomarkers correlated with prognosis. Here, we conduct an integrated analysis using the weighted gene co-expression network analysis (WGCNA) to explore the clinically significant gene sets and identify candidate hub genes associated with OC clinical phenotypes. The gene expression profiles were obtained from the MERAV database. Validations of candidate hub genes were performed with RNASeqV2 data and the corresponding clinical information available from The Cancer Genome Atlas (TCGA) database. In addition, we examined the candidate genes in ovarian cancer cells. Totally, 19 modules were identified and 26 hub genes were extracted from the most significant module (R² = .53) in clinical stages. Through the validation of TCGA data, we found that five hub genes (COL1A1, DCN, LUM, POSTN and THBS2) predicted poor prognosis. Receiver operating characteristic (ROC) curves demonstrated that these five genes exhibited diagnostic efficiency for early-stage and advanced-stage cancer. The protein expression of these five genes in tumour tissues was significantly higher than that in normal tissues. Besides, the expression of COL1A1 was associated with the TAX resistance of tumours and could be affected by the autophagy level in OC cell line. In conclusion, our findings identified five genes could serve as biomarkers related to the prognosis of OC and may be helpful for revealing pathogenic mechanism and developing further research.     

Identification and functional analysis of spermatogenesis-associated gene modules in azoospermia by weighted gene coexpression network analysis

Nonobstructive azoospermia (NOA) or testicular failure is the most severe form of male infertility. A variety of conditions, both acquired and congenital, can cause azoospermia. However, in a large number of azoospermia patients who are classified as idiopathic cases, the etiology remains poorly understand mainly due to the lack of knowledge of all the genetic causes and molecular mechanisms responsible for spermatogenesis failure. Identification of the key gene modules and pathways-related spermatogenesis failure might help to reveal the mechanisms of idiopathic azoospermia. Therefore, the expression patterns of spermatogenesis-associated genes in NOA were analyzed by weighted gene coexpression network analysis (WGCNA) based on two public microarray data sets (GSE45885 and GSE45887), which included 51 samples and 32,321 genes. We identified a module (turquoise) that was significantly related to the Johnsen score of the testicular samples. In addition, the results of function and pathway enrichment analyses based on the online bioinformatics database Metascape revealed that genes in the turquoise module were mainly related to the process of spermatogenesis and spermatid development. To further identify spermatogenesis-associated genes, a microarray data set (GSE926) of murine testis at different developmental time points was analyzed by WGCNA. The blue module in GSE926 was significantly related to the time of murine testis development. The overlap study and k-core analysis based on protein–protein interaction network revealed that spermatogenesis- and spermatid development–associated genes, including glyceraldehyde-3-phosphate dehydrogenase, ADAM metallopeptidase domain 2, transition protein 1, testis-specific serine kinase 2, transition protein 2, and germ cell-associated 1 (GSG1), were further identified in the selected modules. The expression profile of GSG1 in human testis was chosen for further study using immunochemistry staining. Taken together, these screened gene modules and pathways provided a more detailed genetic and molecular mechanism underlying spermatogenesis failure occurrence and holds promise as potential diagnosis biomarkers and therapeutic targets.     

Gene coexpression networks analysis of sickle stroke risk

Stroke is one of the most destructive complications of sickle cell disease (SCD), and SCD is also the most common cause of childhood stroke. Sickle cell stroke is complex and has a genetic endothelial basis. Here, we further investigated this genetic basis using weighted gene coexpression network analysis. This systems biology approach revealed the correlation between coexpressed gene modules and sickle stroke risk. The pink module was significantly correlated with stroke risk and genes in this module were mainly related to GO:0044877 (protein-containing complex binding). In addition hub genes were identified through protein-protein interaction enrichment analysis, including CXCR7, VCAM1, CD44, BMP2, SMAD3, BCL2L1, ITPR2, ITPR3, etc. These hub genes were significantly enriched for three Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways including “gastric acid secretion,” “pathways in cancer,” and “TGF- β signaling pathway.” Altogether, our results based on this innovative method provided some novel understanding of the pathology of sickle cell stroke. Hub genes identified in this study could be potential targets for screening and prevention of stroke risk in SCD children.     

LncRNAs related key pathways and genes in ischemic stroke by weighted gene co-expression network analysis (WGCNA)

BackgroundIschemic stroke (IS) was a significant public health concern and long-chain noncoding RNAs (lncRNAs) were gaining particular importance in stroke biology, however, the potential mechanism of lncRNAs in IS was not fully understood.MethodsIn this study, three diagnosed patients with IS and three controls were selected to establish the lncRNA library. Weighted gene co-expression network analysis (WGCNA) was applied to screen key lncRNA modules associated with IS. The key lncRNAs were identified by module membership (MM) and gene significance (GS). The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was used to identify the key pathways and protein-protein interaction (PPI) network method was used to identify the key genes.ResultsA total of 3627 lncRNAs were investigated, followed by an analysis of 17 modules, and only one module was highly associated with the IS. The top 10 lncRNAs were identified based on GS and MM. KEGG pathways analysis revealed the top two pathways of the Human T cell Lymphotropic Virus-1 (HTLV-1) infection and the mTOR signaling pathway might influence the progress of IS. Further, genes meeting the top two degree (AKT1 and MAPK14) were selected as the hub genes in the PPI network.ConclusionTo summarize, this study identified the key pathways and genes, which might serve as biomarkers and targets for precise diagnosis and treatment of IS in the future.     

Fifteen hub genes associated with progression and prognosis of clear cell renal cell carcinoma identified by coexpression analysis

Renal cell carcinoma (RCC) is the most common type of renal tumor, and the clear cell renal cell carcinoma (ccRCC) is the most frequent subtype. In this study, our aim is to identify potential biomarkers that could effectively predict the prognosis and progression of ccRCC. First, we used The Cancer Genome Atlas (TCGA) RNA-sequencing (RNA-seq) data of ccRCC to identify 2370 differentially expressed genes (DEGs). Second, the DEGs were used to construct a coexpression network by weighted gene coexpression network analysis (WGCNA). Moreover, we identified the yellow module, which was strongly related to the histologic grade and pathological stage of ccRCC. Then, the functional annotation of the yellow module and single-samples gene-set enrichment analysis of DEGs were performed and mainly enriched in cell cycle. Subsequently, 18 candidate hub genes were screened through WGCNA and protein–protein interaction (PPI) network analysis. After verification of TCGA’s ccRCC data set, Gene Expression Omnibus (GEO) data set (GSE73731) and tissue validation, we finally identified 15 hub genes that can actually predict the progression of ccRCC. In addition, by using survival analysis, we found that patients of ccRCC with high expression of each hub gene were more likely to have poor prognosis than those with low expression. The receiver operating characteristic curve showed that each hub gene could effectively distinguish between localized and advanced ccRCC. In summary, our study indicates that 15 hub genes have great predictive value for the prognosis and progression of ccRCC, and may contribute to the exploration of the pathogenesis of ccRCC.