Weighted Gene Co-expression Network Analysis of Endometriosis and Identification of Functional Modules Associated With Its Main Hallmarks

Abstract

Although many genes have been identified using high throughput technologies in endometriosis (ES), only a small number of individual genes have been analyzed functionally. This is due to the complexity of the disease that has different stages and is affected by various genetic and environmental factors. Many genes are upregulated or downregulated at each stage of the disease, thus making it difficult to identify key genes. In addition, little is known about the differences between the different stages of the disease. We assumed that the study of the identified genes in ES at a system-level can help to better understand the molecular mechanism of the disease at different stages of the development. We used publicly available microarray data containing archived endometrial samples from women with minimal/mild endometriosis (MMES), mild/severe endometriosis (MSES) and without endometriosis. Using weighted gene co-expression analysis (WGCNA), functional modules were derived from normal endometrium (NEM) as the reference sample. Subsequently, we tested whether the topology or connectivity pattern of the modules was preserved in MMES and/or MSES. Common and specific hub genes were identified in non-preserved modules. Accordingly, hub genes were detected in the non-preserved modules at each stage. We identified sixteen co-expression modules. Of the 16 modules, nine were non-preserved in both MMES and MSES whereas five were preserved in NEM, MMES, and MSES. Importantly, two non-preserved modules were found in either MMES or MSES, highlighting differences between the two stages of the disease. Analyzing the hub genes in the non-preserved modules showed that they mostly lost or gained their centrality in NEM after developing the disease into MMES and MSES. The same scenario was observed, when the severeness of the disease switched from MMES to MSES. Interestingly, the expression analysis of the new selected gene candidates including CC2D2A, AEBP1, HOXB6, IER3, and STX18 as well as IGF-1, CYP11A1 and MMP-2 could validate such shifts between different stages. The overrepresented gene ontology (GO) terms were enriched in specific modules, such as genetic disposition, estrogen dependence, progesterone resistance and inflammation, which are known as endometriosis hallmarks. Some modules uncovered novel co-expressed gene clusters that were not previously discovered.

Transcripto‐based network analysis reveals a model of gene activation in tongue squamous cell carcinomas

“…WGCNA is an effective and accurate network‐based bioinformatic approach to detect gene co‐expressed modules and hub genes related to clinical phenotype. And it has been one of the most widely used methods with the rapid development of high‐throughput research methods . This analysis aims to group synergetic expressed genes into a same model according to pairwise correlations between genes, and explore the relationship between these models and clinical phenotypes, which is based on the assumption that the co‐expression genes are likely to be similar in biological functions .…”

“…And it has been one of the most widely used methods with the rapid development of high-throughput research methods. 9 This analysis aims to group synergetic expressed genes into a same model according to pairwise correlations between genes, and explore the relationship between these models and clinical phenotypes, which is based on the assumption that the co-expression genes are likely to be similar in biological functions. 10 Besides, it can be used to analyze the interactions among differentially expressed genes in different modules.…”

Systems biology approach identifies functional modules and regulatory hubs related to secondary metabolites accumulation after transition from autotrophic to heterotrophic growth condition in microalgae

“…To evaluate the module preservation after the transition to heterotrophic condition, module preservation function implemented in the WGCNA R package was applied. As prescribed in previous studies, Zsummary < 5 or medianRank > 8 was used as criteria for consideration of a module as a non-preserved module [25].…”

“…However, the brown module showed non-preservation between different phases (Table 1). Since the connectivity and expression patterns of the co-expressed genes at non-preserved modules are altered [25], they may be related to the over-production of secondary metabolites at the PR phase. Because Grey and Gold modules contain the genes that not assigned to any modules, these modules were excluded from the analysis.…”

“…Biosynthesis of secondary metabolites 0.0003 ATHMT1,HMT1,ATSK1,SK1,FBA2,HEMA1, AtGNA1,GNA1,GME,CSY2,HEMC, CHLM,CLA,CLA1,DEF, DXPS2,DXS, BCE2,DIN3,LTA1,LYC,ALB1V,ALB1,CHLD,PDE166,V157,DHNS,ECHID,PORA, DHDPS2,HCEF1, ACS2,PDE226,PDS,PDS3,ATCAO,CAO,CH1,ALDH2B,ALDH2B7,SHM1,SHMT1,STM,AGT,AGT1,SGAT, EMB2778,FKP1,HMGS,MVA1,HISN5B, CCR2,CRTISO,NOL,AtSS2,SS2,HEMG1,PPO1,PPOX,DELTA,OAT, ACSF,CHL27,CRD1,PFK5,ATUGD1,UGD1,LUT2,PCK1,PEPCK,CAC2,EMB2728,RPE Systems biology approach identifies functional modules and regulatory hubs related to secondary metabolites low or no such connection were found in the corresponding module at LG and TR phases. The potential of nodes with high intra-module connectivity as key and determinant genes in the different biological processes has been approved previously [25]. Moreover, it has been suggested that important nodes in large networks are often not among the whole-network hubs and selection of the hubs in sub-networks (modules) is more efficient than a whole-network.…”