Dataset Information

Endometriosis gene expression heterogeneity and biosignature: a phylogenetic analysis.

ABSTRACT: Endometriosis is a multifactorial disease with poorly understood etiology, and reflecting an evolutionary nature where genetic alterations accumulate throughout pathogenesis. Our objective was to characterize the heterogeneous pathological process using parsimony phylogenetics. Gene expression microarray data of ovarian endometriosis obtained from NCBI database were polarized and coded into derived (abnormal) and ancestral (normal) states. Such alterations are referred to as synapomorphies in a phylogenetic sense (or biomarkers). Subsequent gene linkage was modeled by Genomatix BiblioSphere Pathway software. A list of clonally shared derived (abnormal) expressions revealed the pattern of heterogeneity among specimens. In addition, it has identified disruptions within the major regulatory pathways including those involved in cell proliferation, steroidogenesis, angiogenesis, cytoskeletal organization and integrity, and tumorigenesis, as well as cell adhesion and migration. Furthermore, the analysis supported the potential central involvement of ESR2 in the initiation of endometriosis. The pathogenesis mapping showed that eutopic and ectopic lesions have different molecular biosignatures.

SUBMITTER: Abu-Asab M

PROVIDER: S-EPMC3238413 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Endometriosis gene expression heterogeneity and biosignature: a phylogenetic analysis.

Abu-Asab Mones M Zhang Ming M Amini Dennis D Abu-Asab Nihad N Amri Hakima H

Obstetrics and gynecology international 20111213

Endometriosis is a multifactorial disease with poorly understood etiology, and reflecting an evolutionary nature where genetic alterations accumulate throughout pathogenesis. Our objective was to characterize the heterogeneous pathological process using parsimony phylogenetics. Gene expression microarray data of ovarian endometriosis obtained from NCBI database were polarized and coded into derived (abnormal) and ancestral (normal) states. Such alterations are referred to as synapomorphies in a ...[more]

PMID: 22203846

Similar Datasets

Project description:BackgroundEndometriosis is recognized as an estrogen-dependent inflammation disorder, estimated to affect 8%-15% of women of childbearing age. Currently, the etiology and pathogenesis of endometriosis are not completely clear. Underlying mechanism for endometriosis is still under debate and needs further exploration. The involvement of transcription factors and immune mediations may be involved in the pathophysiological process of endometriosis, but the specific mechanism remains to be explored. This study aims to investigate the underlying molecular mechanisms in endometriosis.MethodsThe gene expression profile of endometriosis was obtained from the gene expression omnibus (GEO) database. Gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) were applied to the endometriosis GSE7305 datasets. Cibersort and MCP-counter were used to explore the immune response gene sets, immune response pathway, and immune environment. Differentially expressed genes (DEGs) were identified and screened. Common biological pathways were being investigated using the kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis. Transcription factors were from The Human Transcription Factors. The least absolute shrinkage and selection operator (Lasso) model identified four differential expressions of transcription factors (AEBP1, HOXB6, KLF2, and RORB). Their diagnostic value was calculated by receiver operating characteristic (ROC) curve analysis and validated in the validation cohort (GSE11691, GSE23339). By constructing the interaction network of crucial transcription factors, weighted gene coexpression network analysis (WGCNA) was used to search for key module genes. Metascape was used for enrichment analysis of essential module genes and obtained HOXB6, KLF2. The HOXB6 and KLF2 were further verified as the only two intersection genes according to Support Vector Machine Recursive Feature Elimination (SVM-RFE) and random forest models. We constructed ceRNA (lncRNA-miRNA-mRNA) networks with four potential transcription factors. Finally, we performed molecular docking for goserelin and dienogest with four transcription factors (AEBP1, HOXB6, KLF2, and RORB) to screen potential drug targets.ResultsImmune and metabolic pathways were enriched in GSVA and GSEA. In single sample gene set enrichment analysis (ssGSEA), most immune infiltrating cells, immune response gene sets, and immune response pathways are differentially expressed between endometriosis and non-endometriosis. Twenty-seven transcription factors were screened from differentially expressed genes. Most of the twenty-seven transcription factors were correlated with immune infiltrating cells, immune response gene sets and immune response pathways. Furthermore, Adipocyte enhancer binding protein 1 (AEBP1), Homeobox B6 (HOXB6), Kruppel Like Factor 2 (KLF2) and RAR Related Orphan Receptor B (RORB) were selected out from twenty-seven transcription factors. ROC analysis showed that the four genes had a high diagnostic value for endometriosis. In addition, KLF2 and HOXB6 were found to play particularly important roles in multiple modules (String, WGCNA, SVM-RFE, random forest) on the gene interaction network. Using the ceRNA network, we found that NEAT1 may regulate the expressions of AEBP1, HOXB6 and RORB, while X Inactive Specific Transcript (XIST) may control the expressions of HOXB6, RORB and KLF2. Finally, we found that goserelin and dienogest may be potential drugs to regulate AEBP1, HOXB6, KLF2 and RORB through molecular docking.ConclusionsAEBP1, HOXB6, KLF2, and RORB may be potential biomarkers for endometriosis. Two of them, KLF2 and HOXB6, are critical molecules in the gene interaction network of endometriosis. Discovered by molecular docking, AEBP1, HOXB6, KLF2, and RORB are targets for goserelin and dienogest.

Project description:Background: Endometriosis is a common gynecological disorder with high rates of infertility and pelvic pain. However, its pathogenesis and diagnostic biomarkers remain unclear. This study aimed to elucidate potential hub genes and key pathways associated with endometriosis in ectopic endometrium (EC) and eutopic endometrium (EU). Material and Method: EC and EU-associated microarray datasets were obtained from the gene expression omnibus (GEO) database. Gene set enrichment analysis was performed to obtain further biological insight into the EU and EC-associated genes. Weighted gene co-expression network analysis (WGCNA) was performed to find clinically significant modules of highly-correlated genes. The hub genes that belong to both the weighted gene co-expression network and protein-protein interaction (PPI) network were identified using a Venn diagram. Results: We obtained EC and EU-associated microarray datasets GSE7305 and GSE120103. Genes in the EC were mainly enriched in the immune response and immune cell trafficking, and genes in the EU were mainly enriched in stress response and steroid hormone biosynthesis. PPI networks and weighted gene co-expression networks were constructed. An EC-associated blue module and an EU-associated magenta module were identified, and their function annotations revealed that hormone receptor signaling or inflammatory microenvironments may promote EU passing through the oviducts and migrating to the ovarian surfaces, and adhesion and immune correlated genes may induce the successful ectopic implantation of the endometrium (EC). Twelve hub genes in the EC and sixteen hub genes in the EU were recognized and further validated in independent datasets. Conclusion: Our study identified, for the first time, the hub genes and enrichment pathways in the EC and EU using WGCNA, which may provide a comprehensive understanding of the pathogenesis of endometriosis and have important clinical implications for the treatment and diagnosis of endometriosis.

Dataset Information

Endometriosis gene expression heterogeneity and biosignature: a phylogenetic analysis.

Publications

Endometriosis gene expression heterogeneity and biosignature: a phylogenetic analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets