A relational database to identify differentially expressed genes in the endometrium and endometriosis lesions.
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SUBMITTER: Gabriel M
PROVIDER: S-EPMC7455745 | biostudies-literature | 2020 Aug
REPOSITORIES: biostudies-literature
Publications
A relational database to identify differentially expressed genes in the endometrium and endometriosis lesions.
Gabriel Michael M Fey Vidal V Heinosalo Taija T Adhikari Prem P Rytkönen Kalle K Komulainen Tuomo T Huhtinen Kaisa K Laajala Teemu Daniel TD Siitari Harri H Virkki Arho A Suvitie Pia P Kujari Harry H Aittokallio Tero T Perheentupa Antti A Poutanen Matti M
Scientific data 20200828 1
Endometriosis is a common inflammatory estrogen-dependent gynecological disorder, associated with pelvic pain and reduced fertility in women. Several aspects of this disorder and its cellular and molecular etiology remain unresolved. We have analyzed the global gene expression patterns in the endometrium, peritoneum and in endometriosis lesions of endometriosis patients and in the endometrium and peritoneum of healthy women. In this report, we present the EndometDB, an interactive web-based user ...[more]
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Project description:AimEndometriosis is one of the most common reproductive system diseases, but the mechanisms of disease progression are still unclear. Due to its high recurrence rate, searching for potential therapeutic biomarkers involved in the pathogenesis of endometriosis is an urgent issue.MethodsDue to the similarities between endometriosis and ovarian cancer, four endometriosis datasets and one ovarian cancer dataset were downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified, followed by gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and protein-protein interaction (PPI) analyses. Then, we validated gene expression and performed survival analysis with ovarian serous cystadenocarcinoma (OV) datasets in TCGA/GTEx database, and searched for potential drugs in the Drug-Gene Interaction Database. Finally, we explored the miRNAs of key genes to find biomarkers associated with the recurrence of endometriosis.ResultsIn total, 104 DEGs were identified in the endometriosis datasets, and the main enriched GO functions included cell adhesion, extracellular exosome and actin binding. Fifty DEGs were identified between endometriosis and ovarian cancer datasets including 11 consistently regulated genes, and nine DEGs with significant expression in TCGA/GTEx. Only IGHM had both significant expression and an association with survival, three module DEGs and two significantly expressed DEGs had drug associations, and 10 DEGs had druggability.ConclusionsITGA7, ITGBL1 and SORBS1 may help us understand the invasive nature of endometriosis, and IGHM might be related to recurrence; moreover, these genes all may be potential therapeutic targets.KEY MESSAGEThis manuscript used a bioinformatics approach to find target genes for the treatment of endometriosis.This manuscript used a new approach to find target genes by drawing on common characteristics between ovarian cancer and endometriosis.We screened relevant therapeutic agents for target genes in the drug database, and performed histological validation of target genes with both expression and survival analysis difference in cancer databases.
| S-EPMC8381947 | biostudies-literature
Project description:The pathogenesis of endometriosis may result from aberrant angiogenesis that occurs in eutopic endometrium with retrograde menstruation. The difference in gene expression profile between human endometrial endothelial cells (HEECs) from eutopic endometria of patients with and without endometriosis would be determinant that affects the occurrence of endometriosis. To explore this kind of difference, we performed in vitro culture and identified their endothelial origin, as well as observed growth features of HEECs from the two different origins. Finally we identified the difference in gene expression profile when combined suppression subtractive hybridization(SSH) with genechip and confirmed the results by quantitative reverse transcription polymerase chain reaction (qRT-PCR). The HEECs derived from endometriosis exhibited potent survival ability in vitro compared to that from non-endometriosis. We found that gremlin and fibronectin genes were up-regulated in HEECs derived from eutopic endometrium of patients with endometriosis when compared with that from patients without endometriosis. Our study implies that enhanced angiogenic capacity of eutopic HEECs may be an independent determinant in endometriotic aberrant angiogenesis in addition to the interaction of exfoliated endometrium and peritoneal environment elements such as activated macrophages and soluble cytokines. Keywords: human endometrial endothelial cells (HEECs),endometriosis,differentially expressed genes
2007-10-31 | GSE7846 | GEO
Project description:Increasing evidence has indicated that modulation of epigenetic mechanisms, especially methylation and long-non-coding RNA (lncRNA) regulation, plays a pivotal role in the process of atherosclerosis; however, few studies focused on revealing the epigenetic-related subgroups during atherosclerotic progression using unsupervised clustering analysis. Hence, we aimed to identify the epigenetics-related differentially expressed genes associated with atherosclerosis subtypes and characterize their clinical utility in atherosclerosis. Eighty samples with expression data (GSE40231) and 49 samples with methylation data (GSE46394) from a large artery plaque were downloaded from the GEO database, and aberrantly methylated-differentially expressed (AMDE) genes were identified based on the relationship between methylation and expression. Furthermore, we conducted weighted correlation network analysis (WGCNA) and co-expression analysis to identify the core AMDE genes strongly involved in atherosclerosis. K-means clustering was used to characterize two subtypes of atherosclerosis in GSE40231, and then 29 samples were recognized as validation dataset (GSE28829). In a blood sample cohort (GSE90074), chi-square test and logistic analysis were performed to explore the clinical implication of the K-means clusters. Furthermore, significance analysis of microarrays and prediction analysis of microarrays (PAM) were applied to identify the signature AMDE genes. Moreover, the classification performance of signature AMDE gene-based classifier from PAM was validated in another blood sample cohort (GSE34822). A total of 1,569 AMDE mRNAs and eight AMDE long non-coding RNAs (lncRNAs) were identified by differential analysis. Through the WGCNA and co-expression analysis, 32 AMDE mRNAs and seven AMDE lncRNAs were identified as the core genes involved in atherosclerosis development. Functional analysis revealed that AMDE genes were strongly related to inflammation and axon guidance. In the clinical analysis, the atherosclerotic subtypes were associated with the severity of coronary artery disease and risk of adverse events. Eight genes, including PARP15, SERGEF, PDGFD, MRPL45, UBR1, STAU1, WIZ, and LSM4, were selected as the signature AMDE genes that most significantly differentiated between atherosclerotic subtypes. Ultimately, the area under the curve of signature AMDE gene-based classifier for atherosclerotic subtypes was 0.858 and 0.812 in GSE90074 and GSE34822, respectively. This study identified the AMDE genes (lncRNAs and mRNAs) that could be implemented in clinical clustering to recognize high-risk atherosclerotic patients.
| S-EPMC7767999 | biostudies-literature
Project description:The aim of the present study was to screen the differentially expressed genes (DEGs) associated with glaucoma and investigate the changing patterns of the expression of these genes. The GSE2378 gene microarray data of glaucoma was downloaded from the Gene Expression Omnibus database, which included seven normal samples and eight glaucoma astrocyte samples. Taking into account the corresponding associations between the probe ID and gene symbols, the DEGs were identified prior to and subsequent to the summation of probe level values using the Limma package in R language, followed by Gene Ontology (GO) and pathway enrichment analyses. Interaction networks of the DEGs were constructed using the Biomolecular Interaction Network Database, and cluster analysis of the genes in the networks was performed using ClusterONE. Subsequent to the summation of probe value, a total of 223 genes were identified as DEGs between the normal and glaucoma samples, including 74 downregulated and 149 upregulated genes. In addition, the DEGs were found to be associated with several functions, including response to wounding, extracellular region part and calcium ion binding. The most significantly enriched pathways were complement and coagulation cascades, arrhythmogenic right ventricular cardiomyopathy and extracellular matrix (ECM)‑receptor interaction. Furthermore, interaction networks were constructed of the DEGs prior to and subsequent to the summation of probe values, and HNF4A and CEBPD were identified as hub genes. Additionally, 37 and 31 GO terms were identified to be enriched in the two DEGs of the networks prior to and subsequent to summation, respectively. The results indicated the identified genes associated with ECM as important, and the CEBPD gene was considered to be a critical gene in glaucoma. The findings of the present study offer a potential reference value in further investigations of glaucoma at the gene level.
| S-EPMC4581750 | biostudies-literature
Project description:BackgroundOsteoarthritis (OA) is the most common degenerative disease in orthopedics. However, the cause and underlying molecular mechanisms are not clear. This study aims to identify the hub genes and pathways involved in the occurrence of osteoarthritis.MethodsThe raw data of GSE89408 were downloaded from the Gene Expression Omnibus (GEO) database, and the differentially expressed genes (DEGs) were identified by R software. The DAVID database was used for pathway and gene ontology analysis, and p<0.05 and gene count >2 were set as the cut-off point. Moreover, protein-protein interaction (PPI) network construction was applied for exploring the hub genes in osteoarthritis. The expression levels of the top ten hub genes in knee osteoarthritis synovial membranes and controls were detected by quantitative real-time PCR system.ResultsA total of 229 DEGs were identified in osteoarthritis synovial membranes compared with normal synovial membranes, including 145 upregulated and 84 downregulated differentially expressed genes. The KEGG pathway analysis results showed that up-DEGs were enriched in proteoglycans in cytokine-cytokine receptor interaction, chemokine signaling pathway, rheumatoid arthritis, and TNF signaling pathway, whereas down-DEGs were enriched in the PPAR signaling pathway and AMPK signaling pathway. The qRT-PCR results showed that the expression levels of ADIPOQ, IL6, and CXCR1 in the synovium of osteoarthritis were significantly increased (p <0.05).
| S-EPMC6500622 | biostudies-literature
Project description:Microarray experiments typically analyze thousands to tens of thousands of genes from small numbers of biological replicates. The fact that genes are normally expressed in functionally relevant patterns suggests that gene-expression data can be stratified and clustered into relatively homogenous groups. Cluster-wise dimensionality reduction should make it feasible to improve screening power while minimizing information loss.We propose a powerful and computationally simple method for finding differentially expressed genes in small microarray experiments. The method incorporates a novel stratification-based tight clustering algorithm, principal component analysis and information pooling. Comprehensive simulations show that our method is substantially more powerful than the popular SAM and eBayes approaches. We applied the method to three real microarray datasets: one from a Populus nitrogen stress experiment with 3 biological replicates; and two from public microarray datasets of human cancers with 10 to 40 biological replicates. In all three analyses, our method proved more robust than the popular alternatives for identification of differentially expressed genes.The C++ code to implement the proposed method is available upon request for academic use.
| S-EPMC3607310 | biostudies-literature
Project description:BackgroundIdentifying differentially expressed genes (DEG) is a fundamental step in studies that perform genome wide expression profiling. Typically, DEG are identified by univariate approaches such as Significance Analysis of Microarrays (SAM) or Linear Models for Microarray Data (LIMMA) for processing cDNA microarrays, and differential gene expression analysis based on the negative binomial distribution (DESeq) or Empirical analysis of Digital Gene Expression data in R (edgeR) for RNA-seq profiling.ResultsHere we present a new geometrical multivariate approach to identify DEG called the Characteristic Direction. We demonstrate that the Characteristic Direction method is significantly more sensitive than existing methods for identifying DEG in the context of transcription factor (TF) and drug perturbation responses over a large number of microarray experiments. We also benchmarked the Characteristic Direction method using synthetic data, as well as RNA-Seq data. A large collection of microarray expression data from TF perturbations (73 experiments) and drug perturbations (130 experiments) extracted from the Gene Expression Omnibus (GEO), as well as an RNA-Seq study that profiled genome-wide gene expression and STAT3 DNA binding in two subtypes of diffuse large B-cell Lymphoma, were used for benchmarking the method using real data. ChIP-Seq data identifying DNA binding sites of the perturbed TFs, as well as known drug targets of the perturbing drugs, were used as prior knowledge silver-standard for validation. In all cases the Characteristic Direction DEG calling method outperformed other methods. We find that when drugs are applied to cells in various contexts, the proteins that interact with the drug-targets are differentially expressed and more of the corresponding genes are discovered by the Characteristic Direction method. In addition, we show that the Characteristic Direction conceptualization can be used to perform improved gene set enrichment analyses when compared with the gene-set enrichment analysis (GSEA) and the hypergeometric test.ConclusionsThe application of the Characteristic Direction method may shed new light on relevant biological mechanisms that would have remained undiscovered by the current state-of-the-art DEG methods. The method is freely accessible via various open source code implementations using four popular programming languages: R, Python, MATLAB and Mathematica, all available at: http://www.maayanlab.net/CD.
| S-EPMC4000056 | biostudies-literature
Project description:UnlabelledLysyl oxidases (LOXs) are enzymes involved in collagen deposition, extracellular membrane remodeling, and invasive/metastatic potential. Previous studies reveal an association of LOXs and endometriosis. We aimed to identify the mechanisms activated by upregulation of lysyl oxidases (LOX) in endometriotic cells and tissues. We hypothesized that LOX plays a role in endometriosis by promoting invasiveness and epithelial to mesenchymal transition (EMT).MethodsThe LOX protein expression levels were measured by immunohistochemistry in lesions and endometrium on a tissue microarray (TMA) and in endometrial biopsies from patients and controls during the window of implantation (WOI). Estradiol regulation of LOX expression was determined by quantitative polymerase chain reaction (qPCR). Proliferation, invasion, and migration assays were performed in epithelial (endometrial epithelial cell), endometrial (human endometrial stromal cell), and endometriotic cell lines (ECL and 12Z). Pathway-focused multiplex qPCR was used to determine transcriptome changes due to LOX overexpression.ResultsLOX protein was differentially expressed in ovarian versus peritoneal lesions. During WOI, LOX levels were higher in luminal epithelium of patients with endometriosis-associated infertility compared to controls. Invasive epithelial cell lines expressed higher levels of LOX than noninvasive ones. Transfection of LOX into noninvasive epithelial cells increased their migration in an LOX inhibitor-sensitive manner. Overexpression of LOX did not fully induce EMT but the expression of genes related to fibrosis and extracellular matrix remodeling were dysregulated.ConclusionsThis study documents that expression of LOX is differentially regulated in endometriotic lesions and endometrium. A role for LOX in mediating proliferation, migration, and invasion of endometrial and endometriotic cells was observed, which may be implicated in the establishment and progression of endometriotic lesions.
| S-EPMC5933196 | biostudies-literature
Project description:Endometriosis is a common gynecological disease characterized by the presence and growth of endometrial tissue outside the uterus, including the pelvis and abdominal cavity. This condition causes various clinical symptoms, such as non-menstrual pelvic pain, dysmenorrhea and infertility, seriously affecting the health and quality of life of women. To date, the specific mechanism and the key molecules of endometriosis remain uncertain. The purpose of the present study was to elucidate the mechanisms involved in the development and persistence of the disease. A number of mRNA expression profile datasets (namely GSE11691, GSE23339, GSE25628 and GSE78851) were downloaded from the Gene Expression Omnibus (GEO) database. These gene expression profiles were normalized, and the differentially expressed genes (DEGs) were identified by integrated bioinformatics analysis. A total of 103 DEGs were screened upon excluding the genes that exhibited inconsistency of expression (P<0.05). Furthermore, the Gene Ontology analysis, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, and construction of protein-protein interaction networks of DEGs were performed using online software. The results revealed that the DEGs were closely associated with cell migration, adherens junction and hypoxia-inducible factor signaling. In addition, immunohistochemical assay results were found to be consistent with the bioinformatics results. The present study may help us understand underlying molecular mechanisms and the development of endometriosis, which has a great clinical significance for early diagnosis of the disease.
| S-EPMC6909483 | biostudies-literature
Project description:BackgroundMeta-analysis of gene expression array databases has the potential to reveal information about gene function. The identification of gene-gene interactions may be inferred from gene expression information but such meta-analysis is often limited to a single microarray platform. To address this limitation, we developed a gene-centered approach to analyze differential expression across thousands of gene expression experiments and created the CO-Regulation Database (CORD) to determine which genes are correlated with a queried gene.ResultsUsing the GEO and ArrayExpress database, we analyzed over 120,000 group by group experiments from gene microarrays to determine the correlating genes for over 30,000 different genes or hypothesized genes. CORD output data is presented for sample queries with focus on genes with well-known interaction networks including p16 (CDKN2A), vimentin (VIM), MyoD (MYOD1). CDKN2A, VIM, and MYOD1 all displayed gene correlations consistent with known interacting genes.ConclusionsWe developed a facile, web-enabled program to determine gene-gene correlations across different gene expression microarray platforms. Using well-characterized genes, we illustrate how CORD's identification of co-expressed genes contributes to a better understanding a gene's potential function. The website is found at http://cord-db.org.
| S-EPMC3944024 | biostudies-literature