Common and specific gene signatures among three different endometriosis subtypes.

To identify the common and specific molecular mechanisms of three well-defined subtypes of endometriosis (EMs): ovarian endometriosis (OE), peritoneal endometriosis (PE), and deep infiltrating endometriosis (DIE). Four microarray datasets: GSE7305 and GSE7307 for OE, E-MTAB-694 for PE, and GSE25628 for DIE were downloaded from public databases and conducted to compare ectopic lesions (EC) with eutopic endometrium (EU) from EMs patients. Differentially expressed genes (DEGs) identified by limma package were divided into two parts: common DEGs among three subtypes and specific DEGs in each subtype, both of which were subsequently performed with the Kyoto Encyclopedia of Genes (KEGG) pathway enrichment analysis. The protein-protein interaction (PPI) network was constructed by common DEGs and five hub genes were screened out from the PPI network. Besides, these five hub genes together with selected interested pathway-related genes were further validated in an independent OE RNA-sequencing dataset GSE105764. A total of 54 EC samples from three EMs subtypes (OE, PE, DIE) and 58 EU samples were analyzed, from which we obtained 148 common DEGs among three subtypes, and 729 specific DEGs in OE, 777 specific DEGs in PE and 36 specific DEGs in DIE. The most enriched pathway of 148 shared DEGs was arachidonic acid (AA) metabolism, in which most genes were up-regulated in EC, indicating inflammation was the most common pathogenesis of three subtypes. Besides, five hub genes AURKB, RRM2, DTL, CCNB1, CCNB2 identified from the PPI network constructed by 148 shared DEGs were all associated with cell cycle and mitosis, and down-regulated in EC, suggesting a slow and controlled proliferation in ectopic lesions. The KEGG pathway analysis of specific DEGs in each subtype revealed that abnormal ovarian steroidogenesis was a prominent feature in OE; OE and DIE seems to be at more risk of malignant development since both of their specific DEGs were enriched in the pathways in cancer, though enriched genes were different, while PE tended to be more associated with dysregulated peritoneal immune and inflammatory microenvironment. By integrated bioinformatic analysis, we explored common and specific molecular signatures among different subtypes of endometriosis: activated arachidonic acid (AA) metabolism-related inflammatory process and a slow and controlled proliferation in ectopic lesions were common features in OE, PE and DIE; OE and DIE seemed to be at more risk of malignant development while PE tended to be more associated with dysregulated peritoneal immune and inflammatory microenvironment, all of which could deepen our perception of endometriosis.

Jiang L ，Zhang M ，Wang S ，Han Y ，Fang X ... -  《PeerJ》

Integrated bioinformatic analysis reveals the gene signatures, epigenetic roles, and regulatory networks in endometriosis.

Amanda CR ，Fadilah ，Hestiantoro A ，Muharam R ，Suryandari DA ，Tulandi T ，Asmarinah ... -  《-》

Analysis of characteristic genes and ceRNA regulation mechanism of endometriosis based on full transcriptional sequencing.

Xie C ，Yin Z ，Liu Y 《Frontiers in Genetics》

Identification of functional TF-miRNA-hub gene regulatory network associated with ovarian endometriosis.

Endometriosis (EMs), one of the most common gynecological diseases, seriously affects the health and wellness of women; however, the underlying pathogenesis remains unclear. This study focused on dysregulated genes and their predicted transcription factors (TFs) and miRNAs, which may provide ideas for further mechanistic research. The microarray expression dataset GSE58178, which included six ovarian endometriosis (OE) samples and six control samples, was downloaded from the Gene Expression Omnibus (GEO) to identify differentially expressed genes (DEGs). Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to study the cellular and organism-level functions of DEGs. The protein-protein interaction (PPI) network was built and visualized using Cytoscape, and modules and hub genes were explored using various algorithms. Furthermore, we predicted miRNAs and TFs of hub genes using online databases, and constructed the TF-miRNA-hub gene network. There were 124 upregulated genes and 66 downregulated genes in EMs tissues. GO enrichment analysis showed that DEGs were concentrated in reproductive structure development and collagen-containing extracellular matrix, while KEGG pathway analysis showed that glycolysis/gluconeogenesis and central carbon metabolism in cancer require further exploration. Subsequently, HIF1A, LDHA, PGK1, TFRC, and CD9 were identified as hub genes, 22 miRNAs and 34 TFs were predicted to be upstream regulators of hub genes, and these molecules were pooled together. In addition, we found three key feedback loops in the network, MYC-miR-34a-5p-LDHA, YY1-miR-155-5p-HIF1A, and RELA-miR-93-5p-HIF1A, which may be closely related to OE development. Taken together, our study structured a TF-miRNA-hub gene network to decipher the molecular mechanism of OE, which may provide novel insights for clinical diagnosis and treatment.

Li L ，Sun B ，Sun Y 《Frontiers in Genetics》

Bioinformatic analysis of key pathways and genes shared between endometriosis and ovarian cancer.

The purpose of the study is to identify potential key genes and pathways, using bioinformatics, underlying the potentially common molecular mechanisms between endometriosis (EMS) and ovarian cancer (OC). Two datasets were collected from the Gene Expression Omnibus database, and the limma package identified common differentially expressed genes (DEGs) in the EMS and OC groups compared to controls. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, gene interaction network, and module analyses identified the enriched pathways associated with DEGs. A protein-protein interaction (PPI) network was then constructed, and the CytoHubba plugin of Cytoscape was used to calculate the degree of connectivity for proteins in the PPI network. A total of 571 overlapping DEGs were identified between EMS and OC (vs. controls). Enriched DEGs were associated with 36 gene ontology terms and 7 Kyoto Encyclopedia of Genes and Genomes pathways, which were mainly associated with deactivation of the p53 signaling pathway. The Kaplan-Meier plotter platform confirmed the expression of the identified hub genes, and survival analysis suggested that CCNB1, CCNB2, BUB1B, CCNA2, KIF2C, and TOP2A are associated with decreased survival and disease-free survival rates of OC. The key pathways identified herein elucidate the possible mechanism by which EMS evolves into OC; further, the identified hub genes may serve as potential biomarkers to predict OC occurrence and prognosis.

Ni L ，Chen Y ，Yang J ，Chen C ... -  《-》