PCGEM1 promotes cell proliferation and migration in endometriosis by targeting miR-124-3p-mediated ANTXR2 expression.

BACKGROUND: Endometriosis, a common gynaecological disease in women, affects 10% of women of childbearing age. Among infertile women, this proportion is as high as 30-50%. Despite the high prevalence of endometriosis, the pathogenesis of endometriosis is still unclear. METHODS: In the present study, bioinformatics analysis and molecular and animal experiments were employed to explore the functions of PCGEM1 in the pathogenesis of endometriosis. We established an endometriosis rat model and isolated endometrial stromal cells (ESCs) and primary normal ESCs (NESCs). Bioinformatics analysis was adopted to study the roles of PCGEM1 in promoting the pathogenesis of endometriosis. Luciferase reporter assays and RNA pull-down assays were carried out to study the mechanism by which PCGEM1 regulates ANTXR2. RESULTS: Our results indicated that PCGEM1 promoted the motility and proliferation of ectopic endometrial cells, and the underlying mechanism was due to the direct binding of PCGEM1 to miR-124-3p to modulate ANTXR2 expression. CONCLUSION: PCGEM1 can influence endometrial stromal cell proliferation and motility and may be a novel therapeutic target for endometriosis.

Integrated bioinformatic analysis of dysregulated microRNA-mRNA co-expression network in ovarian endometriosis.

INTRODUCTION: Ovarian endometriosis is a frequently occurring gynecological disease with large socioeconomic impact. Accumulating evidence has suggested that aberrant miRNA-mRNA interactions are involved in the pathogenesis and progression of ovarian endometriosis. This study aims to identify key miRNAs in ovarian endometriosis by using integrated bioinformatic analysis of a dysregulated miRNA-mRNA co-expression network. MATERIAL AND METHODS: Expression profiling of miRNA and mRNA in three normal endometria and five pairs of ectopic/eutopic endometria from patients with ovarian endometriosis was determined by high-throughput sequencing techniques. The data were then integrated with the public sequencing datasets (GSE105764 and GSE105765) using a non-biased approach and a miRNA-mRNA co-expression regulatory network was constructed by in-depth bioinformatic analysis. RESULTS: The constructed miRNA-mRNA network included 87 functionally DEMs, 482 target mRNAs and 1850 paired miRNA-mRNA regulatory interactions. Specifically, five miRNAs (miR-141-3p, miR-363-3p, miR-577, miR-767-5p, miR-96-5p) were gradually decreased and two miRNAs (miR-493-5p, miR-592) were gradually increased from normal endometria to eutopic endometria, and then ectopic endometria tissues. Importantly, miR-141-3p, miR-363-3p and miR-96-5p belonged to the miR-200 family, miR-106a-363 cluster and miR-183/96/182 cluster, respectively. Their target mRNAs were mainly associated with cell adhesion, locomotion and binding, which are suggested to play vital regulatory roles in the pathogenesis of ovarian endometriosis. CONCLUSIONS: Integrated bioinformatic analysis of the miRNA-mRNA co-expression network defines the crucial roles of the miR-200 family, miR-106a-363 cluster and miR-183/96/182 cluster in the pathogenesis of ovarian endometriosis. Further in-depth functional studies are needed to unveil the molecular mechanisms of these miRNAs, and may provide clues for the optimization of therapeutic strategies for ovarian endometriosis.

Diagnostic gene biomarkers for predicting immune infiltration in endometriosis.

OBJECTIVE: To determine the potential diagnostic markers and extent of immune cell infiltration in endometriosis (EMS). METHODS: Two published profiles (GSE7305 and GSE25628 datasets) were downloaded, and the candidate biomarkers were identified by support vector machine recursive feature elimination analysis and a Lasso regression model. The diagnostic value and expression levels of biomarkers in EMS were verified by quantitative reverse transcription polymerase chain reaction (qRT-PCR) and western blotting, then further validated in the GSE5108 dataset. CIBERSORT was used to estimate the composition pattern of immune cell components in EMS. RESULTS: One hundred and fifty-three differential expression genes (DEGs) were identified between EMS and endometrial with 83 upregulated and 51 downregulated genes. Gene sets related to arachidonic acid metabolism, cytokine-cytokine receptor interactions, complement and coagulation cascades, chemokine signaling pathways, and systemic lupus erythematosus were differentially activated in EMS compared with endometrial samples. Aquaporin 1 (AQP1) and ZW10 binding protein (ZWINT) were identified as diagnostic markers of EMS, which were verified using qRT-PCR and western blotting and validated in the GSE5108 dataset. Immune cell infiltrate analysis showed that AQP1 and ZWINT were correlated with M2 macrophages, NK cells, activated dendritic cells, T follicular helper cells, regulatory T cells, memory B cells, activated mast cells, and plasma cells. CONCLUSION: AQP1 and ZWINT could be regarded as diagnostic markers of EMS and may provide a new direction for the study of EMS pathogenesis in the future.

Gene expression profiles of HTR8-S/Vneo cells after changes in ABCA1 expression.

ABCA1 is expressed in placental trophoblasts, such that when the expression level of ABCA1 changes, the function of trophoblasts dramatically changes. However, the mechanism by which ABCA1 affects the function of trophoblast cells remains unclear. Here, we used biochemical and transcriptomic to uncover the potential mechanism of the effect of ABCA1 on trophoblast function. HTR8/SVneo cells were either treated with the agonist T0901317 or transfected with siRNA to regulate ABCA1 expression levels. A human gene expression microarray was used to analyze the expression spectrum of ABCA1. Microarray results were confirmed by Western blotting and RT-PCR. Immunofluorescence allowed detection of the cellular localization of ABCA1, CCL8, CXCL10, CXCL11, and S1PR1 in HTR8/SVneo cells. Co-immunoprecipitation was used to test interactions among these proteins. Concomitant with an increase in ABCA1 expression, S1PR1 expression increased, whereas expression of CCL8, CXCL10, and CXCL11 decreased significantly; opposite effects were observed with a decrease in ABCA1 expression. Thus, changes in ABCA1 expression may lead to changes in downstream gene expression. Whereas the interaction between ABCA1 and S1PR1 was direct, interactions among ABCA1 and CCL8, CXCL10, and CXCL11 were indirect. We propose that, in conjunction with S1PR1, ABCA1 regulates expression levels of CCL8, CXCL10, and CXCL11; this may lead to changes in the immune function of trophoblastic cells. Thus, we suspect that the effect of ABCA1 on trophoblast function may involve many biological processes, molecular function changes, and the activation of multiple signaling pathways.

Identification and Analysis of Gene Biomarkers for Ovarian Cancer.

Objective: To identify potential diagnostic markers for ovarian cancer (OC) and explore the contribution of immune cells infiltration to the pathogenesis of OC. Methods: As the study cohort, two gene expression datasets of human OC (GSE27651 and GSE26712, taken as the metadata) taken from the Gene Expression Omnibus (GEO) database were combined, comprising 228 OC and 16 control samples. Analysis was performed to identify the differentially expressed genes between the OC and control samples, while support vector machine analysis using the recursive feature elimination algorithm and least absolute shrinkage and selection operator regression were performed to identify candidate biomarkers that could discriminate OC. In addition, immunohistochemistry staining was performed to verify the diagnostic value and protein expression levels of the candidate biomarkers. The GSE146553 dataset (OC n = 40, control n = 3) was used to further validate the diagnostic values of those biomarkers. Further, the proportions of various immune cells infiltration in the OC and control samples were evaluated using the CIBERSORT algorithm. Results: CLEC4M, PFKP, and SCRIB were identified as potential diagnostic markers for OC in both the metadata (area under the receiver operating characteristic curve [AUC] = 0.996, AUC = 1.000, AUC = 1.000) and GSE146553 dataset (AUC = 0.983, AUC = 0.975, AUC = 0.892). Regarding immune cell infiltration, there was an increase in the infiltration of follicular helper dendritic cells, and a decrease in the infiltration of M2 macrophages and neutrophils, as well as activated natural killer (NK) cells and T cells in OC. CLEC4M showed a significantly positive correlation with neutrophils (r = 0.57, p < 0.001) and resting NK cells (r = 0.42, p = 0.0047), but a negative correlation with activated dendritic cells (r = -0.33, p = 0.032). PFKP displayed a significantly positive correlation with activated NK cells (r = 0.36, p = 0.016) and follicular helper T cells (r = 0.32, p = 0.035), but a negative correlation with the naive B cells (r = -0.3, p = 0.049) and resting NK cells (r = -0.41, p = 0.007). SCRIB demonstrated a significantly positive correlation with plasma cells (r = 0.39, p = 0.01), memory B cells (r = 0.34, p = 0.025), and follicular helper T cells (r = 0.31, p = 0.04), but a negative correlation with neutrophils (r = -0.46, p = 0.002) and naive B cells (r = -0.48, p = 0.0012). Conclusion: CLEC4M, PFKP, and SCRIB were identified and verified as potential diagnostic biomarkers for OC. This work and identification of the three biomarkers may provide guidance for future studies into the mechanism and treatment of OC.

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

Background: Endometriosis is a common gynecological disorder that usually causes infertility, pelvic pain, and ovarian masses. This study aimed to mine the characteristic genes of endometriosis, and explore the regulatory mechanism and potential therapeutic drugs based on whole transcriptome sequencing data and resources from public databases, providing a theoretical basis for the diagnosis and treatment of endometriosis. Methods: The transcriptome data of the five eutopic (EU) and ectopic (EC) endometrium samples were obtained from Beijing Obstetrics and Gynecology Hospital, Beijing, China, and dinified as the own data set. The expression and clinical data of EC and EU samples in GSE25628 and GSE7305 datasets were obtained from the GEO database (https://www.ncbi.nlm.nih.gov/gds). Differential gene expression analysis and weighted gene co-expression network analysis (WGCNA) were used to identify the endometriosis-related differentially expressed genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted by the "clusterProfiler" R package. Then, characteristic genes for endometriosis were identified by the least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) algorithm. The expression of characteristic genes was verified by quantitative reverse transcription polymerase chain reaction (qRT-PCR) and western-blot. The receiver operating characteristic (ROC) curve was used to evaluate the discriminatory ability of characteristic genes. We assessed the abundance of infiltrating immune cells in each sample using MCP-counter and ImmuCellAI algorithms. The competitive endogenous RNA (ceRNA) regulatory network of characteristic genes was created by Cytoscape and potential targeting drugs were obtained in the CTD database. Results: 44 endometriosis-related differentially expressed genes were obtained from GSE25628 and the own dataset. Subsequently, LASSO and SVM-RFE algorithms identified four characteristic genes, namely ACLY, PTGFR, ADH1B, and MYOM1. The results of RT-PCR and western-blot were consistent with those of sequencing. The result of ROC curves indicated that the characteristic genes had powerful abilities in distinguishing EC samples from EU samples. Infiltrating immune cells analysis suggested that there was a certain difference in immune microenvironment between EC and EU samples. The characteristic genes were significantly correlated with specific differential immune cells between EC and EU samples. Then, a ceRNA regulatory network of characteristic genes was constructed and showed a total of 7, 11, 11, and 1 miRNA associated with ACLY, ADH1B, PTGFR, and MYOM1, respectively. Finally, we constructed a gene-compound network and mined 30 drugs targeting ACLY, 33 drugs targeting ADH1B, 13 drugs targeting MYOM1, and 12 drugs targeting PTGFR. Conclusion: Comprehensive bioinformatic analysis was used to identify characteristic genes, and explore ceRNA regulatory network and potential therapeutic agents for endometriosis. Altogether, these findings provide new insights into the diagnosis and treatment of endometriosis.