BackgroundThe etiology and pathophysiology of endometriosis remain unclear. Accumulating evidence suggests that aberrant microRNA (miRNA) and transcription factor (TF) expression may be involved in the pathogenesis and development of endometriosis. This study therefore aims to survey the key miRNAs, TFs and genes and further understand the mechanism of endometriosis.MethodsPaired expression profiling of miRNA and mRNA in ectopic endometria compared with eutopic endometria were determined by high-throughput sequencing techniques in eight patients with ovarian endometriosis. Binary interactions and circuits among the miRNAs, TFs, and corresponding genes were identified by the Pearson correlation coefficients. miRNA-TF-gene regulatory networks were constructed using bioinformatic methods. Eleven selected miRNAs and TFs were validated by quantitative reverse transcription-polymerase chain reaction in 22 patients.ResultsOverall, 107 differentially expressed miRNAs and 6112 differentially expressed mRNAs were identified by comparing the sequencing of the ectopic endometrium group and the eutopic endometrium group. The miRNA-TF-gene regulatory network consists of 22 miRNAs, 12 TFs and 430 corresponding genes. Specifically, some key regulators from the miR-449 and miR-34b/c cluster, miR-200 family, miR-106a-363 cluster, miR-182/183, FOX family, GATA family, and E2F family as well as CEBPA, SOX9 and HNF4A were suggested to play vital regulatory roles in the pathogenesis of endometriosis.ConclusionIntegration analysis of the miRNA and mRNA expression profiles presents a unique insight into the regulatory network of this enigmatic disorder and possibly provides clues regarding replacement therapy for endometriosis.Electronic supplementary materialThe online version of this article (10.1186/s12958-017-0319-5) contains supplementary material, which is available to authorized users.
Background/Aims: Mitochondrial homeostasis is implicated in the development and progression of endometriosis through poorly defined mechanisms. Mst1 is the major growth suppressor related to cancer migration, apoptosis and proliferation. However, whether Mst1 is involved in endometriosis apoptosis and migration via regulating the mitochondrial function remains to be elucidated. Methods: Expression of Mst1 in endometriosis was examined via western blots. Cellular apoptosis was detected via MTT and TUNEL assay. Gain of function assay about Mst1 was conducted via adenovirus over-expression. Mitochondrial functions were evaluated via mitochondrial membrane potential JC-1 staining, ROS flow cytometry analysis, mPTP opening assessment and immunofluorescence of HtrA2/Omi. The mitophagy activity were examined via western blots and immunofluorescence. Results: First, we found that Mst1 was significantly downregulated in the ectopic endometrium of endometriosis compared to the normal endometrium. However, the recovery of Mst1 function was closely associated with the inability of endometrial stromal cells (ESCs) to migrate and survive. A functional study indicated that regaining Mst1 enhanced Drp1 post-transcriptional phosphorylation at Ser616 and repressed Parkin transcription activity via p53, leading to mitochondrial fission activation and mitophagy inhibition. Excessive Drp1-related fission forced the mitochondria to liberate HtrA2/Omi into the cytoplasm. Moreover, Mst1-induced defective mitophagy evoked cellular
Classification of ovarian cancer by morphologic features has a limited effect on serous ovarian cancer (SOC) treatment and prognosis. Here, we proposed a new system for SOC subtyping based on the molecular categories from the Cancer Genome Atlas project. We analyzed the DNA methylation, protein, microRNA, and gene expression of 1203 samples from 599 serous ovarian cancer patients. These samples were divided into nine subtypes based on RNA-seq data, and each subtype was found to be associated with the activation and/or suppression of the following four biological processes: immunoactivity, hormone metabolic, mesenchymal development and the MAPK signaling pathway. We also identified four DNA methylation, two protein expression, six microRNA sequencing and four pathway subtypes. By integrating the subtyping results across different omics platforms, we found that most RNA-seq subtypes overlapped with one or two subtypes from other omics data. Our study sheds light on the molecular mechanisms of SOC and provides a new perspective for the more accurate stratification of its subtypes.
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