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Meta-analysis identifies candidate key genes in endometrium as predictive biomarkers for clinical pregnancy in IVF.

ABSTRACT: Genetic factors in endometrium are likely to be involved in the embryo implantation failure (IF), one of the major limiting factors in the success of in vitro fertilization (IVF). In this study, we aimed to identify critical genes from the transcriptional profile for the establishment of the endometrial receptivity which supporting the normal pregnancy. Three GEO datasets, including 12 samples of IF and 12 samples of controls, were used for the meta-analysis. We identified 182 different expression genes (DEGs) by comparing IF with controls and present here the successful clustering according to sample type, not by the origin. The gene ontology (GO) enriched analysis demonstrated the significant downregulation in activation and regulation of inflammatory and immune response in IF patients. Furthermore, network analysis of down-regulated genes identified the significant hub genes containing GADD45A (growth arrest and DNA damage inducible alpha, Degree = 77), GZMB (granzyme B, Degree = 38) and NLRP2 (NLR family pyrin domain containing 2, Degree = 37). The lower expression of NLRP2, related to inflammatory responses with the most degree in the network, was validatied by other GEO data. Besides, it was confirmed that the NLRP2 could act as a predictor for pregnancy after IVF (AUC = 87.93%; sensitivity, 60.00%; specificity, 91.30% ). Our meta-analysis will help us to better understand the molecular regulation of endometrial receptivity, and guiding further line of treatment for IF during IVF.

SUBMITTER: Li J

PROVIDER: S-EPMC5731968 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Meta-analysis identifies candidate key genes in endometrium as predictive biomarkers for clinical pregnancy in IVF.

Li Jingyu J Liu Dongyun D Wang Jiang J Deng Huali H Luo Xiu X Shen Xiaoli X Huan Yanjun Y Huang Guoning G Ye Hong H

Oncotarget 20171026 60

Genetic factors in endometrium are likely to be involved in the embryo implantation failure (IF), one of the major limiting factors in the success of <i>in vitro</i> fertilization (IVF). In this study, we aimed to identify critical genes from the transcriptional profile for the establishment of the endometrial receptivity which supporting the normal pregnancy. Three GEO datasets, including 12 samples of IF and 12 samples of controls, were used for the meta-analysis. We identified 182 different e ...[more]

PMID: 29254258

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Project description:BackgroundOsteosarcoma (OS) metastasis is the most common cause of cancer-related mortality, however, no sufficient clinical biomarkers have been identified. In this study, we identified five genes to help predict metastasis at diagnosis.MethodsWe performed weighted gene co-expression network analysis (WGCNA) to identify the most relevant gene modules associated with OS metastasis. An important machine learning algorithm, the support vector machine (SVM), was employed to predict key genes for classifying the OS metastasis phenotype. Finally, we investigated the clinical significance of key genes and their enriched pathways.ResultsEighteen modules were identified in WGCNA, among which the pink, red, brown, blue, and turquoise modules demonstrated good preservation. In the five modules, the brown and red modules were highly correlated with OS metastasis. Genes in the two modules closely interacted in protein-protein interaction networks and were therefore chosen for further analysis. Genes in the two modules were primarily enriched in the biological processes associated with tumorigenesis and development. Furthermore, 65 differentially expressed genes were identified as common hub genes in both WGCNA and protein-protein interaction networks. SVM classifiers with the maximum area under the curve were based on 30 and 15 genes in the brown and red modules, respectively. The clinical significance of the 45 hub genes was analyzed. Of the 45 genes, 17 were found to be significantly correlated with survival time. Finally, 5/17 genes, including ADAP2 (P = 0.0094), LCP2 (P = 0.013), ARHGAP25 (P = 0.0049), CD53 (P = 0.016), and TLR7 (P = 0.04) were significantly correlated with the metastatic phenotype. In vitro verification, western blotting, wound healing analyses, transwell invasion assays, proliferation assays, and colony formation assays indicated that ARHGAP25 promoted OS cell migration, invasion, proliferation, and epithelial-mesenchymal transition.ConclusionWe identified five genes, namely ADAP2, LCP2, ARHGAP25, CD53, and TLR7, as candidate biomarkers for the prediction of OS metastasis; ARHGAP25 inhibits MG63 OS cell growth, migration, and invasion in vitro, indicating that ARHGAP25 can serve as a promising specific and prognostic biomarker for OS metastasis.

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Meta-analysis identifies candidate key genes in endometrium as predictive biomarkers for clinical pregnancy in IVF.

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Meta-analysis identifies candidate key genes in endometrium as predictive biomarkers for clinical pregnancy in IVF.

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