Differentially expressed mRNAs after silencing lncRNA DDGC
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE158555 | GEO | 2021/02/20
REPOSITORIES: GEO
Similar Datasets
Project description:Differentially expressed mRNAs after silencing lncRNA DDGC
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Project description:We have completed the lncRNA expression profiling of human granulosa cells to reveals the differentially expressed lncRNAs involved in POI.
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Project description:Using the highly sensitive lncRNA array (Arraystar human lncRNA microarray), we screened the lncRNAs and mRNA abundant in bladder cancer stem cells sorted from three bladder cancer cell lines, compared to the bladder cancer cells. And the functions of lncRNAs in bladder cancer stem cells were analyzed along the following research.
2016-12-06 | GSE90903 | GEO
Project description:Our study is designed to demonstrate altered profiles of lncRNAs in the granulosa cells from patients with bPOI. Functional studies will be further confirmed. This study will provide new viewpoint for understanding the roles of non-coding RNA for GCs function, as well as potential etiologic mechanism for POI.
2020-02-17 | GSE135697 | GEO
Project description:LncRNA expression profiling for liver tissues of mice fed for NFD, LSF and HSF groups Summary: An abstract of the experiment and the data analysis. Experiment Workflow: A workflow of the experiment and the data analysis. Project Description: Sample and experiment information. Array Information: Mouse 8 x 60K LncRNA expression array information. Summary Table of Files for Data Delivery: Contains summary table of files for data delivery and the recommended software programs for viewing the data. Data Analysis for LncRNAs 1. Raw LncRNA data normalization and low intensity filtering: Raw signal intensities were normalized in quantile method by GeneSpring GX v11.5.1, and low intensity LncRNAs were filtered (LncRNAs that at least 6 out of 9 samples have flags in Present or Marginal were chosen for further analysis, these LncRNAs can be found from the LncRNA Expression Profiling Data.xls file). 2. Quality assessment of LncRNA data after filtering: Contains Box Plot and Scatter Plot for LncRNAs after filtering (This data can be found from the LncRNA Expression Profiling Data.xls file). 3. Differentially expressed LncRNAs screening: Contains differentially expressed genes with statistical significance that passed Volcano Plot filtering (Fold Change >= 2.0, P-value <= 0.05) (This data can be found from the Differentially Expressed LncRNAs.xls file). 4. Heat Map and Hierarchical Clustering: Hierarchical Clustering of Differentially Expressed LncRNAs (The heat map can be found from the LncRNA Expression Profiling Data.xls file). Data Analysis for mRNAs 1. Raw mRNA data normalization and low intensity filtering: Raw signal intensities were normalized in quantile method by GeneSpring GX v11.5.1, and low intensity mRNAs were filtered (mRNAs that at least 6 out of 9 samples have flags in Present or Marginal were chosen for further analysis, these mRNAs can be found from the mRNA Expression Profiling Data.xls file). 2. Quality assessment of mRNA data after filtering: Contains Box Plot and Scatter Plot for mRNAs after filtering (This data can be found from the mRNA Expression Profiling Data.xls file). 3. Differentially expressed mRNAs screening: Contains differentially expressed genes with statistical significance that passed Volcano Plot filtering (Fold Change >= 2.0, P-value <= 0.05) (This data can be found from the Differentially Expressed mRNAs.xls file). 4. Heat Map and Hierarchical Clustering: Hierarchical Clustering of Differentially Expressed mRNAs (The heat map can be found from the mRNA Expression Profiling Data.xls file). 5. Pathway analysis: Pathway analysis of the differentially expressed mRNAs. 6. GO analysis: GO term analysis of the differentially expressed mRNAs. LncRNA Classification and Subgroup Analysis 1. Rinn lincRNAs profiling: Contains profiling data of all lincRNAs based on John Rinn's papers (This data can be found from the Rinn lincRNAs profiling.xls file). 2. LincRNAs nearby coding gene data table: Contains the differentially expressed lincRNAs and nearby coding gene pairs (distance < 300 kb) (This data can be found from the LincRNAs nearby coding gene data table.xls file). Sample RNA Quality Control: Sample quality control data file from NanoDrop ND-1000 spectrophotometer and standard denaturing agarose gel electrophoresis. Methods: A brief introduction of methods for sample preparation, microarray design, experiment, and data analysis.
2022-11-24 | E-MTAB-9167 | biostudies-arrayexpress
Project description:In this study, we aimed to explore RNAs profiling regulated by LNPPS in bladder cancer. RNA-seq analysis of three pairs of LNPPS-overexpressing 5637 cells and control cells was completed in this project. A total of 17.6Gb raw data was obtained. In concluion, our data showed that the overexpression of LNPPS caused the significantly upregulation of 82 mRNAs, and the downregulation of 102 mRNAs (|Log2FC|>1.5, p<0.05). Further studies will focus on the potential regulatory mechanisms of these significantly differently expressed mRNAs in the LNPPS-mediated development of bladder cancer.
2023-01-04 | GSE190917 | GEO
Project description:To compare lncRNA&mRNA expression diffrence between macrophage M0 and macrophage M2 cells, we performed microarray using Arraystar Human LncRNA + mRNA Microarray v3.0 (8×60K) platform
2021-02-28 | GSE144789 | GEO
Project description:This study aimed to identify the crucial molecules and explore the function of noncoding RNAs and related pathways in IDD. We randomly selected 3 samples each from an IDD and a spinal cord injury group (control) for RNA-sequencing. We identified 463 differentially-expressed long noncoding RNAs (lncRNAs), 47 differentially-expressed microRNAs (miRNAs), and 1,334 differentially-expressed mRNAs in IDD. Three hundred fifty-eight lncRNAs as cis-regulators could potentially target 865 genes. Protein–protein interaction (PPI) network analysis confirmed that IL-6, VEGFA, IGF1, MMP9, CXCL8, FGF2, IL1B, CCND1, ITGAM, PTPRC, FOS and PTGS2 were hub genes. We built a competing endogenous RNA (ceRNA) network and identified lncRNA XIST–hsa-miR-4775–PLA2G7 and lncRNA XIST–hsa-miR-424-5p–AMOT/TGFBR3 ceRNA axes.
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Project description:In the ovary, proliferation and differentiation of granulosa cells (GCs) drive the growth of follicles. This is, in part, dependent on gonadotropins. Our immunohistochemical studies provided hints of mitochondrial biogenesis and intracellular redistribution in GCs of growing follicles. A cellular model, human KGN cells (granulosa cell tumor cells, derived from growing follicles) was used to study aspects of mitochondrial dynamics. To elevate cAMP and thereby mimic gonadotropin actions, forskolin (FSK) was used, which increased KGN cell size and mitochondrial DNA within 24 h. MitoTracker experiments and ultrastructural 3D reconstruction revealed that FSK treatment induced the formation of elaborate mitochondrial networks. H89, a protein kinase A (PKA) inhibitor, reduced network formation. A proteomic analysis indicated that FSK among others elevated levels of regulators of the cytoskeleton and the steroidogenic enzyme CYP11A1, located in mitochondria, was more than 3-fold increased, implying that cAMP/PKA-associated structural changes go in parallel with the acquisition of steroidogenic competence of mitochondria in KGN cells. In summary, in situ observations showed increases in mitochondria and suggested intracellular trafficking in GCs during follicular growth and indicated that they may partially be under the control of gonadotropins/cAMP. In line with this, elevation of cAMP in KGN profoundly affected mitochondria dynamics in a PKA-dependent manner and implicated cytoskeletal changes.
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