Differentially expressed lncRNA in biological premature ovarian insufficient(bPOI)
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA665514 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR12707600_1.fastq.gz | Fastqsanger.gz | |||
| SRR12707600_2.fastq.gz | Fastqsanger.gz | |||
| SRR12707601_1.fastq.gz | Fastqsanger.gz | |||
| SRR12707601_2.fastq.gz | Fastqsanger.gz | |||
| SRR12707602_1.fastq.gz | Fastqsanger.gz |
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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:Differentially expressed lncRNA in biological premature ovarian insufficiency (bPOI)
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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:We first examined changes of miRNAs and expression using microarrays ( Exiqon miRCURY™ microarray LNA v.18.0) of GCs from 10 bPOI and 10 age and BMI matched normal control women. Seventy-five miRNAs (Fold Change [FC]>2, P<0.05) were identified differentially expressed between bPOI patients and control women.
2018-04-11 | GSE100238 | GEO
Project description:The aim of this study is to identify the expression profile of mRNA in granulosa cells of POI We performed gene expression profiling analysis using data obtained from RNA-seq of the GCs in 6 POI patients and 5 control patients.This study provides a better understanding of molecular mechanism of POI and identifies the possible biomarkers of POI.
2023-10-31 | GSE201276 | GEO
Project description:Premature ovarian insufficiency (POI) is defined as a primary ovarian defect characterized by absent menarche (primary amenorrhea) or premature depletion of ovarian follicles before the age of 40 (secondary amenorrhea) with hypergonadotropism and hypoestrogenism. Premature ovarian insufficiency has few known genetic causes but in familial cases a genetic link is often suspected. A large consanguineous family with three female affected with POI was investigated. All samples including 3 affected and 5 unaffecd underwent whole genome SNP genotyping using Affymetric Axiom_GW_Hu_SNP array. Linkage analysis was carried out using HomozygosityMapper and Allegro softwares.Linkage analysis mapped the disease phenotype to long arm of chromosome 20. Sequence data analysis of potential candidate genes failed to detect any pathogenic variant.
2014-03-20 | GSE56043 | GEO
Project description:Premature ovarian insufficiency (POI) is defined as a primary ovarian defect characterized by absent menarche (primary amenorrhea) or premature depletion of ovarian follicles before the age of 40 (secondary amenorrhea) with hypergonadotropism and hypoestrogenism. Premature ovarian insufficiency has few known genetic causes but in familial cases a genetic link is often suspected. A large consanguineous family with three female affected with POI was investigated. All samples including 3 affected and 5 unaffecd underwent whole genome SNP genotyping using Affymetric Axiom_GW_Hu_SNP array. Linkage analysis was carried out using HomozygosityMapper and Allegro softwares.Linkage analysis mapped the disease phenotype to long arm of chromosome 20. Sequence data analysis of potential candidate genes failed to detect any pathogenic variant. Affymetrix SNP arrays were performed according to the manufacturer's directions on DNA extracted from peripheral blood samples. DNA of eight individuals including three affected subjects was used for homozygosity mapping. Genotyping was performed using the Affymetrix Axiom_GW_Hu_SNP array. Briefly, 250 ng genomic DNA was digested with Digestion Master Mix containing 2 µl NE buffer 2 (10X), 0.5 µl BSA (100X; 10 mg/ml) and 1 µl Nsp1. Digested DNA sample was ligated to Nsp1 adaptor using T4 DNA ligase and amplified by 2 µl of TITANIUM Taq DNA polymerase (50X) and 100 µM PCR primer. PCR products were purified on a Clean-Up plate (Clontech Lab, Madison, USA) and eluted by RB buffer. Purified PCR products were fragmented using Fragmentation Reagent (0.05U/µl DNase 1) for 35 minutes at 37°C followed by labeling of fragmented samples with Labeling Master Mix (30 mM GeneChip DNA Labeling Reagent, 30 U/µl Terminal Deoxynucleotidyl Transferase) for 4 hours at 37°C. Labeled samples were hybridized to Axiom_GW_Hu_SNP array by mixing the sample with Hybridization Master Mix, denatured on thermoblock and loaded on to Array. Array was then placed in a hybridization oven (GeneChip Hybridization Oven 640, USA) for 16-18 hours. After hybridization, array was washed and stained on an automated Fluidic Station 450 followed by scanning on GeneChip Scanner 3000 7G using GeneChip Operating Software (GCOS).
2014-03-20 | E-GEOD-56043 | biostudies-arrayexpress
Project description:This study is to identify urinary exosome microRNAs (miRNAs) that are unique to premature ovarian insufficiency (POI) with and without Turner syndrome and to use them as diagnostic markers for POI patients. We examined the miRNAexpression profile in urine exosomes from POI patients with and without Turner syndrome.
2022-09-28 | GSE205700 | GEO
Project description:The purpose of the present study was to identify key genes and investigate the related molecular mechanisms of bladder cancer (BC) progression. From the Gene Expression Omnibus database, the gene expression dataset GSE7476 was downloaded, which contained 43 BC samples and 12 normal bladder tissues. GSE7476 was analyzed to screen the differentially expressed genes (DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed for the DEGs using the DAVID database, and a protein?protein interaction (PPI) network was then constructed using Cytoscape software. The results of the GO analysis showed that the upregulated DEGs were significantly enriched in cell division, nucleoplasm and protein binding, while the downregulated DEGs were significantly enriched in 'extracellular matrix organization', 'proteinaceous extracellular matrix' and 'heparin binding'. The results of the KEGG pathway analysis showed that the upregulated DEGs were significantly enriched in the 'cell cycle', whereas the downregulated DEGs were significantly enriched in 'complement and coagulation cascades'. JUN, cyclin?dependent kinase 1, FOS, PCNA, TOP2A, CCND1 and CDH1 were found to be hub genes in the PPI network. Sub?networks revealed that these gene were enriched in significant pathways, including the 'cell cycle' signaling pathway and 'PI3K?Akt signaling pathway'. In summary, the present study identified DEGs and key target genes in the progression of BC, providing potential molecular targets and diagnostic biomarkers for the treatment of BC.
| S-EPMC5928619 | biostudies-literature
Project description:MotivationHigh-throughput sequencing enables expression analysis at the level of individual transcripts. The analysis of transcriptome expression levels and differential expression (DE) estimation requires a probabilistic approach to properly account for ambiguity caused by shared exons and finite read sampling as well as the intrinsic biological variance of transcript expression.ResultsWe present Bayesian inference of transcripts from sequencing data (BitSeq), a Bayesian approach for estimation of transcript expression level from RNA-seq experiments. Inferred relative expression is represented by Markov chain Monte Carlo samples from the posterior probability distribution of a generative model of the read data. We propose a novel method for DE analysis across replicates which propagates uncertainty from the sample-level model while modelling biological variance using an expression-level-dependent prior. We demonstrate the advantages of our method using simulated data as well as an RNA-seq dataset with technical and biological replication for both studied conditions.AvailabilityThe implementation of the transcriptome expression estimation and differential expression analysis, BitSeq, has been written in C++ and Python. The software is available online from http://code.google.com/p/bitseq/, version 0.4 was used for generating results presented in this article.
| S-EPMC3381971 | biostudies-literature