Ontology highlight
ABSTRACT:
PROVIDER: PRJNA833540 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR19010613.fastq.gz | Fastqsanger.gz | |||
| SRR19010614.fastq.gz | Fastqsanger.gz | |||
| SRR19010615.fastq.gz | Fastqsanger.gz | |||
| SRR19010616.fastq.gz | Fastqsanger.gz | |||
| SRR19010617.fastq.gz | Fastqsanger.gz |
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Project description:Purpose: The aim of this study is to identify known and novel small RNAs (Piwi-interacting RNAs and microRNAs) in normal ovary and epithelial ovarian malignancies by adopting high-throughput RNA sequencing (RNA-Seq) and unveil their possible functions in neoplastic pathways of the two most frequently observed and highly lethal subtypes of epithelial ovarian cancers, endometrioid ovarian cancer (ENOCa) and serous ovarian cancer (SOCa). The study has been performed in normal ovarian tissues as well as malignant tissues of these cancer subtypes. Methods: 1 µg of total RNA was used as the starting material for library preparation as prescribed by Illumina Truseq small RNA library protocol. Small RNA sequencing was carried out by Genotypic Technology, Bangalore, India on Illumina Next-Seq 500 platform. Library preparation was done by performing 3' and 5' adapter ligation followed by reverse transcription of cDNA and amplification of the library. The construct of the library was fractionated to select 16-40 nucleotide insert of small RNAs using PAGE. The quality check of the library was done in Agilent Technologies 2100 Bioanalyzer using a DNA-specific chip, such as High Sensitivity DNA. The sequence reads that passed through Quality Check by FastQC and aligned to the human genome (hg19) were further analysed using in-house pipelines and set of known and novel piRNAs and miRNAs were identified. The sets of known piRNAs and miRNAs identified were assessed for their involvement in neoplastic processes of ovarian cancer subtypes by performing target analysis and GO enrichment studies. Results: Using an in-house prediction pipeline, we mapped about 10-15 million sequence reads per sample to the human genome (hg19) and detected 256, 234 and 219 annotated piRNAs in ENOCa, SOCa, and normal ovary respectively; whereas the average number of known miRNAs present in each sample was estimated to be 480. The annotated piRNAs obtained from each sample exhibited varied length distribution between 26-32 nts. Conclusions: For the first time, our study reported the presence of piRNAs in ENOCa, SOCa and normal ovarian tissue from the next-gen sequencing of small RNAs of 16-40 nts length. The extensive catalogue of human EOCa small RNAs (both piRNAs and miRNAs) detected in this study provides a useful resource to dissect complex neoplastic events that are possibly mediated by these ncRNAs, especially by piRNAs. Moreover, these piRNAs could be used as probable small RNA biomarkers for the EOCa.
2018-01-10 | GSE83794 | GEO
Project description:Background: MicroRNAs (miRNAs) are small regulatory RNAs that are implicated in cancer pathogenesis and have recently shown promise as blood-based biomarkers for cancer detection. Epithelial ovarian cancer is a deadly disease for which improved outcomes could be achieved by successful early detection and enhanced understanding of molecular pathogenesis that leads to improved therapies. A critical step toward these goals is to establish a comprehensive view of miRNAs expressed in epithelial ovarian cancer tissues as well as in normal ovarian surface epithelial cells. Methodology: We used massively parallel pyrosequencing (i.e., M-bM-^@M-^\454 sequencingM-bM-^@M-^]) to discover and characterize novel and known miRNAs expressed in primary cultures of normal human ovarian surface epithelium (HOSE) and in tissue from three of the most common histotypes of ovarian cancer. Deep sequencing of small RNA cDNA libraries derived from normal HOSE and ovarian cancer samples yielded a total of 738,710 high-quality sequence reads, generating comprehensive digital profiles of miRNA expression. Expression profiles for 498 previously annotated miRNAs were delineated and we discovered six novel miRNAs and 39 candidate miRNAs. A set of 124 miRNAs was differentially expressed in normal versus cancer samples and 38 miRNAs were differentially expressed across histologic subtypes of ovarian cancer. Taqman qRT-PCR performed on a subset of miRNAs confirmed results of the sequencing-based study.
2009-03-12 | E-GEOD-15190 | biostudies-arrayexpress
Project description:Intervention type:DRUG. Intervention1:Huaier, Dose form:GRANULES, Route of administration:ORAL, intended dose regimen:20 to 60/day by either bulk or split for 3 months to extended term if necessary. Control intervention1:None.
Primary outcome(s): For mRNA libraries, focus on mRNA studies. Data analysis includes sequencing data processing and basic sequencing data quality control, prediction of new transcripts, differential expression analysis of genes. Gene Ontology (GO) and the KEGG pathway database are used for annotation and enrichment analysis of up-regulated genes and down-regulated genes.
For small RNA libraries, data analysis includes sequencing data process and sequencing data process QC, small RNA distribution across the genome, rRNA, tRNA, alignment with snRNA and snoRNA, construction of known miRNA expression pattern, prediction New miRNA and Study of their secondary structure Based on the expression pattern of miRNA, we perform not only GO / KEGG annotation and enrichment, but also different expression analysis.. Timepoint:RNA sequencing of 240 blood samples of 80 cases and its analysis, scheduled from June 30, 2022..
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Project description:Ras-related associated with diabetes (RRAD) is a small Ras-related GTPase that is frequently inactivated by DNA methylation of the CpG island in its promoter region in cancer tissues. However, the role of the methylation-induced RRAD inactivation in tumorigenesis remains unclear. In this study, the Ras regulated-transcriptome and epigenome were profiled by comparing T29H (a RasV12-transformed human ovarian epithelial cell line) with T29 (an immortalized but non-transformed cell line) through Reduced representation bisulfite sequencing (RRBS-seq) and Digital gene expression (DGE) . We found that RasV12-mediated oncogenic transformation was accompanied by RRAD promoter hypermethylation and a concomitant loss of RRAD expression. In addition, we found that the RRAD promoter was hypermethylated and its transcription was reduced in ovarian cancer versus normal ovarian tissues. Treatment with the DNA methyltransferase inhibitor 5-aza-2M-bM-^@M-2-deoxycytidine (5-aza-dC) resulted in demethylation in RRAD promoter and restored RRAD expression in T29H cells. By employing knockdown and overexpression techniques in T29 and T29H, respectively, we found that RRAD inhibited glucose uptake and lactate production by repressing the expression of glucose transporters. Finally, RRAD overexpression in T29H cells inhibited tumor formation in nude mice, suggesting RRAD is a tumor suppressor gene. Our results indicate that RasV12-mediated oncogenic transformation induces RRAD epigenetic inactivation, which in turn promotes glucose uptake and may contribute to ovarian cancer tumorigenesis Reduced representation bisulfite sequencing (MspI,~40-220bp size fraction) data for two cell lines (T29 and T29H) were generated by deep sequencing, in two replicates, using Illumina HiSeq 2000.
2014-08-21 | E-GEOD-55566 | biostudies-arrayexpress
Project description:The diversity and heterogeneity within high-grade serous ovarian cancer (HGSC) is not well understood. Comprehensive molecular analyses were performed including high-pass whole-genome sequencing, targeted deep DNA sequencing, RNA sequencing, reverse-phase protein arrays, mass spectrometry-based proteomics and phosphoproteomics, and immune profiling on primary and metastatic sites from highly clinically annotated HGSC samples. Samples were obtained pre-treatment based on a laparoscopic triage algorithm from patients who underwent R0 tumor debulking or received neoadjuvant chemotherapy (NACT) with excellent or poor response.
2020-05-27 | PXD014980 | Pride
Project description:The aim of this study was to identify a set of miRNAs which are associated with chemotherapy resistance in ovarian cancer, and to evaluate if these miRNAs are enrich in small nanovesicles, called exosomes, released from ovarian cancer cells.
2021-05-25 | GSE114920 | GEO
Project description:Deregulation of the transforming growth factor-? (TGF?) signaling pathway in epithelial ovarian cancer has been reported, but the precise mechanism underlying disrupted TGF? signaling in the disease remains unclear. We performed chromatin immunoprecipitation followed by sequencing (ChIP-seq) to investigate genome-wide screening of TGF?-induced SMAD4 binding in epithelial ovarian cancer. Following TGF? stimulation of the A2780 epithelial ovarian cancer cell line, we identified 2,362 SMAD4 binding loci and 318 differentially expressed SMAD4 target genes. Comprehensive examination of SMAD4-bound loci, revealed four distinct binding patterns: 1) Basal; 2) Shift; 3) Stimulated Only; 4) Unstimulated Only. SMAD4-bound loci were primarily classified as either Stimulated only (74%) or Shift (25%), indicating that TGF?-stimulation alters SMAD4 binding patterns in epithelial ovarian cancer cells compared to normal epithelial cells. Furthermore, based on gene regulatory network analysis, we determined that the TGF?-induced SMAD4-dependent regulatory network was strikingly different in ovarian cancer compared to normal cells. Importantly, the TGF?/SMAD4 target genes identified in the A2780 epithelial ovarian cancer cell line were predictive of patient survival, based on in silico mining of publically available patient data bases. In conclusion, our data highlight the utility of next generation sequencing technology to identify genome-wide SMAD4 target genes in epithelial ovarian cancer. The results link aberrant TGF?/SMAD signaling to ovarian tumorigenesis. Furthermore, the identified SMAD4 binding loci, combined with gene expression profiling and in silico data mining of patient cohorts, may provide a powerful approach to determine potential gene signatures with biological and future translational research in ovarian and other cancers. ChIP-Seq: 1 control lane. 4 unstimulated lanes 4 stimulated lanes Gene expression: 3 technical replicates each of SMAD4 stimulated and SMAD4 unstimulated cells
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2024-03-30 | GSE262296 | GEO