Characterization of the potential role of NTPCR in epithelial ovarian cancer by integrating transcriptomic and metabolomic analysis
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ABSTRACT:
PROVIDER: PRJNA739199 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR14863552_1.fastq.gz | Fastqsanger.gz | |||
| SRR14863552_2.fastq.gz | Fastqsanger.gz | |||
| SRR14863553_1.fastq.gz | Fastqsanger.gz | |||
| SRR14863553_2.fastq.gz | Fastqsanger.gz | |||
| SRR14863554_1.fastq.gz | Fastqsanger.gz |
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Project description:Background: Epithelial ovarian carcinoma (EOC) is a malignant tumor with high motility in women. Our previous study found that dysregulated NTPCR was associated with the prognosis of ovarian cancer patients, and thus this present study attempted to explore the potential roles of nucleoside-triphosphatase cancer-related (NTPCR) in disease progression. Methods: Expressed level of NTPCR was investigated in ovarian cancer cell lines by RT-qPCR analysis. shRNA targeting NTPCR was generated and transfected into SKOV3 cells to detect the effect of knocking out NTPCR on cell proliferation, cell cycle, cell migration, and invasion. Transcriptomic sequencing and metabolite profiling analysis were performed in shNTPCR groups to identify transcriptome or metabolites alteration that might contribute to ovarian cancer. And finally, we searched the overlapped signaling pathways correlated with differential metabolites and DEGs by integrating analysis. Results: NTPCR was upregulated in EOC cell lines compared with human ovarian epithelium cell IOSE80, especially in SKOV3 and OVCAR-3 (SKOV3 vs IOSE80, P<0.001; OVCAR-3 vs IOSE80, P<0.001). Knocking out NTPCR can induce cell proliferation and S phase arrest, promote migration and invasion in SKOV3 cells. RNA sequencing analysis demonstrated cohorts of differential expressed genes (DEGs) were identified in shNTPCR samples. PPI networks were constructed for DEGs. STAT1 (degree = 43) and OAS2 (degree = 36) were identified as hub genes in network. Several miRNAs together with target genes were predicted to be crucial genes related to disease progression, including hsa-miR-124-3p, hsa-miR-30a-5p, hsa-miR-146a-5, EP300, GATA2, and STAT3. We also screened the differential metabolites from shNTPCR samples, including 22 upregulated and 22 downregulated metabolites. By integrating analysis, eight overlapped pathways were correlated with these DEGs and differential metabolites, such as primary bile acid biosynthesis, protein digestion, and absorption, pentose and glucuronate interconversions. Conclusion: NTPCR might serve as a tumor suppressor in EOC progression. Our results demonstrated that DEGs and differential metabolites were mainly related to several signaling pathways, which might be a crucial role in the progression of ovarian cancer.
2021-12-31 | GSE178486 | GEO
Project description:BackgroundEpithelial ovarian carcinoma (EOC) is a malignant tumor with high motility in women. Our previous study found that dysregulated nucleoside-triphosphatase cancer-related (NTPCR) was associated with the prognosis of EOC patients, and thus, this present study attempted to explore the potential roles of NTPCR in disease progression.MethodsExpressed level of NTPCR was investigated in EOC tissues by RT-qPCR and Western blot analysis. NTPCR shRNA and overexpression vector were generated and transfected into OVCAR-3 or SKOV3 cells to detect the effect of NTPCR on cell proliferation, cell cycle, cell migration, and invasion. Transcriptomic sequencing and metabolite profiling analysis were performed in shNTPCR groups to identify transcriptome or metabolite alteration that might contribute to EOC. Finally, we searched the overlapped signaling pathways correlated with differential metabolites and differentially expressed genes (DEGs) by integrating analysis.ResultsComparing para-cancerous tissues, we found that NTPCR is highly expressed in cancer tissues (p < 0.05). Overexpression of NTPCR inhibited cell proliferation, migration, and invasion and reduced the proportion of S- and G2/M-phase cells, while downregulation of NTPCR showed the opposite results. RNA sequencing analysis demonstrated cohorts of DEGs were identified in shNTPCR samples. Protein-protein interaction networks were constructed for DEGs. STAT1 (degree = 43) and OAS2 (degree = 36) were identified as hub genes in the network. Several miRNAs together with target genes were predicted to be crucial genes related to disease progression, including hsa-miR-124-3p, hsa-miR-30a-5p, hsa-miR-146a-5, EP300, GATA2, and STAT3. We also screened the differential metabolites from shNTPCR samples, including 22 upregulated and 22 downregulated metabolites. By integrating transcriptomics and metabolomics analysis, eight overlapped pathways were correlated with these DEGs and differential metabolites, such as primary bile acid biosynthesis, protein digestion, and absorption, pentose, and glucuronate interconversions.ConclusionNTPCR might serve as a tumor suppressor in EOC progression. Our results demonstrated that DEGs and differential metabolites were mainly related to several signaling pathways, which might be a crucial role in the progression of NTPCR regulation of EOC.
| S-EPMC8442909 | biostudies-literature
Project description:Integrative proteomic characterization of epithelial ovarian cancer.
2024-05-23 | PXD053625 |
Project description:Thiele2013 - Ovary ovarian stroma cells
The model of ovary ovarian stroma cells metabolism is derived from the community-driven global reconstruction of human metabolism (version 2.02, MODEL1109130000
).
This model is described in the article:
A community-driven global reconstruction of human metabolism.
Thiele I, et al
.
Nature Biotechnology
Abstract:
Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven,
consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared
with its predecessors, the reconstruction has improved topological and functional features, including ~2x more reactions and ~1.7x more unique metabolites. Using
Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic
data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically
generated a compendium of 65 cell type-specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will
facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/.
This model is hosted on BioModels Database
and identified by: MODEL1310110029
.
To cite BioModels Database, please use: BioModels Database: An enhanced,
curated and annotated resource for published quantitative kinetic models
.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer
to CC0 Public Domain Dedication
for more information.
2005-01-01 | MODEL1310110029 | BioModels
Project description:This study showed that MeDIP-seq could be used for methylation profiling in an efficient manner and identified a number of potential hub genes and pathways important in the ovarian cancer carcinogenesis that may deepen our understanding of the molecular mechanisms involved inthe development of ovarian cancer.
2023-12-31 | GSE244405 | GEO
Project description:Serum miRNA as Potential Biomarkers for ovarian cancer
| PRJNA832738 | ENA
Project description:Although multiple gene and protein expression have been extensively profiled in human pulmonary arterial hypertension (PAH), the mechanism for the development and progression of pulmonary hypertension remains elusive. Analysis of the global metabolomic heterogeneity within the pulmonary vascular system leads to a better understanding of disease progression. Using a combination of high-throughput liquid-and-gas-chromatography-based mass spectrometry, we showed unbiased metabolomic profiles of disrupted glycolysis, increased TCA cycle, and fatty acid metabolites with altered oxidation pathways in the severe human PAH lung. The results suggest that PAH has specific metabolic pathways contributing to increased ATP synthesis for the vascular remodeling process in severe pulmonary hypertension. These identified metabolites may serve as potential biomarkers for the diagnosis of severe PAH. By profiling metabolomic alterations of the PAH lung, we reveal new pathogenic mechanisms of PAH in its later stage, which may differ from the earlier stage of PAH, opening an avenue of exploration for therapeutics that target metabolic pathway alterations in the progression of PAH. Global profiles were determined in human lung tissue and compared across 11 normal and 12 severe pulmonary arterial hypertension patients. Using a combination of microarray and high-throughput liquid-and-gas-chromatography-based mass spectrometry, we showed unbiased metabolomic profiles of disrupted glycolysis, increased TCA cycle, and fatty acid metabolites with altered oxidation pathways in the severe human PAH lung.
2014-11-14 | E-GEOD-53408 | biostudies-arrayexpress
Project description:Molecular characterization of patients enrolled in a clinical phase II study in relapsed ovarian cancer
2024-11-11 | PRJEB82346 | EVA
Project description:Objective: Ovarian tumors of low-malignant potential (LMP) and low-grade serous ovarian carcinomas are thought to represent different stages on a tumorigenic continuum and to develop along pathways distinct from that of high-grade serous ovarian carcinoma. Past studies have utilized gene expression profiles to support this theory. The objective of the current study was to identify new genes whose expression profiles in LMP ovarian tumors and low-grade ovarian carcinomas differ from that in high-grade ovarian carcinomas. Methods: We used RNA from 3 normal human ovarian surface epithelia (HOSE) and from 10 low-grade and 10 high-grade serous ovarian carcinoma samples to perform gene expression profiling. Using real-time reverse-transcription polymerase chain reaction (RT-PCR), we evaluated changes in PAX2 mRNA expression in cDNA created from RNA extracted from an independent set of ovarian tissue samples (7 LMP tumors and 17 low-grade and 23 high-grade serous carcinomas). We also examined PAX2 expression using Western blot analysis of protein extracted from a set of ovarian LMP and low- and high-grade carcinoma tissue samples. Additionally, we used immunohistochemistry (IHC) to validate PAX2 overexpression in a third independent set of paraffin ovarian tissue sections from 17 LMP tumors and 16 low- and 257 high-grade carcinomas. Results: Gene profiling revealed higher expression of PAX2 in low-grade than in high-grade ovarian carcinomas. Real-time RT-PCR demonstrated a statistically significant difference in median PAX2 mRNA expression, expressed as fold change, among ovarian LMP tumor (1837.38), low-grade (183.12), and high-grade (3.72) carcinoma samples (p=0.015). Western blot analysis revealed strong PAX2 expression in ovarian LMP and low-grade carcinoma samples but no PAX2 protein expression in high-grade carcinomas. On IHC, more LMP tumor and low-grade carcinoma samples expressed moderate to high levels of PAX2 than did high-grade ovarian carcinoma samples. The numbers of samples with strong nuclear staining was significantly higher for ovarian LMP tumors (10 of 17, p<0.001) and low-grade serous carcinomas (10 of 16, p<0.001) than for high-grade carcinomas (27 of 257). Discussion: Our identification and validation of higher PAX2 expression in ovarian LMP tumors and low-grade serous carcinomas than in high-grade carcinomas supports the two-tiered hypothesis that the first two are on a continuum and are distinct from high-grade ovarian carcinomas. PAX2 may represent a potential biomarker and future therapeutic target for individualizing chemotherapy for ovarian LMP tumors and low-grade carcinomas in the future.
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2021-07-08 | GSE179440 | GEO