ArticleProteome science2007
Comparative proteome analysis of human epithelial ovarian cancer.
Article in Proteome science, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
27 citing papers in PubMed, 59 citations in OpenAlex.
- Enhanced precision in cell culture analytics: leveraging artificial intelligence for unbiased and non-destructive assessment of cell growth and viability.Cell death discovery · 2026Article
- Diabetes-mediated promotion of colon mucosa carcinogenesis is associated with mitochondrial dysfunction.Molecular oncology · 2019Article
- Calpain system protein expression and activity in ovarian cancer.Journal of cancer research and clinical oncology · 2019Article
- Identification of proteins associated with paclitaxel resistance of epithelial ovarian cancer using iTRAQ-based proteomics.Oncology letters · 2018Article
- PARP1 depletion improves mitochondrial and heart function in Chagas disease: Effects on POLG dependent mtDNA maintenance.PLoS pathogens · 2018Article
- Identification of candidate biomarkers for epithelial ovarian cancer metastasis using microarray data.Oncology letters · 2017Article
- Master redox regulator Trx1 upregulates SMYD1 & modulates lysine methylation.Biochimica et biophysica acta · 2015Article
- Proteomics of ovarian cancer: functional insights and clinical applications.Cancer metastasis reviews · 2015Review
- A quantitative proteomics-based signature of platinum sensitivity in ovarian cancer cell lines.The Biochemical journal · 2015Article
- Multiple signatures of a disease in potential biomarker space: Getting the signatures consensus and identification of novel biomarkers.BMC genomics · 2015Article
- Calcium calmodulin dependent kinase kinase 2 - a novel therapeutic target for gastric adenocarcinoma.Cancer biology & therapy · 2015Article
- Proteomic Analysis of Matched Formalin-Fixed, Paraffin-Embedded Specimens in Patients with Advanced Serous Ovarian Carcinoma.Proteomes · 2013Article
- Potential tumor biomarkers identified in ovarian cyst fluid by quantitative proteomic analysis, iTRAQ.Clinical proteomics · 2013Article
- Proteomic analysis of temporally stimulated ovarian cancer cells for biomarker discovery.Molecular & cellular proteomics : MCP · 2013Article
- Inverse relationship between TCTP/RhoA and p53 /cyclin A/actin expression in ovarian cancer cells.Folia histochemica et cytobiologica · 2012Article
- Article
- The role of annexin A2 in tumorigenesis and cancer progression.Cancer microenvironment : official journal of the International Cancer Microenvironment Society · 2011Article
- Changes in gene expression and cellular architecture in an ovarian cancer progression model.PloS one · 2011Article
- Differential protein expression profiling by iTRAQ-two-dimensional LC-MS/MS of human bladder cancer EJ138 cells transfected with the metastasis suppressor KiSS-1 gene.Molecular & cellular proteomics : MCP · 2010Article
- Quantitative proteomic analysis by iTRAQ(R) for the identification of candidate biomarkers in ovarian cancer serum.Proteome science · 2010Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEpithelial ovarian cancer is a devastating disease associated with low survival prognosis mainly because of the lack of early detection markers and the asymptomatic nature of the cancer until late stage. Using two complementary proteomics approaches, a differential protein expression profile was carried out between low and highly transformed epithelial ovarian cancer cell lines which realistically mimic the phenotypic changes observed during evolution of a tumour metastasis. This investigation was aimed at a better understanding of the molecular mechanisms underlying differentiation, proliferation and neoplastic progression of ovarian cancer.
resultsThe quantitative profiling of epithelial ovarian cancer model cell lines TOV-81D and TOV-112D generated using iTRAQ analysis and two-dimensional electrophoresis coupled to liquid chromatography tandem mass spectrometry revealed some proteins with altered expression levels. Several of these proteins have been the object of interest in cancer research but others were unrecognized as differentially expressed in a context of ovarian cancer. Among these, series of proteins involved in transcriptional activity, cellular metabolism, cell adhesion or motility and cytoskeleton organization were identified, suggesting their possible role in the emergence of oncogenic pathways leading to aggressive cellular behavior.
conclusionThe differential protein expression profile generated by the two proteomics approaches combined to complementary characterizations studies will open the way to more exhaustive and systematic representation of the disease and will provide valuable information that may be helpful to uncover the molecular mechanisms related to epithelial ovarian cancer.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.