ArticleThe Journal of clinical investigation2013
Predicting time to ovarian carcinoma recurrence using protein markers.
Article in The Journal of clinical investigation, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 39 papers.
What it found
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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
39 citing papers in PubMed, 68 citations in OpenAlex.
- Phase II trial of bevacizumab and sorafenib in recurrent ovarian cancer patients with or without prior-bevacizumab treatment.Gynecologic oncology · 2020Trial
- Review
- Tumoroid model recreates clinically relevant phenotypes of high grade serous ovarian cancer (HGSC) cells, carcinoma associated fibroblasts, and macrophages.Research square · 2025Article
- Machine learning driven dashboard for chronic myeloid leukemia prediction using protein sequences.PloS one · 2025Article
- Proteomic landscape of epithelial ovarian cancer.Nature communications · 2024Article
- StabJGL: a stability approach to sparsity and similarity selection in multiple-network reconstruction.Bioinformatics advances · 2023Article
- Multiregional Sequencing Analysis Reveals Extensive Genetic Heterogeneity in Gastric Tumors from Latinos.Cancer research communications · 2022Article
- BRCA mutations lead to XIAP overexpression and sensitise ovarian cancer to inhibitor of apoptosis (IAP) family inhibitors.British journal of cancer · 2022Article
- Radioproteomics in patients with ovarian cancer.The British journal of radiology · 2021Review
- Recent Advances in Integrative Multi-Omics Research in Breast and Ovarian Cancer.Journal of personalized medicine · 2021Review
- Integrative radiogenomics for virtual biopsy and treatment monitoring in ovarian cancer.Insights into imaging · 2020Review
- Integration of proteomics with CT-based qualitative and radiomic features in high-grade serous ovarian cancer patients: an exploratory analysis.European radiology · 2020Article
- Validation of Androgen Receptor loss as a risk factor for the development of brain metastases from ovarian cancers.Journal of ovarian research · 2020Article
- Phosphoproteomics Reveals Key Regulatory Kinases and Modulated Pathways Associated with Ovarian Cancer Tumors.OncoTargets and therapy · 2020Article
- Proteomics advances for precision therapy in ovarian cancer.Expert review of proteomics · 2019Review
- Proteomics and phosphoproteomics in precision medicine: applications and challenges.Briefings in bioinformatics · 2019Review
- Cytokines secreted by human Wharton's jelly stem cells inhibit the proliferation of ovarian cancer (OVCAR3) cellsOncology letters · 2019Article
- Spectral Clustering via sparse graph structure learning with application to Proteomic Signaling Networks in Cancer.Computational statistics & data analysis · 2019Article
- The Omics Revolution Continues: The Maturation of High-Throughput Biological Data Sources.Yearbook of medical informatics · 2018Review
- Predicting high-risk endometrioid carcinomas using proteins.Oncotarget · 2018Article
Corrections and comments
- Erratum issued
Authors and funding
14 authors at 7 institutions in 2 countries.
Funding
Abstract
Patients with ovarian cancer are at high risk of tumor recurrence. Prediction of therapy outcome may provide therapeutic avenues to improve patient outcomes. Using reverse-phase protein arrays, we generated ovarian carcinoma protein expression profiles on 412 cases from TCGA and constructed a PRotein-driven index of OVARian cancer (PROVAR). PROVAR significantly discriminated an independent cohort of 226 high-grade serous ovarian carcinomas into groups of high risk and low risk of tumor recurrence as well as short-term and long-term survivors. Comparison with gene expression-based outcome classification models showed a significantly improved capacity of the protein-based PROVAR to predict tumor progression. Identification of protein markers linked to disease recurrence may yield insights into tumor biology. When combined with features known to be associated with outcome, such as BRCA mutation, PROVAR may provide clinically useful predictions of time to tumor recurrence.
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Identifiers
What Socratic 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 Socratic graph.