ArticleClinical cancer research : an official journal of the American Association for Cancer Research2021
OCaMIR-A Noninvasive, Diagnostic Signature for Early-Stage Ovarian Cancer: A Multi-cohort Retrospective and Prospective Study.
Article in Clinical cancer research : an official journal of the American Association for Cancer Research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 27 citations in OpenAlex.
- Development and validation of a miRNA-based prognostic model for high-grade serous ovarian cancer: a retrospective cohort study.Lancet regional health. Americas · 2026Article
- Targeting Autophagy in Ovarian Cancer: The Emerging Role of Ginsenosides.Cancer medicine · 2026Review
- Potential function of miRNA as a diagnostic biomarker for human ovarian cancer.Discover oncology · 2025Review
- Ultrasensitive Impedimetric Biosensor for Ovarian Cancer Screening.Indian journal of microbiology · 2025Article
- Upregulation of miRNA-450b-5p targets ACTB to affect drug resistance and prognosis of ovarian cancer via the PI3K/Akt signaling pathway.Translational cancer research · 2024Article
- The emerging roles of miRNA-mediated autophagy in ovarian cancer.Cell death & disease · 2024Review
- Exosomal miR-1470 is a diagnostic biomarker and promotes cell proliferation and metastasis in colorectal cancer.Cancer medicine · 2024Article
- Pan-cancer characterization of cell-free immune-related miRNA identified as a robust biomarker for cancer diagnosis.Molecular cancer · 2024Article
- Ultrahigh Resolution Lipid Mass Spectrometry Imaging of High-Grade Serous Ovarian Cancer Mouse Models.bioRxiv : the preprint server for biology · 2023Article
- Ultrahigh resolution lipid mass spectrometry imaging of high-grade serous ovarian cancer mouse models.Frontiers in chemistry · 2023Article
- The Role of miRNA in Ovarian Cancer: an Overview.Reproductive sciences (Thousand Oaks, Calif.) · 2022Review
- Current and Emerging Methods for Ovarian Cancer Screening and Diagnostics: A Comprehensive Review.Cancers · 2022Review
- Toward More Comprehensive Homologous Recombination Deficiency Assays in Ovarian Cancer, Part 1: Technical Considerations.Cancers · 2022Review
- Non-coding RNAs as liquid biopsy biomarkers in cancer.British journal of cancer · 2022Review
- Predict ovarian cancer by pairing serum miRNAs: Construct of single sample classifiers.Frontiers in medicine · 2022Article
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Authors and funding
12 authors at 8 institutions in 4 countries.
Funding
Abstract
purposeDue to the lack of effective screening approaches and early detection biomarkers, ovarian cancer has the highest mortality rates among gynecologic cancers. Herein, we undertook a systematic biomarker discovery and validation approach to identify microRNA (miRNA) biomarkers for the early detection of ovarian cancer. EXPERIMENTAL
designDuring the discovery phase, we performed small RNA sequencing in stage I high-grade serous ovarian cancer (
resultsThe OCaMIR demonstrated a robust diagnostic accuracy in the stage I high-grade serous ovarian cancer patients in the discovery cohort (AUC = 0.99), which was consistently reproducible in both stage I (AUC = 0.96) and all stage patients (AUC = 0.89) in the TCGA cohort. Logistic regression-based training and validation of OCaMIR achieved AUC values of 0.89 (GSE106817), 0.85 (GSE31568), 0.86 (GSE113486), and 0.82 (Czech Republic cohort) in the retrospective serum validation cohorts, as well as prospective validation cohort (AUC = 0.92). More importantly, OCaMIR demonstrated a significantly superior diagnostic performance compared with CA125 levels, even in stage I patients, and was more cost-effective, highlighting its potential role for screening and early detection of ovarian cancer.
conclusionsSmall RNA sequencing identified a robust noninvasive miRNA signature for early-stage serous ovarian cancer detection.
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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.