OCaMIR-A Noninvasive, Diagnostic Signature for Early-Stage Ovarian Cancer: A Multi-cohort Retrospective and Prospective Study
Jazyk angličtina Země Spojené státy americké Médium print-electronic
Typ dokumentu časopisecké články, Research Support, N.I.H., Extramural, práce podpořená grantem, validační studie
Grantová podpora
R01 CA202797
NCI NIH HHS - United States
R01 CA184792
NCI NIH HHS - United States
U01 CA187956
NCI NIH HHS - United States
R01 CA181572
NCI NIH HHS - United States
R01 CA072851
NCI NIH HHS - United States
R01 CA227602
NCI NIH HHS - United States
PubMed
34035068
PubMed Central
PMC10327469
DOI
10.1158/1078-0432.ccr-21-0267
PII: 1078-0432.CCR-21-0267
Knihovny.cz E-zdroje
- MeSH
- časná detekce nádoru metody MeSH
- dospělí MeSH
- kohortové studie MeSH
- lidé středního věku MeSH
- lidé MeSH
- mikro RNA analýza MeSH
- nádorové biomarkery analýza MeSH
- nádory vaječníků chemie diagnóza patologie MeSH
- prospektivní studie MeSH
- retrospektivní studie MeSH
- senioři nad 80 let MeSH
- senioři MeSH
- serózní cystadenokarcinom chemie diagnóza patologie MeSH
- staging nádorů MeSH
- stupeň nádoru MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- senioři nad 80 let MeSH
- senioři MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Research Support, N.I.H., Extramural MeSH
- validační studie MeSH
- Názvy látek
- mikro RNA MeSH
- nádorové biomarkery MeSH
PURPOSE: Due 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 DESIGN: During the discovery phase, we performed small RNA sequencing in stage I high-grade serous ovarian cancer (n = 31), which was subsequently validated in multiple, independent data sets (TCGA, n = 543; GSE65819, n = 87). Subsequently, we performed multivariate logistic regression-based training in a serum data set (GSE106817, n = 640), followed by its independent validation in three retrospective data sets (GSE31568, n = 85; GSE113486, n = 140; Czech Republic cohort, n = 192) and one prospective serum cohort (n = 95). In addition, we evaluated the specificity of OCaMIR, by comparing its performance in several other cancers (GSE31568 cohort, n = 369). RESULTS: The 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. CONCLUSIONS: Small RNA sequencing identified a robust noninvasive miRNA signature for early-stage serous ovarian cancer detection.
Central European Institute of Technology Masaryk University Brno Czech Republic
Department of Biology Faculty of Medicine Masaryk University Brno Czech Republic
Department of Biomedical Sciences City University of Hong Kong Hong Kong P R China
Department of Gynecology Huzhou Maternity and Child Health Care Hospital Huzhou P R China
Department of Laboratory Medicine West China 2nd Hospital Sichuan University Chengdu P R China
Department of Obstetrics and Gynecology Baylor University Medical Center Dallas Texas
Department of Translational Genomics University of Southern California Los Angeles California
Masaryk Memorial Cancer Institute Brno Czech Republic
Shenzhen Research Institute City University of Hong Kong Shenzhen P R China
The Sixth Affiliated Hospital of Sun Yat Sen University Guangzhou P R China
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