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1000 Titel
  • Biomarker-based early detection of epithelial ovarian cancer based on a five-protein signature in patient’s plasma – a prospective trial
1000 Autor/in
  1. Hasenburg, A. |
  2. Eichkorn, D. |
  3. Vosshagen, F. |
  4. Obermayr, E. |
  5. Geroldinger, A. |
  6. Zeillinger, R. |
  7. Bossart, Michaela |
1000 Erscheinungsjahr 2021
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2021-09-16
1000 Erschienen in
1000 Quellenangabe
  • 21(1):1037
1000 Copyrightjahr
  • 2021
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1186/s12885-021-08682-y |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8447799/ |
1000 Publikationsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • Background!#!Trial on five plasma biomarkers (CA125, HE4, OPN, leptin, prolactin) and their possible role in differentiating benign from malignant ovarian tumors.!##!Methods!#!In this unicentric prospective trial preoperative blood samples of 43 women with ovarian masses determined for ovarian surgery were analyzed. 25 patients had pathologically confirmed benign, 18 malignant ovarian tumors. Blood plasma was analyzed for CA125, HE4, OPN, leptin, prolactin and MIF by multiplex immunoassay analysis. Each single protein and a logistical regression model including all the listed proteins were tested as preoperative predictive marker for suspect ovarian masses.!##!Results!#!Plasma CA125 was confirmed as a highly accurate tumor marker in ovarian cancer. HE4, OPN, leptin and prolactin plasma levels differed significantly between benign and malignant ovarian masses. With a logistical regression model a formula including CA125, HE4, OPN, leptin and prolactin was developed to predict malignant ovarian tumors. With a discriminatory AUC of 0.96 it showed to be a highly sensitive and specific diagnostic test for a malignant ovarian tumor.!##!Conclusions!#!The calculated formula with the combination of CA125, HE4, OPN, leptin and prolactin plasma levels surpasses each single marker in its diagnostic value to discriminate between benign and malignant ovarian tumors. The formula, applied to our patient population was highly accurate but should be validated in a larger cohort.!##!Trial registration!#!Clinical Trials.gov under NCT01763125 , registered Jan. 8, 2013.
1000 Sacherschließung
lokal Area Under Curve [MeSH]
lokal Aged, 80 and over [MeSH]
lokal Aged [MeSH]
lokal Liquid biopsy
lokal Surgical oncology, cancer imaging, and interventional therapeutics
lokal WAP Four-Disulfide Core Domain Protein 2/analysis [MeSH]
lokal Biomarker
lokal Ovarian Neoplasms/blood [MeSH]
lokal Early Detection of Cancer [MeSH]
lokal Carcinoma, Ovarian Epithelial/diagnosis [MeSH]
lokal Research Article
lokal Ovarian Neoplasms/pathology [MeSH]
lokal Female [MeSH]
lokal Osteopontin/blood [MeSH]
lokal Adult [MeSH]
lokal Ovarian cancer
lokal Humans [MeSH]
lokal Prospective Studies [MeSH]
lokal Logistic Models [MeSH]
lokal Middle Aged [MeSH]
lokal CA125
lokal Prolactin/blood [MeSH]
lokal Ovarian Neoplasms/diagnosis [MeSH]
lokal Carcinoma, Ovarian Epithelial/pathology [MeSH]
lokal Carcinoma, Ovarian Epithelial/blood [MeSH]
lokal CA-125 Antigen/blood [MeSH]
lokal Biomarkers, Tumor/blood [MeSH]
lokal Young Adult [MeSH]
lokal Protein panel
lokal Leptin/blood [MeSH]
1000 Liste der Beteiligten
  1. https://frl.publisso.de/adhoc/uri/SGFzZW5idXJnLCBBLg==|https://frl.publisso.de/adhoc/uri/RWljaGtvcm4sIEQu|https://frl.publisso.de/adhoc/uri/Vm9zc2hhZ2VuLCBGLg==|https://frl.publisso.de/adhoc/uri/T2Jlcm1heXIsIEUu|https://frl.publisso.de/adhoc/uri/R2Vyb2xkaW5nZXIsIEEu|https://frl.publisso.de/adhoc/uri/WmVpbGxpbmdlciwgUi4=|https://orcid.org/0000-0003-3218-912X
1000 Hinweis
  • DeepGreen-ID: 970a50a2ed664116b40490ab7a0b92f0 ; metadata provieded by: DeepGreen (https://www.oa-deepgreen.de/api/v1/), LIVIVO search scope life sciences (http://z3950.zbmed.de:6210/livivo), Crossref Unified Resource API (https://api.crossref.org/swagger-ui/index.html), to.science.api (https://frl.publisso.de/), ZDB JSON-API (beta) (https://zeitschriftendatenbank.de/api/), lobid - Dateninfrastruktur für Bibliotheken (https://lobid.org/resources/search)
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1000 Erstellt am 2023-11-15T17:02:41.766+0100
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1000 Zuletzt bearbeitet 2023-11-30T21:10:40.343+0100
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