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Cited by 283

The DOI matches its Crossref record; 92 of the 92 references it cites were found in a registry or an index.

Molecular Biomarkers for the Early Detection of Ovarian Cancer

Open access
Zhang, Ruiqian, Siu, Michelle K. Y., Ngan, Hextan Y. S., Chan, Karen K. L.·2022·International Journal of Molecular Sciences

Abstract

Ovarian cancer is the deadliest gynecological cancer, leading to over 152,000 deaths each year. A late diagnosis is the primary factor causing a poor prognosis of ovarian cancer and often occurs due to a lack of specific symptoms and effective biomarkers for an early detection. Currently, cancer antigen 125 (CA125) is the most widely used biomarker for ovarian cancer detection, but this approach is limited by a low specificity. In recent years, multimarker panels have been developed by combining molecular biomarkers such as human epididymis secretory protein 4 (HE4), ultrasound results, or menopausal status to improve the diagnostic efficacy. The risk of ovarian malignancy algorithm (ROMA), the risk of malignancy index (RMI), and OVA1 assays have also been clinically used with improved sensitivity and specificity. Ongoing investigations into novel biomarkers such as autoantibodies, ctDNAs, miRNAs, and DNA methylation signatures continue to aim to provide earlier detection methods for ovarian cancer. This paper reviews recent advancements in molecular biomarkers for the early detection of ovarian cancer.

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Metadata

Authors
Zhang, R., Siu, M. K. Y., Ngan, H. Y. S., & Chan, K. K. L.
Venue
International Journal of Molecular Sciences
Publisher
MDPI AG
Published
2022-10-10
Volume
23
Issue
19
Pages
12041
ISSN
1422-0067 (ISSN-L)
OpenAlex
W4304136151
Cited by
283
References
92
FWCI
40.07
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Molecular Biomarkers for the Early Detection of Ovarian Cancer. https://app.cochat.ai/papers/10.3390/ijms231912041
The paper
Zhang, R., Siu, M. K. Y., Ngan, H. Y. S., & Chan, K. K. L. (2022). Molecular Biomarkers for the Early Detection of Ovarian Cancer. International Journal of Molecular Sciences. https://doi.org/10.3390/ijms231912041

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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