Discovery and Validation of Novel Biomarkers for Detection of Epithelial Ovarian Cancer Article Swipe
Hagen Kulbe
,
Raik Otto
,
Silvia Darb‐Esfahani
,
Hedwig Lammert
,
Salem Abobaker
,
Gabriele Welsch
,
Radoslav Chekerov
,
Reinhold Schäfer
,
Duska Dragun
,
Michael Hummel
,
Ulf Leser
,
Jalid Sehouli
,
Elena Ioana Braicu
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.3390/cells8070713
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.3390/cells8070713
Detection of epithelial ovarian cancer (EOC) poses a critical medical challenge. However, novel biomarkers for diagnosis remain to be discovered. Therefore, innovative approaches are of the utmost importance for patient outcome. Here, we present a concept for blood-based biomarker discovery, investigating both epithelial and specifically stromal compartments, which have been neglected in search for novel candidates. We queried gene expression profiles of EOC including microdissected epithelium and adjacent stroma from benign and malignant tumours. Genes significantly differentially expressed within either the epithelial or the stromal compartments were retrieved. The expression of genes whose products are secreted yet absent in the blood of healthy donors were validated in tissue and blood from patients with pelvic mass by NanoString analysis. Results were confirmed by the comprehensive gene expression database, CSIOVDB (Ovarian cancer database of Cancer Science Institute Singapore). The top 25% of candidate genes were explored for their biomarker potential, and twelve were able to discriminate between benign and malignant tumours on transcript levels (p < 0.05). Among them T-cell differentiation protein myelin and lymphocyte (MAL), aurora kinase A (AURKA), stroma-derived candidates versican (VCAN), and syndecan-3 (SDC), which performed significantly better than the recently reported biomarker fibroblast growth factor 18 (FGF18) to discern malignant from benign conditions. Furthermore, elevated MAL and AURKA expression levels correlated significantly with a poor prognosis. We identified promising novel candidates and found the stroma of EOC to be a suitable compartment for biomarker discovery.
Related Topics
Concepts
Stroma
Biomarker
Stromal cell
Biomarker discovery
Ovarian cancer
Cancer
Biology
Cancer research
Versican
Pathology
Medicine
Gene
Immunology
Immunohistochemistry
Proteomics
Extracellular matrix
Genetics
Proteoglycan
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/cells8070713
- https://www.mdpi.com/2073-4409/8/7/713/pdf?version=1563182364
- OA Status
- gold
- Cited By
- 46
- References
- 62
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2959755208
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2959755208Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/cells8070713Digital Object Identifier
- Title
-
Discovery and Validation of Novel Biomarkers for Detection of Epithelial Ovarian CancerWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-07-12Full publication date if available
- Authors
-
Hagen Kulbe, Raik Otto, Silvia Darb‐Esfahani, Hedwig Lammert, Salem Abobaker, Gabriele Welsch, Radoslav Chekerov, Reinhold Schäfer, Duska Dragun, Michael Hummel, Ulf Leser, Jalid Sehouli, Elena Ioana BraicuList of authors in order
- Landing page
-
https://doi.org/10.3390/cells8070713Publisher landing page
- PDF URL
-
https://www.mdpi.com/2073-4409/8/7/713/pdf?version=1563182364Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2073-4409/8/7/713/pdf?version=1563182364Direct OA link when available
- Concepts
-
Stroma, Biomarker, Stromal cell, Biomarker discovery, Ovarian cancer, Cancer, Biology, Cancer research, Versican, Pathology, Medicine, Gene, Immunology, Immunohistochemistry, Proteomics, Extracellular matrix, Genetics, ProteoglycanTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
46Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 9, 2023: 7, 2022: 9, 2021: 10Per-year citation counts (last 5 years)
- References (count)
-
62Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| institutions_distinct_count | 13 |
| corresponding_institution_ids | https://openalex.org/I39343248, https://openalex.org/I75951250, https://openalex.org/I7877124 |
| citation_normalized_percentile.value | 0.96451198 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |