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Search: WFRF:(Gyllensten Ulf) > Royal Institute of Technology > University of Gothenburg

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  • Gyllensten, Ulf B., et al. (author)
  • Next Generation Plasma Proteomics Identifies High-Precision Biomarker Candidates for Ovarian Cancer
  • 2022
  • In: Cancers. - : MDPI AG. - 2072-6694. ; 14:7
  • Journal article (peer-reviewed)abstract
    • Simple Summary Ovarian cancer is the eighth most common cancer among women and has a 5-year survival of only 30-50%. The survival is close to 90% for patients in stage I but only 20% for patients in stage IV. The presently available biomarkers have insufficient sensitivity and specificity for early detection and there is an urgent need to identify novel biomarkers. The aim of our study was to broadly measure protein biomarkers to find tests for the early detection of ovarian cancer. We found that combinations of 4-7 protein biomarkers can provide highly accurate detection of early- and late-stage ovarian cancer compared to benign conditions. The performance of the tests was then validated in a second independent cohort. Background: Ovarian cancer is the eighth most common cancer among women and has a 5-year survival of only 30-50%. The survival is close to 90% for patients in stage I but only 20% for patients in stage IV. The presently available biomarkers have insufficient sensitivity and specificity for early detection and there is an urgent need to identify novel biomarkers. Methods: We employed the Explore PEA technology for high-precision analysis of 1463 plasma proteins and conducted a discovery and replication study using two clinical cohorts of previously untreated patients with benign or malignant ovarian tumours (N = 111 and N = 37). Results: The discovery analysis identified 32 proteins that had significantly higher levels in malignant cases as compared to benign diagnoses, and for 28 of these, the association was replicated in the second cohort. Multivariate modelling identified three highly accurate models based on 4 to 7 proteins each for separating benign tumours from early-stage and/or late-stage ovarian cancers, all with AUCs above 0.96 in the replication cohort. We also developed a model for separating the early-stage from the late-stage achieving an AUC of 0.81 in the replication cohort. These models were based on eleven proteins in total (ALPP, CXCL8, DPY30, IL6, IL12, KRT19, PAEP, TSPAN1, SIGLEC5, VTCN1, and WFDC2), notably without MUCIN-16. The majority of the associated proteins have been connected to ovarian cancer but not identified as potential biomarkers. Conclusions: The results show the ability of using high-precision proteomics for the identification of novel plasma protein biomarker candidates for the early detection of ovarian cancer.
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Type of publication
journal article (2)
Type of content
peer-reviewed (2)
Author/Editor
Engstrand, L (1)
Gyllensten, Ulf B. (1)
Edqvist, Per-Henrik ... (1)
Söderkvist, Peter (1)
Uhlén, Mathias (1)
Cavelier, Lucia (1)
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Rosenquist, R. (1)
Jacobsson, Bo, 1960 (1)
Fioretos, Thoas (1)
Nordmark, Gunnel (1)
Stenmark, Bianca, 19 ... (1)
Wedell, A (1)
Axelsson, Tomas (1)
Helenius, Gisela, 19 ... (1)
Enroth, Stefan (1)
Larsson, Pär (1)
Johansson, Åsa (1)
Wirta, Valtteri (1)
Levin, Lars-Åke (1)
Palmqvist, Richard (1)
Wadelius, Mia (1)
Stålberg, Karin (1)
Åberg, Mikael (1)
Johansson, Maria (1)
Lindstrand, A (1)
Sjöblom, Tobias (1)
Repsilber, Dirk, 197 ... (1)
Gyllensten, Ulf (1)
Palmqvist, Lars, 196 ... (1)
Nordlund, Jessica (1)
Liljedahl, Ulrika (1)
Svensson, Johanna (1)
Ehrencrona, Hans (1)
Gisselsson, David (1)
Melin, Malin (1)
Landström, Maréne (1)
Nordgren, Ann, 1964 (1)
Botling, Johan (1)
Öst, Torbjörn (1)
Sundfeldt, Karin, 19 ... (1)
Sikora, P. (1)
Berglund, Eva (1)
Lycke, Maria (1)
Strid, Tobias (1)
Friedman, M. (1)
Lyander, Anna (1)
Mölling, Paula, 1971 ... (1)
Stranneheim, H (1)
Melin, M (1)
Wheelock, C. E. (1)
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University
University of Gothenburg  (2)Undo refine
Royal Institute of Technology  (2)Undo refine
Uppsala University (2)
Umeå University (1)
Örebro University (1)
Linköping University (1)
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Lund University (1)
Karolinska Institutet (1)
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Language
English (2)
Research subject (UKÄ/SCB)
Medical and Health Sciences (2)
Year
2022 (2)

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