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Data on metabolomic profiling of ovarian cancer patients' serum for potential diagnostic biomarkers.

ABSTRACT: The data presented here are related to the research paper entitled "Metabolomic profiling suggests long chain ceramides and sphingomyelins as a possible diagnostic biomarker of epithelial ovarian cancer." (Kozar et al., 2018) [1]. Metabolomic profiling was performed on 15 patients with ovarian cancer, 21 healthy controls and 21 patients with benign gynecological conditions. HPLC-TQ/MS was performed on all samples. PLS-DA was used for the first line classification of epithelial ovarian cancer and healthy control group based on metabolomic profiles. Random forest algorithm was used for building a prediction model based over most significant markers. Univariate analysis was performed on individual markers to determine their distinctive roles. Furthermore, markers were also evaluated for their biological significance in cancer progression.

SUBMITTER: Kozar N

PROVIDER: S-EPMC5998211 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Data on metabolomic profiling of ovarian cancer patients' serum for potential diagnostic biomarkers.

Kozar Nejc N Kruusmaa Kristi K Bitenc Marko M Argamasilla Rosa R Adsuar Antonio A Goswami Nandu N Arko Darja D Takač Iztok I

Data in brief 20180430

The data presented here are related to the research paper entitled "Metabolomic profiling suggests long chain ceramides and sphingomyelins as a possible diagnostic biomarker of epithelial ovarian cancer." (Kozar et al., 2018) [1]. Metabolomic profiling was performed on 15 patients with ovarian cancer, 21 healthy controls and 21 patients with benign gynecological conditions. HPLC-TQ/MS was performed on all samples. PLS-DA was used for the first line classification of epithelial ovarian cancer and ...[more]

PMID: 29904684

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Project description:FDA-cleared ovarian cancer biomarkers are limited to CA-125 and HE4 for monitoring and recurrence and OVA1, a multivariate panel consisting of CA-125 and four additional biomarkers, for referring patients to a specialist. Due to relatively poor performance of these tests, more accurate and broadly applicable biomarkers are needed. We evaluated the dysregulation of 259 candidate cancer markers in serum samples from 499 patients. Sera were collected prospectively at 11 monitored sites under a single well-defined protocol. All stages of ovarian cancer and common benign gynecological conditions were represented. To ensure consistency and comparability of biomarker comparisons, all measurements were performed on a single platform, at a single site, using a panel of rigorously calibrated, qualified, high-throughput, multiplexed immunoassays and all analyses were conducted using the same software. Each marker was evaluated independently for its ability to differentiate ovarian cancer from benign conditions. A total of 175 markers were dysregulated in the cancer samples. HE4 (AUC=0.933) and CA-125 (AUC=0.907) were the most informative biomarkers, followed by IL-2 receptor ?, ?1-antitrypsin, C-reactive protein, YKL-40, cellular fibronectin, CA-72-4 and prostasin (AUC>0.800). To improve the discrimination between cancer and benign conditions, a simple multivariate combination of markers was explored using logistic regression. When combined into a single panel, the nine most informative individual biomarkers yielded an AUC value of 0.950, significantly higher than obtained when combining the markers in the OVA1 panel (AUC 0.912). Additionally, at a threshold sensitivity of 90%, the combination of the top 9 markers gave 88.9% specificity compared to 63.4% specificity for the OVA1 markers. Although a blinded validation study has not yet been performed, these results indicate that alternative biomarker combinations might lead to significant improvements in the detection of ovarian cancer.

Dataset Information

Data on metabolomic profiling of ovarian cancer patients' serum for potential diagnostic biomarkers.

Publications

Data on metabolomic profiling of ovarian cancer patients' serum for potential diagnostic biomarkers.

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