Data on metabolomic profiling of ovarian cancer patients' serum for potential diagnostic biomarkers
Nejc Kozar,
Kristi Kruusmaa,
Marko Bitenc,
Rosa Argamasilla,
Antonio Adsuar,
Nandu Goswami,
Darja Arko,
Iztok Takač
Affiliations
Nejc Kozar
Clinic of Gynaecology and Perinatology, University Medical Centre Maribor, Ljubljanska 5, 2000 Maribor, Slovenia; Faculty of Medicine, University of Maribor, Taborska ulica 8, 2000 Maribor, Slovenia; Corresponding author at: Clinic of Gynaecology and Perinatology, University Medical Centre Maribor, Ljubljanska 5, 2000 Maribor, Slovenia.
Kristi Kruusmaa
Faculty of Pharmacy, University of Ljubljana, Aškerčeva cesta 7, 1000 Ljubljana, Slovenia; Universal Diagnostics, S.L. Centre of Research Technology and Innovation, University of Seville, Avenida Reina Mercedes s/n, 41012 Seville, Spain
Marko Bitenc
Universal Diagnostics, S.L. Centre of Research Technology and Innovation, University of Seville, Avenida Reina Mercedes s/n, 41012 Seville, Spain
Rosa Argamasilla
Universal Diagnostics, S.L. Centre of Research Technology and Innovation, University of Seville, Avenida Reina Mercedes s/n, 41012 Seville, Spain
Antonio Adsuar
Universal Diagnostics, S.L. Centre of Research Technology and Innovation, University of Seville, Avenida Reina Mercedes s/n, 41012 Seville, Spain
Nandu Goswami
Institute of Physiology, Medical University of Graz, Harrachgasse 21/V, 8010 Graz, Austria
Darja Arko
Faculty of Medicine, University of Maribor, Taborska ulica 8, 2000 Maribor, Slovenia
Iztok Takač
Clinic of Gynaecology and Perinatology, University Medical Centre Maribor, Ljubljanska 5, 2000 Maribor, Slovenia; Faculty of Medicine, University of Maribor, Taborska ulica 8, 2000 Maribor, Slovenia
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.
