Data on metabolomic profiling of ovarian cancer patients' serum for potential diagnostic biomarkers
| Autor: | Darja Arko, Marko Bitenc, Nejc Kozar, Rosa Argamasilla, Nandu Goswami, Kristi Kruusmaa, Antonio Adsuar, Iztok Takač |
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| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Oncology medicine.medical_specialty First line lcsh:Computer applications to medicine. Medical informatics 03 medical and health sciences 0302 clinical medicine Metabolomics Internal medicine Healthy control medicine Diagnostic biomarker lcsh:Science (General) Proteomics and Biochemistry Univariate analysis Multidisciplinary business.industry Cancer medicine.disease 030104 developmental biology Metabolomic profiling 030220 oncology & carcinogenesis lcsh:R858-859.7 Ovarian cancer business lcsh:Q1-390 |
| Zdroj: | Data in Brief, Vol 18, Iss, Pp 1825-1831 (2018) Data in Brief |
| ISSN: | 2352-3409 |
| DOI: | 10.1016/j.dib.2018.04.081 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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