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.

Machine Learning Reveals Lipidome Remodeling Dynamics in a Mouse Model of Ovarian Cancer

“…Long-chain ceramides have been identified as possible diagnostic biomarkers of human epithelial ovarian cancer (41). Sphingolipid metabolism has also been implicated in regulating autophagy (42).…”

“…For example, HexCer and sphingomyelins (SM) are derived from Cer, while SM and HexCer can be degraded to Cer by sphingomyelinases (SMAse) and cerebrosidases, respectively. Altered sphingolipid metabolism has been implicated in leukemia (38), hepatocellular (39), colorectal (40) and ovarian cancers (41).…”

Unique Roles of Sphingolipids in Selected Malignant and Nonmalignant Lesions of Female Reproductive System

“…At the same time, the aggressive biological nature of the above cancer contributes to the destruction of endothelial cells at the advanced stages of the disease. We may conclude that, because of the malignancy of this neoplasm, most of the described blood cell sphingolipids are used in many processes and despite the upper level of platelets, the total levels of sphingolipids are quite constant [6, 46, 47].…”

Discovery of potential therapeutic targets for non-small cell lung cancer using high-throughput metabolomics analysis based on liquid chromatography coupled with tandem mass spectrometry

“…Metabolomic features might be applied in investigating the mechanisms of cancer. 21 Targeted metabolite pathway analysis Using the "pathway analysis" module within the MetaboAnalyst soware, the above-mentioned 13 metabolites were further analyzed to gain insight into the metabolic mechanism of NSCLC, and the results show that 11 kinds of metabolic alteration, including glycine, serine and threonine metabolism, aminoacyl-tRNA biosynthesis, tyrosine metabolism and sphingolipid metabolism with impact indices of 0.1360, 0.056, 0.04 and 0.0257, were involved in NSCLC as shown in Fig. S4.…”