Metabolomic profiling suggests long chain ceramides and sphingomyelins as a possible diagnostic biomarker of epithelial ovarian cancer

Kozar, Nejc; Kruusmaa, Kristi; Bitenc, Marko; Argamasilla, Rosa; Adsuar, Antonio; Goswami, Nandu; Arko, Darja; Takač, Iztok

doi:10.1016/j.cca.2018.02.029

Cited by 35 publications

(32 citation statements)

References 29 publications

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“…At present, metabonomics is applicated in a variety of tumors [10][11][12][13]. Biological samples, including urine, plasma and tumor tissue, have been applied to metabolism-based ovarian cancer research [14][15][16][17]. It is often used to distinguish between healthy and ovarian cancer patients, ovarian malignant tumors and borderline tumors, and can also be used to detect recurrent tumors.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Potential Prognostic Tumor Markers of Re-ascites After Chemotherapy for Ovarian Cancer Based on LC/MS Method

Miao¹,

Liu²,

Jing³

et al. 2020

Preprint

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Background: Tumor cells are not only in a state of exuberant metabolism, but also inseparable from the surrounding microenvironment for the occurrence and development of tumors. By means of metabonomics technology, the metabolic profiles of re-ascites after chemotherapy in ovarian cancer ascites microenvironment were compared, and new tumor markers were screened for the treatment and prognosis of ovarian cancer.Methods: Thirty-seven cases of ovarian cancer were selected, of which 12 cases had ovarian cancer tissues, ascites supernatant and precipitated cells. By using UHPLC-Q-TOF MS/MS and combined with HMDB database, differential metabolites are identified and then differential pathways are enriched. Results: The results revealed that 10 differential metabolites were screened between chemotherapy and no-chemotherapy groups, and 9 differential metabolites were found in chemotherapy-sensitive and chemotherapy-resistant groups, in the ascites of ovarian cancer. Among the 19 differential metabolites, 9 metabolites combined with serum CA125 were analyzed by receiver operating characteristic analyses, which showed more the predictive value of diagnosis of malignancy. Among them, Tow differential metabolites (2/9) were correlated with the expression of CA125, five differential metabolite (5/9) related enzyme genes were statistically significant in overall survival and progression free survival (PFS) analysis. Four differential metabolites (4/9) were correlated with the expression of uric acid, creatinine, γ-GGT and ALP in serum. Conclusions: The results suggest that differential metabolites are mainly involved in PPAR signal pathway, steroid biosynthesis, fatty acid metabolism, secondary bile acid biosynthesis and so on. Lipid metabolites of ovarian cancer ascites cells may promote fatty acid oxidation by activating AMPK, promote lipid synthesis through mTOR/PPAR, provide energy for cancer cells, inhibit apoptosis and promote metastasis. Thus targeted lipid metabolism signal transduction axis can inhibit ovarian cancer metastasis and drug resistance.

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Section: Discussionmentioning

confidence: 99%

Prediction of Potential Prognostic Tumor Markers of Re-ascites After Chemotherapy for Ovarian Cancer Based on LC/MS Method

Miao¹,

Liu²,

Jing³

et al. 2020

Preprint

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Section: Metabolomics Diagnostic Biomarkers Of Ocmentioning

confidence: 99%

“…The upregulation of blood Cer has been frequently reported in OC studies [ 38 , 41 , 46 , 52 ]. A wide MS targeted study, encompassing 232 lipids, revealed a significant increase of certain Cer in EOC patients, i.e., C16, C22, and C24 derivatives [ 46 ]. The high levels of these Cer may be associated with the enhanced activity of the ceramide synthases (CerS) CerS6 and CerS2 isoforms that seem to promote tumor growth by acting as apoptosis inhibitors [ 46 , 67 ].…”

Section: Metabolomics Diagnostic Biomarkers Of Ocmentioning

confidence: 99%

“…Various specific lipids have been found to be altered in OC patients compared with healthy individuals ( Figure S1a–c ). In the considered metabolomics studies, OC patients showed an increase of total ceramides (Cer) [ 38 , 41 , 46 , 52 ], carnitines [ 37 , 44 , 45 ] and fatty acids (FA) [ 42 , 43 , 53 ], and a decrease of triglycerides (TG) [ 34 ] and lysophosphatidylcholines (LPC) [ 28 , 39 , 45 , 49 , 52 , 54 ].…”

Section: Metabolomics Diagnostic Biomarkers Of Ocmentioning

confidence: 99%

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Emerging Role of Metabolomics in Ovarian Cancer Diagnosis

et al. 2020

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Ovarian cancer is considered a silent killer due to the lack of clear symptoms and efficient diagnostic tools that often lead to late diagnoses. Over recent years, the impelling need for proficient biomarkers has led researchers to consider metabolomics, an emerging omics science that deals with analyses of the entire set of small-molecules (≤1.5 kDa) present in biological systems. Metabolomics profiles, as a mirror of tumor–host interactions, have been found to be useful for the analysis and identification of specific cancer phenotypes. Cancer may cause significant metabolic alterations to sustain its growth, and metabolomics may highlight this, making it possible to detect cancer in an early phase of development. In the last decade, metabolomics has been widely applied to identify different metabolic signatures to improve ovarian cancer diagnosis. The aim of this review is to update the current status of the metabolomics research for the discovery of new diagnostic metabolomic biomarkers for ovarian cancer. The most promising metabolic alterations are discussed in view of their potential biological implications, underlying the issues that limit their effective clinical translation into ovarian cancer diagnostic tools.

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“… 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.…”

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confidence: 99%

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

et al. 2018

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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.

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Metabolomic profiling suggests long chain ceramides and sphingomyelins as a possible diagnostic biomarker of epithelial ovarian cancer

Cited by 35 publications

References 29 publications

Prediction of Potential Prognostic Tumor Markers of Re-ascites After Chemotherapy for Ovarian Cancer Based on LC/MS Method

Prediction of Potential Prognostic Tumor Markers of Re-ascites After Chemotherapy for Ovarian Cancer Based on LC/MS Method

Emerging Role of Metabolomics in Ovarian Cancer Diagnosis

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

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Metabolomic profiling suggests long chain ceramides and sphingomyelins as a possible diagnostic biomarker of epithelial ovarian cancer

Cited by 35 publications

References 29 publications

Prediction of Potential Prognostic Tumor Markers of Re-ascites After Chemotherapy for Ovarian Cancer Based on LC/MS Method ​

Prediction of Potential Prognostic Tumor Markers of Re-ascites After Chemotherapy for Ovarian Cancer Based on LC/MS Method ​

Emerging Role of Metabolomics in Ovarian Cancer Diagnosis

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

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Prediction of Potential Prognostic Tumor Markers of Re-ascites After Chemotherapy for Ovarian Cancer Based on LC/MS Method

Prediction of Potential Prognostic Tumor Markers of Re-ascites After Chemotherapy for Ovarian Cancer Based on LC/MS Method