Discrimination between malignant and benign ovarian tumors by plasma metabolomic profiling using ultra performance liquid chromatography/mass spectrometry

Tao, Zhen; Wu, Xiaoyan; Yin, Mingzhu; Fan, Lijun; Zhang, Haiyu; Zhao, Fang; Zhang, Wang; Ke, Chaofu; Zhang, Guangming; Hou, Yan; Zhou, Xiao‐Hua; Lou, Ge; Li, Kang

doi:10.1016/j.cca.2012.01.026

Cited by 77 publications

(84 citation statements)

References 41 publications

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“…CYP2E1 is implicated in human malignancies including gastrointestinal and respiratory tumors [20,21], and CYP2E1 gene polymorphisms are associated with the occurrence of breast and cervical cancers in females [22,23]. Another study showed that 2-piperidone is a specific marker of OC [24], which is in accordance with our findings.…”

Section: Discussionsupporting

confidence: 81%

Identification of Potential Biomarkers and Metabolic Profiling of Serum in Ovarian Cancer Patients Using UPLC/Q-TOF MS

Yang¹,

Tian²,

Jiang³

et al. 2018

Cell Physiol Biochem

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Background/Aims: Ovarian cancer (OC) is a malignant neoplasm of the female reproductive system with a high mortality rate. Identifying useful biomarkers and clarifying the molecular pathogenesis of OC are critical for early diagnosis and treatment. The aim of the study was to identify candidate biomarkers and explore metabolic changes of OC. Methods: A two-stage design was used in our study, with a discovery cohort of OC cases (n = 30) and controls (n = 30) and an independent cohort of cases (n = 17) and controls (n = 18) for validation. The serum metabolic profiling was investigated by ultra-performance liquid chromatography and quadrupole time-of-fight mass spectrometry with positive electrospray ionization. Results: A total of 18 metabolites closely related to OC were identified in the discovery stage, of which 12 were confirmed in the validation cohort. Metabolic pathways in OC related to these biomarkers included fatty acid β-oxidation, phospholipid metabolism, and bile acid metabolism, which are closely related to the proliferation, invasion, and metastasis of cancer cells. Multiple logistic regression analysis of these metabolites showed that 2-piperidinone and 1-heptadecanoylglycerophosphoethanolamine were potential biomarkers of OC, with high sensitivity (96.7%), specificity (66.7%), and area under the receiver operating characteristic curve value (0.894). Conclusion: These findings provide insight into the pathogenesis pathogenesis of OC and may be useful for clinical diagnosis and treatment.

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Section: Discussionsupporting

confidence: 81%

Identification of Potential Biomarkers and Metabolic Profiling of Serum in Ovarian Cancer Patients Using UPLC/Q-TOF MS

Yang¹,

Tian²,

Jiang³

et al. 2018

Cell Physiol Biochem

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“…47 Individual discriminating metabolites associated with autism and controls were selected primarily according to the VIP (> 1.0) values and a statistical test for difference (p < 0.05) between patients wiht autism and controls. While some suggest using a VIP greater than 2 52 as a criterion for selecting an individual metabolite, we chose a VIP greater than 1, similar to other studies, 53,54 in order to analyze more metabolites. Our approach took into account both predicting capability and statistical significance of individual metabolites in association with autism.…”

Section: Data Preprocessing and Analysismentioning

confidence: 99%

Potential serum biomarkers from a metabolomics study of autism

Wang

Liang

Wang

et al. 2016

jpn

104

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“…Decreased plasma L-tryptophan, LPC(18:3), LPC(14:0) and 2-piperidinone were observed in EOC patients when compared to the BOT patients. Tryptophan and LPCs have been suspected to participate in cancer progression, and 2-piperidinone might be a novel biomarker for EOC [73]. Accelerated L-tryptophan degradation has been observed in the blood of EOC patients when compared to the healthy controls [74].…”

Section: Ovarian Cancermentioning

confidence: 99%

UPLC-based metabonomic applications for discovering biomarkers of diseases in clinical chemistry

Zhao

Cheng

Vaziri

et al. 2014

Clinical Biochemistry

118

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a b s t r a c t a r t i c l e i n f oObjectives: Metabonomics is a powerful and promising analytic tool that allows assessment of global lowmolecular-weight metabolites in biological systems. It has a great potential for identifying useful biomarkers for early diagnosis, prognosis and assessment of therapeutic interventions in clinical practice. The aim of this review is to provide a brief summary of the recent advances in UPLC-based metabonomic approach for biomarker discovery in a variety of diseases, and to discuss their significance in clinical chemistry.Design and methods: All the available information on UPLC-based metabonomic applications for discovering biomarkers of diseases were collected via a library and electronic search (using Web of Science, Pubmed, ScienceDirect, Springer, Google Scholar, etc.).Results: Metabonomics has been used in clinical chemistry to identify and evaluate potential biomarkers and therapeutic targets in various diseases affecting the liver (hepatocarcinoma and liver cirrhosis), lung (lung cancer and pneumonia), gastrointestinal tract (colorectal cancer) and urogenital tract (prostate cancer, ovarian cancer and chronic kidney disease), as well as metabolic diseases (diabetes) and neuropsychiatric disorders (Alzheimer's disease and schizophrenia), etc.Conclusions: The information provided highlights the potential value of determination of endogenous lowmolecular-weight metabolites and the advantages and potential drawbacks of the application of UPLC-based metabonomics in clinical setting.

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Discrimination between malignant and benign ovarian tumors by plasma metabolomic profiling using ultra performance liquid chromatography/mass spectrometry

Cited by 77 publications

References 41 publications

Identification of Potential Biomarkers and Metabolic Profiling of Serum in Ovarian Cancer Patients Using UPLC/Q-TOF MS

Identification of Potential Biomarkers and Metabolic Profiling of Serum in Ovarian Cancer Patients Using UPLC/Q-TOF MS

Potential serum biomarkers from a metabolomics study of autism

UPLC-based metabonomic applications for discovering biomarkers of diseases in clinical chemistry

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