Ornithine and breast cancer: a matched case–control study

Zhang, Jiayi; Tao, Baihui; Chong, Yiran; Ma, Shuang; Wu, Gang; Zhu, Hailong; Zhao, Yi; Zhao, S; Niu, Mengmeng; Zhang, Shutian; Wang, Tianyi; Yang, Shuman; Qiao, Wenjing; Vuong, Ann M.; Li, Jincheng; Zhu, Demiao; Tao, Wei

doi:10.1038/s41598-020-72699-9

Cited by 10 publications

(5 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Ornithine also plays an important role in the regulation of several metabolic processes leading to diseases like hyperornithinemia, hyperammonemia, gyrate atrophy, and cancer in humans [64]. High levels of ornithine have been reported to be a potential protective factor for BC [65]. Similarly, as we demonstrated for metastatic MDA-MB-231 cell line, OAT is overexpressed in hepatocellular carcinoma, and the inhibition of this enzyme has been suggested to be an effective therapy in mice [66,67].…”

Section: Discussionsupporting

confidence: 53%

Breast Cancer-Derived Microvesicles Are the Source of Functional Metabolic Enzymes as Potential Targets for Cancer Therapy

et al. 2021

View full text Add to dashboard Cite

Membrane-derived extracellular vesicles, referred to as microvesicles (MVs), have been proposed to participate in several cancer diseases. In this study, MV fractions were isolated by differential ultracentrifugation from a metastatic breast cancer (BC) cell line MDA-MB-231 and a non-cancerous breast cell line MCF10A, then analyzed by nano-liquid chromatography coupled to tandem mass spectrometry. A total of 1519 MV proteins were identified from both cell lines. The data obtained were compared to previously analyzed proteins from small extracellular vesicles (sEVs), revealing 1272 proteins present in both MVs and sEVs derived from the MDA-MB-231 cell line. Among the 89 proteins unique to MDA-MB-231 MVs, three enzymes: ornithine aminotransferase (OAT), transaldolase (TALDO1) and bleomycin hydrolase (BLMH) were previously proposed as cancer therapy targets. These proteins were enzymatically validated in cells, sEVs, and MVs derived from both cell lines. The specific activity of OAT and TALDO1 was significantly higher in MDA-MB-231-derived MVs than in MCF10A MVs. BLMH was highly expressed in MDA-MB-231-derived MVs, compared to MCF10A MVs. This study shows that MVs carry functional metabolic enzymes and provides a framework for future studies of their biological role in BC and potential in therapeutic applications.

show abstract

Section: Discussionsupporting

confidence: 53%

Breast Cancer-Derived Microvesicles Are the Source of Functional Metabolic Enzymes as Potential Targets for Cancer Therapy

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Only 16 metabolites (2.4%) were identified by at least five papers as significantly changing between healthy controls and PDAC patients. Notably, glutamate (11 studies), glutamine (10 studies), and ornithine (9 studies) were the metabolites identified in most studies and all have established roles in cancer [43][44][45]. can be found in Supplemental Table S1A.…”

Section: Resultsmentioning

confidence: 99%

Meta-Analysis Reveals Both the Promises and the Challenges of Clinical Metabolomics

Roth

Powers

2022

Cancers

View full text Add to dashboard Cite

Clinical metabolomics is a rapidly expanding field focused on identifying molecular biomarkers to aid in the efficient diagnosis and treatment of human diseases. Variations in study design, metabolomics methodologies, and investigator protocols raise serious concerns about the accuracy and reproducibility of these potential biomarkers. The explosive growth of the field has led to the recent availability of numerous replicate clinical studies, which permits an evaluation of the consistency of biomarkers identified across multiple metabolomics projects. Pancreatic ductal adenocarcinoma (PDAC) is the third-leading cause of cancer-related death and has the lowest five-year survival rate primarily due to the lack of an early diagnosis and the limited treatment options. Accordingly, PDAC has been a popular target of clinical metabolomics studies. We compiled 24 PDAC metabolomics studies from the scientific literature for a detailed meta-analysis. A consistent identification across these multiple studies allowed for the validation of potential clinical biomarkers of PDAC while also highlighting variations in study protocols that may explain poor reproducibility. Our meta-analysis identified 10 metabolites that may serve as PDAC biomarkers and warrant further investigation. However, 87% of the 655 metabolites identified as potential biomarkers were identified in single studies. Differences in cohort size and demographics, p-value choice, fold-change significance, sample type, handling and storage, data collection, and analysis were all factors that likely contributed to this apparently large false positive rate. Our meta-analysis demonstrated the need for consistent experimental design and normalized practices to accurately leverage clinical metabolomics data for reliable and reproducible biomarker discovery.

show abstract

“…Among TNBC isoforms, we observed further upregulation of methionine and acetone expression. Furthermore, ornithine and 3‐hydroxybutyric acid play important roles in the metabolic characterization of TNBC and are useful for predicting BC risk [73]. This study further identified ornithine as a potential target for TNBC research, with ornithine decarboxylase being a hot topic [74, 75].…”

Section: Discussionmentioning

confidence: 97%

Two effective models based on comprehensive lipidomics and metabolomics can distinguish BC versus HCs, and TNBC versus non‐TNBC

Jin

Fan

Jiang

et al. 2022

Proteomics Clinical Apps

View full text Add to dashboard Cite

Background Lipidomics and metabolomics are closely related to tumor phenotypes, and serum lipoprotein subclasses and small‐molecule metabolites are considered as promising biomarkers for breast cancer (BC) diagnosis. This study aimed to explore potential biomarker models based on lipidomic and metabolomic analysis that could distinguish BC from healthy controls (HCs) and triple‐negative BC (TNBC) from non‐TNBC. Methods Blood samples were collected from 114 patients with BC and 75 HCs. A total of 112 types of lipoprotein subclasses and 30 types of small‐molecule metabolites in the serum were detected by 1H‐NMR. All lipoprotein subclasses and small‐molecule metabolites were subjected to a three‐step screening process in the order of significance (p < 0.05), univariate regression (p < 0.1), and lasso regression (nonzero coefficient). Discriminant models of BC versus HCs and TNBC versus non‐TNBC were established using binary logistic regression. Results We developed a valid discriminant model based on three‐biomarker panel (formic acid, TPA2, and L6TG) that could distinguish patients with BC from HCs. The area under the receiver operating characteristic curve (AUC) was 0.999 (95% confidence interval [CI]: 0.995–1.000) and 0.990 (95% CI: 0.959–1.000) in the training and validation sets, respectively. Based on the panel (D‐dimer, CA15‐3, CEA, L5CH, glutamine, and ornithine), a discriminant model was established to differentiate between TNBC and non‐TNBC, with AUC of 0.892 (95% CI: 0.778–0.967) and 0.905 (95% CI: 0.754–0.987) in the training and validation sets, respectively. Conclusion This study revealed lipidomic and metabolomic differences between BC versus HCs and TNBC versus non‐TNBC. Two validated discriminatory models established against lipidomic and metabolomic differences can accurately distinguish BC from HCs and TNBC from non‐TNBC. Impact Two validated discriminatory models can be used for early BC screening and help BC patients avoid time‐consuming, expensive, and dangerous BC screening.

show abstract

Ornithine and breast cancer: a matched case–control study

Cited by 10 publications

References 25 publications

Breast Cancer-Derived Microvesicles Are the Source of Functional Metabolic Enzymes as Potential Targets for Cancer Therapy

Breast Cancer-Derived Microvesicles Are the Source of Functional Metabolic Enzymes as Potential Targets for Cancer Therapy

Meta-Analysis Reveals Both the Promises and the Challenges of Clinical Metabolomics

Two effective models based on comprehensive lipidomics and metabolomics can distinguish BC versus HCs, and TNBC versus non‐TNBC

Contact Info

Product

Resources

About