LC-MS-Based Plasma Metabolomics and Lipidomics Analyses for Differential Diagnosis of Bladder Cancer and Renal Cell Carcinoma

Liu, Xiang; Zhang, Mingxin; Cheng, Xiaofang; Liu, Xiaoyan; Sun, Hui; Li, Jing; Tang, Xiaonan; Wang, Zhan; Sun, Wei; Zhang, Yushi; Ji, Zhigang

doi:10.3389/fonc.2020.00717

Cited by 40 publications

(37 citation statements)

References 61 publications

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“…Blood analysis offers a fast readout of the body health status, its collection procedures are less invasive than biopsies, and data normalization strategies are less controversial for blood than urine . However, only a few studies have focused on the analysis of serum − or plasma − samples . Currently, there is a lack of robust tissue and/or biofluid-based biomarkers for ccRCC early diagnosis in the clinic. ,, …”

Section: Introductionmentioning

confidence: 99%

Coupled Mass-Spectrometry-Based Lipidomics Machine Learning Approach for Early Detection of Clear Cell Renal Cell Carcinoma

et al. 2020

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A discovery-based lipid profiling study of serum samples from a cohort that included patients with clear cell renal cell carcinoma (ccRCC) stages I, II, III, and IV (n = 112) and controls (n = 52) was performed using ultraperformance liquid chromatography coupled to quadrupole-time-of-flight mass spectrometry and machine learning techniques. Multivariate models based on support vector machines and the LASSO variable selection method yielded two discriminant lipid panels for ccRCC detection and early diagnosis. A 16-lipid panel allowed discriminating ccRCC patients from controls with 95.7% accuracy in a training set under cross-validation and 77.1% accuracy in an independent test set. A second model trained to discriminate early (I and II) from late (III and IV) stage ccRCC yielded a panel of 26 compounds that classified stage I patients from an independent test set with 82.1% accuracy. Thirteen species, including cholic acid, undecylenic acid, lauric acid, LPC(16:0/0:0), and PC(18:2/18:2), identified with level 1 exhibited significantly lower levels in samples from ccRCC patients compared to controls. Moreover, 3α-hydroxy-5α-androstan-17-one 3-sulfate, cis-5-dodecenoic acid, arachidonic acid, cis-13-docosenoic acid, PI(16:0/18:1), PC(16:0/18:2), and PC(O−16:0/20:4) contributed to discriminate early from late ccRCC stage patients. The results are auspicious for early ccRCC diagnosis after validation of the panels in larger and different cohorts.

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Section: Introductionmentioning

confidence: 99%

Coupled Mass-Spectrometry-Based Lipidomics Machine Learning Approach for Early Detection of Clear Cell Renal Cell Carcinoma

et al. 2020

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“…A study showed that 9,12-octadecadienoic acid, belonging to linoleic acid metabolism, was significantly increased in preoperative lung cancer patients compared with healthy volunteers and postoperative lung cancer patients (55). Significant alterations of linoleic acid metabolism have been observed in many other cancer types associated with inflammatory-mediated damage, immune response, and cell proliferation (colorectal cancer, bladder cancer, and renal cell carcinoma) (56,57). Linoleic acid has also been reported as one of the biomarkers for the diagnosis (12,13,58) and therapeutic efficacy (59) of patients with ESCC.…”

Section: Discussionmentioning

confidence: 99%

Serum Metabolomic Profiling Reveals Biomarkers for Early Detection and Prognosis of Esophageal Squamous Cell Carcinoma

et al. 2022

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Esophageal squamous cell carcinoma (ESCC) is one of the most common aggressive malignancies worldwide, particularly in northern China. The absence of specific early symptoms and biomarkers leads to late-stage diagnosis, while early diagnosis and risk stratification are crucial for improving overall prognosis. We performed UPLC-MS/MS on 450 ESCC patients and 588 controls consisting of a discovery group and two validation groups to identify biomarkers for early detection and prognosis. Bioinformatics and clinical statistical methods were used for profiling metabolites and evaluating potential biomarkers. A total of 105 differential metabolites were identified as reliable biomarker candidates for ESCC with the same tendency in three cohorts, mainly including amino acids and fatty acyls. A predictive model of 15 metabolites [all-trans-13,14-dihydroretinol, (±)-myristylcarnitine, (2S,3S)-3-methylphenylalanine, 3-(pyrazol-1-yl)-L-alanine, carnitine C10:1, carnitine C10:1 isomer1, carnitine C14-OH, carnitine C16:2-OH, carnitine C9:1, formononetin, hyodeoxycholic acid, indole-3-carboxylic acid, PysoPE 20:3, PysoPE 20:3(2n isomer1), and resolvin E1] was developed by logistic regression after LASSO and random forest analysis. This model held high predictive accuracies on distinguishing ESCC from controls in the discovery and validation groups (accuracies > 89%). In addition, the levels of four downregulated metabolites [hyodeoxycholic acid, (2S,3S)-3-methylphenylalanine, carnitine C9:1, and indole-3-carboxylic acid] were significantly higher in early cancer than advanced cancer. Furthermore, three independent prognostic markers were identified by multivariate Cox regression analyses with and without clinical indicators: a high level of MG(20:4)isomer and low levels of 9,12-octadecadienoic acid and L-isoleucine correlated with an unfavorable prognosis; the risk score based on these three metabolites was able to stratify patients into low or high risk. Moreover, pathway analysis indicated that retinol metabolism and linoleic acid metabolism were prominent perturbed pathways in ESCC. In conclusion, metabolic profiling revealed that perturbed amino acids and lipid metabolism were crucial metabolic signatures of ESCC. Both panels of diagnostic and prognostic markers showed excellent predictive performances. Targeting retinol and linoleic acid metabolism pathways may be new promising mechanism-based therapeutic approaches. Thus, this study would provide novel insights for the early detection and risk stratification for the clinical management of ESCC and potentially improve the outcomes of ESCC.

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“…The obtained data matrices were imported into MetaboAnalyst 4.0 (https: / / www .metaboanalyst .ca), a web server designed to facilitate analysis of metabolites and data pre-processing, including missing value estimation, median centering, log-transformation, and Pareto scaling. Features existed in at least 50% of samples, and features with coefficient of variation less than 25% in the QC samples were further analyzed (Liu et al, 2020). Nonparametric tests (Wilcoxon rank-sum tests) were used to evaluate the significance of variables.…”

Section: Ultra Performance Liquid Chromatography-quadrupole Time-of-f...mentioning

confidence: 99%

“…Nonparametric tests (Wilcoxon rank-sum tests) were used to evaluate the significance of variables. The adjusted P-value (false discovery rate) cutoff was set as 0.05 before further pattern recognition analysis (principal component analysis, PCA; orthogonal partial least square discriminant analysis, OPLS-DA; Liu et al, 2020). To visualize group-based clustering of metabolites, OPLS-DA was used.…”

Section: Ultra Performance Liquid Chromatography-quadrupole Time-of-f...mentioning

confidence: 99%

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Untargeted metabolic footprinting reveals key differences between fermented brown milk and fermented milk metabolomes

Peng

Kwok

et al. 2022

Journal of Dairy Science

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Fermented brown milk has gained popularity because of its unique taste and flavor. Lactobacillus bulgaricus ND02 is a starter culture that has good milk fermentation characteristics. This study aimed to profile the metabolites produced during Maillard browning and to identify metabolomic differences between fermented brown milk and fermented milk produced by the ND02 strain. This study used liquid chromatography-mass spectrometry to compare the metabolomes of milk, fermented milk, brown milk, and fermented brown milk. Significant differences were observed in the abundances of various groups of metabolites, including peptides, AA, aldehydes, ketones, organic acids, vitamins, and nucleosides. The Maillard browning reaction significantly increased the intensity of a wide spectrum of flavor compounds, including short peptides, organic acids, and compounds of aldehydes, ketones, sulfur, and furan, which might together contribute to the unique flavor of brown milk. However, Maillard browning led to an increase in Nε-(carboxymethyl)lysine, which might cause negative health effects such as diabetes, uremia, or Alzheimer's disease. On the other hand, fermenting brown milk with the ND02 strain effectively countered such an effect. Finally, 5 differentially abundant metabolites were identified between fermented brown milk and fermented milk, including l-lysine, methylglyoxal, glyoxal, 2,3-pentanedione, and 3-hydroxybutanoic acid, which might together contribute to the different nutritional qualities of fermented brown milk and fermented milk. This study has provided novel information about the Maillard reaction and compared the metabolomes of the 4 types of dairy products.

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LC-MS-Based Plasma Metabolomics and Lipidomics Analyses for Differential Diagnosis of Bladder Cancer and Renal Cell Carcinoma

Cited by 40 publications

References 61 publications

Coupled Mass-Spectrometry-Based Lipidomics Machine Learning Approach for Early Detection of Clear Cell Renal Cell Carcinoma

Coupled Mass-Spectrometry-Based Lipidomics Machine Learning Approach for Early Detection of Clear Cell Renal Cell Carcinoma

Serum Metabolomic Profiling Reveals Biomarkers for Early Detection and Prognosis of Esophageal Squamous Cell Carcinoma

Untargeted metabolic footprinting reveals key differences between fermented brown milk and fermented milk metabolomes

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