Cancer metabolomic markers in urine: evidence, techniques and recommendations

Dinges, Sarah S.; Hohm, Annika; Vandergrift, Lindsey A.; Nowak, Johannes; Habbel, Piet; Kaltashov, Igor A.; Cheng, Leo L.

doi:10.1038/s41585-019-0185-3

Cited by 125 publications

(120 citation statements)

References 137 publications

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“…Through the advancement of bladder cancer diagnostic techniques, more and more biomarkers have been found [23,24]. However, urine cytology still contributes to disease diagnosis as a classic, inexpensive, and rapid detection method [25].…”

Section: Discussionmentioning

confidence: 99%

A Microfluidic Detection System for Bladder Cancer Tumor Cells

Zhao

et al. 2019

Micromachines

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The clinical characteristics of excreted tumor cells can be found in the urine of bladder cancer patients, meaning the identification of tumor cells in urine can assist in bladder cancer diagnosis. The presence of white blood cells and epithelial cells in the urine interferes with the recognition of tumor cells. In this paper, a technique for detecting cancer cells in urine based on microfluidics provides a novel approach to bladder cancer diagnosis. The bladder cancer cell line (T24) and MeT-5A were used as positive bladder tumor cells and non-tumor cells, respectively. The practicality of the tumor cell detection system based on microfluidic cell chip detection technology is discussed. The tumor cell (T24) concentration was around 1 × 10 4 to 300 × 10 4 cells/mL. When phosphate buffer saline (PBS) was the diluted solution, the tumor cell detected rate was 63-71% and the detection of tumor cell number stability (coefficient of variation, CV%) was 6.7-4.1%, while when urine was the diluted solution, the tumor cell detected rate was 64-72% and the detection of tumor cell number stability (CV%) was 6.3-3.9%. In addition, both PBS and urine are tumor cell dilution fluid solutions. The sample was analyzed at a speed of 750 microns per hour. Based on the above experiments, a system for detecting bladder cancer cells in urine by microfluidic analysis chip technology was reported. The rate of recognizing bladder cancer cells reached 68.4%, and the speed reached 2 mL/h.

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Section: Discussionmentioning

confidence: 99%

A Microfluidic Detection System for Bladder Cancer Tumor Cells

Zhao

et al. 2019

Micromachines

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“…In addition, it was observed that the false recovery ratio was reduced obviously. Moreover, the number of uninformative variables were also suppressed by The optimal regularization parameters of GNNR were determined by DQ 2 . In the optimal model, the regularization parameter for the group norm was 0.1, the weight parameter for the trace norm was 10 −5 .…”

Section: The Recovery Ability Of the Two Longitudinal Methods On Simumentioning

confidence: 99%

“…An object was labeled positive if it was generated from the means defined for the positive class. The normal distributions used to generate the simulating variables were N (2.1, 1.5 2 ), N (1.7, 1.5 2 ), N (1.2, 1.5 2 ), and N (−1.3, 1.5 2 ). The value 2.1 and −1.3 were chosen according to the range of metabolomics data preprocessed in the above section.…”

Section: Simulating Datamentioning

confidence: 99%

“…With the advances of high throughput technologies, such as 1 H nuclear magnetic resonance spectroscopy ( 1 H-NMR) and mass spectrometry (MS), nontargeted metabolomics studies have been widely applied to elucidate biomarkers of diseases, to discover new therapeutic targets, to assign unknown gene function and to gain mechanistic insight into physiological processes in plants, yeast, bacteria, and mammals [1,2]. The high-dimensional nontargeted metabolomics data are typically analyzed with multivariate methods, including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA) [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

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Discovering Temporal Patterns in Longitudinal Nontargeted Metabolomics Data via Group and Nuclear Norm Regularized Multivariate Regression

Lin¹,

Zhang

Dai

et al. 2020

Metabolites

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Temporal associations in longitudinal nontargeted metabolomics data are generally ignored by common pattern recognition methods such as partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA). To discover temporal patterns in longitudinal metabolomics, a multitask learning (MTL) method employing structural regularization was proposed. The group regularization term of the proposed MTL method enables the selection of a small number of tentative biomarkers while maintaining high prediction accuracy. Meanwhile, the nuclear norm imposed into the regression coefficient accounts for the interrelationship of the metabolomics data obtained on consecutive time points. The effectiveness of the proposed method was demonstrated by comparison study performed on a metabolomics dataset and a simulating dataset. The results showed that a compact set of tentative biomarkers charactering the whole antipyretic process of Qingkailing injection were selected with the proposed method. In addition, the nuclear norm introduced in the new method could help the group norm to improve the method’s recovery ability.

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“…Nuclear Magnetic Resonance spectroscopy (NMR) is currently one of the most versatile techniques of chemical and physical analysis. Its range of applications is impressively broad: from analysis of small molecules structures in all states of matter [1], through characterization of complex natural mixtures [2,3], including applications to medical screening (metabolomics) [4] up to the biological studies of structure and dynamics of proteins and ribonucleic acids [5]. The introduction of Fourier transform (FT) in 1966 [6] became a cornerstone of modern NMR spectroscopy, which is based on a measurement of a free induction decay signal (FID) in a time domain.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Compression Level for More Efficient Compressed Sensing and Other Lessons from NMR Spectroscopy

Gołowicz

Kasprzak

Kazimierczuk

2020

Sensors

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Modern nuclear magnetic resonance spectroscopy (NMR) is based on two- and higher-dimensional experiments that allow the solving of molecular structures, i.e., determine the relative positions of single atoms very precisely. However, rich chemical information comes at the price of long data acquisition times (up to several days). This problem can be alleviated by compressed sensing (CS)—a method that revolutionized many fields of technology. It is known that CS performs the most efficiently when measured objects feature a high level of compressibility, which in the case of NMR signal means that its frequency domain representation (spectrum) has a low number of significant points. However, many NMR spectroscopists are not aware of the fact that various well-known signal acquisition procedures enhance compressibility and thus should be used prior to CS reconstruction. In this study, we discuss such procedures and show to what extent they are complementary to CS approaches. We believe that the survey will be useful not only for NMR spectroscopists but also to inspire the broader signal processing community.

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Cancer metabolomic markers in urine: evidence, techniques and recommendations

Cited by 125 publications

References 137 publications

A Microfluidic Detection System for Bladder Cancer Tumor Cells

A Microfluidic Detection System for Bladder Cancer Tumor Cells

Discovering Temporal Patterns in Longitudinal Nontargeted Metabolomics Data via Group and Nuclear Norm Regularized Multivariate Regression

Enhancing Compression Level for More Efficient Compressed Sensing and Other Lessons from NMR Spectroscopy

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