Literature DB >> 24551873

High-throughput and high-sensitivity quantitative analysis of serum unsaturated fatty acids by chip-based nanoelectrospray ionization-Fourier transform ion cyclotron resonance mass spectrometry: early stage diagnostic biomarkers of pancreatic cancer.

Yaping Zhang1, Ling Qiu, Yanmin Wang, Xuzhen Qin, Zhili Li.   

Abstract

In this study, Fourier transform ion cyclotron resonance mass spectrometry (FTICR MS) coupled with chip-based direct-infusion nanoelectrospray ionization source (CBDInanoESI) in a negative ion mode is first employed to evaluate the effect of serum and its corresponding supernatant matrixes on the recoveries of serum free fatty acids (FFAs) based on spike-and-recovery experimental strategy by adding analytes along with analog internal standard (IS). The recoveries between serum (69.8-115.6%) and the supernatant (73.6-99.0%) matrixes are almost identical. Multiple point internal standard calibration curves between the concentration ratios of individual fatty acids to ISs, (C(17:1) as IS of C(16:1), C(18:3), C(18:2), or C(18:1) or C(21:0) as IS of C(20:4) or C(22:6)) versus their corresponding intensity ratios were constructed for C(16:1), C(18:3), C(18:2), C(18:1), C(20:4) and C(22:6), respectively, with correlation coefficients of greater than 0.99, lower limits of detection between 0.3 and 1.8 nM, and intra- and inter-day precision (relative standard deviations <18%), along with the linear dynamic range of three orders of magnitude. Sequentially, this advanced analytical platform was applied to perform simultaneous quantitative and qualitative analysis of multiple targets, e.g., serum supernatant unsaturated FFAs from 361 participants including 95 patients with pancreatic cancer (PC), 61 patients with pancreatitis and 205 healthy controls. Experimental results indicate that the levels of C(18:1), C(18:2), C(18:3), C(20:4) and C(22:6), as well as the level ratios of C(18:2)/C(18:1) and C(18:3)/C(18:1) of the PC patients were significantly decreased compared with those of healthy controls and the patients with pancreatitis (p < 0.01). It is worth noting that the ratio of C(18:2)/C(18:1), polyunsaturated fatty acids (PUFAs) (C(18:2), C(18:3), C(20:4), and C(22:6)), panel a (C(16:1), C(18:3), C(18:2), C(20:4) and C(22:6)) and panel b (C(18:2)/C(18:1) and C(18:3)/C(18:1)) performed excellent diagnostic ability, with an area under the receiver operating characteristic curve of ≥0.869, sensitivity of ≥85.7%, and specificity of ≥86.7% for differentiating the early stage PC from non-cancer subjects, which are greatly higher than those of clinically used serum biomarker CA 19-9. More importantly, this platform can also provide a fast and easy way to quantify the levels of FFAs in less than 30 s per sample.

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Year:  2014        PMID: 24551873     DOI: 10.1039/c3an02130k

Source DB:  PubMed          Journal:  Analyst        ISSN: 0003-2654            Impact factor:   4.616


  12 in total

1.  A systematic review on metabolomics-based diagnostic biomarker discovery and validation in pancreatic cancer.

Authors:  Nguyen Phuoc Long; Sang Jun Yoon; Nguyen Hoang Anh; Tran Diem Nghi; Dong Kyu Lim; Yu Jin Hong; Soon-Sun Hong; Sung Won Kwon
Journal:  Metabolomics       Date:  2018-08-10       Impact factor: 4.290

2.  Serum unsaturated free Fatty acids: potential biomarkers for early detection and disease progression monitoring of non-small cell lung cancer.

Authors:  Yaping Zhang; Chengyan He; Ling Qiu; Yanmin Wang; Li Zhang; Xuzhen Qin; Yujie Liu; Dan Zhang; Zhili Li
Journal:  J Cancer       Date:  2014-09-19       Impact factor: 4.207

3.  Distinguishing between the metabolome and xenobiotic exposome in environmental field samples analysed by direct-infusion mass spectrometry based metabolomics and lipidomics.

Authors:  Andrew D Southam; Anke Lange; Raghad Al-Salhi; Elizabeth M Hill; Charles R Tyler; Mark R Viant
Journal:  Metabolomics       Date:  2014-07-15       Impact factor: 4.290

4.  Development and in silico evaluation of large-scale metabolite identification methods using functional group detection for metabolomics.

Authors:  Joshua M Mitchell; Teresa W-M Fan; Andrew N Lane; Hunter N B Moseley
Journal:  Front Genet       Date:  2014-07-28       Impact factor: 4.599

5.  Direct infusion mass spectrometry metabolomics dataset: a benchmark for data processing and quality control.

Authors:  Jennifer A Kirwan; Ralf J M Weber; David I Broadhurst; Mark R Viant
Journal:  Sci Data       Date:  2014-06-10       Impact factor: 6.444

6.  Serum Unsaturated Free Fatty Acids: A Potential Biomarker Panel for Early-Stage Detection of Colorectal Cancer.

Authors:  Yaping Zhang; Chengyan He; Ling Qiu; Yanmin Wang; Xuzhen Qin; Yujie Liu; Zhili Li
Journal:  J Cancer       Date:  2016-01-29       Impact factor: 4.207

7.  Simultaneous Quantification of Serum Multi-Phospholipids as Potential Biomarkers for Differentiating Different Pathophysiological states of lung, stomach, intestine, and pancreas.

Authors:  Yumei Guo; Junling Ren; Xiaoou Li; Xiaofeng Liu; Ning Liu; Yanmin Wang; Zhili Li
Journal:  J Cancer       Date:  2017-07-15       Impact factor: 4.207

8.  Optimization and Application of Direct Infusion Nanoelectrospray HRMS Method for Large-Scale Urinary Metabolic Phenotyping in Molecular Epidemiology.

Authors:  Elena Chekmeneva; Gonçalo Dos Santos Correia; Queenie Chan; Anisha Wijeyesekera; Adrienne Tin; Jeffery Hunter Young; Paul Elliott; Jeremy K Nicholson; Elaine Holmes
Journal:  J Proteome Res       Date:  2017-03-14       Impact factor: 4.466

Review 9.  Identifying Novel Biomarkers Ready for Evaluation in Low-Prevalence Populations for the Early Detection of Upper Gastrointestinal Cancers: A Systematic Review.

Authors:  Natalia Calanzani; Paige E Druce; Claudia Snudden; Kristi M Milley; Rachel Boscott; Dawnya Behiyat; Smiji Saji; Javiera Martinez-Gutierrez; Jasmeen Oberoi; Garth Funston; Mike Messenger; Jon Emery; Fiona M Walter
Journal:  Adv Ther       Date:  2020-12-11       Impact factor: 3.845

10.  Serum Unsaturated Free Fatty Acids: A Potential Biomarker Panel for Differentiating Benign Thyroid Diseases from Thyroid Cancer.

Authors:  Yaping Zhang; Ling Qiu; Chengyan He; Yanmin Wang; Yujie Liu; Dan Zhang; Zhili Li
Journal:  J Cancer       Date:  2015-10-20       Impact factor: 4.207

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