Literature DB >> 24842401

Tissue imaging and serum lipidomic profiling for screening potential biomarkers of thyroid tumors by matrix-assisted laser desorption/ionization-Fourier transform ion cyclotron resonance mass spectrometry.

Shuai Guo1, Ling Qiu, Yanming Wang, Xuzhen Qin, Hui Liu, Manwen He, Yaping Zhang, Zhili Li, Xiaohong Chen.   

Abstract

Changes in serum lipidome and in tissue lipidome are associated with cancer. In this study, tissue mass spectrometry imaging (MSI) and serum lipid profiling by matrix-assisted laser desorption/ionization-Fourier transform ion cyclotron resonance mass spectrometry (MALDI-FTICR MS) were performed to investigate significantly changed lipids in both tumor (malignant thyroid cancer (MTC) and benign thyroid tumor (BTT)) tissues and sera. Y-scatterplots of variable importance in the projection (VIP) values vs. fold change values indicate that change trends in the levels of ten lipids (i.e., phosphatidylcholine (PC)(34:1), PC(36:1), PC(38:6), phosphatidic acid (PA) (36:2), PA(36:3), PA(38:3), PA(38:4), PA(38:5), PA(40:5), and sphingomyelin (SM)(34:1)) in both tissues and sera from MTC patients, BTT patients, and normal individuals are significantly associated with these three types of pathophysiological status. In order to examine their diagnostic ability, 289 serum samples from 124 MTC patients, 43 BTT patients, and 122 normal controls were randomly divided into the training set and validation set. A biomarker of PC(34:1) exhibited excellent diagnostic ability to differentiate both MTC and BTT patients from normal individuals, with an area under the receiver operating characteristic (ROC) curve value of 0.984, a sensitivity of 96.4 %, and a specificity of 92.7 %. A panel which included PA(36:3) and SM(34:1) could distinguish between MTC and BTT, with an area under receiver operating characteristic curve (AUC) of 0.961, a sensitivity of 87.8 %, and a specificity of 92.9 %. It is worth noting that a panel consisting of PC(34:1), PA(36:3), and SM(34:1) could differentiate MTC patients from both BTT patients and normal individuals, with an AUC of 0.841, a sensitivity of 86.6 %, and a specificity of 75.5 %.

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Year:  2014        PMID: 24842401     DOI: 10.1007/s00216-014-7846-0

Source DB:  PubMed          Journal:  Anal Bioanal Chem        ISSN: 1618-2642            Impact factor:   4.142


  20 in total

Review 1.  Advances in metabolomics of thyroid cancer diagnosis and metabolic regulation.

Authors:  Raziyeh Abooshahab; Morteza Gholami; Maryam Sanoie; Fereidoun Azizi; Mehdi Hedayati
Journal:  Endocrine       Date:  2019-04-01       Impact factor: 3.633

2.  Diagnosis of post-surgical fine-needle aspiration biopsies of thyroid lesions with indeterminate cytology using HRMAS NMR-based metabolomics.

Authors:  Lamya Rezig; Adele Servadio; Liborio Torregrossa; Paolo Miccoli; Fulvio Basolo; Laetitia Shintu; Stefano Caldarelli
Journal:  Metabolomics       Date:  2018-10-10       Impact factor: 4.290

Review 3.  Mass spectrometry imaging to detect lipid biomarkers and disease signatures in cancer.

Authors:  Matthias Holzlechner; Eliseo Eugenin; Brendan Prideaux
Journal:  Cancer Rep (Hoboken)       Date:  2019-12

4.  In situ characterizing membrane lipid phenotype of breast cancer cells using mass spectrometry profiling.

Authors:  Manwen He; Shuai Guo; Zhili Li
Journal:  Sci Rep       Date:  2015-06-10       Impact factor: 4.379

Review 5.  Application of metabolomics in thyroid cancer research.

Authors:  Anna Wojakowska; Mykola Chekan; Piotr Widlak; Monika Pietrowska
Journal:  Int J Endocrinol       Date:  2015-04-20       Impact factor: 3.257

6.  Heat fixation inactivates viral and bacterial pathogens and is compatible with downstream MALDI mass spectrometry tissue imaging.

Authors:  Lisa H Cazares; Sean A Van Tongeren; Julie Costantino; Tara Kenny; Nicole L Garza; Ginger Donnelly; Douglas Lane; Rekha G Panchal; Sina Bavari
Journal:  BMC Microbiol       Date:  2015-05-13       Impact factor: 3.605

7.  In Situ Characterizing Membrane Lipid Phenotype of Human Lung Cancer Cell Lines Using Mass Spectrometry Profiling.

Authors:  Manwen He; Shuai Guo; Junling Ren; Zhili Li
Journal:  J Cancer       Date:  2016-04-26       Impact factor: 4.207

Review 8.  Advances in Lipidomics for Cancer Biomarkers Discovery.

Authors:  Francesca Perrotti; Consuelo Rosa; Ilaria Cicalini; Paolo Sacchetta; Piero Del Boccio; Domenico Genovesi; Damiana Pieragostino
Journal:  Int J Mol Sci       Date:  2016-11-28       Impact factor: 5.923

9.  Assessment of pathological response to therapy using lipid mass spectrometry imaging.

Authors:  Nathan Heath Patterson; Balqis Alabdulkarim; Anthoula Lazaris; Aurélien Thomas; Mieczyslaw M Marcinkiewicz; Zu-Hua Gao; Peter B Vermeulen; Pierre Chaurand; Peter Metrakos
Journal:  Sci Rep       Date:  2016-11-14       Impact factor: 4.379

10.  Monitoring changes of docosahexaenoic acid-containing lipids during the recovery process of traumatic brain injury in rat using mass spectrometry imaging.

Authors:  Shuai Guo; Dan Zhou; Mo Zhang; Tiejun Li; Yujie Liu; Yupin Xu; Tianjing Chen; Zhili Li
Journal:  Sci Rep       Date:  2017-07-11       Impact factor: 4.379

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