Evidence map›Paper›PMID 28247296›Full record

ArticleJournal of the American Society for Mass Spectrometry2017

Detection of Metastatic Breast and Thyroid Cancer in Lymph Nodes by Desorption Electrospray Ionization Mass Spectrometry Imaging.

Jialing Zhang, Clara L Feider, Chandandeep Nagi, Wendong Yu, Stacey A Carter, James Suliburk, Hop S Tran Cao, Livia S Eberlin

Abstract read
In one paragraph

Article in Journal of the American Society for Mass Spectrometry, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed
4.3field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

27 citing papers in PubMed, 56 citations in OpenAlex.

  1. Review
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  9. Article
  10. Clinical Chemistry for Developing Countries: Mass Spectrometry.Annual review of analytical chemistry (Palo Alto, Calif.) · 2021
    Review
  11. Review
  12. Mass Spectrometry-Based Shotgun Lipidomics for Cancer Research.Advances in experimental medicine and biology · 2021
    Article
  13. Article
  14. The Potential of Metabolomics in the Diagnosis of Thyroid Cancer.International journal of molecular sciences · 2020
    Review
  15. Article
  16. Review
  17. Review
  18. Review
  19. Preoperative metabolic classification of thyroid nodules using mass spectrometry imaging of fine-needle aspiration biopsies.Proceedings of the National Academy of Sciences of the United States of America · 2019
    Article
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 2 institutions in 1 country.

Jialing ZhangDepartment of Chemistry, The University of Texas at Austin, Austin, TX, 78712, USA.
Clara L FeiderDepartment of Chemistry, The University of Texas at Austin, Austin, TX, 78712, USA.
Chandandeep NagiDepartment of Pathology and Immunology, Baylor College of Medicine, Houston, TX, 77030, USA.
Wendong YuDepartment of Pathology and Immunology, Baylor College of Medicine, Houston, TX, 77030, USA.
Stacey A CarterDepartment of Surgery, Baylor College of Medicine, Houston, TX, 77030, USA.
James SuliburkDepartment of Surgery, Baylor College of Medicine, Houston, TX, 77030, USA.
Hop S Tran CaoDepartment of Surgery, Baylor College of Medicine, Houston, TX, 77030, USA.
Livia S EberlinDepartment of Chemistry, The University of Texas at Austin, Austin, TX, 78712, USA. liviase@utexas.edu.
Baylor College of Medicine · USThe University of Texas at Austin · US

Funding

Mass Spectrometry Imaging for Clinical Diagnosis and Prognosis of Human CancersR00CA190783 · NCI · UNIVERSITY OF TEXAS AT AUSTIN · PI SCHIAVINATO EBERLIN, LIVIA · 2016 to 2018
$677k
NCI NIH HHS R00 CA190783
6 · The paper itself

Abstract

Ambient ionization mass spectrometry has been widely applied to image lipids and metabolites in primary cancer tissues with the purpose of detecting and understanding metabolic changes associated with cancer development and progression. Here, we report the use of desorption electrospray ionization mass spectrometry (DESI-MS) to image metastatic breast and thyroid cancer in human lymph node tissues. Our results show clear alterations in lipid and metabolite distributions detected in the mass spectra profiles from 42 samples of metastatic thyroid tumors, metastatic breast tumors, and normal lymph node tissues. 2D DESI-MS ion images of selected molecular species allowed discrimination and visualization of specific histologic features within tissue sections, including regions of metastatic cancer, adjacent normal lymph node, and fibrosis or adipose tissues, which strongly correlated with pathologic findings. In thyroid cancer metastasis, increased relative abundances of ceramides and glycerophosphoinisitols were observed. In breast cancer metastasis, increased relative abundances of various fatty acids and specific glycerophospholipids were seen. Trends in the alterations in fatty acyl chain composition of lipid species were also observed through detailed mass spectra evaluation and chemical identification of molecular species. The results obtained demonstrate DESI-MSI as a potential clinical tool for the detection of breast and thyroid cancer metastasis in lymph nodes, although further validation is needed. Graphical Abstract Desorption electrospray ionization mass spectrometry imaging is used to differentiate metastatic cancer from adjacent lymph node tissue.

Indexed as

Breast NeoplasmsFatty AcidsFemaleGlycerophospholipidsGlycolipidsHumansImage Processing, Computer-AssistedLymph NodesSpectrometry, Mass, Electrospray IonizationSphingolipidsThyroid NeoplasmsFatty AcidsGlycerophospholipidsGlycolipidsSphingolipidsAmbient mass spectrometryCancer stagingLipid profilesMass spectrometry imagingMetastatic cancer

Identifiers

PMID28247296
PMCPMC5750372
OpenAlexW2593183284

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.