Evidence map›Paper›PMID 30792417›Full record

ArticleScientific reports2019

Detection of Volatile Organic Compounds (VOCs) in Urine via Gas Chromatography-Mass Spectrometry QTOF to Differentiate Between Localized and Metastatic Models of Breast Cancer.

Mark Woollam, Meghana Teli, Paula Angarita-Rivera, Shengzhi Liu, Amanda P Siegel, Hiroki Yokota, Mangilal Agarwal

Abstract read
In one paragraph

Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Review
  5. Article
  6. Article
  7. Urinary VOCs as biomarkers of early stage lung tumour development in mice.Cancer biomarkers : section A of Disease markers · 2024
    Article
  8. Review
  9. Article
  10. Article
  11. Article
  12. Article
  13. Biomedicines · 2022
    Review
  14. Article
  15. Article
  16. Article
  17. Review
  18. Article
  19. Article
  20. Article
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

7 authors.

Mark WoollamIUPUI, Department of Chemistry and Chemical Biology, Indianapolis, 46202, USA.
Meghana TeliIUPUI, Department of Biomedical Engineering, Indianapolis, 46202, USA.
Paula Angarita-RiveraIUPUI, Department of Biomedical Engineering, Indianapolis, 46202, USA.
Shengzhi LiuIUPUI, Department of Biomedical Engineering, Indianapolis, 46202, USA.
Amanda P SiegelIUPUI, Department of Chemistry and Chemical Biology, Indianapolis, 46202, USA.
Hiroki YokotaIUPUI, Department of Biomedical Engineering, Indianapolis, 46202, USA.
Mangilal AgarwalIUPUI, Department of Chemistry and Chemical Biology, Indianapolis, 46202, USA. agarwal@iupui.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Breast cancer is the most common cancer detected in women and current screening methods for the disease are not sensitive. Volatile organic compounds (VOCs) include endogenous metabolites that provide information about health and disease which might be useful to develop a better screening method for breast cancer. The goal of this study was to classify mice with and without tumors and compare tumors localized to the mammary pad and tumor cells injected into the iliac artery by differences in VOCs in urine. After 4T1.2 tumor cells were injected into BALB/c mice either in the mammary pad or into the iliac artery, urine was collected, VOCs from urine headspace were concentrated by solid phase microextraction and results were analyzed by gas chromatography-mass spectrometry quadrupole time-of-flight. Multivariate and univariate statistical analyses were employed to find potential biomarkers for breast cancer and metastatic breast cancer in mice models. A set of six VOCs classified mice with and without tumors with an area under the receiver operator characteristic (ROC AUC) of 0.98 (95% confidence interval [0.85, 1.00]) via five-fold cross validation. Classification of mice with tumors in the mammary pad and iliac artery was executed utilizing a different set of six VOCs, with a ROC AUC of 0.96 (95% confidence interval [0.75, 1.00]).

Indexed as

AnimalsBiomarkers, TumorBreast NeoplasmsFemaleGas Chromatography-Mass SpectrometryHumansLimit of DetectionMammary Neoplasms, AnimalMass ScreeningMiceNeoplasm MetastasisROC CurveVolatile Organic CompoundsBiomarkers, TumorVolatile Organic Compounds

Identifiers

PMID30792417
PMCPMC6384920

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.