Evidence map›Paper›PMID 34287708›Full record

ArticleMetabolomics : Official journal of the Metabolomic Society2021

Spatially resolved metabolomic characterization of muscle invasive bladder cancer by mass spectrometry imaging.

Anqi Tu, Neveen Said, David C Muddiman

Open access · greenAbstract read
In one paragraph

Article in Metabolomics : Official journal of the Metabolomic Society, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
1.3field-weighted citation impact, top 20% 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

12 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.

  1. Unmasking the Metabolite Signature of Bladder Cancer: A Systematic Review.International journal of molecular sciences · 2024
    Pooled it
  2. Review
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  8. Emerging Computational Methods in Mass Spectrometry Imaging.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022
    Review
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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

3 authors at 2 institutions in 1 country.

Anqi TuFTMS Laboratory for Human Health Research, Department of Chemistry, North Carolina State University, Raleigh, NC, 27695, USA.
Neveen SaidDepartments of Cancer Biology, Pathology, and Urology, Wake Forest University School of Medicine, Wake Forest Baptist Comprehensive Cancer Center, Winston Salem, NC, 27157, USA.
David C MuddimanFTMS Laboratory for Human Health Research, Department of Chemistry, North Carolina State University, Raleigh, NC, 27695, USA. dcmuddim@ncsu.edu.ORCID http://orcid.org/0000-0003-2216-499X
North Carolina State University · USAtrium Health Wake Forest Baptist · US

Funding

Tumor Tissue CoreP30CA012197 · NCI · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI Ruben A. Mesa · 1985 to 2026
$55.4M
Wake Forest Clinical and Translational Science AwardUL1TR001420 · NCATS · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI ARD, JAMY D, FOLEY, KRISTIE L · 2015 to 2023
$32.3M
Development and Application of New Ionization Methods for Biological Mass SpectrometryR01GM087964 · NIGMS · NORTH CAROLINA STATE UNIVERSITY RALEIGH · PI MUDDIMAN, DAVID C. · 2010 to 2025
$4.7M
Regulation of Urinary Bladder Carcinogenesis and Progression by SPARCR01CA193437 · NCI · WAKE FOREST UNIVERSITY HEALTH SCIENCES · PI SAID, NEVEEN · 2016 to 2020
$1.8M
NCATS NIH HHS UL1 TR001420NCI NIH HHS P30 CA012197NCI NIH HHS P30CA012197NCI NIH HHS R01 CA193437NIGMS NIH HHS R01 GM087964NIGMS NIH HHS R01GM087964NIH HHS R01CA193437
6 · The paper itself

Abstract

introductionMuscle invasive bladder cancer (MIBC) is an advanced stage of bladder cancer which poses a severe threat to life. Cancer development is usually accompanied by remarkable alterations in cell metabolism, and hence deep insights into MIBC at the metabolomic level can facilitate the understanding of the biochemical mechanisms involved in the cancer development and progression.

methodsIn this proof-of-concept study, the optimal cutting temperature (OCT)-embedded MIBC samples were first washed with pure water to remove the polymer compounds which could cause severe signal suppression during mass spectrometry. Further, the tissue sections were analyzed by infrared matrix-assisted laser desorption electrospray ionization mass spectrometry imaging (IR-MALDESI MSI), providing an overview on the spatially resolved metabolomic profiles.

resultsThe MSI data enabled the discrimination between not only the cancerous and normal tissues, but also the subregions within a tissue section associated with different disease states. Using t-Distributed Stochastic Neighbor Embedding (t-SNE), the hyperdimensional MSI data was mapped into a two-dimensional space to visualize the spectral similarity, providing evidence that metabolomic alterations might have occurred outside the histopathological tumor border. Least absolute shrinkage and selection operator (LASSO) was further employed to classify sample pathology in a pixel-wise manner, yielding excellent prediction sensitivity and specificity up to 96% based on the statistically characteristic spectral features.

conclusionThe results demonstrate great promise of IR-MALDESI MSI to identify molecular changes derived from cancer and unveil tumor heterogeneity, which can potentially promote the discovery of clinically relevant biomarkers and allow for applications in precision medicine.

Indexed as

MetabolomicsSpectrometry, Mass, Matrix-Assisted Laser Desorption-IonizationUrinary Bladder NeoplasmsFeasibility StudiesHumansMuscle, SmoothBladder cancerIntratumor heterogeneityIR-MALDESI mass spectrometry imagingLipidomicsMetabolomics

Identifiers

PMID34287708
PMCPMC8893274
OpenAlexW3184021072

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.