Evidence map›Paper›PMID 35629952›Full record

ArticleMetabolites2022

Metabolomic Analysis of Plasma from Breast Cancer Patients Using Ultra-High-Performance Liquid Chromatography Coupled with Mass Spectrometry: An Untargeted Study.

Patricia A Da Cunha, Diana Nitusca, Luisa Matos Do Canto, Rency S Varghese, Habtom W Ressom, Shawna Willey, Catalin Marian, Bassem R Haddad

Open access · goldAbstract read
In one paragraph

Article in Metabolites, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.0field-weighted citation impact, top 25% 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

6 citing papers in PubMed, 12 citations in OpenAlex.

  1. Article
  2. Article
  3. Machine learning of cellular metabolic rewiring.Biology methods & protocols · 2024
    Article
  4. Review
  5. Review
  6. 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 2 countries.

Patricia A Da CunhaLombardi Comprehensive Cancer Center and Department of Oncology, Georgetown University Medical Center, Georgetown University, Washington, DC 20057, USA.
Diana NituscaDepartment of Biochemistry and Pharmacology, Victor Babeş University of Medicine and Pharmacy, Pta Eftimie Murgu Nr. 2, 300041 Timişoara, Romania.ORCID 0000-0002-5325-5221
Luisa Matos Do CantoLombardi Comprehensive Cancer Center and Department of Oncology, Georgetown University Medical Center, Georgetown University, Washington, DC 20057, USA.ORCID 0000-0001-9095-9431
Rency S VargheseLombardi Comprehensive Cancer Center and Department of Oncology, Georgetown University Medical Center, Georgetown University, Washington, DC 20057, USA.
Habtom W RessomLombardi Comprehensive Cancer Center and Department of Oncology, Georgetown University Medical Center, Georgetown University, Washington, DC 20057, USA.ORCID 0000-0001-9296-2132
Shawna WilleyLombardi Comprehensive Cancer Center and Department of Oncology, Georgetown University Medical Center, Georgetown University, Washington, DC 20057, USA.
Catalin MarianDepartment of Biochemistry and Pharmacology, Victor Babeş University of Medicine and Pharmacy, Pta Eftimie Murgu Nr. 2, 300041 Timişoara, Romania.ORCID 0000-0002-7749-1384
Bassem R HaddadLombardi Comprehensive Cancer Center and Department of Oncology, Georgetown University Medical Center, Georgetown University, Washington, DC 20057, USA.
Georgetown University · USVictor Babeș University of Medicine and Pharmacy Timișoara · RO

Funding

Tissue Culture Shared ResourceP30CA051008 · NCI · GEORGETOWN UNIVERSITY · PI MARCUS S NOEL · 1990 to 2026
$71.5M
NCI NIH HHS P30 CA051008
6 · The paper itself

Abstract

Breast cancer (BC) is one of the leading causes of cancer mortality in women worldwide, and therefore, novel biomarkers for early disease detection are critically needed. We performed herein an untargeted plasma metabolomic profiling of 55 BC patients and 55 healthy controls (HC) using ultra-high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC/Q-TOF-MS). Pre-processed data revealed 2494 ions in total. Data matrices’ paired t-tests revealed 792 ions (both positive and negative) which presented statistically significant changes (FDR < 0.05) in intensity levels between cases versus controls. Metabolites identified with putative names via MetaboQuest using MS/MS and mass-based approaches included amino acid esters (i.e., N-stearoyl tryptophan, L-arginine ethyl ester), dipeptides (ile-ser, met-his), nitrogenous bases (i.e., uracil derivatives), lipid metabolism-derived molecules (caproleic acid), and exogenous compounds from plants, drugs, or dietary supplements. LASSO regression selected 16 metabolites after several variables (TNM Stage, Grade, smoking status, menopausal status, and race) were adjusted. A predictive conditional logistic regression model on the 16 LASSO selected ions provided a high diagnostic performance with an area-under-the-curve (AUC) value of 0.9729 (95% CI 0.96−0.98) on all 55 samples. This study proves that BC possesses a specific metabolic signature that could be exploited as a novel metabolomics-based approach for BC detection and characterization. Future studies of large-scale cohorts are needed to validate these findings.

Indexed as

biomarkersbreast cancerdiagnosticmetabolomicsUHPLC/Q-TOF-MS

Identifiers

PMID35629952
PMCPMC9147455
OpenAlexW4280548444

What Socratic 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 Socratic graph.