Evidence map›Paper›PMID 28076849›Full record

ArticleOncotarget2017

A plasma metabolomic signature discloses human breast cancer.

Mariona Jové, Ricardo Collado, José Luís Quiles, Mari-Carmen Ramírez-Tortosa, Joaquim Sol, Maria Ruiz-Sanjuan, Mónica Fernandez, Capilla de la Torre Cabrera, Cesar Ramírez-Tortosa, Sergio Granados-Principal and 2 more

Abstract readComparative Study
In one paragraph

Article in Oncotarget, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
44citing 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

44 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  20. Metabolomics and EMT Markers of Breast Cancer: A Crosstalk and Future Perspective.Pathophysiology : the official journal of the International Society for Pathophysiology · 2022
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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

12 authors.

Mariona JovéDepartment of Experimental Medicine, University of Lleida-Institute for Research in Biomedicine of Lleida (UdL-IRBLleida), Lleida, Spain.
Ricardo ColladoDepartment of Oncology, Medical Oncology Unit, Hospital San Pedro de Alcántara, Cáceres, Official Postgraduate Programme in Nutrition and Food Technology, University of Granada, Spain.
José Luís QuilesInstitute of Nutrition and Food Technology "José Mataix", Biomedical Research Center, Department of Physiology, University of Granada, Granada, Spain.
Mari-Carmen Ramírez-TortosaInstitute of Nutrition and Food Technology "José Mataix", Biomedical Research Center, Department of Biochemistry and Molecular Biology II, University of Granada, Granada, Spain.
Joaquim SolDepartment of Experimental Medicine, University of Lleida-Institute for Research in Biomedicine of Lleida (UdL-IRBLleida), Lleida, Spain.
Maria Ruiz-SanjuanDepartment of Medical Oncology, Hospital of Jaén, Jaén, Spain.
Mónica FernandezDepartment of Medical Oncology, Hospital of Jaén, Jaén, Spain.
Capilla de la Torre CabreraDepartment of Medical Oncology, Hospital of Jaén, Jaén, Spain.
Cesar Ramírez-TortosaDepartment of Pathological Anatomy, Hospital of Jaén, Jaén, Spain.
Sergio Granados-PrincipalDepartment of Medical Oncology, Hospital of Jaén, Jaén, Spain.
Pedro Sánchez-RoviraDepartment of Medical Oncology, Hospital of Jaén, Jaén, Spain.
Reinald PamplonaDepartment of Experimental Medicine, University of Lleida-Institute for Research in Biomedicine of Lleida (UdL-IRBLleida), Lleida, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeMetabolomics is the comprehensive global study of metabolites in biological samples. In this retrospective pilot study we explored whether serum metabolomic profile can discriminate the presence of human breast cancer irrespective of the cancer subtype.

methodsPlasma samples were analyzed from healthy women (n = 20) and patients with breast cancer after diagnosis (n = 91) using a liquid chromatography-mass spectrometry platform. Multivariate statistics and a Random Forest (RF) classifier were used to create a metabolomics panel for the diagnosis of human breast cancer.

resultsMetabolomics correctly distinguished between breast cancer patients and healthy control subjects. In the RF supervised class prediction analysis comparing breast cancer and healthy control groups, RF accurately classified 100% both samples of the breast cancer patients and healthy controls. So, the class error for both group in and the out-of-bag error were 0. We also found 1269 metabolites with different concentration in plasma from healthy controls and cancer patients; and basing on exact mass, retention time and isotopic distribution we identified 35 metabolites. These metabolites mostly support cell growth by providing energy and building stones for the synthesis of essential biomolecules, and function as signal transduction molecules. The collective results of RF, significance testing, and false discovery rate analysis identified several metabolites that were strongly associated with breast cancer.

conclusionsIn breast cancer a metabolomics signature of cancer exists and can be detected in patient plasma irrespectively of the breast cancer type.

Indexed as

MetabolomeBiomarkers, TumorBreast NeoplasmsCase-Control StudiesFemaleFollow-Up StudiesGas Chromatography-Mass SpectrometryHumansMetabolomicsPilot ProjectsPlasmaRetrospective StudiesBiomarkers, Tumorbiomarkerbreast cancermass spectrometrymetabolitesmetabolomics

Identifiers

PMID28076849
PMCPMC5386702

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Registered trials

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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.