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1000 Titel
  • Potential of pre-diagnostic metabolomics for colorectal cancer risk assessment or early detection
1000 Autor/in
  1. Seum, Teresa |
  2. Frick, Clara |
  3. Cardoso, Rafael |
  4. Bhardwaj, Megha |
  5. Hoffmeister, Michael |
  6. Brenner, Hermann |
1000 Verlag Nature Publishing Group UK
1000 Erscheinungsjahr 2024
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2024-10-27
1000 Erschienen in
1000 Quellenangabe
  • 8(1):244
1000 Copyrightjahr
  • 2024
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1038/s41698-024-00732-5 |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11514036/ |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • <jats:title>Abstract</jats:title><jats:p>This systematic review investigates the efficacy of metabolite biomarkers for risk assessment or early detection of colorectal cancer (CRC) and its precursors, focusing on pre-diagnostic biospecimens. Searches in PubMed, Web of Science, and SCOPUS through December 2023 identified relevant prospective studies. Relevant data were extracted, and the risk of bias was assessed with the QUADAS-2 tool. Among the 26 studies included, significant heterogeneity existed for case numbers, metabolite identification, and validation approaches. Thirteen studies evaluated individual metabolites, mainly lipids, while eleven studies derived metabolite panels, and two studies did both. Nine panels were internally validated, resulting in an area under the curve (AUC) ranging from 0.69 to 0.95 for CRC precursors and 0.72 to 1.0 for CRC. External validation was limited to one panel (AUC = 0.72). Metabolite panels and lipid-based biomarkers show promise for CRC risk assessment and early detection but require standardization and extensive validation for clinical use.</jats:p>
1000 Sacherschließung
lokal Article
lokal /692/499
lokal /692/4028/67/2322
lokal /692/53/2423
lokal article
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
  1. https://orcid.org/0009-0003-4475-0583|https://orcid.org/0000-0001-7953-3018|https://frl.publisso.de/adhoc/uri/Q2FyZG9zbywgUmFmYWVs|https://frl.publisso.de/adhoc/uri/QmhhcmR3YWosIE1lZ2hh|https://orcid.org/0000-0002-8307-3197|https://orcid.org/0000-0002-6129-1572
1000 Hinweis
  • DeepGreen-ID: 100865f09a20474b891e0e315355046a ; metadata provieded by: DeepGreen (https://www.oa-deepgreen.de/api/v1/), LIVIVO search scope life sciences (http://z3950.zbmed.de:6210/livivo), Crossref Unified Resource API (https://api.crossref.org/swagger-ui/index.html), to.science.api (https://frl.publisso.de/), ZDB JSON-API (beta) (https://zeitschriftendatenbank.de/api/), lobid - Dateninfrastruktur für Bibliotheken (https://lobid.org/resources/search)
1000 Label
1000 Förderer
  1. German Federal Ministry of Education and Research |
1000 Fördernummer
  1. -
1000 Förderprogramm
  1. -
1000 Dateien
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer German Federal Ministry of Education and Research |
    1000 Förderprogramm -
    1000 Fördernummer -
1000 Objektart article
1000 Beschrieben durch
1000 @id frl:6519937.rdf
1000 Erstellt am 2025-07-05T21:31:26.282+0200
1000 Erstellt von 322
1000 beschreibt frl:6519937
1000 Zuletzt bearbeitet 2025-08-11T08:10:20.103+0200
1000 Objekt bearb. Mon Aug 11 08:10:20 CEST 2025
1000 Vgl. frl:6519937
1000 Oai Id
  1. oai:frl.publisso.de:frl:6519937 |
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