Evidence map›Paper›PMID 31344914›Full record

ReviewInternational journal of molecular sciences2019

Targeting Long Chain Acyl-CoA Synthetases for Cancer Therapy.

Matteo Rossi Sebastiano, Georgia Konstantinidou

Abstract readReview
In one paragraph

Review in International journal of molecular sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 84 papers.

0numbers the graph read from it
0cells of the map it votes in
84citing papers in PubMed
–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

84 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Review
  5. Review
  6. Article
  7. Insights Into circRNA-Mediated Lipid Metabolism in Cancer Progression.Journal of cellular and molecular medicine · 2026
    Review
  8. Article
  9. Article
  10. Article
  11. The Association ofBalkan journal of medical genetics : BJMG · 2025
    Article
  12. Review
  13. Review
  14. Article
  15. Review
  16. Article
  17. Review
  18. Article
  19. Article
  20. Article

24 more citing papers are in PubMed but not listed here.

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

2 authors.

Matteo Rossi SebastianoInstitute of Pharmacology, University of Bern, 3010 Bern, Switzerland.
Georgia KonstantinidouInstitute of Pharmacology, University of Bern, 3010 Bern, Switzerland. georgia.konstantinidou@pki.unibe.ch.ORCID 0000-0001-9513-0286

Funding

Novartis Stiftung für Medizinisch-Biologische Forschung #16C190Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung PP00P3_163929
6 · The paper itself

Abstract

The deregulation of cancer cell metabolic networks is now recognized as one of the hallmarks of cancer. Abnormal lipid synthesis and extracellular lipid uptake are advantageous modifications fueling the needs of uncontrolled cancer cell proliferation. Fatty acids are placed at the crossroads of anabolic and catabolic pathways, as they are implicated in the synthesis of phospholipids and triacylglycerols, or they can undergo β-oxidation. Key players to these decisions are the long-chain acyl-CoA synthetases, which are enzymes that catalyze the activation of long-chain fatty acids of 12-22 carbons. Importantly, the long-chain acyl-CoA synthetases are deregulated in many types of tumors, providing a rationale for anti-tumor therapeutic opportunities. The purpose of this review is to summarize the last up-to-date findings regarding their role in cancer, and to discuss the related emerging tumor targeting opportunities.

Indexed as

AnimalsCoenzyme A LigasesHumansLipidsNeoplasmsPhospholipidsTriglyceridesCoenzyme A LigasesLipidsPhospholipidsTriglyceridesACSL1ACSL3ACSL4ACSL5ACSL6cancercancer therapyfatty acidlipid metabolismtherapy target

Identifiers

PMID31344914
PMCPMC6696099

What OpenQuestion holds

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LicenceCC BY
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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.