Evidence map›Paper›PMID 35925428›Full record

ReviewJournal of cancer research and clinical oncology2023

Metabolic targeting of malignant tumors: a need for systemic approach.

Aggelos T Margetis

Open access · greenAbstract readReview
In one paragraph

Review in Journal of cancer research and clinical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.6field-weighted citation impact, top 39% 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

3 citing papers in PubMed, 6 citations in OpenAlex.

  1. Review
  2. Article
  3. 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

1 author at 1 institution in 1 country.

Aggelos T MargetisInternal Medicine-Oncology Residency Program, 2nd Department of Internal Medicine, Athens Naval and Veterans Hospital, 11521, Athens, Greece. margetis.aggelos.md@gmail.com.
Athens Naval & Veterans Hospital · GR

Funding

State Scholarships Foundation MIS-5000432
6 · The paper itself

Abstract

purposeDysregulated metabolism is now recognized as a fundamental hallmark of carcinogenesis inducing aggressive features and additional hallmarks. In this review, well-established metabolic changes displayed by tumors are highlighted in a comprehensive manner and corresponding therapeutical targets are discussed to set up a framework for integrating basic research findings with clinical translation in oncology setting.

methodsRecent manuscripts of high research impact and relevant to the field from PubMed (2000-2021) have been reviewed for this article.

resultsMetabolic pathway disruption during tumor evolution is a dynamic process potentiating cell survival, dormancy, proliferation and invasion even under dismal conditions. Apart from cancer cells, though, tumor microenvironment has an acting role as extracellular metabolites, pH alterations and stromal cells reciprocally interact with malignant cells, ultimately dictating tumor-promoting responses, disabling anti-tumor immunity and promoting resistance to treatments.

conclusionIn the field of cancer metabolism, there are several emerging prognostic and therapeutic targets either in the form of gene expression, enzyme activity or metabolites which could be exploited for clinical purposes; both standard-of-care and novel treatments may be evaluated in the context of metabolism rewiring and indeed, synergistic effects between metabolism-targeting and other therapies would be an attractive perspective for further research.

Indexed as

NeoplasmsCarcinogenesisEnergy MetabolismHumansTumor MicroenvironmentCancerMetabolismTumor microenvironment

Identifiers

PMID35925428
PMCPMC11797171
OpenAlexW4289745359

What OpenQuestion holds

Textmetadata
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.