Evidence map›Paper›PMID 34680164›Full record

ReviewBiomolecules2021

MicroRNAs and Metabolism: Revisiting the Warburg Effect with Emphasis on Epigenetic Background and Clinical Applications.

Zsuzsanna Gaál

Open access · goldAbstract readReview
In one paragraph

Review in Biomolecules, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.9field-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

4 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. 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.

Zsuzsanna GaálDepartment of Pediatric Hematology-Oncology, Institute of Pediatrics, University of Debrecen, 4032 Debrecen, Hungary.
University of Debrecen · HU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Since the well-known hallmarks of cancer were described by Hanahan and Weinberg, fundamental advances of molecular genomic technologies resulted in the discovery of novel puzzle pieces in the multistep pathogenesis of cancer. MicroRNAs are involved in the altered epigenetic pattern and metabolic phenotype of malignantly transformed cells. They contribute to the initiation, progression and metastasis-formation of cancers, also interacting with oncogenes, tumor-suppressor genes and epigenetic modifiers. Metabolic reprogramming of cancer cells results from the dysregulation of a complex network, in which microRNAs are located at central hubs. MicroRNAs regulate the expression of several metabolic enzymes, including tumor-specific isoforms. Therefore, they have a direct impact on the levels of metabolites, also influencing epigenetic pattern due to the metabolite cofactors of chromatin modifiers. Targets of microRNAs include numerous epigenetic enzymes, such as sirtuins, which are key regulators of cellular metabolic homeostasis. A better understanding of reversible epigenetic and metabolic alterations opened up new horizons in the personalized treatment of cancer. MicroRNA expression levels can be utilized in differential diagnosis, prognosis stratification and prediction of chemoresistance. The therapeutic modulation of microRNA levels is an area of particular interest that provides a promising tool for restoring altered metabolism of cancer cells.

Indexed as

Warburg Effect, OncologicDNA MethylationEpigenesis, GeneticGene Expression Regulation, NeoplasticHumansMicroRNAsNeoplasmsMicroRNAscancer epigeneticsmetabolismmicroRNAspersonalized treatmentWarburg effect

Identifiers

PMID34680164
PMCPMC8533942
OpenAlexW3207185776

What OpenQuestion 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 OpenQuestion graph.