Evidence map›Paper›PMID 34722292›Full record

ReviewFrontiers in oncology2021

High ATP Production Fuels Cancer Drug Resistance and Metastasis: Implications for Mitochondrial ATP Depletion Therapy.

Marco Fiorillo, Béla Ózsvári, Federica Sotgia, Michael P Lisanti

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers.

0numbers the graph read from it
0cells of the map it votes in
63citing papers in PubMed
4.9field-weighted citation impact, top 3% 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

63 citing papers in PubMed, 94 citations in OpenAlex.

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  16. Reagentless Real-Time ATP Monitoring with New DNA Aptamers.Small (Weinheim an der Bergstrasse, Germany) · 2025
    Article
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  20. Review

3 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

4 authors at 2 institutions in 2 countries.

Marco FiorilloTranslational Medicine, School of Science, Engineering and Environment (SEE), University of Salford, Greater Manchester, United Kingdom.
Béla ÓzsváriTranslational Medicine, School of Science, Engineering and Environment (SEE), University of Salford, Greater Manchester, United Kingdom.
Federica SotgiaTranslational Medicine, School of Science, Engineering and Environment (SEE), University of Salford, Greater Manchester, United Kingdom.
Michael P LisantiTranslational Medicine, School of Science, Engineering and Environment (SEE), University of Salford, Greater Manchester, United Kingdom.
University of Salford · GBUniversity of Calabria · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recently, we presented evidence that high mitochondrial ATP production is a new therapeutic target for cancer treatment. Using ATP as a biomarker, we isolated the "metabolically fittest" cancer cells from the total cell population. Importantly, ATP-high cancer cells were phenotypically the most aggressive, with enhanced stem-like properties, showing multi-drug resistance and an increased capacity for cell migration, invasion and spontaneous metastasis. In support of these observations, ATP-high cells demonstrated the up-regulation of both mitochondrial proteins and other protein biomarkers, specifically associated with stemness and metastasis. Therefore, we propose that the "energetically fittest" cancer cells would be better able to resist the selection pressure provided by i) a hostile micro-environment and/or ii) conventional chemotherapy, allowing them to be

Indexed as

anti-oxidant capacityATPbedaquilinecancer stem cells (CSCs)dormancymetastasismitochondriamulti-drug resistance

Identifiers

PMID34722292
PMCPMC8554334
OpenAlexW3206314245

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.