Evidence mapPaperPMID 36949046Full record

ReviewSignal transduction and targeted therapy2023

To metabolomics and beyond: a technological portfolio to investigate cancer metabolism.

Federica Danzi, Raffaella Pacchiana, Andrea Mafficini, Maria T Scupoli, Aldo Scarpa, Massimo Donadelli, Alessandra Fiore

Open access · goldFull text readReview
In one paragraph

Review in Signal transduction and targeted therapy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 122 papers.

0numbers the graph read from it
0cells of the map it votes in
122citing papers in PubMed
34.8field-weighted citation impact, top 1% 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

122 citing papers in PubMed, 220 citations in OpenAlex.

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  11. The metabolic profiles of cancer stem cells.Stem cell research & therapy · 2026
    Review
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62 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

7 authors at 1 institution in 1 country.

Federica DanziDepartment of Neurosciences, Biomedicine and Movement Sciences, Section of Biochemistry, University of Verona, Verona, Italy.
Raffaella PacchianaDepartment of Neurosciences, Biomedicine and Movement Sciences, Section of Biochemistry, University of Verona, Verona, Italy.
Andrea MafficiniDepartment of Diagnostics and Public Health, University of Verona, Verona, Italy.
Maria T ScupoliDepartment of Neurosciences, Biomedicine and Movement Sciences, Biology and Genetics Section, University of Verona, Verona, Italy.
Aldo ScarpaDepartment of Diagnostics and Public Health, University of Verona, Verona, Italy.ORCID 0000-0003-1678-739X
Massimo Donadelli *Department of Neurosciences, Biomedicine and Movement Sciences, Section of Biochemistry, University of Verona, Verona, Italy. massimo.donadelli@univr.it.
Alessandra Fiore *Department of Neurosciences, Biomedicine and Movement Sciences, Section of Biochemistry, University of Verona, Verona, Italy.
University of Verona · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tumour cells have exquisite flexibility in reprogramming their metabolism in order to support tumour initiation, progression, metastasis and resistance to therapies. These reprogrammed activities include a complete rewiring of the bioenergetic, biosynthetic and redox status to sustain the increased energetic demand of the cells. Over the last decades, the cancer metabolism field has seen an explosion of new biochemical technologies giving more tools than ever before to navigate this complexity. Within a cell or a tissue, the metabolites constitute the direct signature of the molecular phenotype and thus their profiling has concrete clinical applications in oncology. Metabolomics and fluxomics, are key technological approaches that mainly revolutionized the field enabling researchers to have both a qualitative and mechanistic model of the biochemical activities in cancer. Furthermore, the upgrade from bulk to single-cell analysis technologies provided unprecedented opportunity to investigate cancer biology at cellular resolution allowing an in depth quantitative analysis of complex and heterogenous diseases. More recently, the advent of functional genomic screening allowed the identification of molecular pathways, cellular processes, biomarkers and novel therapeutic targets that in concert with other technologies allow patient stratification and identification of new treatment regimens. This review is intended to be a guide for researchers to cancer metabolism, highlighting current and emerging technologies, emphasizing advantages, disadvantages and applications with the potential of leading the development of innovative anti-cancer therapies.

Indexed as

MetabolomicsNeoplasmsBiomarkersEnergy MetabolismHumansBiomarkers

Identifiers

PMID36949046
PMCPMC10033890
OpenAlexW4353031848

What Socratic holds

Textfull text, public
LicenceCC BY
measurements read26
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 Socratic graph.