Evidence map›Paper›PMID 33182674›Full record

ReviewCancers2020

Animal Models: A Useful Tool to Unveil Metabolic Changes in Hepatocellular Carcinoma.

Marina Serra, Amedeo Columbano, Andrea Perra, Marta Anna Kowalik

Abstract readReview
In one paragraph

Review in Cancers, 2020. 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
–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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  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

4 authors.

Marina SerraUnit of Oncology and Molecular Pathology, Department of Biomedical Sciences, University of Cagliari, 09042 Monserrato, Italy.
Amedeo ColumbanoUnit of Oncology and Molecular Pathology, Department of Biomedical Sciences, University of Cagliari, 09042 Monserrato, Italy.ORCID 0000-0002-6956-9030
Andrea PerraUnit of Oncology and Molecular Pathology, Department of Biomedical Sciences, University of Cagliari, 09042 Monserrato, Italy.
Marta Anna KowalikUnit of Oncology and Molecular Pathology, Department of Biomedical Sciences, University of Cagliari, 09042 Monserrato, Italy.

Funding

Associazione Italiana per la Ricerca sul Cancro IG 20176Regione Autonoma Sardegna RICRAS_CTC_2018 grant to AC
6 · The paper itself

Abstract

Hepatocellular carcinoma (HCC) is one the most frequent and lethal human cancers. At present, no effective treatment for advanced HCC exist; therefore, the overall prognosis for HCC patients remains dismal. In recent years, a better knowledge of the signaling pathways involved in the regulation of HCC development and progression, has led to the identification of novel potential targets for therapeutic strategies. However, the obtained benefits from current therapeutic options are disappointing. Altered cancer metabolism has become a topic of renewed interest in the last decades, and it has been included among the core hallmarks of cancer. In the light of growing evidence for metabolic reprogramming in cancer, a wide number of experimental animal models have been exploited to study metabolic changes characterizing HCC development and progression and to further expand our knowledge of this tumor. In the present review, we discuss several rodent models of hepatocarcinogenesis, that contributed to elucidate the metabolic profile of HCC and the implications of these changes in modulating the aggressiveness of neoplastic cells. We also highlight the apparently contrasting results stemming from different animal models. Finally, we analyze whether these observations could be exploited to improve current therapeutic strategies for HCC.

Indexed as

glycolysisHCCmiRNAOXPHOSPPP

Identifiers

PMID33182674
PMCPMC7696782

What OpenQuestion holds

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Registered trials

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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.