Evidence map›Paper›PMID 35319749›Full record

ReviewDisease models & mechanisms2022

Cancer cachexia: lessons from Drosophila.

Ying Liu, Pedro Saavedra, Norbert Perrimon

Open access · goldAbstract readReview
In one paragraph

Review in Disease models & mechanisms, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.

0numbers the graph read from it
0cells of the map it votes in
37citing papers in PubMed
4.1field-weighted citation impact, top 4% 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

37 citing papers in PubMed, 46 citations in OpenAlex.

  1. Macrophage restriction inScience advances · 2026
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  4. Science advances · 2026
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  6. Review
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  16. A health-system-scale, episode-resolved multimodal atlas of cancer cachexia.medRxiv : the preprint server for health sciences · 2025
    Article
  17. International journal of molecular sciences · 2025
    Review
  18. Article
  19. Article
  20. Article
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

3 authors at 2 institutions in 1 country.

Ying LiuDepartment of Genetics, Harvard Medical School, Boston, MA 02115, USA.ORCID 0000-0002-6419-1980
Pedro SaavedraDepartment of Genetics, Harvard Medical School, Boston, MA 02115, USA.
Norbert PerrimonDepartment of Genetics, Harvard Medical School, Boston, MA 02115, USA.ORCID 0000-0001-7542-472X
Harvard University · USHoward Hughes Medical Institute · US

Funding

Cancer Research UK CGCATF-2021/100026Howard Hughes Medical Institute
6 · The paper itself

Abstract

Cachexia, a wasting syndrome that is often associated with cancer, is one of the primary causes of death in cancer patients. Cancer cachexia occurs largely due to systemic metabolic alterations stimulated by tumors. Despite the prevalence of cachexia, our understanding of how tumors interact with host tissues and how they affect metabolism is limited. Among the challenges of studying tumor-host tissue crosstalk are the complexity of cancer itself and our insufficient knowledge of the factors that tumors release into the blood. Drosophila is emerging as a powerful model in which to identify tumor-derived factors that influence systemic metabolism and tissue wasting. Strikingly, studies that are characterizing factors derived from different fly tumor cachexia models are identifying both common and distinct cachectic molecules, suggesting that cachexia is more than one disease and that fly models can help identify these differences. Here, we review what has been learned from studies of tumor-induced organ wasting in Drosophila and discuss the open questions.

Indexed as

CachexiaNeoplasmsAnimalsDrosophilaMuscle, SkeletalCachectic factorsCancer cachexiaDrosophilaOrgan wasting

Identifiers

PMID35319749
PMCPMC8961677
OpenAlexW4220713300

What Socratic 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 Socratic graph.