Evidence map›Paper›PMID 34528213›Full record

ReviewAging clinical and experimental research2022

Aging preclinical models in oncology field: from cells to aging.

Wafa Bouleftour, Nicolas Magne

Abstract readReview
PubMed Publisher
In one paragraph

Review in Aging clinical and experimental research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.3field-weighted citation impact, top 50% 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

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Review
  2. Article
  3. 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

2 authors at 1 institution in 1 country.

Wafa BouleftourMedical Oncology Department, Lucien Neuwirth Cancer Institute, 108 bis avenue Albert Raimond, 42270, Saint Priest en Jarez, France. wafa.bouleftour@icloire.fr.ORCID http://orcid.org/0000-0002-8485-9386
Nicolas MagneRadiotherapy Department, Lucien Neuwirth Cancer Institute, 42270, Saint Priest en Jarez, France.
Institute Cancer De La Loire Lucien Neuwirth · FR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aging is a universal complex and multifactorial physiological process that leads to the increasing incidence of various diseases including cancer. Indeed, 40% of individuals aged 65 years and over will have newly diagnosed cancers. Although most treated patients are elderly people, a low inclusion of the geriatric population is observed in most clinical trials. Furthermore, lethal side effects of antineoplastic therapy are markedly exacerbated with aging. Most cancer therapies were validated on young mice models, complicating results transposition to elderly patients. Thus, understanding the role of aging in tumor progression and response to cancer therapies with accurate preclinical models must be investigated. Therefore, this review aimed to summarize the state of the literature about preclinical models used to investigate the impact of aging microenvironment on tumorigenic potential, and on antineoplastic therapy response. Despite the advances in technology, and the increasing incidence of cancer in the elderly population, this present review focuses on the few studies using preclinical tumor model of aging. Since the biology of aging is challenging, aging animal models are an inevitable prelude. New emerging tools such as human organoid offer a promising path in research dedicated to aging.

Indexed as

Antineoplastic AgentsNeoplasmsAgedAgingAnimalsHumansMiceTumor MicroenvironmentAntineoplastic AgentsAgingAntineoplastic treatmentsCancerPreclinical model

Identifiers

PMID34528213
OpenAlexW3200555227

What OpenQuestion holds

Textmetadata
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.