Evidence map›Paper›PMID 35267646›Full record

ArticleCancers2022

Stem-Cell Theory of Cancer: Implications for Antiaging and Anticancer Strategies.

Shi-Ming Tu, Louis L Pisters

Open access · goldAbstract read
In one paragraph

Article in Cancers, 2022. 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
0.5field-weighted citation impact, top 35% 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

4 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
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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 2 institutions in 1 country.

Shi-Ming TuDivision of Hematology/Oncology, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.ORCID 0000-0003-0015-2111
Louis L PistersDepartment of Urology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
The University of Texas MD Anderson Cancer Center · USUniversity of Arkansas for Medical Sciences · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A stem-cell theory of cancer predicates that not only does the cell affect the niche, the niche also affects the cell. It implicates that even though genetic makeup may be supreme, cellular context is key. When we attempt to solve the mystery of a long cancer-free life, perhaps we need to search no further than the genetics and epigenetics of the naked mole-rat. When we try to unlock the secrets in the longevity and quality of life, perhaps we need to look no further than the lifestyle and habits of the super centenarians. We speculate that people with Down's syndrome and progeria age faster but have fewer cancers, because they are depleted of stem cells, and, as a consequence, have fewer opportunities for stem cell defects that could predispose them to the development of cancer. We contemplate whether these incredible experiments of nature may provide irrefutable evidence that cancer is a stem-cell disease-fewer aberrant stem cells, fewer cancers; no defective stem cells, no cancer. In this perspective, we investigate a stem-cell origin of aging and cancer. We elaborate an intriguing inverse relationship between longevity and malignancy in the naked mole-rat, in Down's syndrome, and in progeria. We postulate that stem-cell pools and stemness factors may affect aging and dictate cancer. We propose that a healthy microbiome may protect and preserve stem cell reserves and provide meaningful antiaging effects and anticancer benefits.

Indexed as

agingantiagingcancer stem cellsDown’s syndromemicrobiomenaked mole-ratprogeria

Identifiers

PMID35267646
PMCPMC8909197
OpenAlexW4220802030

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.