Evidence map›Paper›PMID 35921025›Full record

ArticleTheory in biosciences = Theorie in den Biowissenschaften2022

Growth dynamics of breast cancer stem cells: effects of self-feedback and EMT mechanisms.

Liuyong Pang, Sanhong Liu, Zhong Zhao, Tianhai Tian, Xinan Zhang, Qiuying Li

Abstract read
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In one paragraph

Article in Theory in biosciences = Theorie in den Biowissenschaften, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.1field-weighted citation impact, top 54% 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

0 citing papers in PubMed, 1 citations in OpenAlex.

No citing paper in PubMed yet.

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

6 authors at 4 institutions in 2 countries.

Liuyong PangSchool of Mathematics and Statistics, Huanghuai University, Zhumadian, 463000, People's Republic of China.
Sanhong LiuSchool of Mathematics and Statistics, Hubei University of Science and Technology, Xianning, 437100, People's Republic of China. jerrylsh@163.com.ORCID http://orcid.org/0000-0002-2197-2955
Zhong ZhaoSchool of Mathematics and Statistics, Huanghuai University, Zhumadian, 463000, People's Republic of China. zhaozhong8899@163.com.
Tianhai TianSchool of Mathematical Sciences, Monash University, Melbourne, VIC, 3800, Australia.
Xinan ZhangSchool of Mathematics and Statistics, Central China Normal University, Wuhan, 430079, People's Republic of China.
Qiuying LiSchool of Mathematics and Statistics, Huanghuai University, Zhumadian, 463000, People's Republic of China.
Huanghuai University · CNCentral China Normal University · CNHubei University of Science and Technology · CNMonash University · AU

Funding

National Natural Science Foundation of China 11871060National Natural Science Foundation of China 12071407National Natural Science Foundation of China 61973177
6 · The paper itself

Abstract

Breast cancer stem cells (BCSCs) with the ability to self-renew and differentiate have been identified in primary breast cancer tissues and cell lines. The BCSCs are often resistant to traditional radiation and/or chemotherapies. Previous studies have also shown that successful therapy must eradicate cancer stem cells. The purpose of this paper is to develop a mathematical model with self-feedback mechanism to illustrate the issues regarding the difficulties of absolutely eliminating a breast cancer. In addition, we introduce the mechanism of the epithelial-mesenchymal transition (EMT) to investigate the influence of EMT on the effects of breast cancer growth and treatment. Results indicate that the EMT mechanism facilitates the growth of breast cancer and makes breast cancer more difficult to be cured. Therefore, targeting the signals involved in EMT can halt tumor progression in breast cancer. Finally, we apply the experimental data to carry out numerical simulations and validate our theoretical conclusions.

Indexed as

Breast NeoplasmsEpithelial-Mesenchymal TransitionFeedbackFemaleHumansNeoplastic Stem CellsBreast cancer stem cellsEpithelial-mesenchymal transitionMathematical modellingNumerical simulationsSelf-feedback

Identifiers

PMID35921025
OpenAlexW4289517653

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.