Evidence map›Paper›PMID 35205794›Full record

ReviewCancers2022

Matrix Stiffness Contributes to Cancer Progression by Regulating Transcription Factors.

Seiichiro Ishihara, Hisashi Haga

Open access · goldAbstract readReview
In one paragraph

Review in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 126 papers.

0numbers the graph read from it
0cells of the map it votes in
126citing papers in PubMed
17.7field-weighted citation impact, top 1% 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

126 citing papers in PubMed, 167 citations in OpenAlex.

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66 more citing papers are in PubMed but not listed here.

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.

Seiichiro IshiharaDepartment of Advanced Transdisciplinary Sciences, Faculty of Advanced Life Science, Hokkaido University, N10-W8, Kita-ku, Sapporo 060-0810, Japan.
Hisashi HagaDepartment of Advanced Transdisciplinary Sciences, Faculty of Advanced Life Science, Hokkaido University, N10-W8, Kita-ku, Sapporo 060-0810, Japan.
Hokkaido University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Matrix stiffness is critical for the progression of various types of cancers. In solid cancers such as mammary and pancreatic cancers, tumors often contain abnormally stiff tissues, mainly caused by stiff extracellular matrices due to accumulation, contraction, and crosslinking. Stiff extracellular matrices trigger mechanotransduction, the conversion of mechanical cues such as stiffness of the matrix to biochemical signaling in the cells, and as a result determine the cellular phenotypes of cancer and stromal cells in tumors. Transcription factors are key molecules for these processes, as they respond to matrix stiffness and are crucial for cellular behaviors. The Yes-associated protein (YAP)/transcriptional coactivator with PDZ-binding motif (TAZ) is one of the most studied transcription factors that is regulated by matrix stiffness. The YAP/TAZ are activated by a stiff matrix and promotes malignant phenotypes in cancer and stromal cells, including cancer-associated fibroblasts. In addition, other transcription factors such as β-catenin and nuclear factor kappa B (NF-κB) also play key roles in mechanotransduction in cancer tissues. In this review, the mechanisms of stiffening cancer tissues are introduced, and the transcription factors regulated by matrix stiffness in cancer and stromal cells and their roles in cancer progression are shown.

Indexed as

cancercancer associated fibroblastscollagencontractioncrosslinkingextracellular matrixmechanotransductionstiffnesstranscription factorsYAP/TAZ

Identifiers

PMID35205794
PMCPMC8870363
OpenAlexW4213077358

What OpenQuestion holds

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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.