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Cited by 472

The DOI matches its Crossref record; 274 of the 274 references it cites were found in a registry or an index.

Targeting cancer stem cells for reversing therapy resistance: mechanism, signaling, and prospective agents

Open access
Zhou, He-Ming, Zhang, Ji-Gang, Zhang, Xue, Li, Qin·2021·Signal Transduction and Targeted Therapy

Abstract

Cancer stem cells (CSCs) show a self-renewal capacity and differentiation potential that contribute to tumor progression and therapy resistance. However, the underlying processes are still unclear. Elucidation of the key hallmarks and resistance mechanisms of CSCs may help improve patient outcomes and reduce relapse by altering therapeutic regimens. Here, we reviewed the identification of CSCs, the intrinsic and extrinsic mechanisms of therapy resistance in CSCs, the signaling pathways of CSCs that mediate treatment failure, and potential CSC-targeting agents in various tumors from the clinical perspective. Targeting the mechanisms and pathways described here might contribute to further drug discovery and therapy.

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Crossref · 10.1038/s41392-020-00430-1

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Metadata

Authors
Zhou, H.-M., Zhang, J.-G., Zhang, X., & Li, Q.
Venue
Signal Transduction and Targeted Therapy
Published
2021-02-15
Volume
6
Issue
1
Pages
62-62
ISSN
2059-3635 (ISSN-L)
OpenAlex
W3131854624
Cited by
472
References
274
FWCI
25.27
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Targeting cancer stem cells for reversing therapy resistance: mechanism, signaling, and prospective agents. https://app.cochat.ai/papers/10.1038/s41392-020-00430-1
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Zhou, H.-M., Zhang, J.-G., Zhang, X., & Li, Q. (2021). Targeting cancer stem cells for reversing therapy resistance: mechanism, signaling, and prospective agents. Signal Transduction and Targeted Therapy. https://doi.org/10.1038/s41392-020-00430-1

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.