Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 899

The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.

Targeting mTOR for cancer therapy

Open access
Hua, Hui, Kong, Qingbin, Zhang, Hongying, Wang, Jiao, Luo, Ting, Jiang, Yangfu·2019·Journal of Hematology & Oncology

Abstract

Mechanistic target of rapamycin (mTOR) is a protein kinase regulating cell growth, survival, metabolism, and immunity. mTOR is usually assembled into several complexes such as mTOR complex 1/2 (mTORC1/2). In cooperation with raptor, rictor, LST8, and mSin1, key components in mTORC1 or mTORC2, mTOR catalyzes the phosphorylation of multiple targets such as ribosomal protein S6 kinase β-1 (S6K1), eukaryotic translation initiation factor 4E binding protein 1 (4E-BP1), Akt, protein kinase C (PKC), and type-I insulin-like growth factor receptor (IGF-IR), thereby regulating protein synthesis, nutrients metabolism, growth factor signaling, cell growth, and migration. Activation of mTOR promotes tumor growth and metastasis. Many mTOR inhibitors have been developed to treat cancer. While some of the mTOR inhibitors have been approved to treat human cancer, more mTOR inhibitors are being evaluated in clinical trials. Here, we update recent advances in exploring mTOR signaling and the development of mTOR inhibitors for cancer therapy. In addition, we discuss the mechanisms underlying the resistance to mTOR inhibitors in cancer cells.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1186/s13045-019-0754-1

Cites

21 of 21 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 21 references

Cited by

See all 899

Metadata

Authors
Hua, H., Kong, Q., Zhang, H., Wang, J., Luo, T., & Jiang, Y.
Venue
Journal of Hematology & Oncology
Published
2019-07-05
Volume
12
Issue
1
Pages
71-71
ISSN
1756-8722 (ISSN-L)
OpenAlex
W2954917865
Cited by
899
References
21
FWCI
28.57
Type
journal article
Language
en
Open access
gold

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Targeting mTOR for cancer therapy. https://app.cochat.ai/papers/10.1186/s13045-019-0754-1
The paper
Hua, H., Kong, Q., Zhang, H., Wang, J., Luo, T., & Jiang, Y. (2019). Targeting mTOR for cancer therapy. Journal of Hematology & Oncology. https://doi.org/10.1186/s13045-019-0754-1

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.