The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Breaking the Intracellular Redox Balance with Diselenium Nanoparticles for Maximizing Chemotherapy Efficacy on Patient-Derived Xenograft Models
Abstract
Excessive oxidative stress in cancer cells can induce cancer cell death. Anticancer activity and drug resistance of chemotherapy are closely related to the redox state of tumor cells. Herein, five lipophilic Pt(IV) prodrugs were synthesized on the basis of the most widely used anticancer drug cisplatin, whose anticancer efficacy and drug resistance are closely related to the intracellular redox state. Subsequently, a series of cisplatin-sensitive and drug-resistant cell lines as well as three patient-derived primary ovarian cancer cells have been selected to screen those prodrugs. To verify if the disruption of redox balance can be combined with these Pt(IV) prodrugs, we then synthesized a polymer with a diselenium bond in the main chain for encapsulating the most effective prodrug to form nanoparticles (NP(Se)s). NP(Se)s can efficiently break the redox balance via simultaneously depleting GSH and augmenting ROS, thereby achieving a synergistic effect with cisplatin. In addition, genome-wide analysis via RNA-seq was employed to provide a comprehensive understanding of the changes in transcriptome and the alterations in redox-related pathways in cells treated with NP(Se)s and cisplatin. Thereafter, patient-derived xenograft models of hepatic carcinoma (PDX HCC ) and multidrug-resistant lung cancer (PDX MDR ) were established to evaluate the therapeutic effect of NP(Se)s, and a significant antitumor effect was achieved on both models with NP(Se)s. Overall, this study provides a promising strategy to break the redox balance for maximizing the efficacy of platinum-based cancer therapy.
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.
Integrity signals
Registry
Cites
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.
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
Targeting cancer cells by ROS-mediated mechanisms: a radical therapeutic approach?
Trachootham, D., Alexandre, J., & Huang, P. (2009) · Nature Reviews Drug Discovery · doi:10.1038/nrd2803 · Cited by 5,452
Modulation of oxidative stress as an anticancer strategy
Gorrini, C., Harris, I. S., & Mak, T. W. (2013) · Nature Reviews Drug Discovery · doi:10.1038/nrd4002 · Cited by 3,579
Glutathione: overview of its protective roles, measurement, and biosynthesis
Forman, H. J., Zhang, H., & Rinna, A. (2009) · Molecular Aspects of Medicine · doi:10.1016/j.mam.2008.08.006 · Cited by 2,636
Sahay, G., Alakhova, D. Y., & Kabanov, A. V. (2010) · Journal of Controlled Release · doi:10.1016/j.jconrel.2010.01.036 · Cited by 2,181
Reactive Oxygen Species in Metabolic and Inflammatory Signaling
Forrester, S. J., Kikuchi, D. S., Hernandes, M. S., Xu, Q., & Griendling, K. K. (2018) · Circulation Research · doi:10.1161/circresaha.117.311401 · Cited by 2,081
Gao, H., Korn, J. M., Ferretti, S., Monahan, J. E., Wang, Y., Singh, M., Zhang, C., Schnell, C., Yang, G., Zhang, Y., et al. (2015) · Nature Medicine · doi:10.1038/nm.3954 · Cited by 1,393
Self-citationShen, D.-W., Pouliot, L. M., Hall, M. D., & Gottesman, M. M. (2012) · Pharmacological Reviews · doi:10.1124/pr.111.005637 · Cited by 955
ROS Generation and Antioxidant Defense Systems in Normal and Malignant Cells
Snezhkina, A. V., Kudryavtseva, A. V., Kardymon, O. L., Savvateeva, M. V., Melnikova, N. V., Krasnov, G. S., & Dmitriev, A. A. (2019) · Oxidative Medicine and Cellular Longevity · doi:10.1155/2019/6175804 · Cited by 934
Kong, N., Tao, W., Ling, X., Wang, J., Xiao, Y., Shi, S., Ji, X., Shajii, A., Gan, S. T., Kim, N. Y., et al. (2019) · Science Translational Medicine · doi:10.1126/scitranslmed.aaw1565 · Cited by 314
Cited by
Theranostic Fluorescent Probes
Sharma, A., Verwilst, P., Li, M., Ma, D., Singh, N., Yoo, J., Kim, Y., Yang, Y., Zhu, J.-H., Huang, H., et al. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.3c00778 · Cited by 499
Stimuli-Responsive Polymer-Based Nanosystems for Cancer Theranostics
Wei, D., Sun, Y., Zhu, H., & Fu, Q. (2023) · ACS Nano · doi:10.1021/acsnano.3c06019 · Cited by 303
Self-citationStimuli‐Responsive Nanoparticles for Controlled Drug Delivery in Synergistic Cancer Immunotherapy
Zhang, J., Lin, Y., Lin, Z., Wei, Q., Qian, J., Ruan, R., Jiang, X., Hou, L., Song, J., Ding, J., et al. (2022) · Advanced Science · doi:10.1002/advs.202103444 · Cited by 292
Metadata
- Authors
- Wei, D., Yu, Y., Zhang, X., Wang, Y., Chen, H., Zhao, Y., Wang, F., Rong, G., Wang, W., Kang, X., Cai, J., Wang, Z., Yin, J.-Y., Hanif, M., Sun, Y., Zha, G., Li, L., Nie, G., & Xiao, H.
- Venue
- ACS Nano
- Publisher
- American Chemical Society (ACS)
- Published
- 2020-12-07
- Volume
- 14
- Issue
- 12
- Pages
- 16984-16996
- ISSN
- 1936-0851 (ISSN-L)
- 1936-086X
- OpenAlex
- W3111454367
- Cited by
- 170
- References
- 9
- FWCI
- 5.89
- Type
- journal article
- Language
- en
- Open access
- closed
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
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.