It is well known that protein tyrosine phosphatases (PTPs) that become oxidized due to exposure to reactive oxygen species (ROS) undergo a conformational change and are inactivated. However, whether PTPs can actively regulate ROS levels in order to prevent PTP inhibition has yet to be investigated. Here, we demonstrate that PTP non-receptor type 12 (PTPN12) protects cells against aberrant ROS accumulation and death induced by oxidative stress. Murine embryonic fibroblasts (MEFs) deficient in PTPN12 underwent increased ROS-induced apoptosis under conditions of antioxidant depletion. Cells lacking PTPN12 also showed defective activation of FOXO1/3a, transcription factors required for the upregulation of several antioxidant genes. PTPN12-mediated regulation of ROS appeared to be mediated by phosphoinositide-dependent kinase-1 (PDK1), which was hyperstimulated in the absence of PTPN12. As tight regulation of ROS to sustain survival is a key feature of cancer cells, we examined PTPN12 levels in tumors from a cohort of breast cancer patients. Patients whose tumors showed high levels of PTPN12 transcripts had a significantly poorer prognosis. Analysis of tissues from patients with various breast cancer subtypes revealed that more triple-negative breast cancers, the most aggressive breast cancer subtype, showed high PTPN12 expression than any other subtype. Furthermore, both human breast cancer cells and mouse mammary epithelial tumor cells engineered to lack PTPN12 exhibited reduced tumorigenic and metastatic potential in vivo that correlated with their elevated ROS levels. The involvement of PTPN12 in the antioxidant response of breast cancer cells suggests that PTPN12 may represent a novel therapeutic target for this disease.
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http://dx.doi.org/10.1038/onc.2013.24 | DOI Listing |
Pharm Dev Technol
January 2025
Department of Pharmacy, School of Chemistry and Chemical Engineering, Liaoning Normal University, Dalian 116029, China.
In this paper, the pH-sensitive targeting functional material NGR-poly(2-ethyl-2-oxazoline)-cholesteryl methyl carbonate (NGR-PEtOz-CHMC, NPC) modified quercetin (QUE) liposomes (NPC-QUE-L) was constructed. The structure of NPC was confirmed by infrared spectroscopy (IR) and nuclear magnetic resonance hydrogen spectrum (H-NMR). Pharmacokinetic results showed that the accumulation of QUE in plasma of the NPC-QUE-L group was 1.
View Article and Find Full Text PDFJ Med Econ
January 2025
UNESCO-TWAS, The World Academy of Sciences, Trieste, Italy.
Aim: Dynamic cancer control is a current health system priority, yet methods for achieving it are lacking. This study aims to review the application of system dynamics modeling (SDM) on cancer control and evaluate the research quality.
Methods: Articles were searched in PubMed, Web of Science, and Scopus from the inception of the study to November 15th, 2023.
Int J Surg
January 2025
Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen, China.
Detection of biomarkers of breast cancer incurs additional costs and tissue burden. We propose a deep learning-based algorithm (BBMIL) to predict classical biomarkers, immunotherapy-associated gene signatures, and prognosis-associated subtypes directly from hematoxylin and eosin stained histopathology images. BBMIL showed the best performance among comparative algorithms on the prediction of classical biomarkers, immunotherapy related gene signatures, and subtypes.
View Article and Find Full Text PDFInt J Gen Med
December 2024
Department of Thyroid and Breast Surgery, Quzhou People's Hospital, Quzhou, 324000, People's Republic of China.
Objective: This study aims to demonstrate the impact of sarcopenia on the prognosis of early breast cancer and its role in early multimodal intervention.
Methods: The clinical data of patients (n=285) subjected to chemotherapy for early-stage breast cancer diagnosed pathologically between January 1, 2016, and December 31, 2020, in our hospital were retrospectively analyzed. Accordingly, the recruited subjects were divided into sarcopenia (n=85) and non-sarcopenia (n=200) groups according to CT diagnosis correlating with single-factor and multifactorial logistic regression analyses.
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