Background: TIPARP (TCDD-inducible poly-ADP-ribose polymerase), a mono-ADP-ribosyltransferase and a transcriptional repressor of aryl hydrocarbon receptor (AHR), was one of the potential therapeutic targets for human cancers identified by CRISPR-Cas9 screens recently. Studies about TIPARP on cancers are scarce till now, most of which just focus on expressions, while the functions have not been widely reported yet. Moreover, the TIPARP prognostic significance and therapeutic value of breast cancer is also uncertain.
Methods: The present study was performed to comprehensively analyze the expression pattern, prognostic effect, potential therapeutic function of TIPARP in breast cancer by pooling all currently available databases online including Oncomine, UALCAN, bc-GenExMiner, Kaplan-Meier Plotter, COSMIC, UCSC Xena, STRING, DAVID and Comparative Toxicogenomics Database. Further, we also performed several cell biology experiments including RT-qPCR, Western blot and CCK-8 in cellular and clinical sample levels to confirm the conclusions from bioinformatics analysis.
Results: TIPARP was expressed lower in tumor tissues comparing with normal tissues. Meanwhile, several clinical parameters of breast cancer patients were correlated with TIPARP expression. Further, higher TIPARP expression was related to preferable survival. Moreover, the mutations and DNA methylation of TIPARP might contribute to TIPARP dysregulation in breast cancer. Interactors with TIPARP were significantly enriched in telomere maintenance, telomere organization and mainly participated in pathways in cancer. Finally, several common drugs including metformin were observed to up-regulate the expression of TIPARP.
Conclusion: TIPARP might act as a preferable prognostic marker of breast cancer through multiple biological processes such as DNA methylation, mutation as well as pathway related to telomere and so on. TIPARP could be considered as a potential therapeutic target for breast cancer. However, large-scale and comprehensive research is needed to clarify our results.
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http://dx.doi.org/10.2147/CMAR.S219289 | 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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