Background: This study is to explore the relationship between the ZBRK1/ZNF350 (Zinc finger and BRCA1-interacting protein with KRAB domain-1; also known as zinc-finger protein 350) gene polymorphism and early-onset breast cancer.
Methods: The ZBRK1/ZNF350 gene exon detection analysis was performed with the direct sequencing and Snapshot methods in 80 cases of breast cancer (aged ≤ 40 years old) and 240 healthy subjects (aged ≤ 40 years old).
Results: Totally 9 sequence variants were detected, including 5 missense mutations and 4 synonymous mutations, located at EXON3, EXON4 and EXON5, respectively. The rs4987241 and rs3764538 variants were published for the first time, while the remaining variants had been reported before. There were significant differences in the frequency distribution of family history between the breast cancer and control groups. Moreover, there were significant differences in the CT genotype frequency at the rs138898320 locus between the breast cancer and healthy control groups. Compared with the carriers of CC wild genotype at rs138898320, the risk of breast cancer was reduced by 88.3% in the CT mutant genotype carriers, with significant difference. In the stratification with no family history, compared with the carriers of CC wild genotype at rs138898320, significant differences were observed for the CT mutant genotype carriers. In the stratification with family history, there was no significant difference in the variation of rs138898320.
Conclusion: The rs138898320 CT mutation genotype of ZBRK1/ZNF350 may reduce the risk of breast cancer, and the protecting effect would be increased in the stratification with no family history. Trial registration Not applicable.
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http://dx.doi.org/10.1186/s12920-020-00862-2 | 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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