Breast cancer (BC) is a significant threat to female health, with both modifiable andnon-modifiable risk factors. It is essential to monitor patients regularly and to raise population awareness. Increasing research also suggests that E-selectin (SELE) may increase tumor angiogenesis and the development of cancer. This study investigated SELE single-nucleotide polymorphisms (SNPs) in the following positions: rs5367T/C, rs5368C/T, rs5362T/G,and rs5362T/C. Using polymerase chain reaction, significant differences in allele and genotype frequencies were found between BC patients and controls. Position rs5368 was associated with an increased risk of BC for the CT and TT genotypes, with odds ratios (ORs) of16.3 and 6.90 (Fisher probability = 0.0001, p = 0.005). Women with the T allele had a 19.3-fold higher incidence of BC, while allele C may be a protective allele against BC (OR, 0.05).Heterozygous genotypes at rs5367, rs5362, and rs5362 were significantly more common inBC patients, with ORs of 5.70, 4.50, and 3.80, respectively. These SNPs may be associatedwith the risk of BC, because the frequency of mutant alleles was significantly higher in patients (OR: 4.26, 3.83, and 4.30, respectively) than in controls (OR: 0.23, 0.30, and 0.20, respectively). These SNPs may be considered a common genotype in the Iraqi population,with the wild-type allele having a protective fraction and the mutant allele having an environmental fraction. The results also revealed a 2-fold increase in gene expression in BCpatients compared to controls, with a significant effect (p = 0.017). This study's findingsconfirm the importance of SELE polymorphisms in cancer risk prediction.
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http://dx.doi.org/10.5808/gi.22042 | 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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