Unlabelled: Patients with low-risk node negative breast cancer have an excellent prognosis with 5% breast cancer mortality at 10 years. However, prognostic factors are needed to identify poor prognostic patients who might benefit from adjuvant systemic therapy. Proliferation has been identified as the most important component of gene expression profiles. Cyclin B is a proliferative marker easily assessed by immunohistochemistry. We wanted to examine cyclin B as a prognostic factor in low-risk breast cancer patients.
Patients And Methods: Using an experimental study design, we compared women dying early from their breast cancer (n=17) with women free from relapse more than eight years after initial diagnosis (n=24). All women had stage I, node negative and hormone receptor positive disease. None had received adjuvant chemotherapy. Tumor samples were immunostained for cyclin B using commercial antibodies.
Results: The mean percentage of cyclin B (12%) was significantly higher (p=0.001) in women dying from their breast cancer compared with women free from relapse (5%). High cyclin B (> or =9%) identified 11/17 patients dying from breast cancer and low cyclin B identified 22/24 patients free from relapse. The sensitivity and specificity of cyclin B was 65% and 92%, respectively.
Discussion: We found that low-risk node negative patients with high expression of cylin B had a significantly worse outcome than patients with low expression of cyclin B. Cyclin B could separate patients with poor survival from those with good survival with 80% accuracy. We suggest that cyclin B might be a potent prognostic factor in this low-risk patient group.
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http://dx.doi.org/10.3109/02841861003691937 | 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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