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Distinct expression of CDCA3, CDCA5, and CDCA8 leads to shorter relapse free survival in breast cancer patient / Nam Nhut Phan, Chih-Yang Wang, Kuan-Lun Li, Chien-Fu Chen, Chung-Chieh Chiao, Han-Gang Yu, Pung-Ling Huang, Yen-Chang Lin // . - . - Vol. 9 (2018), P.6977-6992. - ISSN:
// Oncotarget. - . - . - ISSN:
New York : Impact Journals, LLC, 2018
16 p.
Ký hiệu phân loại (DDC): 660
Breast cancer is a dangerous disease that results in high mortality rates for cancer patients. Many methods have been developed for the treatment and prevention of this disease. Determining the expression patterns of certain target genes in specific subtypes of breast cancer is important for developing new therapies for breast cancer. In the present study, we performed a holistic approach to screening the mRNA expression of six members of the cell division cycle-associated gene family (CDCA) with a focus on breast cancer using the Oncomine and The Cancer Cell Line Encyclopedia (CCLE) databases. Furthermore, Gene Expression-Based Outcome for Breast Cancer Online (GOBO) was also used to deeply mine the expression of each CDCA gene in clinical breast cancer tissue and breast cancer cell lines. Finally, the mRNA expression of the CDCA genes as related to breast cancer patient survival were analyzed using a Kaplan-Meier plot. CDCA3, CDCA5, and CDCA8 mRNA expression levels were significantly higher than the control sample in both clinical tumor sample and cancer cell lines. These highly expressed genes in the tumors of breast cancer patients dramatically reduced patient survival. The interaction network of CDCA3, CDCA5, and CDCA8 with their co-expressed genes also revealed that CDCA3 expression was highly correlated with cell cycle related genes such as CCNB2, CDC20, CDKN3, and CCNB1. CDCA5 expression was correlated with BUB1 and TRIP13, while CDCA8 expression was correlated with BUB1 and CCNB1. Altogether, these findings suggested CDCA3, CDCA5, and CDCA8 could have a high potency as targeted breast cancer therapies.
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UGGNet : Bridging U-Net and VGG for Advanced Breast Cancer Diagnosis / Tran Cao Minh, Nguyen Kim Quoc, Phan Cong Vinh, Dang Nhu Phu, [...] // EAI Endorsed Transactions on Contex-aware Systems and Applications. - . - . - ISSN: 2409-0026
DOAJ, 2024
8 tr. : picture, tables ; 24 cm.
Ký hiệu phân loại (DDC): 616
In the field of medical imaging, breast ultrasound has emerged as a crucial diagnostic tool for the early detection of breast cancer. However, the accuracy of diagnosing the location of the affected area and the extent of the disease depends on the experience of the physician. In this paper, we propose a novel model called UGGNet, combining the power of the U-Net and VGG architectures to enhance the performance of breast ultrasound image analysis. The U-Net component of the model helps accurately segment the lesions, while the VGG component utilizes deep convolutional layers to extract features. The fusion of these two architectures in UGGNet aims to optimize both segmentation and feature representation, providing a comprehensive solution for accurate diagnosis in breast ultrasound images. Experimental results have demonstrated that the UGGNet model achieves a notable accuracy of 78.2% on the "Breast Ultrasound Images Dataset."
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