Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (231)
  • Open Access

    ARTICLE

    MCU-i4, a mitochondrial Ca2+ uniporter modulator, induces breast cancer BT474 cell death by enhancing glycolysis, ATP production and reactive oxygen species (ROS) burst

    EDMUND CHEUNG SO1,2,#, LOUIS W. C. CHOW3,#, CHIN-MIN CHUANG4, CING YU CHEN5,6, CHENG-HSUN WU7, LIAN-RU SHIAO8, TING-TSZ OU9, KAR-LOK WONG10, YUK-MAN LEUNG8,*, YI-PING HUANG8,*

    Oncology Research, Vol.33, No.2, pp. 397-406, 2025, DOI:10.32604/or.2024.052743 - 16 January 2025

    Abstract Objectives: Mitochondrial Ca2+ uniporter (MCU) provides a Ca2+ influx pathway from the cytosol into the mitochondrial matrix and a moderate mitochondrial Ca2+ rise stimulates ATP production and cell growth. MCU is highly expressed in various cancer cells including breast cancer cells, thereby increasing the capacity of mitochondrial Ca2+ uptake, ATP production, and cancer cell proliferation. The objective of this study was to examine MCU inhibition as an anti-cancer mechanism. Methods: The effects of MCU-i4, a newly developed MCU inhibitor, on cell viability, apoptosis, cytosolic Ca2+, mitochondrial Ca2+ and potential, glycolytic rate, generation of ATP, and reactive oxygen species,… More >

  • Open Access

    ARTICLE

    Hsa-miR-214-3p inhibits breast cancer cell growth and improves the tumor immune microenvironment by downregulating B7H3

    YAN LU1,2,#, KANG WANG3,#, YUANHONG PENG3,4, MENG CHEN5, LIN ZHONG3,4, LUJI HUANG3,4, FU CHENG3,4, XINDAN SHENG4,6, XIN YANG4,6, MANZHAO OUYANG3,4, GEORGE A. CALIN5,*, ZHIWEI HE1,*

    Oncology Research, Vol.33, No.1, pp. 103-121, 2025, DOI:10.32604/or.2024.057472 - 20 December 2024

    Abstract Background: Immune checkpoint inhibitors play an important role in the treatment of solid tumors, but the currently used immune checkpoint inhibitors targeting programmed cell death-1 (PD-1), programmed cell death ligand-1 (PD-L1), and cytotoxic T-lymphocyte antigen-4 (CTLA-4) show limited clinical efficacy in many breast cancers. B7H3 has been widely reported as an immunosuppressive molecule, but its immunological function in breast cancer patients remains unclear. Methods: We analyzed the expression of B7H3 in breast cancer samples using data from the Cancer Genome Atlas Program (TCGA) and the Gene Expression Omnibus (GEO) databases. MicroRNAs were selected using the… More >

  • Open Access

    ARTICLE

    Deep learning identification of novel autophagic protein-protein interactions and experimental validation of Beclin 2-Ubiquilin 1 axis in triple-negative breast cancer

    XIANG LI1,#, WENKE JIN2,#, LIFENG WU2, HUAN WANG1, XIN XIE1, WEI HUANG1,*, BO LIU2,*

    Oncology Research, Vol.33, No.1, pp. 67-81, 2025, DOI:10.32604/or.2024.055921 - 20 December 2024

    Abstract Background: Triple-negative breast cancer (TNBC), characterized by its lack of traditional hormone receptors and HER2, presents a significant challenge in oncology due to its poor response to conventional therapies. Autophagy is an important process for maintaining cellular homeostasis, and there are currently autophagy biomarkers that play an effective role in the clinical treatment of tumors. In contrast to targeting protein activity, intervention with protein-protein interaction (PPI) can avoid unrelated crosstalk and regulate the autophagy process with minimal interference pathways. Methods: Here, we employed Naive Bayes, Decision Tree, and k-Nearest Neighbors to elucidate the complex PPI… More >

  • Open Access

    ARTICLE

    Advancing Breast Cancer Diagnosis: The Development and Validation of the HERA-Net Model for Thermographic Analysis

    S. Ramacharan1,*, Martin Margala1, Amjan Shaik2, Prasun Chakrabarti3, Tulika Chakrabarti4

    CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3731-3760, 2024, DOI:10.32604/cmc.2024.058488 - 19 December 2024

    Abstract Breast cancer remains a significant global health concern, with early detection being crucial for effective treatment and improved survival rates. This study introduces HERA-Net (Hybrid Extraction and Recognition Architecture), an advanced hybrid model designed to enhance the diagnostic accuracy of breast cancer detection by leveraging both thermographic and ultrasound imaging modalities. The HERA-Net model integrates powerful deep learning architectures, including VGG19, U-Net, GRU (Gated Recurrent Units), and ResNet-50, to capture multi-dimensional features that support robust image segmentation, feature extraction, and temporal analysis. For thermographic imaging, a comprehensive dataset of 3534 infrared (IR) images from the… More >

  • Open Access

    ARTICLE

    Modeling and Predictive Analytics of Breast Cancer Using Ensemble Learning Techniques: An Explainable Artificial Intelligence Approach

    Avi Deb Raha1, Fatema Jannat Dihan2, Mrityunjoy Gain1, Saydul Akbar Murad3, Apurba Adhikary2, Md. Bipul Hossain2, Md. Mehedi Hassan1, Taher Al-Shehari4, Nasser A. Alsadhan5, Mohammed Kadrie4, Anupam Kumar Bairagi1,*

    CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4033-4048, 2024, DOI:10.32604/cmc.2024.057415 - 19 December 2024

    Abstract Breast cancer stands as one of the world’s most perilous and formidable diseases, having recently surpassed lung cancer as the most prevalent cancer type. This disease arises when cells in the breast undergo unregulated proliferation, resulting in the formation of a tumor that has the capacity to invade surrounding tissues. It is not confined to a specific gender; both men and women can be diagnosed with breast cancer, although it is more frequently observed in women. Early detection is pivotal in mitigating its mortality rate. The key to curbing its mortality lies in early detection.… More >

  • Open Access

    ARTICLE

    Densely Convolutional BU-NET Framework for Breast Multi-Organ Cancer Nuclei Segmentation through Histopathological Slides and Classification Using Optimized Features

    Amjad Rehman1, Muhammad Mujahid1, Robertas Damasevicius2,*, Faten S Alamri3, Tanzila Saba1

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2375-2397, 2024, DOI:10.32604/cmes.2024.056937 - 31 October 2024

    Abstract This study aims to develop a computational pathology approach that can properly detect and distinguish histology nuclei. This is crucial for histopathological image analysis, as it involves segmenting cell nuclei. However, challenges exist, such as determining the boundary region of normal and deformed nuclei and identifying small, irregular nuclei structures. Deep learning approaches are currently dominant in digital pathology for nucleus recognition and classification, but their complex features limit their practical use in clinical settings. The existing studies have limited accuracy, significant processing costs, and a lack of resilience and generalizability across diverse datasets. We… More >

  • Open Access

    ARTICLE

    A Genetic Algorithm-Based Optimized Transfer Learning Approach for Breast Cancer Diagnosis

    Hussain AlSalman1, Taha Alfakih2, Mabrook Al-Rakhami2, Mohammad Mehedi Hassan2,*, Amerah Alabrah2

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2575-2608, 2024, DOI:10.32604/cmes.2024.055011 - 31 October 2024

    Abstract Breast cancer diagnosis through mammography is a pivotal application within medical image-based diagnostics, integral for early detection and effective treatment. While deep learning has significantly advanced the analysis of mammographic images, challenges such as low contrast, image noise, and the high dimensionality of features often degrade model performance. Addressing these challenges, our study introduces a novel method integrating Genetic Algorithms (GA) with pre-trained Convolutional Neural Network (CNN) models to enhance feature selection and classification accuracy. Our approach involves a systematic process: first, we employ widely-used CNN architectures (VGG16, VGG19, MobileNet, and DenseNet) to extract a… More >

  • Open Access

    ARTICLE

    Unveiling the therapeutic potential: KBU2046 halts triple-negative breast cancer cell migration by constricting TGF-β1 activation in vitro

    JINXIA CHEN1,2,3,#, SULI DAI1,2,#, GENG ZHANG4,5, SISI WEI1,2, XUETAO ZHAO3, YANG ZHENG1,2, YAOJIE WANG1,2, XIAOHAN WANG1,2, YUNJIANG LIU4,5,*, LIANMEI ZHAO1,2,*

    Oncology Research, Vol.32, No.11, pp. 1791-1802, 2024, DOI:10.32604/or.2024.049348 - 16 October 2024

    Abstract Background: Triple-negative breast cancer (TNBC) is a heterogeneous, recurring cancer characterized by a high rate of metastasis, poor prognosis, and lack of efficient therapies. KBU2046, a small molecule inhibitor, can inhibit cell motility in malignant tumors, including breast cancer. However, the specific targets and the corresponding mechanism of its function remain unclear. Methods: In this study, we employed (3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H tetrazolium) (MTS) assay and transwell assay to investigate the impact of KBU2046 on the proliferation and migration of TNBC cells in vitro. RNA-Seq was used to explore the targets of KBU2046 that inhibit the motility of TNBC.… More > Graphic Abstract

    Unveiling the therapeutic potential: KBU2046 halts triple-negative breast cancer cell migration by constricting TGF-β1 activation <i>in vitro</i>

  • Open Access

    ARTICLE

    An Improved Artificial Rabbits Optimization Algorithm with Chaotic Local Search and Opposition-Based Learning for Engineering Problems and Its Applications in Breast Cancer Problem

    Feyza Altunbey Özbay1, Erdal Özbay2, Farhad Soleimanian Gharehchopogh3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1067-1110, 2024, DOI:10.32604/cmes.2024.054334 - 27 September 2024

    Abstract Artificial rabbits optimization (ARO) is a recently proposed biology-based optimization algorithm inspired by the detour foraging and random hiding behavior of rabbits in nature. However, for solving optimization problems, the ARO algorithm shows slow convergence speed and can fall into local minima. To overcome these drawbacks, this paper proposes chaotic opposition-based learning ARO (COARO), an improved version of the ARO algorithm that incorporates opposition-based learning (OBL) and chaotic local search (CLS) techniques. By adding OBL to ARO, the convergence speed of the algorithm increases and it explores the search space better. Chaotic maps in CLS… More > Graphic Abstract

    An Improved Artificial Rabbits Optimization Algorithm with Chaotic Local Search and Opposition-Based Learning for Engineering Problems and Its Applications in Breast Cancer Problem

  • Open Access

    RETRACTION

    Retraction: Long noncoding RNA CAMTA1 promotes proliferation and mobility of the human breast cancer cell line MDA-MB-231 via targeting miR-20b

    Oncology Research Editorial Office

    Oncology Research, Vol.32, No.10, pp. 1679-1680, 2024, DOI:10.32604/or.2024.056894 - 18 September 2024

    Abstract This article has no abstract. More >

Displaying 21-30 on page 3 of 231. Per Page