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  • Open Access

    ARTICLE

    Cell cycle and HIF-1 related gene expression alteration in thyroid cell lines under microgravity

    JONG-HYUK AHN1, JIN WOOK YI2,3,*

    Oncology Research, Vol.33, No.8, pp. 1909-1931, 2025, DOI:10.32604/or.2025.065847 - 18 July 2025

    Abstract Background: With growing interest in space exploration, understanding microgravity’s impact on human health is essential. This study aims to investigate gene expression changes and migration and invasion potential in five thyroid-related cell lines cultured under simulated microgravity. Methods: Five thyroid-related cell lines—normal thyrocytes (Nthy-ori 3-1), papillary thyroid cancer (PTC) cells (SNU-790, TPC-1), poorly differentiated thyroid cancer cell (BCPAP), and anaplastic thyroid cancer cell (SNU-80)—were cultured under simulated microgravity (10−3 g) using a clinostat. Differentially expressed genes (DEGs) were analyzed using cDNA microarray, followed by functional annotation and assessment of aggressiveness via Transwell migration and invasion assays.… More >

  • Open Access

    ARTICLE

    A New Optimized Wrapper Gene Selection Method for Breast Cancer Prediction

    Heyam H. Al-Baity*, Nourah Al-Mutlaq

    CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 3089-3106, 2021, DOI:10.32604/cmc.2021.015291 - 01 March 2021

    Abstract Machine-learning algorithms have been widely used in breast cancer diagnosis to help pathologists and physicians in the decision-making process. However, the high dimensionality of genetic data makes the classification process a challenging task. In this paper, we propose a new optimized wrapper gene selection method that is based on a nature-inspired algorithm (simulated annealing (SA)), which will help select the most informative genes for breast cancer prediction. These optimal genes will then be used to train the classifier to improve its accuracy and efficiency. Three supervised machine-learning algorithms, namely, the support vector machine, the decision… More >

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