AIMC Topic: Epithelial-Mesenchymal Transition

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Machine learning reveals mesenchymal breast carcinoma cell adaptation in response to matrix stiffness.

PLoS computational biology Jul 23, 2021
Epithelial-mesenchymal transition (EMT) and its reverse process, mesenchymal-epithelial transition (MET), are believed to play key roles in facilitating the metastatic cascade. Metastatic lesions often exhibit a similar epithelial-like state to that ...
Humans Machine Learning Female Triple Negative Breast Neoplasms Cell Proliferation Antigens, CD Cell Count Tumor Microenvironment Biomarkers, Tumor Cell Line, Tumor Computational Biology Cell Adhesion Cell Shape Adaptation, Physiological Vimentin Neoplasm Metastasis Extracellular Matrix Biophysical Phenomena Epithelial-Mesenchymal Transition Cadherins Models, Biological
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Global gene network exploration based on explainable artificial intelligence approach.

PloS one Nov 6, 2020
In recent years, personalized gene regulatory networks have received significant attention, and interpretation of the multilayer networks has been a critical issue for a comprehensive understanding of gene regulatory systems. Although several statist...
Epithelial-Mesenchymal Transition Gene Regulatory Networks Transcription Factors Principal Component Analysis Humans Artificial Intelligence Software Biomarkers
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Explaining the dynamics of tumor aggressiveness: At the crossroads between biology, artificial intelligence and complex systems.

Seminars in cancer biology Jul 11, 2018
Facing metastasis is the most pressing challenge of cancer research. In this review, we discuss recent advances in understanding phenotypic plasticity of cancer cells, highlighting the kinetics of cancer stem cell and the role of the epithelial mesen...
Humans Artificial Intelligence Macrophages Neoplastic Stem Cells Cancer-Associated Fibroblasts Epithelial-Mesenchymal Transition Tumor Microenvironment Neoplasm Metastasis Neoplasms Gene Regulatory Networks
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The Interplay Between Epithelial-Mesenchymal Transition (EMT) and the Thyroid Hormones-αvβ3 Axis in Ovarian Cancer.

Hormones & cancer Dec 19, 2017
Ovarian cancer is a highly metastatic disease. The metastatic potential is enhanced by epithelial to mesenchymal transition (EMT) in which αvβ3 integrin plays a role. Thyroid hormones (L-thyroxine, T4, and 3,5,3'-triiodo-L-thyronine, T3) bind this in...
Cadherins Humans Thyroid Hormones Cell Line, Tumor Female beta Catenin Zonula Occludens-1 Protein Gene Expression Regulation, Neoplastic Integrin alphaVbeta3 Vimentin Snail Family Transcription Factors Epithelial-Mesenchymal Transition Signal Transduction Zinc Finger E-box-Binding Homeobox 1 Thyroxine Triiodothyronine Ovarian Neoplasms
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Epithelial-mesenchymal transition effect of fine particulate matter from the Yangtze River Delta region in China on human bronchial epithelial cells.

Journal of environmental sciences (China) May 9, 2017
Epidemiological studies have demonstrated that fine particulate matter (PM) exposure causes airway inflammation, which may lead to lung cancer. The activation of epithelial-mesenchymal transition (EMT) is assumed to be a crucial step in lung tumor me...
Toxicity Tests Humans Cell Survival China Air Pollutants Epithelial Cells Particulate Matter Epithelial-Mesenchymal Transition
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Integrative PPI network and random forest analysis identifies KIF4A, TOP2A, and ASPM protein macromolecules as novel protein biomarkers in renal carcinoma pathogenesis.

International journal of biological macromolecules Jun 1, 2025
The pathogenesis of kidney cancer is not fully understood, so there is an urgent need to identify new biomarkers to improve diagnosis and treatment. The study identified KIF4A, TOP2A and ASPM as novel protein biomarkers for renal cancer and explored ...
Kidney Neoplasms Carcinoma, Renal Cell Kinesins Poly-ADP-Ribose Binding Proteins DNA Topoisomerases, Type II Gene Expression Regulation, Neoplastic Nerve Tissue Proteins Epithelial-Mesenchymal Transition Random Forest Cell Line, Tumor Humans Protein Interaction Maps Prognosis Computational Biology Biomarkers, Tumor Gene Expression Profiling
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An Attention-Based Deep Neural Network Model to Detect Cis-Regulatory Elements at the Single-Cell Level From Multi-Omics Data.

Genes to cells : devoted to molecular & cellular mechanisms Mar 1, 2025
Cis-regulatory elements (cREs) play a crucial role in regulating gene expression and determining cell differentiation and state transitions. To capture the heterogeneous transitions of cell states associated with these processes, detecting cRE activi...
Humans Neural Networks, Computer Leukocytes, Mononuclear Single-Cell Analysis Zinc Finger E-box-Binding Homeobox 1 SOXB1 Transcription Factors Regulatory Elements, Transcriptional Regulatory Sequences, Nucleic Acid Genomics Multiomics Glioma Epithelial-Mesenchymal Transition
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Deep learning-based classification of breast cancer cells using transmembrane receptor dynamics.

Bioinformatics (Oxford, England) Dec 22, 2021
MOTIVATION: Motions of transmembrane receptors on cancer cell surfaces can reveal biophysical features of the cancer cells, thus providing a method for characterizing cancer cell phenotypes. While conventional analysis of receptor motions in the cell...
MCF-7 Cells Epithelial-Mesenchymal Transition ErbB Receptors Humans Female Breast Neoplasms Deep Learning Cell Line, Tumor
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Morphology-based prediction of cancer cell migration using an artificial neural network and a random decision forest.

Integrative biology : quantitative biosciences from nano to macro Dec 19, 2018
Metastasis is the cause of death in most patients of breast cancer and other solid malignancies. Identification of cancer cells with highly migratory capability to metastasize relies on markers for epithelial-to-mesenchymal transition (EMT), a proces...
Models, Biological Neoplasm Metastasis Lab-On-A-Chip Devices Humans Neoplasms Single-Cell Analysis Neural Networks, Computer Deep Learning Mice Algorithms Cell Line, Tumor Cell Movement Decision Support Techniques Female Epithelial-Mesenchymal Transition Reproducibility of Results Breast Neoplasms Animals
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