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Because activity of the epithelial–mesenchymal transition (EMT) is involved in anti-cancer drug resistance, cancer malignancy, and shares some characteristics with cancer stem cells (CSCs), we used artificial intelligence (AI) modeling to identify the cancer-related activity of the EMT-related pathway in datasets of gene expression. We generated images of gene expression overlayed onto molecular pathways with Ingenuity Pathway Analysis (IPA). A dataset of 50 activated and 50 inactivated pathway images of EMT regulation in the development pathway was then modeled by the DataRobot Automated Machine Learning platform. The most accurate models were based on the Elastic-Net Classifier algorithm. The model was validated with 10 additional activated and 10 additional inactivated pathway images. The generated models had false-positive and false-negative results. These images had significant features of opposite labels, and the original data were related to Parkinson’s disease. This approach reliably identified cancer phenotypes and treatments where EMT regulation in the development pathway was activated or inactivated thereby identifying conditions where therapeutics might be applied or developed. As there are a wide variety of cancer phenotypes and CSC targets that provide novel insights into the mechanism of CSCs’ drug resistance and cancer metastasis, our approach holds promise for modeling and simulating cellular phenotype transition, as well as predicting molecular-induced responses.

The mechanism of epithelial-mesenchymal transition (EMT) consists of the cellular phenotypic transition from epithelial to mesenchymal status. The cells exhibiting EMT exist in cancer stem cell (CSC) population, which is involved in drug resistance. CSCs demonstrating EMT feature remain after cancer treatment, which leads to drug resistance, recurrence, metastasis and malignancy of cancer. In this context, the recent advance of nanotechnology in the medical application has ascended the possibility to target CSCs using nanomedicines. In this review article, we focused on the mechanism of CSCs and EMT, especially into the signaling pathways in EMT, regulation of EMT and CSCs by microRNAs and nanomedicine-based approaches to target CSCs.

The crystal structure of the title compound, C27H45N3, has been determined as part of our investigation into the hydrophobic modification of aminoglycoside antibiotics. The isopropyl group showed disorder for the tertiary carbon (equal occupancies), with high thermal motion for the peripheral atoms of the isopropyl and azide groups also apparent in the structure. The axial disposition of the azide group is consistent with the clean inversion of stereochemistry at C-3 under Mitsunobu conditions.