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- Title
Machine Learning Model for Prediction of Development of Cancer Stem Cell Subpopulation in Tumurs Subjected to Polystyrene Nanoparticles.
- Authors
Ramović Hamzagić, Amra; Gazdić Janković, Marina; Cvetković, Danijela; Nikolić, Dalibor; Nikolić, Sandra; Milivojević Dimitrijević, Nevena; Kastratović, Nikolina; Živanović, Marko; Miletić Kovačević, Marina; Ljujić, Biljana
- Abstract
Cancer stem cells (CSCs) play a key role in tumor progression, as they are often responsible for drug resistance and metastasis. Environmental pollution with polystyrene has a negative impact on human health. We investigated the effect of polystyrene nanoparticles (PSNPs) on cancer cell stemness using flow cytometric analysis of CD24, CD44, ABCG2, ALDH1 and their combinations. This study uses simultaneous in vitro cell lines and an in silico machine learning (ML) model to predict the progression of cancer stem cell (CSC) subpopulations in colon (HCT-116) and breast (MDA-MB-231) cancer cells. Our findings indicate a significant increase in cancer stemness induced by PSNPs. Exposure to polystyrene nanoparticles stimulated the development of less differentiated subpopulations of cells within the tumor, a marker of increased tumor aggressiveness. The experimental results were further used to train an ML model that accurately predicts the development of CSC markers. Machine learning, especially genetic algorithms, may be useful in predicting the development of cancer stem cells over time.
- Subjects
CANCER stem cells; CARCINOGENESIS; MACHINE learning; POLYSTYRENE; NANOPARTICLES; PREDICTION models
- Publication
Toxics, 2024, Vol 12, Issue 5, p354
- ISSN
2305-6304
- Publication type
Article
- DOI
10.3390/toxics12050354