Machine Learning Model for Prediction of Development of Cancer Stem Cell Subpopulation in Tumurs Subjected to Polystyrene Nanoparticles.

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.

Modeling 5-FU-Induced Chemotherapy Selection of a Drug-Resistant Cancer Stem Cell Subpopulation.

(1) Background: Cancer stem cells (CSCs) are a subpopulation of cells in a tumor that can self-regenerate and produce different types of cells with the ability to initiate tumor growth and dissemination. Chemotherapy resistance, caused by numerous mechanisms by which tumor tissue manages to overcome the effects of drugs, remains the main problem in cancer treatment. The identification of markers on the cell surface specific to CSCs is important for understanding this phenomenon. (2) Methods: The expression of markers CD24, CD44, ALDH1, and ABCG2 was analyzed on the surface of CSCs in two cancer cell lines, MDA-MB-231 and HCT-116, after treatment with 5-fluorouracil (5-FU) using flow cytometry analysis. A machine learning model (ML)-genetic algorithm (GA) was used for the in silico simulation of drug resistance. (3) Results: As evaluated through the use of flow cytometry, the percentage of CD24-CD44+ MDA-MB-231 and CD44, ALDH1 and ABCG2 HCT-116 in a group treated with 5-FU was significantly increased compared to untreated cells. The CSC population was enriched after treatment with chemotherapy, suggesting that these cells have enhanced drug resistance mechanisms. (4) Conclusions: Each individual GA prediction model achieved high accuracy in estimating the expression rate of CSC markers on cancer cells treated with 5-FU. Artificial intelligence can be used as a powerful tool for predicting drug resistance.

Combined Biological and Numerical Modeling Approach for Better Understanding of the Cancer Viability and Apoptosis.

Nowadays, biomedicine is a multidisciplinary science that requires a very broad approach to the study and analysis of various phenomena essential for a better understanding of human health. This study deals with the use of numerical simulations to better understand the processes of cancer viability and apoptosis in treatment with commercial chemotherapeutics. Starting from many experiments examining cell viability in real-time, determining the type of cell death and genetic factors that control these processes, a lot of numerical results were obtained. These in vitro test results were used to create a numerical model that gives us a new angle of observation of the proposed problem. Model systems of colon and breast cancer cell lines (HCT-116 and MDA-MB-231), as well as a healthy lung fibroblast cell line (MRC-5), were treated with commercial chemotherapeutics in this study. The results indicate a decrease in viability and the appearance of predominantly late apoptosis in the treatment, a strong correlation between parameters. A mathematical model was created and employed for a better understanding of investigated processes. Such an approach is capable of accurately simulating the behavior of cancer cells and reliably predicting the growth of these cells.