Multilayer Analysis of Nicotine-Induced Gene Expression Alterations in Breast Cancer Cells Using Clustering and Supervised Learning Methods

Nicotine is known not only for its addictive properties but also for its potential to alter the genetic structure of cancer cells. This study investigates the genetic alterations caused by chronic nicotine exposure in HCC38 breast cancer cells through a multi-layered computational analysis. Using K-means clustering and seven supervised machine learning algorithms, differentially expressed genes were grouped into three clusters and used as class labels in classification models. Logistic Regression achieved the highest performance with 98.76% accuracy and an F1 score of 0.9869. The Friedman test was applied to evaluate the statistical significance of performance differences among classifiers, and multi-class ROC curves were used to demonstrate their discriminative power. The findings indicate that nicotine exposure leads to genetic reprogramming and activates inflammation-related gene pathways in specific cellular subpopulations. These results highlight the utility of machine learning methods in uncovering biologically meaningful gene expression patterns in cancer research.