The concept of entropy exceeds the boundaries of theoretical physics and can be expanded on the theory of information where it is known as Shannon entropy. Shannon entropy is used for quantifying information content of a message. This approach can be used to evaluate the variation in DNA methylation, which represents an important epigenetic mechanism. Entropy of DNA methylation patterns (methylome entropy) correlates with chronological age, and changes in DNA methylation are associated with age-related diseases, such as cancer and metabolic disease. On cellular level, stress activates defensive mechanisms, which are epigenetically conditioned. So far it has been shown that epigenetic changes caused by aging and malignant transformation are reflected on information content, or in other words, on methylation entropy. In this thesis we selected data sets from Gene Expression Omnibus (GEO) database, we implemented the method for calculating methylation entropy, we determined the epigenetic age of selected data and we tested the hypothesis that changes caused by various types of stress on a cellular level are synonymous to changes happening in degenerative processes like aging and malignant transformation. Obtained data doesn’t have statistic value, and this experiment didn’t prove the hypothesis. In further experiments of this type. Choosing data sets with bigger number of samples or determinating entropy of carefully chosen CpG places which are in specific correlation with stress could in future scientific research give statistically significant results.