Arsenic poisoning significantly elevates the risk of cancer and other chronic illnesses. The goal of this research is to identify important genes whose expression changes in response to arsenic toxicity, and the molecular pathways affected by arsenic, using computational analysis of arsenic toxicity profiles. This approach will computationally identify and analyze genes whose expression changes in response to arsenic, thereby elucidating the heightened risk of carcinogenesis in arsenic-exposed individuals. This work employed high-throughput arsenic toxicity profiles to computationally identify and analyze expressed genes (DEGs) differentially in Affymetrix microarray datasets from the Gene Expression Omnibus (GEO) database, which were screened using the GEO2R program. A protein-protein interaction (PPI) network was constructed using STRING to elucidate the functional links between these DEGs and DNA repair genes. Interactions between the seven central genes (E2F1, EXO1, EZH2, FEN1, HIST1H3A, POLA1, and TIMELESS) and the repair genes PARP1, NBN, PMS1, MSH3, XRCC5, XRCC6, MGMT, and MLH1 were discovered. We employed the DAVID and Enrichr-KG platforms to investigate the functions of these genes and their associations with cellular and molecular processes in greater detail. Two hundred eighty-one non-synonymous single-nucleotide polymorphisms (nsSNPs) in the 07 genes linked to arsenic toxicity were found using the COSMIC database. Based on our analysis, mutations in E2F1, EXO1, EZH2, FEN1, HIST1H3A, POLA1, and TIMELESS can hinder DNA repair mechanisms, ultimately leading to cancer. Our computational analysis demonstrated that these non-synonymous SNPs can affect gene function, potentially altering protein stability and activity. Furthermore, according to Metal-Protein docking and protein-protein docking, these genes and their mutations appear to affect interactions with repair proteins substantially. Specific dietary consumption may lessen the detrimental effects of arsenic poisoning on protein function. We hypothesized that the mutations might be reversed by attaching particular molecules to these mutants. The protective effects of six curcumin compounds were examined using molecular docking with AutoDock 4.2.6 to assess protein dynamics and binding interactions. Optimal complexes were selected for dynamics simulation using GROMACS, and potential strategies for long-term cancer prevention related to arsenic exposure were identified.
Parida et al. (Sun,) studied this question.