Title:Deciphering the Influence of Cigarette Smoke Carcinogens on CNS Associated Biomolecules: A Computational Synergistic Approach
Volume: 20
Issue: 6
Author(s): Qazi M.S. Jamal*, Ali H. Alharbi, Anupam Dhasmana, Anukriti Saxena, Fahad Albejaidi and Mohammad Sajid
Affiliation:
- Department of Health Informatics, College of Public Health and Health Informatics, Qassim University, Al Bukayriyah,Saudi Arabia
Keywords:
Cigarette smoke, carcinogens, CNS, network analysis, system biology, molecular interaction.
Abstract:
Background: Human health issues caused by Cigarette Smoke Carcinogens (CSC) are
increasing rapidly every day and challenging the scientific community to provide a better understanding
in order to avoid its impact on communities. Cigarette smoke also contains tobacco-based
chemical compounds harmful to human beings, either smokers or non-smokers.
Objective: We have tested 7H-Dibenzo[c,g]carbazole (7H-DBC) and Dibenz[a,h]acridine (DBAD)
derivatives of Asz-arenes along with N'-Nitrosoanabasine (NAB) and N-Nitrosoanatabine (NAT)
derivatives of N-Nitrosamines molecular interaction with CNS biomolecules.
Methods: Computational synergistic approaches like system biology and molecular interaction
techniques were implemented to conduct the analysis.
Results: CSC efficiently interacted with NRAS, KRAS, CDH1, and RAC1 molecular targets in
CNS. We have also performed the interactome analysis followed by system biology approaches
and found that HSPA8 is the most important hub protein for the network generated for CSC-hampered
genes of CNS. We have also identified 6 connector proteins, namely TP53, HSP90AA1, PPP2CA,
CDH1, CTNNB1, and ARRB1. Further analysis revealed that NRAS and CDH1 have maximum
interactions with all the selected CSC.
Conclusion: The obtained structural analysis data could be utilized to assess the carcinogenic effect
of CSC and could be useful in the treatment of CNS diseases and disorders induced, especially
by tobacco-specific carcinogens, or it could also be used in vivo/ in vitro experimentation model designing.