Abstract
Background: Anticipating the correlation between SARS-CoV-2 infection and ‘triplenegative breast cancer (TNBC)’ remains challenging. It has been reported that people currently diagnosed with cancer have a higher risk of severe complications if they are affected by the viral infection. Cancer treatments, including chemotherapy, targeted therapies, and immunotherapy, may weaken the immune system and possibly cause critical lung damage and breathing problems. Special attention must be paid to the ‘comorbidity condition’ while estimating the risk of severe SARSCoV- 2 infection in TNBC patients. Hence the work aims to study the correlation between triplenegative breast cancer (TNBC) and SARS-CoV-2 using biomolecular networking.
Methods: The genes associated with SARS CoV-2 have been collected from curated data in Bio- GRID. TNBC-related genes have been collected from expression profiles. Molecular networking has generated a Protein-Protein Interaction (PPI) network and a Protein-Drug Interaction (PDI) network. The network results were further evaluated through molecular docking studies followed by molecular dynamic simulation.
Results: The genetic correlation of TNBC and SARS-Cov-2 has been observed from the combined PPI of their proteins. The drugs interacting with the disease's closely associated genes have been identified. The docking and simulation study showed that anti-TNBC and anti-viral drugs interact with these associated targets, suggesting their influence in inhibiting both the disease mutations.
Conclusion: The study suggests a slight influence of SARS-CoV-2 viral infection on Triple Negative Breast Cancer. Few anticancer drugs such as Lapatinib, Docetaxel and Paclitaxel are found to inhibit both TNBC and viral mutations. The computational studies suggest these molecules are also useful for TNBC patients to control SARS-CoV-2 infection.
Keywords: Covid 19, SARS-CoV-2, BRCA, TNBC, Biomolecular Networking
Graphical Abstract
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