No abstract

No abstract

Background: Anticipating the correlation between SARS-CoV-2 infection and ‘triple negative breast cancer (TNBC)’ remains a challenge. It has been reported that people who are currently diagnosed with cancer have a higher risk of severe complications if they affected by the viral infection. In general, the cancer treatments including chemotherapy, targeted therapies, and immunotherapy may weaken the immune system and possibly lead to cause critical lung damage and breathing problems. Special attention must be paid to the ‘comorbidity condition’ while estimating the risk of severe SARS-CoV-2 infection in TNBC patients. Hence the work aims to study the correlation between triple-negative breast cancer (TNBC) and SARS-CoV-2 using biomolecular networking. Methods: The genes associated with SARS CoV-2 has been collected from curated data in BioGRID. TNBC related genes have been collected from expression profiles. Molecular networking has been performed for generating a Protein-Protein Interaction (PPI) network as well as 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 closely associated genes of both the disease have been identified. The docking and simulation study showed that anti-TNBC drugs as well as anti-viral drugs interacting 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 to be useful for the TNBC patients to control SARS-CoV-2 infection also.

Introduction: The need for designing and developing personalized drugs for various diseases has become a challenging research topic at present. The individual variation towards susceptibility of a drug depends upon the genomic, epigenomic, metagenomic and environmental genomic factors. Areas covered: The ‘Single Nucleotide Variant (SNV)’ has been identified as the functional feature corresponding these factors. The need for personalized drug designing for the ERBB2 mutation related to Breast Cancer has been proposed by taking the South Asian (SA) population as the test sample. The SNVs corresponding to SA population for the ERBB2 mutation has been identified. The ‘convolution neural network-based deep learning technique’ (DeepCNN) has been used for computing the clinical significance of the SNVs, whose clinical significance values are unknown, using the functional variants as the attributes for the ethnic group. Expert opinion: The population has been classified into four groups based upon the probability of variants. The population-specific gene models and protein models have been designed. The potential molecules that control ERBB2 mutation specific to the South Asian population have been identified through docking/interaction score values

Tumor hypoxia results in most of the anticancer drugs becoming ineffective. However, due to lack of proper signaling in the hypoxic micro environment, the condition cannot be detected in advance, leading into unnecessary delay in the diagnosis and treatment. The main objective of the work is to identify the hypoxia prone SNPs to help the patients to predict their possibility of hypoxia formation and to Design and develop a machine helping in diagnosing the hypoxia from pathological images using deep learning with 'convolution neural network. The genetic signatures corresponding to 'tumor hypoxia development' have been identified by pharmacogenomic method, comprising of genomics, epigenomics, metagenomics and environmental genomics. All the common hypoxia related mutations have been included in the study. The formation of the hypoxia condition has to be carefully identified and monitored during the process of treatment to ensure that the right drug is being administered. In the present manuscript, a novel method of elucidating the condition using deep convolution network from simple pathological image has been suggested. The efficiency of the suggested machine is found to be 92.8% making it as a potential device for prediction of hypoxia mutation and thereby helping us to monitor the hypoxic conditions effectively. Thus, the hypoxia prone SNPs corresponding to common mutations have been identified. The patients having the hypoxia prone SNPs are advised to guard against hypoxia formation with the help of diagnostic tests using the machine. The machine helps to warn the patients against the respective mutations from simple pathological image of the tumor cells.