The TP53 Gene and COVID-19 Virus: A Correlation Analysis

Author(s):C. Lakshmi Anand* and P.K. Krishnan Namboori

Aim: This study aimed to discover the most effective anti-cancer medicine for cancer patients infected with SARS-CoV-2.

Background: The correlation between TP53 and SARS-CoV-2 was examined using biomolecular networking analysis.

Objective: Cancer patients with TP53 gene mutations are more likely to be infected with the SARS-CoV-2 virus since it is the most frequently mutated tumor suppressor gene in human cancer. The main goal of this study is to discover the most effective and efficient anti-cancer therapy for patients with SARS-CoV-2 infection.

Materials and Methods: Topp gene analysis was used to prioritize candidate genes based on molecular function, biological process, and pathway analysis. Biomolecular networking was carried out using Cytoscape 2.8.2. The protein-protein interaction network was used to identify the functionally associated proteins. The protein-drug interaction network was used to observe the molecular therapeutic efficiency of drugs. The network was further analyzed using CytoHubba to find the hub nodes. The molecular docking was used to study the proteinligand interaction, and the protein-ligand complex was further evaluated through molecular dynamic simulation to determine its stability.

Results: Functionally relevant genes were prioritized through Toppgene analysis. Using Cytohabba, it was found that the genes UBE2N, BRCA1, BARD1, TP53, and DPP4 had a high degree and centrality score. The drugs 5-fluorouracil, Methotrexate, Temozolomide, Favipiravir, and Levofloxacin have a substantial association with the hub protein, according to protein-drug interaction analysis. Finally, a docking study revealed that 5-fluorouracil has the highest connection value and stability compared to Methotrexate, Favipiravir, and Levofloxacin.

Conclusion: The biomolecular networking study was used to discover the link between TP53 and SARS-CoV-2, and it was found that 5-fluorouracil had a higher affinity for binding to TP53 and its related genes, such as UBE2N, BRCA1, RARD1, and SARS-CoV-2 specific DPP4. For cancer patients with TP53 gene mutations and Covid-19 infection, this treatment is determined to be the most effective.

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Most Cited Article – Identification and Characterization of SNP Mutation in Genes Related to Non-small Cell Lung Cancer

Author(s):Neelambika B. Hiremath and P. Dayananda*

Volume 16, Issue 3, 2021

Published on: 19 August, 2020

Page: [253 – 261]

Pages: 9

DOI: 10.2174/1574362415999200819202218


Background and Objective: The advent of Next Generation Sequencing (NGS) has created a high throughput platform to identify disease traits and phenotypic characteristics using RNASeq Sequencing analysis in humans. Non-small cell lung cancer (NSCLC), a lethal disease, accounts for 85 percent of most lung cancers with a very small window of survival rate. The decision of tumour image biomarker impression can be improved by gene profile. Hence there is a need to characterise the variants in the disease manifestation.

Methods: To understand the SNPs in the major genes responsible for NSCLC, RNASeq data of patients aged above 50 years were downloaded from the SRA database. The quality matrix analysis is mapped to Genome reference consortium human build 38 (GRCh38) to call the variants and identify SNPs with the tuxedo protocol.

Results: The SNPs and the patterns of variants were analysed to see the comparison between healthy individuals and NSCLC patients, and in between patients of different age. Oncogenes commonly associated with the NSCLC like KRAS, EGFR, ALK, BRAF and HER2 were mainly analysed to see the SNPs and their characterisations with respect to the functional change done.

Conclusion: The SNPs with the greater quality scores belonging to the above-said genes were identified, which gives us a baseline to understand the NSCLC at the Genomic level. Further fold change of these genes to the frequency of variants can be mapped to understand the NSCLC at a greater depth. Read now:

Part-of-Speech Tagging for Arabic Text using Particle Swarm Optimization and Genetic Algorithm

Ahmad T. Al-Taani, Fadi A. ALkhazaaleh

What is it about?

In this study, we propose a supervised POS tagging system for the Arabic language using Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) as well as Hidden Markov Model (HMM). The tagging process is considered as an optimization problem and illustrated as a swarm, which consists of a group of particles. Each particle represents a sequence of tags. The PSO algorithm is applied to find the best sequence of tags, which represent the correct tags of the sentence. The genetic operators: crossover and mutation are used to find personal best, global best, and velocity of the PSO algorithm. HMM is used to find fitness of the particles in the swarm. Read now:

BSP Patent Journal- Recent Patents on Biomarkers

Recent Patents on Biomarkers

7-24-2014 11-31-47 AM

ISSN: 2210-3104 (Online)
ISSN: 2210-3090 (Print)

Volume 4, 3 Issues, 2014

Aims & Scope

Recent Patents on Biomarkers publishes review and research articles, and guest edited thematic issues on important recent patents on biomarkers. The coverage includes novel biomarkers in basic, medical, environmental, and pharmaceutical research. A selection of important and recent patents on biomarkers is also included in the journal. The journal is essential reading for all researchers involved in biomarker research and discovery. The journal also covers recent research (where patents have been registered) in fast emerging patent biomarker applications; discovery and validation are covered for drug discovery, clinical development and molecular diagnostics.

Abstracted & Indexed in:

Chemical Abstracts, MediaFinder®-Standard Periodical Directory, J-Gate, PubsHub, CABI.

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