A Novel Dynamic Model identifies Anticancer Target Genes

Agrima Bhatt, Rajasthan University

Drugs identifying cancer genes:

To understand drug-target interaction, let’s first cover our bases to know what is meant by receptor and a ligand. Receptors are defined as the macromolecules present either on the cell surface or in the cytoplasm which is involved in the cell interaction through signalling. Molecules like drugs, hormones, etc are known as ligands which show binding to the receptor molecules and cascades a signalling pathway.

A drug’s ability to bind to a specific receptor which is most likely to be a protein and the intrinsic efficacy of the drug which activates the receptor molecule, both are determined by the chemical and physical structure of the drug. Such identification of the drug target gene is a crucial barrier in anticancer drug development. The binding of the drug to the specific target or a gene stimulates the downstream processing and eventually alters the transcriptome of the tumour cell.

Drug–target interaction data has been employed as one component of several contemporary drug repurposing strategies, generally as the chemical or structural component of methods that try to integrate both biological and chemical data types.

A dynamic model to identify drug target genes:

Drug target gene identification is the core part of the science and research behind anti-cancer drug development for cancer patients. Thus, to overcome this barrier, scientists have proposed a dynamic model which identifies the target genes through calculation of the total support of each agent in the signalling network of cancerous cells.

In previous studies, researchers have gradually become interested in the identification of such target genes since the availability of new molecular data consisting of metabolic networks, protein interaction networks, heterogeneous and cancer signalling networks. Since cancer signalling networks are a type of heterogeneous network, they can provide the most information for dynamic analysis. With structural analysis, it has been evidenced that genes causing cancer via mutation often get located in the core of the cancer signalling networks.

Results:

Scientists investigated this model’s performance for the identification of genes and found out that among the top 3 genes having the highest total support, 82% of those genes were anticancer drug target genes. The rest of the uninvestigated genes also show to come out as anticancer genes. In conclusion, the outer competitive dynamics model aids in the identification of anticancer medication targets as well as cancer signalling network driving agents. The top forecast of this novel dynamic model presented the most consistency across all methods of prediction.

With the experimentation on 17 out 100 directed random networks, the model concludes that the overall support of each node has a positive relationship with its closeness and hierarchical closeness. This conclusion suggests that the less distance between nodes, the more support it receives and thus having a larger impact. Total support which was evidenced in the studies had a novel dynamic centrality metric for the identification of anticancer treatment target genes on cancer signalling networks.

Future studies on this model will experiment with reducing the long-running period of the algorithm and the issue of finding the key evidence for the model to run properly remains a challenge for the future.

Also read: Effect of pollution on thyroxine levels in newborns

References:

1. Tran, T.-D., & Pham, D.-T. (2021). Identification of anticancer drug target genes using an outside competitive dynamics model on cancer signaling networks. Scientific Reports, 11(1), 14095. https://doi.org/10.1038/s41598-021-93336-z

Author info:

Agrima Bhatt is an undergraduate student studying BSc. Biotechnology in Jaipur, Rajasthan. She is a science and research enthusiast who also loves to write articles and short snippets.

Some of her published articles at BioXone are:

1. https://bioxone.in/news/worldnews/molecular-mechanisms-underlying-virescent-mutation-in-cotton/
2. https://bioxone.in/news/worldnews/multi-angle-projection-microscope-a-novel-imaging-technique/
3. https://bioxone.in/news/worldnews/scientists-develop-novel-cholera-vaccine-from-rice-grains/
4. https://bioxone.in/news/worldnews/ai-predicts-the-relation-between-viruses-and-mammals/

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