Metabologenomics and network pharmacology to understand the molecular mechanism of cancer research

Abstract

In this editorial I comment on the article “Network pharmacological and molecular docking study of the effect of Liu-Wei-Bu-Qi capsule on lung cancer” published in the recent issue of the World Journal of Clinical Cases 2023 November 6; 11 (31): 7593-7609. Almost all living forms are able to manufacture particular chemicals-metabolites that enable them to differentiate themselves from one another and to overcome the unique obstacles they encounter in their natural habitats. Numerous methods for chemical warfare, communication, nutrition acquisition, and stress prevention are made possible by these specialized metabolites. Metabolomics is a popular technique for collecting direct measurements of metabolic activity from many biological systems. However, confusing metabolite identification is a typical issue, and biochemical interpretation is frequently constrained by imprecise and erroneous genome-based estimates of enzyme activity. Metabolite annotation and gene integration uses a biochemical reaction network to obtain a metabolite-gene association so called metabologenomics. This network uses an approach that emphasizes metabolite-gene consensus via biochemical processes. Combining metabolomics and genomics data is beneficial. Furthermore, computer networking proposes that using metabolomics data may improve annotations in sequenced species and provide testable hypotheses for specific biochemical processes.

Keywords: Network pharmacology, Metabologenomics, Genome, Pathways, Cancer

Core Tip: Regulation of biochemical pathways is similar to Le Chatelier chemical dynamic equilibrium principle. It states that the equilibrium is disrupted by changing the conditions, and the position of dynamic equilibrium shifts to counteract the change to reestablish an equilibrium. Metabolites disrupt pathways either themselves or by enhancing gene expression levels. The equilibrium or “homeostasis” always reestablishes itself to survive the organism. The relationship between genes and metabolites may be discovered by understanding the link through computational networking.