Discovering a combination of natural compounds for treatment of chronic cancer pain based on bioinformatics

Abstract

Background & Objective: The incidence of malignant disease has been increasing the world over, and with it the number of patients suffering from severe cancer pain is also increasing with every passing day. We aimed to find a potentially effective combination of natural compounds for the treatment of chronic cancer pain (CCP) using bioinformatics methods, and provide a theoretical basis for subsequent laboratory experiments and clinical trials.

Methodology: Six natural compounds, including bakuchiol, cinnamaldehyde, curcumin, eugenol, salicin and xanthoxylin, were selected as candidates for new drugs by consulting a large number of relevant literatures. Their targets were obtained from the Swiss Target Prediction database. The related targets of CCP were obtained from GeneCards, OMIM and DisGent databases. Cytoscape software was used to construct the "compound-target-disease" network. Protein-protein interaction network (PPI) was constructed by STRING platform; GO function and KEGG pathway enrichment analysis were performed by R language software.

Results: A total of 197 targets were obtained from the Swiss Target Prediction database; 10767 CCP of disease targets were obtained after searching and removing duplications in the disease database. Twelve core targets, AKT1, SRC, STAT3, ESR1, RELA, EP300, MAPK1, MAPK14, CCNA2, EGFR, GSK3B and PRKCZ, were identified by PPI analysis. GO functional enrichment analysis yielded 1950 entries. KEGG enrichment analysis revealed 134 pathways with statistical significance, among which pathways related to CCP were more than those related to nerve.

Conclusion: The mechanism of action of these six compounds in the treatment of chronic cancer pain is multi-target and multi-pathway related. Through protein-protein interaction analysis, GO function and KEGG pathway enrichment analysis, it is found that these six compounds can be used as candidate drugs for the treatment of chronic cancer pain, which will be further studied in the future.

Key words: Bioinformatics; Natural Compounds; Chronic Cancer Pain; New Drug Discovery

Citation: Si W. Discovering a combination of natural compounds for treatment of chronic cancer pain based on bioinformatics. Anaesth. pain intensive care 2023;27(5):431−439; DOI: 10.35975/apic.v27i5.2293

Received: February 21, 2023; Reviewed: March 06, 2023; Accepted: April 28, 2023