BACKGROUND: The network pharmacological approach was used to identity the anti-colorectal cancer (CRC) targets of formononetin (FN) and the molecular mechanisms of FN against CRC.

MATERIAL AND METHODS: A tool of the DisGeNET database was used for collection of CRC-based targets. Other tools of SuperPred, herbal ingredients target (HIT), and SwissTargetPrediction databases were applied in prediction of pharmacological targets of FN against cancer. A protein-protein interaction (PPI) network of FN against CRC was obtained by using a STRING database. All top biological functional processes and signaling pathways of FN against CRC were identified by using Database for Annotation, Visualization and Integrated Discovery (DAVID) software and Omicshare cloud platform.

RESULTS: The most key anti-CRC targets of FN were identified as tumor protein p53 (TP53), cytochrome P450 3A4 (CYP3A4), ATP binding cassette subfamily G member 2 (ABCG2), tumor necrosis factor (TNF), epidermal growth factor receptor (EGFR), Erb-B2 receptor tyrosine kinase 2 (ERBB2), and cytochrome P450 1A1 (CYP1A1). In further assays, the treatment of CRC by FN was mainly involved in biological functional processes of reactive oxygen species metabolic process, positive regulation of transcription, DNA-templated, positive regulation of nucleic acid-templated transcription, and positive regulation of RNA metabolic process. anti-CRC by FN of signaling pathways were associated with amyotrophic lateral sclerosis (ALS), allograft rejection, cytokine-cytokine receptor interaction, asthma, mitogen-activated protein kinase (MAPK) signaling pathways, and others.

CONCLUSIONS: The anti-CRC molecular mechanisms of FN are implicated in suppression of cellular proliferation and regulation of cancer-related metabolic pathways. Interestingly, 8 optimal biological targets may be used as potential molecular markers for predicting and treating CRC.

Keywords: Colorectal Neoplasms, Molecular Mechanisms of Pharmacological Action, Pharmacology, Cluster Analysis, Gene Regulatory Networks, Protein Interaction Maps