Integrated in-silico approach to explore the therapeutic potential of RNAs and druggable polyphenols to mine alternative breast cancer therapeutic strategies targeting cancer hallmarks.

Abstract

Breast cancer (BC) is a global disease. A polyphenol-based therapeutic strategy is utilised to discover novel biotargets for breast cancer by assessing their drug-likeliness and toxicity. 1067 mRNAs associated with the ten initial hallmarks are retrieved from a publicly available database. However, no interacting mRNA data were found for two of the new hallmarks. The mRNAs are compared with the GEPIA database data to obtain the final 15 differentially expressed genes (DEGs) for the hallmarks. The interacting miRNAs of the DEGs are retrieved from a publicly available database. 56 druggable polyphenols are finalised for the study owing to their drug-likeliness and toxicity. Finally, a comprehensive interaction network-based analysis was carried out for the DEGs-interacting miRNAs and common druggable polyphenols. This revealed daidzein (DAI), resveratrol and 6-Gingerol; miR-663, miR-148a, miR328 and miR27b; BIRC5, CCNA2, EGFR, STAT5B and CDKN2A as significant polyphenols, miRNAs and mRNAs, respectively. Subsequently, a two-step docking approach along with molecular dynamics simulation (MDS) was also used to assess the therapeutic potential of the three polyphenols. Molecular docking revealed DAI-CCNA2 as the best fit among all the test complexes. For MDS, DAI-CCNA2 was simulated in comparison with CCNA2-Olaparib (OLA), an approved drug for breast cancer. MDS results verified DAI (the proposed drug) to be a potential candidate to combat breast cancer. Identification of druggable polyphenols using such a comprehensive in-silico approach can aid in providing a novel therapeutic strategy to combat the drawbacks associated with conventional therapies that can be further validated in an experimental setup.