In-silico investigation of curcumin drug-likeness, gene-targets and prognostic relevance of the targets in panels of human cancer cohorts

Abstract

Despite advancements in diagnostic and standard treatment modalities, cancer survival rate remains disappointing globally. It has however, been recognized that exploring the therapeutic properties of secondary metabolite from natural products may alleviate the problems of drug resistance and toxicity that besiege the conventional therapies, and hence improve the overall prognosis of cancer patient. To this end curcumin, a polyphenolic natural compound has been widely studied for it anticancer activities in in vitro and in vivo models. Computational technology has significantly improved the success rate of drug discovery and development, hence, it has become a widely explore tool in drug candidate identification. In this study we used computational approached to identify 12 genes that are potential druggable candidate for curcumin. The genes identified were found to be enriched in cancer and drug resistance associated signaling pathways. Interestingly, the top 3 identified genes; Microtubule-associated protein tau (MAPT), Toll-like receptor 9 (TLR9) and Tyrosyl-DNA phosphodiesterase 1 (TDP1) were observed to be over expressed in multiple cancer cohorts and were associated with poor prognoses of the patients. Curcumin has good physicochemical, bioavailability and ADMET properties. Importantly, it met the Lipinski's Rule of 5 for drug likeness and thus worthy of further in vitro and in vivo confirmation studies.

Identification of NSP3 (SH2D3C) as a Prognostic Biomarker of Tumor Progression and Immune Evasion for Lung Cancer and Evaluation of Organosulfur Compounds from Allium sativum L. as Therapeutic Candidates

“…It contains hundreds of phytochemicals, among which sulfur-containing compounds are of therapeutic interest in the field of oncology [ 32 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 ]. Previous studies appraised the uses of in silico approaches to assessing biomarkers and the prognostic relevance of genes and gene signatures in a diseased condition based on the available clinical information of cohorts, and also aid in identifying drug-like molecules for targeted therapy [ 42 , 43 , 44 ]. In this study, we used a bioinformatics approach to evaluate the effect of NSP3 expression as well as genetic and epigenetic alterations on the TME with respect to tumor immune evasion mechanisms and prognoses in lung cancer cohorts.…”