In Vitro Inhibition of Colorectal Cancer Gene Targets by Withania somnifera L. Methanolic Extracts: A Focus on Specific Genome Regulation.
John M MachariaDaniel O PandeAfshin ZandFerenc BudanZsolt KáposztásOrsolya KövesdiTímea VarjasLászló Bence RaposaPublished in: Nutrients (2024)
An approach that shows promise for quickening the evolution of innovative anticancer drugs is the assessment of natural biomass sources. Our study sought to assess the effect of W. somnifera L. (WS) methanolic root and stem extracts on the expression of five targeted genes (cyclooxygenase-2, caspase-9, 5-Lipoxygenase, B-cell lymphoma-extra-large, and B-cell lymphoma 2) in colon cancer cell lines (Caco-2 cell lines). Plant extracts were prepared for bioassay by dissolving them in dimethyl sulfoxide. Caco-2 cell lines were exposed to various concentrations of plant extracts, followed by RNA extraction for analysis. By explicitly relating phytoconstituents of WS to the dose-dependent overexpression of caspase-9 genes and the inhibition of cyclooxygenase-2, 5-Lipoxygenase, B-cell lymphoma-extra-large, and B-cell lymphoma 2 genes, our novel findings characterize WS as a promising natural inhibitor of colorectal cancer (CRC) growth. Nonetheless, we recommend additional in vitro research to verify the current findings. With significant clinical benefits hypothesized, we offer WS methanolic root and stem extracts as potential organic antagonists for colorectal carcinogenesis and suggest further in vivo and clinical investigations, following successful in vitro trials. We recommend more investigation into the specific phytoconstituents in WS that contribute to the regulatory mechanisms that inhibit the growth of colon cancer cells.
Keyphrases
- genome wide
- diffuse large b cell lymphoma
- genome wide identification
- transcription factor
- cell death
- dna methylation
- genome wide analysis
- cell proliferation
- induced apoptosis
- oxidative stress
- drinking water
- copy number
- gene expression
- risk assessment
- signaling pathway
- drug delivery
- cell wall
- deep learning
- drug induced