Natural Flavonoids Quercetin and Kaempferol Targeting G2/M Cell Cycle-Related Genes and Synergize with Smac Mimetic LCL-161 to Induce Necroptosis in Cholangiocarcinoma Cells.
Thanpisit LomphithakPatthorn JaiklaApiwit Sae-FungSasiprapa SonkaewSiriporn JitkaewPublished in: Nutrients (2023)
Cholangiocarcinoma (CCA) is an aggressive cancer associated with a very poor prognosis and low survival rates, primarily due to late-stage diagnosis and low response rates to conventional chemotherapy. Therefore, there is an urgent need to identify effective therapeutic strategies that can improve patient outcomes. Flavonoids, such as quercetin and kaempferol, are naturally occurring compounds that have attracted significant attention for their potential in cancer therapy by targeting multiple genes. In this study, we employed network pharmacology and bioinformatic analysis to identify potential targets of quercetin and kaempferol. The results revealed that the target genes of these flavonoids were enriched in G2/M-related genes, and higher expression of G2/M signature genes was significantly associated with shorter survival in CCA patients. Furthermore, in vitro experiments using CCA cells demonstrated that quercetin or kaempferol induced cell-cycle arrest in the G2/M phase. Additionally, when combined with a Smac mimetic LCL-161, an IAP antagonist, quercetin or kaempferol synergistically induced RIPK1/RIPK3/MLKL-mediated necroptosis in CCA cells while sparing non-tumor cholangiocyte cells. These findings shed light on an innovative therapeutic combination of flavonoids, particularly quercetin and kaempferol, with Smac mimetics, suggesting great promise as a necroptosis-based approach for treating CCA and potentially other types of cancer.
Keyphrases
- cell cycle arrest
- poor prognosis
- induced apoptosis
- cell death
- cell cycle
- cancer therapy
- pi k akt
- end stage renal disease
- long non coding rna
- oxidative stress
- cell proliferation
- chronic kidney disease
- drug delivery
- papillary thyroid
- squamous cell carcinoma
- diabetic rats
- high glucose
- endothelial cells
- machine learning
- free survival
- genome wide analysis
- robot assisted
- human health
- patient reported outcomes