Synthesis of a celastrol derivative as a cancer stem cell inhibitor through regulation of the STAT3 pathway for treatment of ovarian cancer.
Meijuan LiuNa LiZhaoxue WangShuo WangShaoda RenXiaojing LiPublished in: RSC medicinal chemistry (2024)
Accumulating evidence suggests that the root of drug chemoresistance in ovarian cancer is tightly associated with subpopulations of cancer stem cells (CSCs), whose activation is largely associated with signal transducer and activator of transcription 3 (STAT3) signaling. Recently, celastrol has shown a significant anti-cancer effect on ovarian cancer, but its clinical translation is very challenging due to its oral bioavailability and high organ toxicity. In this study, a celastrol derivative ( Cel-N ) was synthesized to augment the overall efficacy, and its underlying mechanisms were also explored. Different ovarian cancer cells, SKOV3 and A2780, were used to evaluate and compare the anticancer effects. Cel-N displayed potent activities against all the tested ovarian cancer cells, with the lowest IC 50 value of 0.14-0.25 μM. Further studies showed that Cel-N effectively suppressed the colony formation and sphere formation ability, decreased the percentage of CD44 + CD24 - and ALDH + cells, and induced ROS production. Furthermore, western blot analysis indicated that Cel-N significantly inhibited both Tyr705 and Ser727 phosphorylation and reduced the protein expression of STAT3. In addition, Cel-N could dramatically induce apoptosis and cell cycle arrest, and inhibit migration and invasion. Importantly, Cel-N showed a potent antitumor efficacy with no or limited systemic toxicity in mice xenograft models. The anticancer effect of Cel-N is stronger than celastrol. Cel-N attenuates cancer cell stemness, inhibits the STAT3 pathway, and exerts anti-ovarian cancer effects in cell and mouse models. Our data support that Cel-N is a potent drug candidate for ovarian cancer.
Keyphrases
- cancer stem cells
- cell cycle arrest
- cell death
- cell proliferation
- pi k akt
- oxidative stress
- stem cells
- drug induced
- machine learning
- type diabetes
- single cell
- epithelial mesenchymal transition
- bone marrow
- anti inflammatory
- transcription factor
- signaling pathway
- immune response
- adipose tissue
- inflammatory response
- nuclear factor
- high glucose
- data analysis
- protein kinase
- deep learning
- big data