Ruxolitinib induces apoptosis and pyroptosis of anaplastic thyroid cancer via the transcriptional inhibition of DRP1-mediated mitochondrial fission.
Ya-Wen GuoLei ZhuYan-Ting DuanYi-Qun HuLe-Bao LiWei-Jiao FanFa-Huan SongYe-Feng CaiYun-Ye LiuGuo-Wan ZhengMing-Hua GePublished in: Cell death & disease (2024)
Anaplastic thyroid carcinoma (ATC) has a 100% disease-specific mortality rate. The JAK1/2-STAT3 pathway presents a promising target for treating hematologic and solid tumors. However, it is unknown whether the JAK1/2-STAT3 pathway is activated in ATC, and the anti-cancer effects and the mechanism of action of its inhibitor, ruxolitinib (Ruxo, a clinical JAK1/2 inhibitor), remain elusive. Our data indicated that the JAK1/2-STAT3 signaling pathway is significantly upregulated in ATC tumor tissues than in normal thyroid and papillary thyroid cancer tissues. Apoptosis and GSDME-pyroptosis were observed in ATC cells following the in vitro and in vivo administration of Ruxo. Mechanistically, Ruxo suppresses the phosphorylation of STAT3, resulting in the repression of DRP1 transactivation and causing mitochondrial fission deficiency. This deficiency is essential for activating caspase 9/3-dependent apoptosis and GSDME-mediated pyroptosis within ATC cells. In conclusion, our findings indicate DRP1 is directly regulated and transactivated by STAT3; this exhibits a novel and crucial aspect of JAK1/2-STAT3 on the regulation of mitochondrial dynamics. In ATC, the transcriptional inhibition of DRP1 by Ruxo hampered mitochondrial division and triggered apoptosis and GSDME-pyroptosis through caspase 9/3-dependent mechanisms. These results provide compelling evidence for the potential therapeutic effectiveness of Ruxo in treating ATC.
Keyphrases
- induced apoptosis
- oxidative stress
- cell cycle arrest
- endoplasmic reticulum stress
- signaling pathway
- cell death
- pi k akt
- nlrp inflammasome
- gene expression
- cell proliferation
- transcription factor
- systematic review
- randomized controlled trial
- heat shock
- epithelial mesenchymal transition
- cardiovascular disease
- cardiovascular events
- machine learning
- risk factors
- deep learning
- artificial intelligence