Targeting the IRE1α-XBP1s axis confers selective vulnerability in hepatocellular carcinoma with activated Wnt signaling.
Tingting ZhangFaming ZhaoYi ZhangJi-Hua ShiFengzhen CuiWeixiang MaKai WangChuanrui XuQingping ZengRong ZhongNingning LiYong LiuYang JinXia ShengPublished in: Oncogene (2024)
Liver-specific Ern1 knockout impairs tumor progression in mouse models of hepatocellular carcinoma (HCC). However, the mechanistic role of IRE1α in human HCC remains unclear. In this study, we show that XBP1s, the major downstream effector of IRE1α, is required for HCC cell survival both in vitro and in vivo. Mechanistically, XBP1s transactivates LEF1, a key co-factor of β-catenin, by binding to its promoter. Moreover, XBP1s physically interacts with LEF1, forming a transcriptional complex that enhances classical Wnt signaling. Consistently, the activities of XBP1s and LEF1 are strongly correlated in human HCC and with disease prognosis. Notably, selective inhibition of XBP1 splicing using an IRE1α inhibitor significantly repressed the viability of tumor explants as well as the growth of tumor xenografts derived from patients with distinct Wnt/LEF1 activities. Finally, machine learning algorithms developed a powerful prognostic signature based on the activities of XBP1s/LEF1. In summary, our study uncovers a key mechanistic role for the IRE1α-XBP1s pathway in human HCC. Targeting this axis could provide a promising therapeutic strategy for HCC with hyperactivated Wnt/LEF1 signaling.
Keyphrases
- endoplasmic reticulum stress
- endothelial cells
- machine learning
- cell proliferation
- stem cells
- induced pluripotent stem cells
- pluripotent stem cells
- gene expression
- mouse model
- dna methylation
- transcription factor
- deep learning
- immune response
- oxidative stress
- cancer therapy
- drug delivery
- binding protein
- long non coding rna
- signaling pathway