Downregulation of RIPK4 Expression Inhibits Epithelial-Mesenchymal Transition in Ovarian Cancer through IL-6.
Huan YiYan-Zhao SuRong LinXiang-Qin ZhengDiling PanDan-Mei LinXiang GaoRong ZhangPublished in: Journal of immunology research (2021)
RIPK4 has been implicated in multiple cancer types, but its role in ovarian cancer (OC) has not been clearly elucidated. Our data from Gene Expression Profiling Interactive Analysis, RT-PCR, and immunohistochemical analysis showed that RIPK4 was expressed at higher levels in OC tissues and cells than in normal ovarian tissues and cells. Increased RIPK4 expression in OC markedly correlated with a worse overall survival than lower RIPK4 expression levels (hazard rate (HR) 1.5 (1.45-1.87); P = 0.001). In functional experiments, RIPK4 downregulation significantly inhibited metastatic behaviours in OC cells. Subsequently, based on data from 593 OC patients in the TCGA database, gene set enrichment analysis revealed that RIPK4 was involved in epithelial-mesenchymal transition (EMT) in OC. At the molecular level, silencing RIPK4 significantly downregulated vimentin, N-cadherin, and Twist expression but induced an increase in the protein level of E-cadherin and inhibited the IL-6 and STAT3 levels. Moreover, IL-6 levels were significantly decreased in RIPK4-silenced OC cells (P < 0.05). The addition of IL-6 to OC cells rescued the suppressive effect of RIPK4 knockdown on EMT. Thus, our data illustrate that downregulation of RIPK4 expression can restrain EMT in OC by inhibiting IL-6. This finding may provide a novel diagnostic and therapeutic target for improving the poor prognoses of OC patients.
Keyphrases
- epithelial mesenchymal transition
- induced apoptosis
- signaling pathway
- poor prognosis
- cell cycle arrest
- cell proliferation
- end stage renal disease
- squamous cell carcinoma
- ejection fraction
- endoplasmic reticulum stress
- gene expression
- transforming growth factor
- chronic kidney disease
- newly diagnosed
- binding protein
- small cell lung cancer
- electronic health record
- genome wide
- prognostic factors
- deep learning
- copy number
- young adults
- data analysis