The transcription factor RUNX2 fuels YAP1 signaling and gastric cancer tumorigenesis.
Zhengjun GuoKai ZhouQiang WangYusheng HuangJia JiYuan PengXiaoyue ZhangTaihao ZhengZhen ZhangDaochen ChongZhen Zhou YangPublished in: Cancer science (2021)
Despite considerable efforts in the detection and treatment of gastric cancer (GC), the underlying mechanism of the progression of GC remains unknown. Our previous work has demonstrated the remarkable role of Runt-related transcription factor 2 (RUNX2), in fueling the invasion and metastasis of GC. The present study aimed to elucidate the role of RUNX2 in tumorigenesis of GC. We assessed Runx2 expression and its clinical significance via bioinformatic analysis of the Cancer Genome Atlas and Gene Expression Omnibus databases. Roles for Runx2 in self-renewal and tumorigenesis were examined in vitro and in vivo. Further bioinformatic analysis was applied to study the mechanism of GC progression. We found that Runx2 was highly expressed in the early stage of GC and positively correlated with a poor clinical outcome of patients. Runx2 was also significantly correlated with clinicopathological features, such as Hp infection, new neoplastic events, primary therapeutic outcome, ethnicity, race, and tumor stage. Multivariate analysis revealed that together with Runx2, age, cancer status, M stage, and T stage were independent prognostic factors for the outcome of GC patients. RUNX2 overexpression induced increased anchorage-independent colony formation, sphere formation, and tumorigenesis in GC cells in vitro and in vivo. Mechanistically, bioinformatic analysis indicated that yes1 associated transcriptional regulator (YAP1) might be a downstream target of RUNX2. Specific knockdown of YAP1 reduced the tumor-initiating ability of GC cells induced by ectopic Runx2 expression. Our findings support the hypothesis that RUNX2 exerts oncogenic properties via YAP1 regulation, highlighting essential roles for RUNX2 and YAP1 in gastric carcinogenesis and suggesting potential therapeutic targets.
Keyphrases
- transcription factor
- prognostic factors
- dna binding
- gene expression
- early stage
- poor prognosis
- end stage renal disease
- induced apoptosis
- genome wide identification
- oxidative stress
- machine learning
- risk assessment
- dna methylation
- quality improvement
- lymph node
- cell cycle arrest
- single cell
- patient reported outcomes
- endoplasmic reticulum stress
- simultaneous determination
- heat shock
- deep learning
- cell death
- stress induced
- real time pcr