Identification of a Novel Epithelial-Mesenchymal Transition-Related Gene Signature for Endometrial Carcinoma Prognosis.
Tianyuan RuanJing WanQian SongPeigen ChenXiaomao LiPublished in: Genes (2022)
(1) Background: Endometrial cancer is the most prevalent cause of gynecological malignant tumor worldwide. The prognosis of endometrial carcinoma patients with distant metastasis is poor. (2) Method: The RNA-Seq expression profile and corresponding clinical data were downloaded from the Cancer Genome Atlas database and the Gene Expression Omnibus databases. To predict patients' overall survival, a 9 EMT-related genes prognosis risk model was built by machine learning algorithm and multivariate Cox regression. Expressions of nine genes were verified by RT-qPCR. Responses to immune checkpoint blockades therapy and drug sensitivity were separately evaluated in different group of patients with the risk model. (3) Endometrial carcinoma patients were assigned to the high- and low-risk groups according to the signature, and poorer overall survival and disease-free survival were showed in the high-risk group. This EMT-related gene signature was also significantly correlated with tumor purity and immune cell infiltration. In addition, eight chemical compounds, which may benefit the high-risk group, were screened out. (4) Conclusions: We identified a novel EMT-related gene signature for predicting the prognosis of EC patients. Our findings provide potential therapeutic targets and compounds for personalized treatment. This may facilitate decision making during endometrial carcinoma treatment.
Keyphrases
- epithelial mesenchymal transition
- endometrial cancer
- end stage renal disease
- machine learning
- chronic kidney disease
- rna seq
- ejection fraction
- newly diagnosed
- free survival
- genome wide
- single cell
- copy number
- dna methylation
- decision making
- big data
- stem cells
- lymph node
- young adults
- bioinformatics analysis
- climate change
- transcription factor
- combination therapy
- data analysis