A Novel Ferroptosis-Related Gene Signature for Prognosis Prediction in Ewing Sarcoma.
Runhan ZhaoZefang LiYanran HuangChuang XiongChao ZhangHao LiangJingtao XuXiaoji LuoPublished in: Analytical cellular pathology (Amsterdam) (2022)
Ferroptosis, as a form of programmed cell death independent of apoptosis, has been demonstrated that plays a major role in tumorigenesis and cancer treatment. A comprehensive analysis of ferroptosis-related genes (FRGs) may lead to a novel choice for the treatment of Ewing sarcoma (ES). Here, 148 differentially expressed FRGs (DEFRGs) were identified between normal and ES tissue. And the GO and KEGG analyses of DEFRGs indicated that these genes were enriched in cancer and immune-related signaling pathways. Then, the GSE17679 cohort was randomly divided into train and test cohorts. Based on the train cohort, AURKA, RGS4, and RIPK1 were identified as key genes through the univariate Cox regression analysis, the random survival forest algorithm, and the multivariate Cox regression analysis and utilized to establish a prognostic FRG signature. The validation results demonstrated that the gene signature has not only excellent prediction performance and generalization ability but is also good at predicting the response of immunotherapy and chemotherapy. Subsequent analysis indicated that all 3 key genes play key roles in tumor immunity and prognosis of ES. Of these, AURKA was highly associated with EWSR1, which was verified by a single-cell dataset (GSE130019). Therefore, the 3 genes may be potential therapeutic targets for ES. At the end of this study, we also constructed an accurate nomogram that helps clinicians to assess the survival time of ES patients. In conclusion, our study constructed an excellent gene signature, which is helpful in improving the prognosis of ES patients.
Keyphrases
- genome wide
- genome wide identification
- end stage renal disease
- cell death
- ejection fraction
- chronic kidney disease
- single cell
- genome wide analysis
- copy number
- machine learning
- signaling pathway
- squamous cell carcinoma
- high resolution
- deep learning
- transcription factor
- wastewater treatment
- bioinformatics analysis
- mass spectrometry
- lymph node metastasis
- endoplasmic reticulum stress
- cell cycle arrest
- high speed
- high throughput
- free survival
- neural network
- patient reported outcomes
- human health