Conclusions
The five FRGs risk signature prognostic model having a superior and drug sensitivity predictive performance for OS in UCS, and HSF1 is a potential marker sensitive to doxorubicin and gemcitabine in UCS patients.
Methods
Prognostic differentially expressed FRGs were identified of in the TCGA cohort. Integrated analysis, cox regression, and the least absolute shrinkage and selection operator (LASSO) methods of FRGs were performed to construct a multigene signature prognostic model. Moreover, a dataset from Gene Expression Omnibus (GEO) served as an external validation. HSF1 was knockdown in MES-SA and FU-MMT-1 cells, and cell viability, lipid ROS, and intracellular iron level were detected when combined with doxorubicin or gemcitabine. Result: Five FRGs were selected to construct a prognostic model of UCS. The group with high-risk signature score exhibited obviously lower overall survival (OS) than the group with low risk signature score in both TCGA and validated GEO cohorts. Multivariate Cox regression analysis further indicated that the risk score was an independent factor for the prognosis of UCS patients. The high-risk group of UCS has a higher sensitivity in the treatment of doxorubicin and gemcitabine. Knocking down of HSF1 in MES-SA and FU-MMT-1 cells was more sensitive to doxorubicin and gemcitabine via increasing ferroptosis. Conclusions: The five FRGs risk signature prognostic model having a superior and drug sensitivity predictive performance for OS in UCS, and HSF1 is a potential marker sensitive to doxorubicin and gemcitabine in UCS patients.
Purpose
Iron metabolism and ferroptosis play crucial roles in the pathogenesis of cancer. In this study, we aim to study the role of ferroptosis-related genes (FRGs) in uterine carcinosarcoma (UCS) and identify potential target for UCS.