{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["9"],"submitter":["Liu S"],"pubmed_abstract":["<b>Background:</b> Ferroptosis, a form of non-apoptotic cell death, has aroused worldwide interest in cancer researchers. However, the current study about the correlation between ferroptosis-related genes (FRGs) and endometrial cancer (EC) remains limited. <b>Methods:</b> First, the transcriptome profiling and clinical data of EC patients were downloaded from The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) program as the training group and testing group, respectively. FRGs were acquired through literature mining. Then, we used R 4.1.1 software to screen the differently expressed FRGs from TCGA, which was also connected with the prognosis of EC patients. Subsequently, the risk score of each tumor sample was identified by LASSO regression analysis, and"],"journal":["Frontiers in molecular biosciences"],"pagination":["916689"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9334791"],"repository":["biostudies-literature"],"pubmed_title":["Prediction of Prognosis in Patients With Endometrial Carcinoma and Immune Microenvironment Estimation Based on Ferroptosis-Related Genes."],"pmcid":["PMC9334791"],"pubmed_authors":["Huang X","Liu S","Zhang Q","Liu W"],"additional_accession":[]},"is_claimable":false,"name":"Prediction of Prognosis in Patients With Endometrial Carcinoma and Immune Microenvironment Estimation Based on Ferroptosis-Related Genes.","description":"<b>Background:</b> Ferroptosis, a form of non-apoptotic cell death, has aroused worldwide interest in cancer researchers. However, the current study about the correlation between ferroptosis-related genes (FRGs) and endometrial cancer (EC) remains limited. <b>Methods:</b> First, the transcriptome profiling and clinical data of EC patients were downloaded from The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) program as the training group and testing group, respectively. FRGs were acquired through literature mining. Then, we used R 4.1.1 software to screen the differently expressed FRGs from TCGA, which was also connected with the prognosis of EC patients. Subsequently, the risk score of each tumor sample was identified by LASSO regression analysis, and","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022","modification":"2025-04-04T19:27:22.919Z","creation":"2025-04-04T19:27:22.919Z"},"accession":"S-EPMC9334791","cross_references":{"pubmed":["35911966"],"doi":["10.3389/fmolb.2022.916689"]}}