{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["12"],"submitter":["Han Z"],"pubmed_abstract":["<h4>Background</h4>Kidney cancer (KC) is one of the most challenging cancers due to its delayed diagnosis and high metastasis rate. The 5-year survival rate of KC patients is less than 11.2%. Therefore, identifying suitable biomarkers to accurately predict KC outcomes is important and urgent.<h4>Methods</h4>Corresponding data for KC patients were obtained from the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) databases. Systems biology/bioinformatics/computational approaches were used to identify suitable biomarkers for predicting the outcome and immune landscapes of KC patients.<h4>Results</h4>We found two ferroptosis- and immune-related differentially expressed genes (FI-DEGs) (<i>Klotho</i> (<i>KL</i>) and <i>Sortilin 1</i> (<i>SORT1</i>)) independently correlated with the overall survival of KC patients. The area under the curve (AUC) values of the prognosis model using these two FI-DEGs exceeded 0.60 in the training, validation, and entire groups. The AUC value of the 1-year receiver operating characteristic (ROC) curve reached 0.70 in all the groups.<h4>Conclusions</h4>Our present study indicated that <i>KL</i> and <i>SORT1</i> could be prognostic biomarkers for KC patients. Whether this model can be used in clinical settings requires further validation."],"journal":["Frontiers in oncology"],"pagination":["931383"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9459019"],"repository":["biostudies-literature"],"pubmed_title":["Establishing a prognostic model of ferroptosis- and immune-related signatures in kidney cancer: A study based on TCGA and ICGC databases."],"pmcid":["PMC9459019"],"pubmed_authors":["Qiu Y","Xing XL","Long J","Han Z","Wang H"],"additional_accession":[]},"is_claimable":false,"name":"Establishing a prognostic model of ferroptosis- and immune-related signatures in kidney cancer: A study based on TCGA and ICGC databases.","description":"<h4>Background</h4>Kidney cancer (KC) is one of the most challenging cancers due to its delayed diagnosis and high metastasis rate. The 5-year survival rate of KC patients is less than 11.2%. Therefore, identifying suitable biomarkers to accurately predict KC outcomes is important and urgent.<h4>Methods</h4>Corresponding data for KC patients were obtained from the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) databases. Systems biology/bioinformatics/computational approaches were used to identify suitable biomarkers for predicting the outcome and immune landscapes of KC patients.<h4>Results</h4>We found two ferroptosis- and immune-related differentially expressed genes (FI-DEGs) (<i>Klotho</i> (<i>KL</i>) and <i>Sortilin 1</i> (<i>SORT1</i>)) independently correlated with the overall survival of KC patients. The area under the curve (AUC) values of the prognosis model using these two FI-DEGs exceeded 0.60 in the training, validation, and entire groups. The AUC value of the 1-year receiver operating characteristic (ROC) curve reached 0.70 in all the groups.<h4>Conclusions</h4>Our present study indicated that <i>KL</i> and <i>SORT1</i> could be prognostic biomarkers for KC patients. Whether this model can be used in clinical settings requires further validation.","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022","modification":"2025-04-05T10:25:33.892Z","creation":"2025-04-05T10:25:33.892Z"},"accession":"S-EPMC9459019","cross_references":{"pubmed":["36091132"],"doi":["10.3389/fonc.2022.931383"]}}