Dataset Information
Establishing a prognostic model of ferroptosis- and immune-related signatures in kidney cancer: A study based on TCGA and ICGC databases.
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ABSTRACT: Background
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.Methods
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.Results
We found two ferroptosis- and immune-related differentially expressed genes (FI-DEGs) (Klotho (KL) and Sortilin 1 (SORT1)) 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.Conclusions
Our present study indicated that KL and SORT1 could be prognostic biomarkers for KC patients. Whether this model can be used in clinical settings requires further validation.
SUBMITTER: Han Z
PROVIDER: S-EPMC9459019 | biostudies-literature | 2022
REPOSITORIES: biostudies-literature
Publications
Establishing a prognostic model of ferroptosis- and immune-related signatures in kidney cancer: A study based on TCGA and ICGC databases.
Frontiers in oncology 20220826
<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/computatio ...[more]