Ferroptosis-based molecular prognostic model for adrenocortical carcinoma based on least absolute shrinkage and selection operator regression

Abstract

Background: This study aimed to find ferroptosis-related genes linked to clinical outcomes of adrenocortical carcinoma (ACC) and assess the prognostic value of the model. Methods: We downloaded the mRNA sequencing data and patient clinical data of 78 ACC patients from the TCGA data portal. Candidate ferroptosis-related genes were screened by univariate regression analysis, machine-learning least absolute shrinkage, and selection operator (LASSO). A ferroptosis-related gene-based prognostic model was constructed. The effectiveness of the prediction model was accessed by KM and ROC analysis. External validation was done using the GSE19750 cohort. A nomogram was generated. The prognostic accuracy was measured and compared with conventional staging systems (TNM stage). Functional analysis was conducted to identify biological characterization of survival-associated ferroptosis-related genes. Results: Seventy genes were identified as survival-associated ferroptosis-related genes. The prognostic model was constructed with 17 ferroptosis-related genes including STMN1, RRM2, HELLS, FANCD2, AURKA, GABARAPL2, SLC7A11, KRAS, ACSL4, MAPK3, HMGB1, CXCL2, ATG7, DDIT4, NOX1, PLIN4, and STEAP3. A RiskScore was calculated for each patient. KM curve indicated good prognostic performance. The AUC of the ROC curve for predicting 1-, 3-, and 5- year(s) survival time was 0.975, 0.913, and 0.915 respectively. The nomogram prognostic evaluation model showed better predictive ability than conventional staging systems. Conclusion: We constructed a prognosis model of ACC based on ferroptosis-related genes with better predictive value than the conventional staging system. These efforts provided candidate targets for revealing the molecular basis of ACC, as well as novel targets for drug development.