Research Paper Volume 16, Issue 2 pp 1781—1795
Identification of key ferroptosis genes and mechanisms associated with breast cancer using bioinformatics, machine learning, and experimental validation
Figure 3. Screening of key ferroptosis-related genes in breast cancer. (A) Protein-protein interaction network of ferroptosis-related DEGs. (B) Top 20 genes with the best connectivity identified by the MCC algorithm. (C) SVM-REF analysis of the feature ferroptosis-related genes for differentiating breast cancer tissues from adjacent tissues. (D) Random forest analysis of the feature ferroptosis-related genes for differentiating breast cancer tissues from adjacent tissues. (E) LASSO regression analysis of the ferroptosis-related genes associated with the prognosis of breast cancer patients. (F) Venn diagram showing the common ferroptosis-related genes identified by the 4 machine learning algorithms. (G) Expression levels of TXNIP, SLC2A1, and ATF3 in breast cancer tissues (BC group, n = 1091) and adjacent tissues (NC group, n = 113).