Identification of key ferroptosis genes and mechanisms associated with breast cancer using bioinformatics, machine learning, and experimental validation

This study identified three key ferroptosis-related genes (TXNIP, SLC2A1, ATF3) associated with breast cancer, which are closely related to the occurrence, development, and prognosis of breast cancer.

Transcriptional and clinical data of breast cancer patients were downloaded from TCGA database and ferroptosis-related genes were obtained from FerrDB database. Significant differentially expressed ferroptosis-related genes between breast cancer tissues and adjacent normal tissues were selected. Functional enrichment analysis was performed on these differentially expressed genes. Four machine learning algorithms were used to identify key ferroptosis-related genes associated with breast cancer. A multi-factor Cox regression analysis was used to construct a risk score model for the key ferroptosis-related genes. The accuracy of the risk score model was validated using Kaplan-Meier survival curve analysis and receiver operating characteristic (ROC) curve analysis. Finally, cell experiments were conducted to validate the biological functions of the key ferroptosis-related genes in breast cancer cells MCF-7, further confirming the accuracy of the analysis

The aim of this paper is to mine ferroptosis genes associated with breast cancer based on bioinformatics and machine learning, and to perform in vitro functional validation.

A total of 52 significantly differentially expressed ferroptosis-related genes were identified, which were mainly enriched in cancer pathways, central carbon metabolism in cancer, HIF-1 signaling pathway, and NOD-like receptor signaling pathway. Three key ferroptosis-related genes (TXNIP, SLC2A1, ATF3) closely related to the occurrence, development, and prognosis of breast cancer were identified using machine learning algorithms. The risk model constructed using these three key ferroptosis-related genes showed that the prognosis of the low-risk group was better than that of the high-risk group (P < 0.001). The ROC curve analysis showed that the prognosis model had good predictive ability. In vitro experiments validated the reliability of the bioinformatics and machine learning screening results. Downregulation of SLC2A1 expression promoted ferroptosis and suppressed tumor cell growth in breast cancer cells (P < 0.01), while overexpression of TXNIP or ATF3 had the same effect (P < 0.01).