ArticleAging2024
Identification of key ferroptosis genes and mechanisms associated with breast cancer using bioinformatics, machine learning, and experimental validation.
Article in Aging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 11 citations in OpenAlex.
- The SLC7A11 Thermostat: A Molecular Signaling Switch Between Ferroptosis and Disulfidptosis in Neurodegenerative Disease.Molecular neurobiology · 2026Review
- Reactive Oxygen Species in Breast Cancer: From Redox Dysregulation to ROS-Responsive Therapeutics.Cell biochemistry and function · 2026Review
- Artesunate induces ferroptosis in osteosarcoma through NCOA4-mediated ferritinophagy.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2025Article
- Identification of Novel lncRNAs Related to Colorectal Cancer Through Bioinformatics Analysis.BioMed research international · 2025Article
- Targeting ferroptosis reveals a new strategy for breast cancer treatment: a bibliometric study.Discover oncology · 2024Article
- Applying whole-genome and whole-exome sequencing in breast cancer: a review of the landscape.Breast cancer (Tokyo, Japan) · 2024Review
- Single-cell omics and machine learning integration to develop a polyamine metabolism-based risk score model in breast cancer patients.Journal of cancer research and clinical oncology · 2024Article
- Identification and validation of ferroptosis-related gene SLC2A1 as a novel prognostic biomarker in AKI.Aging · 2024Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe aim of this paper is to mine ferroptosis genes associated with breast cancer based on bioinformatics and machine learning, and to perform
methodsTranscriptional 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 results.
resultsA 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 (
conclusionThis 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.