A Machine Learning-Driven Four-Gene Signature Integrating Ferroptosis, Cuproptosis, and Immune Pathways for Diagnosis, Prognosis, and Therapeutic Guidance in Lung Adenocarcinoma
- 18 August 2026
- journal article
- research article
- Published by Springer Nature in Journal of Pharmaceutical Innovation
- Vol. 22 (1) , 85
Abstract
No abstract availableKeywords
Funding Information
- King Faisal University (ORF-2025-97)
This publication has 52 references indexed in Scilit:
- A novel network-based method identifies a cuproplasia-related pan-cancer gene signature to predict patient outcomeHuman Genetics, 2024
- Applied machine learning in cancer research: A systematic review for patient diagnosis, classification and prognosisComputational and Structural Biotechnology Journal, 2021
- Cystine transporter SLC7A11/xCT in cancer: ferroptosis, nutrient dependency, and cancer therapyProtein & Cell, 2020
- The Chemistry and Biology of FerroptosisCell Chemical Biology, 2020
- Matplotlib and Seaborn2019
- The ATP-binding cassette transporter ABCF1 is a hepatic oncofetal protein that promotes chemoresistance, EMT and cancer stemness in hepatocellular carcinomaCancer Letters, 2019
- Targeting Ferroptosis to Iron Out CancerCancer Cell, 2019
- Similarities and Distinctions of Cancer and Immune Metabolism in Inflammation and TumorsCell Metabolism, 2017
- Immune Checkpoint Blockade: A Common Denominator Approach to Cancer TherapyCancer Cell, 2015
- Machine learning applications in cancer prognosis and predictionComputational and Structural Biotechnology Journal, 2014