ArticleOxidative medicine and cellular longevity2022
Machine Learning Assistants Construct Oxidative Stress-Related Gene Signature and Discover Potential Therapy Targets for Acute Myeloid Leukemia.
Article in Oxidative medicine and cellular longevity, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 6 citations in OpenAlex.
- Pediatric leukemia: origins, pathogenesis, the role of microenvironment and immunological modulation.Frontiers in immunology · 2026Review
- A prognostic model for gastric cancer constructed by multiple machine learning algorithms.Journal of molecular histology · 2025Article
- Feasibility of machine learning-based modeling and prediction to assess osteosarcoma outcomes.Scientific reports · 2025Article
- Machine learning in TCM with natural products and molecules: current status and future perspectives.Chinese medicine · 2023Review
- Comprehensive analysis of prognosis of cuproptosis-related oxidative stress genes in multiple myeloma.Frontiers in genetics · 2023Article
- A novel oxidative stress- and ferroptosis-related gene prognostic signature for distinguishing cold and hot tumors in colorectal cancer.Frontiers in immunology · 2022Article
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
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Abstract
Background: Oxidative stress (OS) is associated with the development of acute myeloid leukemia (AML). However, there is lack of relevant research to confirm that OS-related genes can guide patients in risk stratification and predict their survival probability. Method: First, we Data from three public databases, respectively. Then, we use batch univariate Cox regression and machine learning to select important characteristic genes; next, we build the model and use receiver operating characteristic curve (ROC) to evaluate the accuracy. Moreover, GSEAs were performed to discover the molecular mechanism and conduct nomogram visualization. In addition, the relative importance value was used to identify the hub gene, and GSE9476 was to validate hub gene difference expression. Finally, we use symptom mapping to predict the candidate herbs, targeting the hub gene, and put these candidate herbs into Traditional Chinese Medicine Systems Pharmacology (TCMSP) to identify the main small molecular ingredients and then docking hub proteins with this small molecular. Results: A total of 313 candidate oxidative stress-related genes could affect patients' outcomes and machine learning to select six potential genes to construct a gene signature model to predict the overall survival (OS) of AML patients. Patients in a high group will obtain a short survival time when compared with the low-risk group (HR = 3.97, 95% CI: 2.48-6.36; Conclusion: We use two different machine learning methods to build six oxidative stress-related gene signatures that could assist clinical decisions and identify PLA2G4A as a potential biomarker for AML. Nobiletin, targeting PLA2G4, may provide a third pathway for therapy AML.
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