ArticleFrontiers in genetics2023
Employing machine learning using ferroptosis-related genes to construct a prognosis model for patients with osteosarcoma.
Article in Frontiers in genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 11 citations in OpenAlex.
- ELANE inhibits the progression of osteosarcoma via suppressing the CXCL12/CXCR4 axis.Molecular and cellular biochemistry · 2026Article
- Iron-Related Metabolic Targets in the Treatment of Osteosarcoma: Research Progress and Prospects.Biomedicines · 2025Review
- Identification and tissue-level validation of ferroptosis-related genes in small intestinal neuroendocrine neoplasms based on machine learning.BMC gastroenterology · 2025Article
- Feasibility of machine learning-based modeling and prediction to assess osteosarcoma outcomes.Scientific reports · 2025Article
- LncRNA-associated ceRNA Network Revealing the Potential Regulatory Roles of Ferroptosis and Immune Infiltration in Osteosarcoma as well as Construction of the Prognostic Model.Current medicinal chemistry · 2025Article
- A multifunctional biomimetic nanoplatform for image-guideded photothermal-ferroptotic synergistic osteosarcoma therapy.Bioactive materials · 2024Article
- Harnessing ferroptosis for enhanced sarcoma treatment: mechanisms, progress and prospects.Experimental hematology & oncology · 2024Review
- Comprehensive investigation of tumor immune microenvironment and prognostic biomarkers in osteosarcoma through integrated bulk and single-cell transcriptomic analysis.Frontiers in immunology · 2024Article
- Identification and validation of novel biomarkers associated with immune infiltration for the diagnosis of osteosarcoma based on machine learning.Frontiers in genetics · 2023Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying effective biomarkers in osteosarcoma (OS) is important for predicting prognosis. We investigated the prognostic value of ferroptosis-related genes (FRGs) in OS. Transcriptome and clinical data were obtained from The Cancer Genome Atlas and Gene Expression Omnibus. FRGs were obtained from the ferroptosis database. Univariate COX regression and LASSO regression screening were performed and an FRG-based prognostic model was constructed, which was validated using the Gene Expression Omnibus cohort. The predictive power of the model was assessed
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Registered trials
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