ArticleTranslational cancer research2025
Machine learning screening for disulfidptosis genes-associated immunosuppression status in osteosarcoma and rhabdomyosarcoma.
Article in Translational cancer research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: The recent identification of disulfidptosis, a novel form of cell death, offers significant potential for advancing cancer therapeutics. Osteosarcoma (OS) and rhabdomyosarcoma (RMS) are prevalent malignant sarcomas in children and young adults, yet their molecular underpinnings remain poorly understood, hampering treatment options. This study aimed to delineate the role of disulfidptosis in these malignancies and to identify key molecular determinants of prognosis. Methods: We analyzed extensive RNA transcriptome data from the Gene Expression Omnibus (GEO) and the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) databases. Non-negative matrix factorization (NMF) clustering was employed to identify disulfidptosis-associated molecular subtypes. Furthermore, we integrated 100 machine-learning algorithms to pinpoint core prognostic genes. Results: Based on disulfidptosis-related genes, we identified a distinct immunosuppressive subtype in both OS and RMS, characterized by significantly poorer immune cell infiltration. A robust prognostic signature comprising five genes ( Conclusions: Our findings illuminate the landscape of disulfidptosis in OS and RMS, revealing a novel immunosuppressive subtype and a defined five-gene signature for risk stratification. The identification of
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