骨肉腫および横紋筋肉腫におけるジスルフィドプトーシス関連免疫抑制状態の機械学習スクリーニング

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.

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.

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 (ACTN4, MYH9, FLNA, MYH10, and IQGAP1) was established, which effectively characterized the immunosuppressive subtypes and stratified patient risk. Notably, IQGAP1 emerged as an independent prognostic factor in RMS, highlighting its potential as both a therapeutic target and biomarker. Additionally, our analysis revealed a strong correlation between macrophage infiltration and disulfidptosis, suggesting a potential role in shaping the tumor immune microenvironment and influencing patient prognosis.

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 IQGAP1 and the link to macrophages provides new insights for developing targeted therapies and immunotherapeutic strategies for these malignancies.