Comprehensive machine learning analysis of PANoptosis signatures in multiple myeloma identifies prognostic and immunotherapy biomarkers.

ABSTRACT: PANoptosis is closely associated with tumorigenesis and therapeutic response, yet its role in multiple myeloma (MM) remains unclear. This study analyzed bulk transcriptomic and clinical data from the TCGA and GEO databases to identify seven PANoptosis-related genes (PRGs) using machine learning (LASSO regression and random forest models) and univariate Cox analysis, and constructed a prognostic risk model. The model demonstrated robust predictive performance across three external validation cohorts. High-risk patients exhibited higher tumor purity, increased tumor mutational burden, and distinct immune cell infiltration patterns. Drug sensitivity analysis revealed heightened sensitivity to cyclophosphamide, Sinularin, Wee1 inhibitor, osimertinib, JQ1, VE-822, and AZD6738 in high-risk patie

SUBMITTER: Feng Y 

PROVIDER: S-EPMC12219422 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature