Unraveling the Molecular Cross-talk in the Comorbidity of Multiple Myeloma and Systemic Light-Chain Amyloidosis through Multi-Dataset Analysis

Background: The co-occurrence of multiple myeloma (MM) and light-chain amyloidosis (AL) accelerates disease progression, but their shared mechanisms remain unclear. Methods: We integrated bulk transcriptomic (GSE6477, GSE16558, GSE175384) and single-cell RNA-seq (GSE188222, GSE271107) data. Using WGCNA, PPI networks, and machine learning, we developed a prognostic signature validated in MMRF (N=859) and external cohorts. Immune infiltration and drug sensitivity were analyzed. Results: We identified 41 shared genes and established a 12-gene prognostic signature. High-risk patients showed distinct immune microenvironments and drug responses. Single-cell analysis revealed cell-type-specific expression patterns, with PTP4A3 emerging as a key regulator. Conclusions: This multi-omics study reveals shared molecular mechanisms in MM-AL comorbidity and provides a robust prognostic signature. PTP4A3 represents a potential therapeutic target, offering insights for precision medicine.