Investigation of fatty acid metabolism in chronic lymphocytic leukemia to guide clinical outcome and therapy.

Chronic lymphocytic leukemia (CLL) is the most common leukemia in the West. With CLL's heterogeneity, some people still develop disease refractory and relapse despite advances in treatment. Thus, early diagnosis and treatment of high-risk CLL patients is critical. Fatty acid (FA) metabolism contributes to tumorigenesis, progression, and therapy resistance through enhanced lipid synthesis, storage, and catabolism. In this study, we aimed to construct a prognostic model to improve the risk stratification of CLL and reveal the link between FA metabolism and CLL. The differentially expressed FA metabolism-related genes (FMGs) in CLL were filtered through univariate Cox regression analysis based on public databases. Functional enrichment was examined using prognostic FA metabolism-related gene enrichment analysis. CIBERSORT and single-sample gene set enrichment analysis (ssGSEA) estimated immune infiltration score and immune-related pathways. Pearson's correlation analysis investigated FA metabolism-related genes and drug sensitivity. A novel prognostic model was built using least absolute shrinkage and selection operator (LASSO) Cox algorithms. This validation cohort included 36 CLL patients from our center. We obtained CLL RNA microarray profiles from public databases and identified 15 prognostic-related FMGs. CLL patients were divided into two molecular clusters based on the expression of FMGs. The Kaplan-Meier analysis revealed a significant difference in TFS (P < 0.001) and OS (P < 0.001) between the two clusters. KEGG functional analysis showed that several pathways were enriched, including the chemokine and immune-related signaling pathways. In the training and validation cohorts, patients with higher FA metabolism-related prognostic index (FAPI) levels had worse outcomes. Finally, a novel nomogram prognostic model including CLL international prognostic index (CLL-IPI) was constructed, exhibiting reliable effectiveness and accuracy. In conclusion, we established a reliable predictive signature based on FA metabolism-related genes and constructed a novel nomogram prognostic model, supporting the potential preclinical implications of FA metabolism in CLL research.