Current medicinal chemistry · 2026
Single-cell RNA Sequencing Identifies Prognostic Biomarkers in Extramedullary Multiple Myeloma.
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Yang Menghan, Yu Fan, Qin Hui
Аннотация
BACKGROUND: Multiple myeloma (MM) is the second most common hematologic malignancy, accounting for approximately 10% of all hematological cases, with higher morbidity and mortality.
OBJECTIVE: This study aimed to investigate the clonal evolutionary characteristics to identify novel prognostic biomarkers associated with extramedullary progression in MM.
METHODS: We downloaded transcriptomic profiles and single-cell microarray (scRNA-seq) data from public databases. Then, we used the LASSO method to develop a prognostic signature and validated its efficacy using external MM cohorts. We evaluated the differences in the immune microenvironment and drug sensitivity (IC50) between the different risk score groups. scRNA-seq analysis identified key cell types through AUCell scores, cell communication, and differentiation trajectory analyses.
RESULTS: In total, 126 DEGs were identified as crucial genes associated with extramedullary and intramedullary MM. After LASSO analysis, seven signature genes were selected to develop a risk score model, and high-risk patients showed worse outcomes. Subsequently, the nomogram incorporating age, albumin, b2m, LDH, and RiskScore predicted 1-, 3-, and 5-year outcomes with high AUCs. Immune analyses showed that 25 immune cell types, 35 immune checkpoints, 27 chemokines, 20 MHC molecules, and 14 receptor- related genes differed significantly between the two risk groups. We also identified 116 drugs (roscovitine and JNK inhibitor VIII) with significantly different IC50 values between the two risk groups. CD4+ T cells exhibited the highest signature gene activity. CellChat analysis demonstrated enhanced communication between CD4+, NK, and CD8+ T cells.
CONCLUSION: Our study has proposed a risk score model based on seven identified signature genes for MM prognosis and revealed CD4+ T cells to be a major immune cell type associated with MM progression, contributing to personalized treatment decision-making and precise risk stratification of MM.
OBJECTIVE: This study aimed to investigate the clonal evolutionary characteristics to identify novel prognostic biomarkers associated with extramedullary progression in MM.
METHODS: We downloaded transcriptomic profiles and single-cell microarray (scRNA-seq) data from public databases. Then, we used the LASSO method to develop a prognostic signature and validated its efficacy using external MM cohorts. We evaluated the differences in the immune microenvironment and drug sensitivity (IC50) between the different risk score groups. scRNA-seq analysis identified key cell types through AUCell scores, cell communication, and differentiation trajectory analyses.
RESULTS: In total, 126 DEGs were identified as crucial genes associated with extramedullary and intramedullary MM. After LASSO analysis, seven signature genes were selected to develop a risk score model, and high-risk patients showed worse outcomes. Subsequently, the nomogram incorporating age, albumin, b2m, LDH, and RiskScore predicted 1-, 3-, and 5-year outcomes with high AUCs. Immune analyses showed that 25 immune cell types, 35 immune checkpoints, 27 chemokines, 20 MHC molecules, and 14 receptor- related genes differed significantly between the two risk groups. We also identified 116 drugs (roscovitine and JNK inhibitor VIII) with significantly different IC50 values between the two risk groups. CD4+ T cells exhibited the highest signature gene activity. CellChat analysis demonstrated enhanced communication between CD4+, NK, and CD8+ T cells.
CONCLUSION: Our study has proposed a risk score model based on seven identified signature genes for MM prognosis and revealed CD4+ T cells to be a major immune cell type associated with MM progression, contributing to personalized treatment decision-making and precise risk stratification of MM.