Abstract
Molecular heterogeneity has great significance in the disease biology of multiple myeloma (MM). Thus, the analysis combined single-cell RNA-seq (scRNA-seq) and bulk RNA-seq data were performed to investigate the clonal evolution characteristics and to find novel prognostic targets in MM. The scRNA-seq data were analyzed by the Seurat pipeline and Monocle 2 to identify MM cell branches with different differentiation states. Marker genes in each branch were uploaded to the STRING database to construct the Protein-Protein Interaction (PPI) network, followed by the detection of hub genes by Cytoscape software. Using bulk RNA-seq data, Kaplan-Meier (K-M) survival analysis was then carried out to determine prognostic biomarkers in MM. A total of 342 marker genes in two branches with different differentiation states were identified, and the top 20 marker genes with the highest scores in the network calculated by the MCC algorithm were selected as hub genes in MM. Furthermore, K-M survival analysis revealed that higher NDUFB8, COX6C, NDUFA6, USMG5, and COX5B expression correlated closely with a worse prognosis in MM patients. Moreover, ssGSEA and Pearson analyses showed that their expression had a significant negative correlation with the proportion of Tcm (central memory cell) immune cells. Our findings identified NDUFB8, COX6C, NDUFA6, USMG5, and COX5B as novel prognostic biomarkers in MM, and also revealed the significance of genetic heterogeneity during cell differentiation in MM prognosis.