基于 bulk RNA-Seq 和单细胞 RNA-Seq 的综合分析揭示多发性骨髓瘤的新型预后生物标志物。
Integrative Analysis of Bulk RNA-Seq and Single-Cell RNA-Seq Unveils Novel Prognostic Biomarkers in Multiple Myeloma.
机构信息
Department of Hematology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China.
出版信息
Biomolecules. 2022 Dec 12;12(12):1855. doi: 10.3390/biom12121855.
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.
分子异质性在多发性骨髓瘤（MM）的疾病生物学中具有重要意义。因此，进行了单细胞 RNA 测序（scRNA-seq）和批量 RNA-seq 数据的联合分析，以研究 MM 中的克隆进化特征并寻找新的预后靶标。scRNA-seq 数据通过 Seurat 管道和 Monocle 2 进行分析，以鉴定具有不同分化状态的 MM 细胞分支。每个分支中的标记基因被上传到 STRING 数据库，以构建蛋白质-蛋白质相互作用（PPI）网络，然后使用 Cytoscape 软件检测枢纽基因。使用批量 RNA-seq 数据，然后进行 Kaplan-Meier（K-M）生存分析，以确定 MM 中的预后生物标志物。鉴定出两个具有不同分化状态的分支中的 342 个标记基因，并选择网络中 MCC 算法得分最高的前 20 个标记基因作为 MM 中的枢纽基因。此外，K-M 生存分析表明，较高的 NDUFB8、COX6C、NDUFA6、USMG5 和 COX5B 表达与 MM 患者的预后较差密切相关。此外，ssGSEA 和 Pearson 分析表明，它们的表达与 Tcm（中央记忆细胞）免疫细胞的比例呈显著负相关。我们的研究结果确定了 NDUFB8、COX6C、NDUFA6、USMG5 和 COX5B 作为 MM 中的新型预后生物标志物，并揭示了 MM 预后中细胞分化过程中遗传异质性的重要性。
Zotero 插件