Single cell RNA-seq data and bulk gene profiles reveal a novel signature of disease progression in multiple myeloma

Zhiyong Zeng; Junfang Lin; Kejie Zhang; Xizhe Guo; Xiaoqiang Zheng; Apeng Yang; Junmin Chen

doi:10.1186/s12935-021-02190-6

Single cell RNA-seq data and bulk gene profiles reveal a novel signature of disease progression in multiple myeloma

Cancer Cell Int. 2021 Sep 25;21(1):511. doi: 10.1186/s12935-021-02190-6.

Authors

Zhiyong Zeng¹, Junfang Lin¹, Kejie Zhang², Xizhe Guo³, Xiaoqiang Zheng¹, Apeng Yang¹, Junmin Chen⁴

Affiliations

¹ Department of Hematology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.
² Department of Hematology, Zhongshan Hospital Xiamen University, Xiamen, China.
³ Department of Hematology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, China.
⁴ Department of Hematology, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China. cheney@fjmu.edu.cn.

Abstract

Background: The development of multiple myeloma (MM) is considered to involve a multistep transformation process, but the role of cytogenetic abnormalities and molecular alterations in determining the cell fate of multiple myeloma (MM) remains unclear. Here, we have analyzed single cell RNA-seq data and bulk gene profiles to reveal a novel signature associated with MM development.

Methods: The scRNA-seq data from GSE118900 was used to profile the transcriptomes of cells from MM patients at different stages. Pseudotemporal ordering of the single cells was performed using Monocle package to feature distinct transcriptomic states of the developing MM cells. The bulk microarray profiles from GSE24080 and GSE9782 were applied to identify a signature associated with MM development.

Results: The 597 cells were divided into 7 clusters according to different risk levels. They were initiated mainly from monoclonal gammopathy of undetermined significance (MGUS), newly diagnosed MM (NDMM), or relapsed and/or refractory myeloma (RRMM) with cytogenetically favorable t(11;14), moved towards the cells from smoldering MM (SMM) or NDMM without t(11;14) or t(4;14), and then finally to cells from SMM or RRMM with t(4;14). Based on the markers identified in the late stage, the bulk data was used to develop a 20-gene signature stratifying patients into high and low-risk groups (GSE24080: HR = 3.759, 95% CI 2.746-5.145; GSE9782: HR = 2.612, 95% CI 1.894-3.603), which was better than the previously published gene signatures (EMC92, UAMS70, and UAMS17) and International Staging System. This signature also succeeded in predicting the clinical outcome of patients treated with bortezomib (HR = 2.884, 95% CI 1.994-4.172, P = 1.89e-8). The 20 genes were further verified by quantitative real-time polymerase chain reaction using samples obtained from the patients with MM.

Conclusion: Our comprehensive analyses offered new insights in MM development, and established a 20-gene signature as an independent biomarker for MM.

Keywords: Bulk gene profiles; Disease progression; Multiple myeloma; Novel signature; Single cell RNA-seq.

Abstract

Grants and funding