Gene Dosage Analysis on the Single-Cell Transcriptomes Linking Cotranslational Protein Targeting to Metastatic Triple-Negative Breast Cancer.

Yining Liu,Min Zhao

doi:10.3390/ph14090918

Abstract

Many recent efforts have been put into the association between expression heterogeneity and different cell types and states using single-cell RNA transcriptome analysis. There is only limited understanding of gene dosage effects for the genetic heterogeneity at the single-cell level. By focusing on concordant copy number variation (CNV) and expression, we presented a computational framework to explore dosage effect for aggressive metastatic triple-negative breast cancer (TNBC) at the single-cell level. In practice, we collected CNV and single-cell expression data from the same patients with independent technologies. By focusing on 47,198 consistent copy number gains (CNG) and gene up-regulation from 1145 single cells, ribosome proteins with important roles in protein targeting were enriched. Independent validation in another metastatic TNBC dataset further prioritized signal recognition particle-dependent protein targeting as the top functional module. More interesting, the increased ribosome gene copies in TNBC may associate with their enhanced stemness and metastatic potential. Indeed, the prioritization of a well-upregulated functional module confirmed by high copy numbers at the single-cell level and contributing to patient survival may indicate the possibility of targeted therapy based on ribosome proteins for TNBC.

Highlights

Introductiontriple-negative breast cancer (TNBC) cannot be treated by blocking these receptors
Accepted: 5 September 2021Triple-negative breast cancer (TNBC) is characterized by none of the cell surface receptors for estrogen, progesterone, or human epidermal growth factor receptor 2 (HER2) [1].Because of this, triple-negative breast cancer (TNBC) cannot be treated by blocking these receptors
By applying the computational framework to two independent metastatic TNBC single-cell transcriptome datasets, we identified the common functional modules for further functional and clinical evaluation

Summary

Introduction

TNBC cannot be treated by blocking these receptors. Current treatment for TNBC has mainly relied on the response to chemotherapy, the potential excessive side effects may cause the treatment to fail. Identification and understanding of TNBC have a profound influence on the development of effective drugs for cancer therapies. Several large-scale cancer genomics projects focused on identifying novel receptors for complementary treatment [2]. Recent advances in single-cell sequencing technology enable the characterization of spatiotemporal features for thousands of cells simultaneously [3]. The single-cell-based transcriptome analyses mostly rely on cell typespecific marker genes. The precise identification of the cell types is important, many common genetic features and molecular consequences across different cell types may be overlooked during cancer development

Methods

Results

Discussion

Conclusion