Systemically Identifying Triple-Negative Breast Cancer Subtype-Specific Prognosis Signatures, Based on Single-Cell RNA-Seq Data
Kaiyuan Xing,
Bo Zhang,
Zixuan Wang,
Yanru Zhang,
Tengyue Chai,
Jingkai Geng,
Xuexue Qin,
Xi Steven Chen,
Xinxin Zhang,
Chaohan Xu
Affiliations
Kaiyuan Xing
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
Bo Zhang
Department of Pharmacology, State-Province Key Laboratories of Biomedicine-Pharmaceutics of China, Key Laboratory of Cardiovascular Medicine Research, Ministry of Education, College of Pharmacy, Harbin Medical University, Harbin 150081, China
Zixuan Wang
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
Yanru Zhang
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
Tengyue Chai
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
Jingkai Geng
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
Xuexue Qin
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
Xi Steven Chen
Department of Public Health Sciences, Division of Biostatistics, University of Miami Miller School of Medicine, Miami, FL 33136, USA
Xinxin Zhang
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
Chaohan Xu
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
Triple-negative breast cancer (TNBC) is a highly heterogeneous disease with different molecular subtypes. Although progress has been made, the identification of TNBC subtype-associated biomarkers is still hindered by traditional RNA-seq or array technologies, since bulk data detected by them usually have some non-disease tissue samples, or they are confined to measure the averaged properties of whole tissues. To overcome these constraints and discover TNBC subtype-specific prognosis signatures (TSPSigs), we proposed a single-cell RNA-seq-based bioinformatics approach for identifying TSPSigs. Notably, the TSPSigs we developed mostly were found to be disease-related and involved in cancer development through investigating their enrichment analysis results. In addition, the prognostic power of TSPSigs was successfully confirmed in four independent validation datasets. The multivariate analysis results showed that TSPSigs in two TNBC subtypes-BL1 and LAR, were two independent prognostic factors. Further, analysis results of the TNBC cell lines revealed that the TSPSigs expressions and drug sensitivities had significant associations. Based on the preceding data, we concluded that TSPSigs could be exploited as novel candidate prognostic markers for TNBC patients and applied to individualized treatment in the future.
