Single-cell RNA sequencing reveals gene expression signatures of breast cancer-associated endothelial cells
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Zhengda Sun1,*, Chih-Yang Wang2,5,*, Devon A. Lawson3, Serena Kwek4, Hugo Gonzalez Velozo2, Mark Owyong2, Ming-Derg Lai5, Lawrence Fong4, Mark Wilson1, Hua Su6, Zena Werb2 and Daniel L. Cooke1
1Division of Neurointerventional Radiology, Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94143, USA
2Department of Anatomy, University of California, San Francisco, CA 94143, USA
3Department of Physiology and Biophysics, University of California, Irvine, CA 92697, USA
4Division of Hematology and Oncology, Department of Medicine, University of California, San Francisco, CA 94143, USA
5Department of Biochemistry and Molecular Biology, Institute of Basic Medical Sciences, College of Medicine, National Cheng Kung University, Tainan 70101, Taiwan
6Center for Cerebrovascular Research, Department of Anesthesia and Perioperative Care, University of California, San Francisco, CA 94143, USA
Daniel L. Cooke, email: Daniel.firstname.lastname@example.org
Zena Werb, email: email@example.com
Keywords: breast cancer; endothelial cell; single-cell RNA sequencing; extracellular matrix
Received: November 08, 2017 Accepted: December 22, 2017 Published: December 29, 2017
Tumor endothelial cells (TEC) play an indispensible role in tumor growth and metastasis although much of the detailed mechanism still remains elusive. In this study we characterized and compared the global gene expression profiles of TECs and control ECs isolated from human breast cancerous tissues and reduction mammoplasty tissues respectively by single cell RNA sequencing (scRNA-seq). Based on the qualified scRNA-seq libraries that we made, we found that 1302 genes were differentially expressed between these two EC phenotypes. Both principal component analysis (PCA) and heat map-based hierarchical clustering separated the cancerous versus control ECs as two distinctive clusters, and MetaCore disease biomarker analysis indicated that these differentially expressed genes are highly correlated with breast neoplasm diseases. Gene Set Enrichment Analysis software (GSEA) enriched these genes to extracellular matrix (ECM) signal pathways and highlighted 127 ECM-associated genes. External validation verified some of these ECM-associated genes are not only generally overexpressed in various cancer tissues but also specifically overexpressed in colorectal cancer ECs and lymphoma ECs. In conclusion, our data demonstrated that ECM-associated genes play pivotal roles in breast cancer EC biology and some of them could serve as potential TEC biomarkers for various cancers.
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