Identification of immune cell function in breast cancer by integrating multiple single-cell data

被引:4
作者
Zhang, Liyuan [1 ]
Qin, Qiyuan [2 ]
Xu, Chen [3 ]
Zhang, Ningyi [1 ]
Zhao, Tianyi [4 ]
机构
[1] Harbin Inst Technol, Dept Comp Sci, Harbin, Peoples R China
[2] Sun Yat sen Univ, Affiliated Hosp 6, Dept Colorectal Surg, Guangzhou, Peoples R China
[3] Harbin Inst Technol, Ctr Bioinformat, Sch Comp Sci & Technol, Harbin, Peoples R China
[4] Harbin Inst Technol, Sch Med & Hlth, Harbin, Peoples R China
关键词
scRNA-seq; integration analysis; immune cells; T cell; functional analysis; GENES;
D O I
10.3389/fimmu.2022.1058239
中图分类号
R392 [医学免疫学]; Q939.91 [免疫学];
学科分类号
100102 ;
摘要
Breast cancer has now become the most commonly diagnosed cancer worldwide. It is a highly complex and heterogeneous disease that comprises distinct histological features and treatment response. With the development of molecular biology and immunology, immunotherapy has become a new field of breast cancer treatment. Identifying cell-type-specific genes critical to the immune microenvironment contributes to breast cancer treatment. Single-cell RNA sequencing (scRNA-seq) technology could serve as a powerful tool to analyze cellular genetic information at single-cell resolution and to uncover the gene expression status of each cell, thus allowing comprehensive assessment of intercellular heterogeneity. Because of the influence of sample size and sequencing depth, the specificity of genes in different cell types for breast cancer cannot be fully revealed. Therefore, the present study integrated two public breast cancer scRNA-seq datasets aiming to investigate the functions of different type of immune cells in tumor microenvironment. We identified total five significant differential expressed genes of B cells, T cells and macrophage and explored their functions and immune mechanisms in breast cancer. Finally, we performed functional annotation analyses using the top fifteen differentially expressed genes in each immune cell type to discover the immune-related pathways and gene ontology (GO) terms.
引用
收藏
页数:10
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