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. 2022 Nov 21:13:1058239.
doi: 10.3389/fimmu.2022.1058239. eCollection 2022.

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

Liyuan Zhang  1 , Qiyuan Qin  2 , Chen Xu  3 , Ningyi Zhang  1 , Tianyi Zhao  4
Affiliations

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

Liyuan Zhang et al. Front Immunol. .

Abstract

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.

Keywords: T cell; functional analysis; immune cells; integration analysis; scRNA-seq.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
The flowchart of the overall analysis.
Figure 2
Figure 2
The Scatter plot of the feature–feature relationships in GSE75688 dataset after quality control.
Figure 3
Figure 3
The elbow plot of the principal component analysis.
Figure 4
Figure 4
t-SNE of the integration for datasets GSE75688 and GSE118389. t-SNE, t-distributed stochastic neighbor embedding.
Figure 5
Figure 5
t-SNE of cell clustering result. t-SNE, t-distributed stochastic neighbor embedding.
Figure 6
Figure 6
t-SNE visualization of the cluster of immune cells. t-SNE, t-distributed stochastic neighbor embedding.
Figure 7
Figure 7
GO and KEGG enrichment analysis of differentially expressed genes in immune cell types.
See this image and copyright information in PMC

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