Deep learning using bulk RNA-seq data expands cell landscape identification in tumor microenvironment

Xin Wang; Hongjiu Wang; Dan Liu; Na Wang; Danni He; Zheyu Wu; Xu Zhu; Xiaoling Wen; Xuhua Li; Jin Li; Zhenzhen Wang

doi:10.1080/2162402X.2022.2043662

Deep learning using bulk RNA-seq data expands cell landscape identification in tumor microenvironment

Oncoimmunology. 2022 Feb 25;11(1):2043662. doi: 10.1080/2162402X.2022.2043662. eCollection 2022.

Authors

Xin Wang^{1

2

3}, Hongjiu Wang^{1

2}, Dan Liu³, Na Wang², Danni He², Zheyu Wu², Xu Zhu¹, Xiaoling Wen¹, Xuhua Li¹, Jin Li¹, Zhenzhen Wang^{1

2}

Affiliations

¹ Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Medical University, Haikou, China.
² College of Bioinformatics Science and Technology, Harbin Medical University, Harbin,China.
³ The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.

Abstract

The tumor microenvironment (TME) profoundly influences tumor progression and affects immunotherapy responses and resistance. Understanding its heterogeneity is the key for developing immunotherapy. However, the available methods can only partially portray the TME heterogeneity with a small number of cell types. Here, we developed a deep learning-based frame with a design visible, DCNet, that embeds the relationships between cells and their marker genes in the neural network, and can infer the cell landscape with more than 400 cell types based on bulk RNA-seq data. DCNet accurately recapitulated the cell landscape of multiple single cell RNA-seq datasets, which showed better robustness and stability. Based on the cell landscape of TCGA patients, which was built with DCNet, the patients were divided into two groups with significant differences in survival time and distinct cell-type populations. DCNet provides a foundation for decoding TME heterogeneity. The source code of DCNet can be found on GitHub: https://github.com/xindd/DCNet.

Keywords: Tumor microenvironment; cell landscape; deep learning.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Deep Learning*
Exome Sequencing
Humans
Immunotherapy
RNA-Seq
Tumor Microenvironment* / genetics

Grants and funding

This work was supported by the Natural Science Foundation of Hainan Province [No.821MS045, 821MS0777, 621MS041]; National Natural Science Foundation of China [No.3170115932160179]; Major Science and Technology Program of Hainan Province [No.ZDKJ202003].