Journal of Advanced Research

Journal of Advanced Research

Volume 44, February 2023, Pages 149-160
Journal of Advanced Research

Original Article
Integrated immunogenomic analysis of single-cell and bulk tissue transcriptome profiling unravels a macrophage activation paradigm associated with immunologically and clinically distinct behaviors in ovarian cancer

https://doi.org/10.1016/j.jare.2022.04.006Get rights and content
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open access

Highlights

  • Integrated immunogenomic analysis of single-cell and bulk tissue transcriptome profiling was conducted.

  • Two novel OV subgroups were characterized by a balance between M0, M1 and M2 macrophages with distinct clinical and immunological behaviors.

  • A macrophage polarization-derived artificial neural network model (MacroANN) was developed to predict patient outcomes and therapeutic efficacy.

Abstract

Introduction

Increasing evidence demonstrates that the activation states and diverse spectrum of macrophage subtypes display dynamic heterogeneity in the tumor microenvironment, which plays a critical role in a variety of cancer types.

Objectives

To investigate the heterogeneity and the homeostasis of different macrophage subtypes, as well as their effect on biological and clinical manifestations of ovarian cancer (OV).

Method

Integrated immunogenomic analysis of single-cell and bulk tissue transcriptome profiling was performed to systematically investigate the association between macrophage activation and prognostic and therapeutic efficacy. Consensus clustering analysis was used to define novel macrophage subtypes. An artificial neural network was used to simulate the dynamic activation of macrophages.

Results

The pan-cohort results suggested that high relative infiltration abundance of M0 and M1 macrophages was associated with improved outcome and therapeutic efficacy. However, it was the opposite for M2 macrophages. Unsupervised consensus clustering analysis revealed two OV subgroups characterized by a balance between M0, M1 and M2 macrophages with distinct clinical and immunological behaviors. Finally, a macrophage polarization-derived artificial neural network model was proposed to serve as a robust prognostic factor and predictive biomarker for therapeutic efficacy, which was validated in different independent patient cohorts.

Conclusion

The present study provides a new understanding of macrophage heterogeneity and its association with OV prognosis and underlines the future clinical potential of a macrophage activation model for tumor prevention and treatment.

Keywords

Macrophages
Single-cell
Artificial neural network
Immune microenvironment

Cited by (0)

Peer review under responsibility of Cairo University.

1

These authors contributed equally.