Multi-omics analysis reveals a macrophage-related marker gene signature for prognostic prediction, immune landscape, genomic heterogeneity, and drug choices in prostate cancer

Weian Zhu; Jianjie Wu; Jiongduan Huang; Dongming Xiao; Fengao Li; Chenglun Wu; Xiaojuan Li; Hengda Zeng; Jiayu Zheng; Wenjie Lai; Xingqiao Wen

doi:10.3389/fimmu.2023.1122670

Multi-omics analysis reveals a macrophage-related marker gene signature for prognostic prediction, immune landscape, genomic heterogeneity, and drug choices in prostate cancer

Front Immunol. 2023 Apr 14:14:1122670. doi: 10.3389/fimmu.2023.1122670. eCollection 2023.

Authors

Weian Zhu¹, Jianjie Wu¹, Jiongduan Huang¹, Dongming Xiao¹, Fengao Li², Chenglun Wu¹, Xiaojuan Li³, Hengda Zeng¹, Jiayu Zheng¹, Wenjie Lai^{4

1}, Xingqiao Wen¹

Affiliations

¹ Department of Urology, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.
² Department of Urology, Anqing First People's Hospital of Anhui Medical University, Anqing, China.
³ Department of Health Care, Shenzhen Hospital, Southern Medical University, Shenzhen, China.
⁴ Laboratory of Biomaterials and Translational Medicine, Center for Nanomedicine, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China.

Abstract

Introduction: Macrophages are components of the innate immune system and can play an anti-tumor or pro-tumor role in the tumor microenvironment owing to their high heterogeneity and plasticity. Meanwhile, prostate cancer (PCa) is an immune-sensitive tumor, making it essential to investigate the value of macrophage-associated networks in its prognosis and treatment.

Methods: Macrophage-related marker genes (MRMGs) were identified through the comprehensive analysis of single-cell sequencing data from GSE141445 and the impact of macrophages on PCa was evaluated using consensus clustering of MRMGs in the TCGA database. Subsequently, a macrophage-related marker gene prognostic signature (MRMGPS) was constructed by LASSO-Cox regression analysis and grouped based on the median risk score. The predictive ability of MRMGPS was verified by experiments, survival analysis, and nomogram in the TCGA cohort and GEO-Merged cohort. Additionally, immune landscape, genomic heterogeneity, tumor stemness, drug sensitivity, and molecular docking were conducted to explore the relationship between MRMGPS and the tumor immune microenvironment, therapeutic response, and drug selection.

Results: We identified 307 MRMGs and verified that macrophages had a strong influence on the development and progression of PCa. Furthermore, we showed that the MRMGPS constructed with 9 genes and the predictive nomogram had excellent predictive ability in both the TCGA and GEO-Merged cohorts. More importantly, we also found the close relationship between MRMGPS and the tumor immune microenvironment, therapeutic response, and drug selection by multi-omics analysis.

Discussion: Our study reveals the application value of MRMGPS in predicting the prognosis of PCa patients. It also provides a novel perspective and theoretical basis for immune research and drug choices for PCa.

Keywords: drug choices; genomic heterogeneity; macrophage-related marker gene; prognostic signature; prostate cancer; single-cell RNA-sequencing; tumor immunity.

Publication types

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

MeSH terms

Genomics
Humans
Macrophages
Male
Molecular Docking Simulation
Multiomics*
Prognosis
Prostatic Neoplasms* / drug therapy
Prostatic Neoplasms* / genetics
Tumor Microenvironment / genetics

Grants and funding

This work was supported by the National Natural Science Foundation of China (grant numbers: 81874095, 82072820); Basic and Applied Basic Research Foundation of Guangdong Province (grant numbers: 2019B1515120007, 2021A1515111210, 2023A1515010588), and National Natural Science Foundation Cultivation Project of Shenzhen Hospital of Southern Medical University (grant numbers: 22H3AGZR09).