Research Paper Volume 16, Issue 8 pp 6809—6838
Comprehensive analysis of macrophage-related genes in prostate cancer by integrated analysis of single-cell and bulk RNA sequencing
- 1 Department of Urology, Fujian Union Hospital, Fujian Medical University, Fuzhou, Fujian, China
- 2 Department of Urology, The First Navy Hospital of Southern Theater Command, Zhanjiang, Guangdong, China
- 3 Center of Reproductive Medicine, Fujian Maternity and Child Health Hospital, Fujian Medical University, Fuzhou, Fujian, China
Received: October 30, 2023 Accepted: January 30, 2024 Published: April 24, 2024
https://doi.org/10.18632/aging.205727How to Cite
Copyright: © 2024 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Macrophages, as essential components of the tumor immune microenvironment (TIME), could promote growth and invasion in many cancers. However, the role of macrophages in tumor microenvironment (TME) and immunotherapy in PCa is largely unexplored at present. Here, we investigated the roles of macrophage-related genes in molecular stratification, prognosis, TME, and immunotherapeutic response in PCa. Public databases provided single-cell RNA sequencing (scRNA-seq) and bulk RNAseq data. Using the Seurat R package, scRNA-seq data was processed and macrophage clusters were identified automatically and manually. Using the CellChat R package, intercellular communication analysis revealed that tumor-associated macrophages (TAMs) interact with other cells in the PCa TME primarily through MIF - (CD74+CXCR4) and MIF - (CD74+CD44) ligand-receptor pairs. We constructed coexpression networks of macrophages using the WGCNA to identify macrophage-related genes. Using the R package ConsensusClusterPlus, unsupervised hierarchical clustering analysis identified two distinct macrophage-associated subtypes, which have significantly different pathway activation status, TIME, and immunotherapeutic efficacy. Next, an 8-gene macrophage-related risk signature (MRS) was established through the LASSO Cox regression analysis with 10-fold cross-validation, and the performance of the MRS was validated in eight external PCa cohorts. The high-risk group had more active immune-related functions, more infiltrating immune cells, higher HLA and immune checkpoint gene expression, higher immune scores, and lower TIDE scores. Finally, the NCF4 gene has been identified as the hub gene in MRS using the “mgeneSim” function.
Abbreviations
PCa: prostate cancer; TIME: tumor immune microenvironment; TME: tumor microenvironment; scRNA-seq: single-cell RNA sequencing; MRS: macrophage-related risk signature; TAMs: tumor-associated macrophages; WGCNA: weighted gene coexpression network analysis; GEO: Gene Expression Omnibus; TCGA: The Cancer Genome Atlas; UCSC: University of California, Santa Cruz; CPGEA: Chinese Prostate Cancer Genome and Epigenome Atlas; DFKZ: The German Cancer Research Center, Deutsches Krebsforschungszentrum; MSKCC: The Memorial Sloan Kettering Cancer Center; PCA: principal component analysis; t-SNEs: t-distributed stochastic neighbor embeddings; MEs: module eigengenes; KM: Kaplan-Meier; GSVA: gene set variation analysis; ssGSEA: single-sample gene set enrichment analysis; HLA: human leukocyte antigen; IPS: immunophenoscore; TCIA: The Cancer Immunome Atlas; TIDE: tumor immune dysfunction and exclusion; DEGs: differentially expressed genes; LASSO: least absolute shrinkage and selection operator; AUC: area under the curve; ROC: receiver operating characteristic; DCA: decision curve analysis; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; GSEA: gene set enrichment analysis; TISCH: tumor immune single-cell hub; TIMER2.0: Tumor Immune Estimation Resource 2.0; HPA: Human Protein Atlas; PCs: principal components.