Integrated single-cell and transcriptome sequencing to construct a prognostic model of M2 macrophage-related genes in prostate cancer

doi:10.3969/j.issn.1674-8115.2025.05.003

Abstract

Abstract:

Objective To explore the prognostic value of M2 macrophage-related genes in prostate cancer (PCa), aiming to predict tumor prognosis more accurately and enable personalized treatment. Methods ·RNA sequencing (RNA-seq) data of PCa were downloaded from The Cancer Genome Atlas (TCGA) database, and single-cell RNA sequencing (scRNA-seq) data were obtained from the Gene Expression Omnibus (GEO) database. The immune infiltration of TCGA samples was assessed using the CIBERSORTx algorithm. Differential genes in scRNA-seq data were identified using the FindMarkers function, and immune cell subtypes were characterized. M2 macrophage-related pathways and interactions with surrounding cells were explored through Gene Set Enrichment Analysis (GSEA) and the CellChat algorithm. M2 macrophage signature genes were selected to construct a prognostic model for PCa using univariate Cox and LASSO analyses. Based on the risk model, clinical characteristics, immune suppression, drug resistance, and drug sensitivity analyses were conducted. Results ·In TCGA samples, patients with high M2 macrophage infiltration exhibited significantly lower progression-free survival (PFS). scRNA-seq analysis identified multiple subpopulations of tumor microenvironment (TME) cells. M2 macrophages interacted with various immune cells in TME, contributing to an immunosuppressive microenvironment and playing a key role in tumor promotion. Based on these findings, a PCa risk model was developed, incorporating TREM2, OTOA, SIGLEC1, and PLXDC1, which showed robust predictive performance in both training and validation cohorts. Patients with higher risk scores demonstrated a more immunosuppressive TME, decreased androgen receptor (AR) signaling activity, and worse clinical characteristics, leading to poorer outcomes. Drug prediction and sensitivity analyses identified six potential therapeutic agents that may offer improved efficacy for patients with higher risk scores. Conclusion ·A prognostic model based on M2 macrophage-related genes in the TME has been constructed, providing a theoretical foundation for precision treatment in PCa.

Key words: tumor-associated macrophage, prostate cancer, prognostic model, tumor microenvironment

CLC Number:

R737.25

TANG Kairan, FENG Chengling, HAN Bangmin. Integrated single-cell and transcriptome sequencing to construct a prognostic model of M2 macrophage-related genes in prostate cancer[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2025, 45(5): 549-561.

Figures/Tables 11

TrendMD

References 33

1	SUNG H, FERLAY J, SIEGEL R L, et al. Global cancer statistics 2020: globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2021, 71(3): 209-249.
2	JAMES N D, TANNOCK I, N'DOW J, et al. The lancet commission on prostate cancer: planning for the surge in cases[J]. Lancet, 2024, 403(10437): 1683-1722.
3	XIANG X N, WANG J G, LU D, et al. Targeting tumor-associated macrophages to synergize tumor immunotherapy[J]. Signal Transduct Target Ther, 2021, 6(1): 75.
4	MANTOVANI A, MARCHESI F, MALESCI A, et al. Tumour-associated macrophages as treatment targets in oncology[J]. Nat Rev Clin Oncol, 2017, 14(7): 399-416.
5	CASSETTA L, POLLARD J W. Targeting macrophages: therapeutic approaches in cancer[J]. Nat Rev Drug Discov, 2018, 17(12): 887-904.
6	EL-KENAWI A, DOMINGUEZ-VIQUEIRA W, LIU M, et al. Macrophage-derived cholesterol contributes to therapeutic resistance in prostate cancer[J]. Cancer Res, 2021, 81(21): 5477-5490.
7	WANG D, CHENG C P, CHEN X Y, et al. IL-1β is an androgen-responsive target in macrophages for immunotherapy of prostate cancer[J]. Adv Sci (Weinh), 2023, 10(17): e2206889.
8	PARTIN A W, MANGOLD L A, LAMM D M, et al. Contemporary update of prostate cancer staging nomograms (Partin Tables) for the new millennium[J]. Urology, 2001, 58(6): 843-848.
9	PUNNEN S, FREEDLAND S J, PRESTI J C Jr, et al. Multi-institutional validation of the CAPRA-S score to predict disease recurrence and mortality after radical prostatectomy[J]. Eur Urol, 2014, 65(6): 1171-1177.
10	BOSTRÖM P J, BJARTELL A S, CATTO J W F, et al. Genomic predictors of outcome in prostate cancer[J]. Eur Urol, 2015, 68(6): 1033-1044.
11	GOLDMAN M J, CRAFT B, HASTIE M, et al. Visualizing and interpreting cancer genomics data via the Xena platform[J]. Nat Biotechnol, 2020, 38(6): 675-678.
12	CHEN S J, ZHU G H, YANG Y, et al. Single-cell analysis reveals transcriptomic remodellings in distinct cell types that contribute to human prostate cancer progression[J]. Nat Cell Biol, 2021, 23(1): 87-98.
13	TAYLOR B S, SCHULTZ N, HIERONYMUS H, et al. Integrative genomic profiling of human prostate cancer[J]. Cancer Cell, 2010, 18(1): 11-22.
14	ABIDA W, CYRTA J, HELLER G, et al. Genomic correlates of clinical outcome in advanced prostate cancer[J]. Proc Natl Acad Sci USA, 2019, 116(23): 11428-11436.
15	NEWMAN A M, STEEN C B, LIU C L, et al. Determining cell type abundance and expression from bulk tissues with digital cytometry[J]. Nat Biotechnol, 2019, 37(7): 773-782.
16	TANG Z F, KANG B X, LI C W, et al. GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis[J]. Nucleic Acids Res, 2019, 47(W1): W556-W560.
17	YU G C, WANG L G, HAN Y Y, et al. ClusterProfiler: an R package for comparing biological themes among gene clusters[J]. OMICS, 2012, 16(5): 284-287.
18	JIN S Q, GUERRERO-JUAREZ C F, ZHANG L H, et al. Inference and analysis of cell-cell communication using CellChat[J]. Nat Commun, 2021, 12(1): 1088.
19	FRIEDMAN J, HASTIE T, TIBSHIRANI R. Regularization paths for generalized linear models via coordinate descent[J]. J Stat Softw, 2010, 33(1): 1-22.
20	LYU A, FAN Z H, CLARK M, et al. Evolution of myeloid-mediated immunotherapy resistance in prostate cancer[J]. Nature, 2025, 637: 1207-1217.
21	FANG L Y, IZUMI K, LAI K P, et al. Infiltrating macrophages promote prostate tumorigenesis via modulating androgen receptor-mediated CCL4-STAT3 signaling[J]. Cancer Res, 2013, 73(18): 5633-5646.
22	BLUM D L, KOYAMA T, M'KOMA A E, et al. Chemokine markers predict biochemical recurrence of prostate cancer following prostatectomy[J]. Clin Cancer Res, 2008, 14(23): 7790-7797.
23	GANTA V C, CHOI M, FARBER C R, et al. Antiangiogenic VEGF₁₆₅b regulates macrophage polarization via S100A8/S100A9 in peripheral artery disease[J]. Circulation, 2019, 139(2): 226-242.
24	LIU Y, KONG X H, YOU Y, et al. S100A8-mediated NLRP3 inflammasome-dependent pyroptosis in macrophages facilitates liver fibrosis progression[J]. Cells, 2022, 11(22): 3579.
25	OBRADOVIC A, CHOWDHURY N, HAAKE S M, et al. Single-cell protein activity analysis identifies recurrence-associated renal tumor macrophages[J]. Cell, 2021, 184(11): 2988-3005.e16.
26	MOLGORA M, ESAULOVA E, VERMI W, et al. TREM2 modulation remodels the tumor myeloid landscape enhancing anti-PD-1 immunotherapy[J]. Cell, 2020, 182(4): 886-900.e17.
27	BINNEWIES M, POLLACK J L, RUDOLPH J, et al. Targeting TREM2 on tumor-associated macrophages enhances immunotherapy[J]. Cell Rep, 2021, 37(3): 109844.
28	LAURENT S, GEHRIG C, NOUSPIKEL T, et al. Molecular characterization of pathogenic OTOA gene conversions in hearing loss patients[J]. Hum Mutat, 2021, 42(4): 373-377.
29	LUKASHKIN A N, KEVIN LEGAN P, WEDDELL T D, et al. A mouse model for human deafness DFNB22 reveals that hearing impairment is due to a loss of inner hair cell stimulation[J]. Proc Natl Acad Sci USA, 2012, 109(47): 19351-19356.
30	HOFMANN O, CABALLERO O L, STEVENSON B J, et al. Genome-wide analysis of cancer/testis gene expression[J]. Proc Natl Acad Sci USA, 2008, 105(51): 20422-20427.
31	SINGH R, CHOI B K. Siglec1-expressing subcapsular sinus macrophages provide soil for melanoma lymph node metastasis[J]. eLife, 2019, 8: e48916.
32	CASSETTA L, FRAGKOGIANNI S, SIMS A H, et al. Human tumor-associated macrophage and monocyte transcriptional landscapes reveal cancer-specific reprogramming, biomarkers, and therapeutic targets[J]. Cancer Cell, 2019, 35(4): 588-602.e10.
33	FUCHS B, MAHLUM E, HALDER C, et al. High expression of tumor endothelial marker 7 is associated with metastasis and poor survival of patients with osteogenic sarcoma[J]. Gene, 2007, 399(2): 137-143.

Item	Coef	Exp(coef)	Se(coef)	Z	Pr(>\|z\|)
RiskScore	0.041 055	1.041 909	0.020 700	1.983	0.047 3
Age	-0.002 394	0.997 609	0.016 200	-0.148	0.882 5
Gleason score	0.641 375	1.899 091	0.133 834	4.792	1.65×10^-6
AJCCⅡ	0.050 297	1.051 583	1.060 082	0.047	0.962 2
AJCCⅢ	0.581 300	1.788 361	1.060 250	0.548	0.583 5
AJCCⅣ	0.524 520	1.689 648	1.087 386	0.482	0.629 5

Item	Coef	Exp(coef)	Se(coef)	Z	Pr(>\|z\|)
RiskScore	0.041 055	1.041 909	0.020 700	1.983	0.047 3
Age	-0.002 394	0.997 609	0.016 200	-0.148	0.882 5
Gleason score	0.641 375	1.899 091	0.133 834	4.792	1.65×10^-6
AJCCⅡ	0.050 297	1.051 583	1.060 082	0.047	0.962 2
AJCCⅢ	0.581 300	1.788 361	1.060 250	0.548	0.583 5
AJCCⅣ	0.524 520	1.689 648	1.087 386	0.482	0.629 5

Item	Coef	Exp(coef)	Se(coef)	Z	Pr(>\|z\|)
Age	0.000 538 3	1.000 538 5	0.015 995 0	0.034	0.973
Gleason score	0.705 826 4	2.025 519 8	0.131 378 0	5.372	7.77×10^-6
AJCCⅡ	-0.088 018 0	0.915 744 4	1.057 486 1	-0.083	0.934
AJCCⅢ	0.488 412 6	1.629 727 2	1.059 586 6	0.461	0.645
AJCCⅣ	0.420 683 5	1.523 002 2	1.088 112 2	0.387	0.699

Item	Coef	Exp(coef)	Se(coef)	Z	Pr(>\|z\|)
Age	0.000 538 3	1.000 538 5	0.015 995 0	0.034	0.973
Gleason score	0.705 826 4	2.025 519 8	0.131 378 0	5.372	7.77×10^-6
AJCCⅡ	-0.088 018 0	0.915 744 4	1.057 486 1	-0.083	0.934
AJCCⅢ	0.488 412 6	1.629 727 2	1.059 586 6	0.461	0.645
AJCCⅣ	0.420 683 5	1.523 002 2	1.088 112 2	0.387	0.699

Drug ID	Drug name	Pathway name	Target
1021	Axitinib	RTK signaling	PDGFR, KIT, VEGFR
1022	AZD7762	Cell cycle	CHEK1, CHEK2
1059	AZD8055	PI3K/mTOR signaling	MTORC1, MTORC2
1192	GSK269962A	Cytoskeleton	ROCK1, ROCK2
1615	CZC24832	PI3K/mTOR signaling	PI3KG
1632	Ribociclib	Cell cycle	CDK4, CDK6