基于单细胞测序与转录组测序构建M2巨噬细胞基因相关的前列腺癌预后模型

doi:10.3969/j.issn.1674-8115.2025.05.003

上海交通大学学报（医学版） ›› 2025, Vol. 45 ›› Issue (5): 549-561.doi: 10.3969/j.issn.1674-8115.2025.05.003

基于单细胞测序与转录组测序构建M2巨噬细胞基因相关的前列腺癌预后模型

汤开然(), 冯成领, 韩邦旻()

上海交通大学医学院附属第一人民医院泌尿外科，上海 200080

收稿日期:2024-10-17 接受日期:2025-01-07 出版日期:2025-05-28 发布日期:2025-05-28
通讯作者: 韩邦旻，主任医师，博士；电子信箱：hanbm@163.com。
作者简介:汤开然（1999—），女，博士生；电子信箱：sjtu-tkr-01005@sjtu.edu.cn
冯成领（1998—），男，住院医师，博士；电子信箱：jsgyfcl@163.com。
基金资助:
国家自然科学基金(82473440);上海市重中之重研究中心建设项目(2023ZZ02015)

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

TANG Kairan(), FENG Chengling, HAN Bangmin()

Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, China

Received:2024-10-17 Accepted:2025-01-07 Online:2025-05-28 Published:2025-05-28
Contact: HAN Bangmin, E-mail: hanbm@163.com.
Supported by:
National Natural Science Foundation of China(82473440);Shanghai Key Research Center Construction Project(2023ZZ02015)

摘要/Abstract

摘要：

目的·探讨M2巨噬细胞相关基因在前列腺癌（prostate cancer，PCa）中的预后评估价值，以便更准确地预测患者预后并实现个性化治疗。方法·从TCGA数据库下载PCa的普通转录组测序（RNA sequencing，RNA-seq）数据，从GEO数据库获取PCa的单细胞RNA测序（single-cell RNA sequencing，scRNA-seq）数据。通过CIBERSORTx算法评估TCGA样本中的免疫浸润情况，使用FindMarkers功能识别scRNA-seq数据中的差异基因并鉴定免疫细胞亚型，通过基因集富集分析（Gene Set Enrichment Analysis，GSEA）和CellChat算法探究M2巨噬细胞参与通路以及与周围细胞的相互作用情况。最后筛选出M2巨噬细胞特征基因，通过单变量Cox回归和LASSO分析构建PCa预后模型，并基于该模型进行患者临床病理特征分析、免疫抑制与耐药性分析、药物敏感性分析。结果·分析TCGA样本发现，M2巨噬细胞高度浸润的PCa患者显著表现出更低的无进展生存期（progression-free survival，PFS）。分析scRNA-seq发现，肿瘤微环境中的细胞可以分为多个亚群，M2巨噬细胞可以与肿瘤微环境中多种免疫细胞相互作用，促进免疫抑制性微环境的形成，进而发挥促肿瘤关键作用。基于此构建了PCa风险评分模型，模型中包含TREM2、OTOA、SIGLEC1和PLXDC1 4个基因，在测试集与验证集中均表现出良好的预测性能。高风险评分患者的肿瘤微环境呈现免疫抑制特征，并伴有雄激素受体（androgen receptor，AR）信号通路活性降低，同时他们表现出更差的临床分期与病理分级水平，进而导致更差的预后结果。基于药物预测与药物敏感性分析，最终筛选出6种治疗药物对高风险评分患者疗效更佳。结论·基于PCa肿瘤微环境中M2巨噬细胞相关基因构建了一个风险预后模型，为PCa的精准治疗提供了理论基础。

关键词: 肿瘤相关巨噬细胞, 前列腺癌, 预后模型, 肿瘤微环境

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

中图分类号:

R737.25

汤开然, 冯成领, 韩邦旻. 基于单细胞测序与转录组测序构建M2巨噬细胞基因相关的前列腺癌预后模型[J]. 上海交通大学学报（医学版）, 2025, 45(5): 549-561.

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.

图/表 11

图1 PCa中M2巨噬细胞相关特征的鉴定Note： A‒C.Comparison of PFS between low and high infiltration groups for M2 (A), M0 (B) and M1 (C) macrophages. D. Dimension reduction plot displaying the composition of 14 major cell clusters derived from prostate cancer samples. E. Bubble plot showing the marker genes of each cell cluster. F. Violin plot presenting the M2 signature scores across each cell cluster. ①P<0.001, ②P=0.830, ③P=0.700.

Fig 1 Identification of M2 macrophage-related characteristics in PCa

图2 TME中细胞间相互作用分析Note： A. Volcano plot illustrating the differentially expressed genes between M2 macrophages and non-M2 macrophages (genes with significant differences are highlighted in red; non-significant genes in blue). B. Gene enrichment analysis represented as a bubble diagram. C/D. Heatmap (C) and network plot (D)visualizing cell communication in sc-RNA seq. E. Violin plot indicating the predominant secretion of SPP1 by M2 macrophages. F. Kaplan-Meier survival curve showing DFS difference between high and low SPP1 expression groups in the TCGA cohort. ①P=0.034.

Fig 2 Analysis of cell-cell interactions in TME

图3 M2巨噬细胞与TME中其他细胞交互的主要通路Note： A‒C. The signals of CCL3 and CCL4 from M2 macrophages primarily targeted NK/T cells, Treg cells and B cells in PCa. D‒F. DFS differences based on these pathways. ①P=0.036, ②P=0.032, ③P=0.013.

Fig 3 Major pathways of interaction between M2 macrophages and other cells in TME

图4 PCa预后模型的构建与测试Note： A. The construction of a LASSO regression model based on four characteristic genes (TREM2, OTOA, SIGLEC1, and PLXDC1). B. Identification process of genes for construction of prognostic risk score signature. C. The coefficients in LASSO Cox regression analysis of the 38 prognostic M2 macrophages-related genes. D. The comparison of PFS between low- and high-risk score groups in the training set. E. The distribution of risk scores ranked from low to high and the comparison of survival status between low- and high- risk score groups in the training set. F. The prediction accuracy of the risk score measured by ROC curves at 1, 3, 5 years in the training set. The AUC values are 0.728, 0.674, 0.681 respectively. ①P<0.001, ②P=0.001.

Fig 4 Construction and testing of the prognostic model in PCa

图5 通过外部数据库对预后模型进行验证Note： A. The comparison of PFS between low- and high-risk score groups in the test set. B. The prediction accuracy of the risk score measured by ROC curves at 1, 3, 5 years in the test set. The AUC values were 0.693, 0.659, and 0.649 for 1, 3, and 5 years, respectively. C. Distribution of risk in ascending order and comparison of survival status between low- and high- risk score groups in the test set. D. The heatmap of four signature genes between high- and low-risk group. E/F. The comparison of BCR (E) and OS (F) between low- and high-risk score groups in the SU2C/PCF Dream Team cohort. G. Nomogram for predicting PFS of patients in training set. H. The calibration plots of the nomogram at 1, 3, 5 years. The x coordinate value represents the nomogram-predicted survival, and the y coordinate value represents observed PFS. I. ROC curves for nomogram at 1, 3, 5 years. ①P=0.018, ②P<0.001, ③P=0.013.

Fig 5 External database validation of prognostic model

表1 包含风险评分的多变量Cox回归模型

Tab 1 Multivariable Cox regression models with risk score

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

表2 不包含风险评分的多变量Cox回归模型

Tab 2 Multivariable Cox regression models without risk score

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

图6 临床特征与风险评分的关联Note： A/B. The analysis of the RiskScore distribution based on Gleason score (A) and AJCC stage (B). C‒H. Kaplan-Meier analysis of PFS between high-risk and low-risk score groups among patients with different clinical pathological features. C. Patients with Gleason scores 6‒7. D. Patients with Gleason scores 8‒10. E. Patients with AJCC stage Ⅰ/Ⅱ. F. Patients with AJCC stage Ⅲ/Ⅳ. G. Patients aged ≤65 years. H. Patients aged >65 years. ①P<0.001, ②P=0.011, ③P=0.010, ④P=0.024.

Fig 6 Association of clinical characteristics with RiskScore

图7 风险评分与免疫抑制和耐药性的关联Note： A/B. GSEA of patients with high or low Risk Score. C. Analysis of immune cell infiltration in PCa samples with high or low Risk Score. D. Analysis of immune checkpoint-related genes in PCa samples with high or low Risk Score. E. Analysis ofAR pathway activity in PCa samples with high or low Risk Score. ①P>0.05, ②P<0.05, ③P<0.01, ④P<0.001.

Fig 7 Association of immunosuppression and drug resistance with RiskScore

表3 与风险评分呈负相关的药物

Tab 3 Drugs negatively correlated with risk score

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

图8 潜在药物预测Note： A/B. GSEA of potential drug resistance-related pathways. C. A comprehensive analysis of drug sensitivity between the high and low Risk Score groups. ①P<0.001.

Fig 8 Prediction of potential drugs

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基于单细胞测序与转录组测序构建M2巨噬细胞基因相关的前列腺癌预后模型

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

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