图1 本研究的工作流程

Fig.1 Workflow of this study. Single-cell sequencing data were obtained from principal components analysis (A), followed by annotation and clustering of the data (B), resulting in the identification of two types of neutrophils: Neu_1 and Neu_2 (C). Tissue immunofluorescence verification, Gene Ontology (GO) enrichment analysis, cell interaction studies, and counter-time sequence analysis of these two cell types were used to elucidate their roles within the tumor microenvironment (TME) (D). Combining our findings with data from the TCGA database, we identified 4 independent prognostic factors for constructing and validating the prognostic models (E, F). We performed an analysis of immunotherapy predictions by analyzing and scoring the proportion of immune cells within the TME (G, H). ***P<0.001, ****P<0.0001. The patients with high levels of infiltrating Neu_2 neutrophils are likely to have poor responses to immunotherapy, as indicated by TIDE analysis (I). THPA: The Human Protein Atlas; ESTIMATE: Estimation of stromal and immune cells in malignant tumor tissues using expression data; TIDE: Tumor immune dysfunction and exclusion.

图2 PCa scRNA-Seq数据的集成和聚类

Fig.2 Integration and clustering of PCa scRNA-Seq data. A: t-SNE of 15 PCa samples. B: t-SNE of 15 cell clusters. C: Identification of 12 cell types by marker genes. D: Cell types exist in different samples. E: Dot plot showed the expression differences of various genes across the 12 cell types. F: Expression differences of 12 cell types between the control and tumor groups. G: Heat map showing expressions of the characteristic genes across different cell subpopulations.

图3 中性粒细胞亚型的细胞图谱

Fig.3 Cell map of neutrophil subtypes. A: t-SNE of the 20 cell clusters. B: t-SNE of control group and tumor group. C: Neu_1 and Neu_2 neutrophils identified by marker genes. D: Heat map showing differential expressions of the marker genes between Neu_1 and Neu_2 neutrophils. E: Expressions of the marker genes as signatures of the two cell types. F: GO enrichment analysis of signaling pathways associated with Neu_1 and Neu_2 neutrophils. G: Immunofluorescent staining of the marker genes in a subset of neutrophils (CD66b), specifically WTAP in Neu_1 and IIFI30 in Neu_2, within prostate cancer (PCa) tissue. H: GO enrichment analysis chord diagram Neu_1 signaling pathways involved in neutrophils. I: GO enrichment analysis chord diagram Neu_2 signaling pathways involved in neutrophils. J: KEGG analysis of signaling pathways involved in Neu_1 and Neu_2 neutrophils.

图4 与中性粒细胞相关的细胞间通讯分析

Fig.4 Analysis of intercellular communication related to neutrophils. A: Number of interactions in the intercellular communication network. B: Interaction weights/strengths in intercellular communication networks. C: Neu_1 interaction between neutrophils and other cells. D: Neu_2 interaction between neutrophils and other cells. E: Number and intensity of interactions between Neu_1 neutrophils and different cell types. F: Number and intensity of interactions between Neu_2 neutrophils and different cell types. G: Bubble diagram of ligand-receptor pair-mediated interactions between Neu_1 cells and Neu_2 neutrophils and other cells.

图6 中性粒细胞预后风险模型的构建和验证

Fig.6 Construction and validation of a neutrophil prognostic risk model. A, B: Screening of prognostic-related core genes by lasso-cox regression in TCGA training group. C, G: Forest diagram in TCGA training group and GSE70770 validation set. D, H: Kaplan-Meier curve for overall survival between different ICPI risk groups in TCGA training group and GSE70770 validation set. E, I: Validation of centralized risk scores and expression heat maps of 4 genes in TCGA training group and GSE70770 validation set. F, J: Time-dependent ROC curve analysis in TCGA training group and GSE70770 validation set. K: Verification of expressions of the 4 key genes in PCa tissues by immunohistochemical staining from THPA database.

图7 中性粒细胞风险预后模型与PCa免疫浸润及免疫应答的相关性

Fig.7 Correlation between neutrophil risk prognostic model and immune infiltration as well as immune response in PCa. A: Calculation of 22 immune cell infiltration ratios in PCa tissues based on CIBERSORT. B: Correlation analysis between immune cells in PCa tissues. C: Differences in immune cell infiltration expression between high-risk and low-risk groups. D: Differences in immune scores and infiltration ratios of some immune cells (plasma cells, Tregs cells, and M2-TAMs cells) between high-risk and low-risk groups. E, F: Correlation analysis between TIDE score expression and risk score. G: Differences in microsatellite instability, immune dysfunction and immune rejection scores between high-risk and low-risk groups. *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001.