HNRNPC suppresses tumor immune microenvironment by activating Treg cells promoting the progression of prostate cancer
HNRNPC 通过激活 Treg 细胞抑制肿瘤免疫微环境，促进前列腺癌的进展

2.1. Data processing 2.1. 数据处理

Figure 1 shows a flowchart of this study. The mRNA expression profiles and related clinical data were collected from The Cancer Genome Atlas (TCGA) data portal and Gene Expression Omnibus (GEO) datasets (GSE70768, GSE32571, GSE60329, GSE46602). The mRNA expression data were transformed to values in transcripts per million (TPM). A total of 23 m6A genes were integrated from several studies, as shown in Figure S1. The single‐cell transcriptome data were extracted from GEO dataset: GSE141445 including 13 samples, 12 primary samples, and 1 metastatic sample.
图 1 显示了本研究的流程图。mRNA 表达谱和相关临床数据来自癌症基因组图谱 (TCGA) 数据门户和基因表达综合数据库 (GEO) 数据集 (GSE70768, GSE32571, GSE60329, GSE46602)。mRNA 表达数据被转换为每百万转录本 (TPM) 的值。总共整合了 23 个 m6A 基因，来自多个研究，如图 S1 所示。单细胞转录组数据来自 GEO 数据集：GSE141445，包括 13 个样本，12 个原发样本和 1 个转移样本。

2.2. Differentially expressed m6A genes (DEMGs) and cluster analysis
2.2. 差异表达的 m6A 基因 (DEMGs) 和聚类分析

We used Wilcoxon rank sum test to perform the differential expression analyses. M6A genes that met the criterion of false discovery rate (FDR) <0.05 between tumor and normal samples were identified as DEMGs. Based on the expression level of DEMGs, patients were then separated into subgroups using unsupervised clustering (named immune microenvironment subtypes [IMSs] subsequently).
我们使用 Wilcoxon 秩和检验进行差异表达分析。满足肿瘤和正常样本之间假发现率（FDR）<0.05 标准的 M6A 基因被确定为差异表达基因（DEMGs）。根据 DEMGs 的表达水平，患者随后通过无监督聚类分为亚组（随后称为免疫微环境亚型[IMSs]）。

Differences in the clinical characteristics of patients between IMSs were measured, including clinical M stage, pathological T stage, pathological N stage, Gleason score, and biochemical recurrence (BCR). In addition, we investigated the differences in tumor purity and immune scores between clusters. Next, we calculated the ssGSEA score of immunostimulatory and inhibitory states using gene lists collected from previous studies, which are provided in Table S1.
在 IMS 之间测量患者的临床特征差异，包括临床 M 期、病理 T 期、病理 N 期、Gleason 评分和生化复发（BCR）。此外，我们还研究了不同簇之间肿瘤纯度和免疫评分的差异。接下来，我们使用从之前研究中收集的基因列表计算了免疫刺激和抑制状态的 ssGSEA 评分，这些基因列表在表S1中提供。

2.3. Identifying the immune landscape of IMS clusters
2.3. 识别 IMS 簇的免疫环境

First, we counted the proportion of C1‐C6 immune subtypes in the two IMSs. Considering that immune infiltration was related to “measures of DNA damage,” we compared several measures of DNA damage, including fraction alteration, number of segments, aneuploidy score (AS), homologous recombination defects (HRD), and intratumor heterogeneity (ITH) between IMSs. Next, a comparison was performed of the genome changes, including silent/nonsilent mutation, indel neoantigens, and SNV neoantigen counts. Differences in BCR and TCR diversity between clusters were also measured using the Shannon index. All data used were obtained from Thorsson et al ²⁰ Finally, we compared the expression levels of ICD and ICP genes between the two IMSs.
首先，我们计算了两个 IMS 中 C1-C6 免疫亚型的比例。考虑到免疫浸润与“DNA 损伤的测量”相关，我们比较了几种 DNA 损伤的测量，包括分数改变、片段数量、非整倍体评分（AS）、同源重组缺陷（HRD）和肿瘤内异质性（ITH）。接下来，我们对基因组变化进行了比较，包括沉默/非沉默突变、插入缺失新抗原和 SNV 新抗原计数。还使用香农指数测量了不同簇之间 BCR 和 TCR 多样性的差异。所有使用的数据均来自 Thorsson 等人 ²⁰ 。最后，我们比较了两个 IMS 之间 ICD 和 ICP 基因的表达水平。

Furthermore, whole‐exome sequencing (WES) from TCGA database data was used to calculate the tumor mutation burden (TMB) score in PCa with exons uniformly counted as 40 M regions and to describe the mutation spectrum of two clusters to search for specific mutations.
此外，使用 TCGA 数据库的数据进行全外显子测序（WES）来计算前列腺癌（PCa）的肿瘤突变负荷（TMB）评分，外显子统一计为 40 M 区域，并描述两个簇的突变谱以寻找特定突变。

2.4. Correlations of DEMGs with immune microenvironment
2.4. DEMGs 与免疫微环境的相关性

We performed correlation analyses of DEMGs with immune score and tumor purity to identify genes related to the immune microenvironment (named immune‐related genes [IRGs]). Genes with expression levels negatively correlated with immune score and positively correlated with tumor purity were determined as IRGs. To assess the outcome of immunotherapy for IMSs, we compared the expression of the following marker genes, which have been proven to affect immunotherapy in different ways: CD274 (PD‐L1), ²¹ CXCL9, ²⁴ MEX3B, ²⁵ HAVCR2 (TIM3), ²² CTLA4, and CD38. ²⁶
我们对与免疫评分和肿瘤纯度相关的差异表达免疫相关基因（DEMGs）进行了相关性分析，以识别与免疫微环境相关的基因（称为免疫相关基因 [IRGs]）。那些表达水平与免疫评分呈负相关而与肿瘤纯度呈正相关的基因被确定为 IRGs。为了评估免疫治疗对 IMS 的结果，我们比较了以下标记基因的表达，这些基因已被证明以不同方式影响免疫治疗：CD274 (PD‐L1), ²¹ CXCL9, ²⁴ MEX3B, ²⁵ HAVCR2 (TIM3), ²² CTLA4，和 CD38. ²⁶

2.5. Identification of hub genes
2.5. 中心基因的识别

The STRING database (https://string‐db.org/) and Cytoscape software were used to establish a protein‐protein interaction (PPI) network with a minimum required interaction score of 0.40 to evaluate the interactions between proteins coded by IRGs and immune modules, including the major histocompatibility complex (MHC) module, receptor module, chemokine module, immunostimulatory module, and immunoinhibitory module. IRGs that interacted with immune modules were identified as hub genes.
STRING 数据库 (https://string‐db.org/) 和 Cytoscape 软件被用来建立一个蛋白质-蛋白质相互作用 (PPI) 网络，最低所需相互作用评分为 0.40，以评估 IRGs 编码的蛋白质与免疫模块之间的相互作用，包括主要组织相容性复合体 (MHC) 模块、受体模块、趋化因子模块、免疫刺激模块和免疫抑制模块。与免疫模块相互作用的 IRGs 被确定为中心基因。

2.6. Validation of hub genes through in silico analyses
2.6. 通过计算机分析验证中心基因

First, GEO datasets (GSE70768, GSE32571, GSE60329, and GSE46602) were applied to validate the correlations between IRGs and the immune microenvironment. Second, we analyzed the correlation between the expression of hub genes and MEX3B, which can downregulate HLA‐A expression on the surface of tumor cells, thereby rendering the tumor cells unable to be recognized and killed by T cells. ²⁵ Next, tumor patients were separated into a high‐expression group and a low‐expression group according to the median hub gene expression levels; differences in the abundance of immune cells between the two groups were measured. Finally, based on TCGA database, we compared the differences in the expression levels of hub genes between different clinical conditions. Kaplan‐Meier (K‐M) plots were developed to perform survival analysis of hub genes.
首先，GEO 数据集（GSE70768，GSE32571，GSE60329 和 GSE46602）被应用于验证 IRGs 与免疫微环境之间的相关性。其次，我们分析了中心基因的表达与 MEX3B 之间的相关性，MEX3B 可以下调肿瘤细胞表面 HLA‐A 的表达，从而使肿瘤细胞无法被 T 细胞识别和杀死。 ²⁵ 接下来，肿瘤患者根据中心基因表达水平的中位数被分为高表达组和低表达组；测量了两组之间免疫细胞丰度的差异。最后，基于 TCGA 数据库，我们比较了不同临床条件下中心基因表达水平的差异。Kaplan‐Meier (K‐M) 图被开发用于对中心基因进行生存分析。

2.7. Single‐cell transcriptome data analyses
2.7. 单细胞转录组数据分析

The single‐cell transcriptome data were preprocessed using the “Seurat” package. Well‐established markers for each cell type of PCa, integrated with the automatic cell annotation tool “SingleR,” ²⁷ were applied to annotate the cell types, dividing the cells into luminal epithelial cells (AR), basal epithelial cells (TP63), T cells (CD3E), B cells (CD19), fibroblasts (MYL9), mast cells (KIT), monocytes (CD14), and endothelial cells (CD34). We calculated percentages of cell types among samples and analyzed their correlation with HNRNPC‐positive cell rate. “ProjecTIL” was used to parse human scRNA‐seq T cell data in the context of murine TIL profiles. ²⁸ We then compared the difference of T cell subgroup composition between HNRNPC‐positive and ‐negative cells.
单细胞转录组数据使用“Seurat”包进行了预处理。针对前列腺癌（PCa）每种细胞类型的成熟标记，与自动细胞注释工具“SingleR,” ²⁷ 结合，应用于细胞类型的注释，将细胞分为腔道上皮细胞（AR）、基底上皮细胞（TP63）、T 细胞（CD3E）、B 细胞（CD19）、成纤维细胞（MYL9）、肥大细胞（KIT）、单核细胞（CD14）和内皮细胞（CD34）。我们计算了样本中细胞类型的百分比，并分析了它们与HNRNPC‐阳性细胞比例的相关性。“ProjecTIL”用于解析人类 scRNA-seq T 细胞数据，以小鼠 TIL 特征为背景。 ²⁸ 然后，我们比较了HNRNPC‐阳性和阴性细胞之间 T 细胞亚群组成的差异。

We used “CellChat” R package to study cell‐cell communication. ²⁹ The samples were divided into positive and negative groups according to whether the positive rate of HNRNPC was greater than 70%. We compared the strength of interaction among different cell types.
我们使用“CellChat” R 包来研究细胞间通信。 ²⁹ 根据 HNRNPC 的阳性率是否大于 70%，将样本分为阳性组和阴性组。我们比较了不同细胞类型之间的相互作用强度。

2.8. Cell lines and cell culture
2.8. 细胞系和细胞培养

Prostate cancer cells (C4‐2B, LNCaP, DU145, and PC3) and normal human prostate epithelial cells (RWPE‐1) were purchased from the American Type Culture Collection (ATCC). All cells were tested and confirmed to be free of mycoplasma contamination before use. C4‐2B cells were cultured in DMEM/F12 (4:1). LNCaP cells were cultured in RPMI‐1640. DU145 cells were cultured in MEM. PC3 cells were cultured in the F‐12 K medium. RWPE‐1 cells were cultured in keratinocyte serum‐free medium (K‐SFM). All media were provided by Gibco and were treated with 10% FBS and 100 U/mL penicillin/streptomycin in a 5% CO₂ incubator.
前列腺癌细胞（C4‐2B、LNCaP、DU145 和 PC3）和正常人前列腺上皮细胞（RWPE‐1）均购自美国典型培养物保藏中心（ATCC）。所有细胞在使用前均经过检测，确认无支原体污染。C4‐2B 细胞在 DMEM/F12（4:1）中培养。LNCaP 细胞在 RPMI‐1640 中培养。DU145 细胞在 MEM 中培养。PC3 细胞在 F‐12 K 培养基中培养。RWPE‐1 细胞在无角质形成细胞血清培养基（K‐SFM）中培养。所有培养基均由 Gibco 提供，并在 5% CO₂ 培养箱中处理了 10% FBS 和 100 U/mL 青霉素/链霉素。

2.9. Cell transfection 2.9. 细胞转染

Three specific shRNAs targeting HNRNPC and scrambled shRNA were synthesized by GenePharma. Cells were seeded in six‐well plates at a density of 8 × 10⁶ cells/well, and the plates were placed at 37°C with 5% CO₂. When cell confluence reached 70%, transfections were performed using the Lipofectamine 3000 kit (Invitrogen) according to the manufacturer's instructions.
三种特定的 shRNA 靶向HNRNPC，以及随机 shRNA 由 GenePharma 合成。细胞以 8 × 10⁶个细胞/孔的密度接种在六孔板中，板子放置在 37°C、5% CO₂的环境中。当细胞融合度达到 70%时，按照制造商的说明使用 Lipofectamine 3000 试剂盒（Invitrogen）进行转染。

2.10. Quantitative real‐time PCR (RT‐qPCR)
2.10. 定量实时 PCR (RT-qPCR)

Cells were seeded in six‐well plates at a density of 1.5 × 10⁶ cells/well and incubated for 24 hours. Total RNA was extracted from the cells using TRIzol® reagent (Invitrogen), and the extracted RNA was reverse‐transcribed into cDNA using the ReverTra Ace qPCR RT Kit (Toyobo). Quantitative PCR was performed using SYBR® qPCR Mix (Toyobo) based on the 2^−ΔΔCt method. GAPDH and U6 were used as the endogenous control genes. All experiments were performed in triplicates.
细胞以每孔 1.5 × 10⁶个细胞的密度接种在六孔板中，并孵育 24 小时。使用 TRIzol®试剂（Invitrogen）从细胞中提取总 RNA，提取的 RNA 使用 ReverTra Ace qPCR RT Kit（Toyobo）反转录为 cDNA。基于 2^−ΔΔCt方法，使用 SYBR® qPCR Mix（Toyobo）进行定量 PCR。GAPDH 和 U6 被用作内源性对照基因。所有实验均以三次重复进行。

2.11. Cell‐counting kit‐8 (CCK‐8) assays
2.11. 细胞计数试剂盒-8 (CCK-8) 检测

Cell viability was determined using a CCK‐8 kit (Beyotime Institute of Biotechnology), following the manufacturer's instructions. The cells (2 × 10⁴ cells/well) were seeded in a 96‐well plate. After cell growth for 12, 24, 48, or 72 hours, 10 μL of CCK‐8 solution was added to each well. Two hours later, absorbance was measured at 450 nm using a microplate reader. All experiments were performed in triplicates.
细胞活力使用 CCK-8 试剂盒（碧云天生物科技）进行测定，按照制造商的说明进行操作。将细胞（2 × 10⁴ 细胞/孔）接种在 96 孔板中。经过 12、24、48 或 72 小时的细胞生长后，向每个孔中添加 10 μL CCK-8 溶液。两小时后，使用微孔板读数仪在 450 nm 处测量吸光度。所有实验均以三次重复进行。

2.12. EdU assays 2.12. EdU 检测

The cells were cultured in 24‐well plates until 70% confluence. After 10 μL EdU solution was added and 2 hours incubation, the cells were fixed with 4% paraformaldehyde. After washing, a Click‐iTR EdU kit was used to detect EdU. The nuclei were stained with DAPI and the cells were observed using a fluorescence microscope (Olympus). All experiments were performed in triplicates.
细胞在 24 孔板中培养至 70%汇合度。加入 10 μL EdU 溶液并孵育 2 小时后，细胞用 4%多聚甲醛固定。洗涤后，使用 Click-iTR EdU 试剂盒检测 EdU。细胞核用 DAPI 染色，并使用荧光显微镜（奥林巴斯）观察。所有实验均进行三次重复。

2.13. TUNEL assays 2.13. TUNEL 检测

Cells were grown in 24‐well plates until 70% confluence. Cultured cells were fixed with 4% paraformaldehyde and permeabilized with 0.25% Triton‐X 100. TUNEL assays were performed according to the manufacturer's instructions (Roche). Briefly, cells were first incubated in a terminal deoxynucleotidyl transferase (TdT) reaction cocktail, followed by treatment with a Click‐iT reaction cocktail. Nuclei were stained with DAPI. The cells were observed and imaged using a fluorescence microscope (Olympus). All experiments were performed in triplicates.
细胞在 24 孔板中培养至 70%融合。培养的细胞用 4%多聚甲醛固定，并用 0.25% Triton-X 100 透化。根据制造商的说明（罗氏）进行 TUNEL 检测。简而言之，细胞首先在末端脱氧核苷酸转移酶（TdT）反应混合物中孵育，然后用 Click-iT 反应混合物处理。细胞核用 DAPI 染色。使用荧光显微镜（奥林巴斯）观察和成像细胞。所有实验均进行三次重复。

2.14. Flow cytometry analysis
2.14. 流式细胞术分析

Cells (2 × 10⁶) were seeded into each well of a six‐well plate and incubated for 24 hours. To assess apoptosis, cells were harvested after transfection for flow cytometry analysis. Briefly, after double‐staining with an Annexin V‐FITC/PI apoptosis kit (Multi Sciences), apoptosis was determined using a flow cytometer (BD). The apoptotic cells were gated as Annexin V‐FITC⁺PI⁺ and Annexin V‐FITC⁺PI⁻. The apoptosis rate was defined as the percentage of apoptotic cells in total cells. Flow cytometric analysis was performed to determine the percentage of Treg cells. The cocultured peripheral blood mononuclear cells (PBMCs) were digested with 0.25% trypsin and washed with PBS containing 0.5% (w/v) bovine serum albumin (BSA). Lymphocytes in PBMCs were gated by forward scattering (FSC) and lateral scattering (SSC). CD4⁺ T cells were gated by CD3 and CD4 staining. Isolated CD4⁺ T cells were then incubated with FITC‐conjugated anti‐CD25 and anti‐CD127 antibodies. ³⁰ All experiments were performed in triplicates.
细胞 (2 × 10⁶) 被接种到六孔板的每个孔中，并孵育 24 小时。为了评估凋亡，转染后收集细胞进行流式细胞术分析。简而言之，使用 Annexin V‐FITC/PI 凋亡试剂盒 (Multi Sciences) 进行双重染色后，使用流式细胞仪 (BD) 确定凋亡。凋亡细胞被划分为 Annexin V‐FITC⁺PI⁺ 和 Annexin V‐FITC⁺PI⁻。凋亡率定义为凋亡细胞在总细胞中的百分比。进行流式细胞术分析以确定 Treg 细胞的百分比。共培养的外周血单核细胞 (PBMCs) 用 0.25% 胰蛋白酶消化，并用含有 0.5% (w/v) 牛血清白蛋白 (BSA) 的 PBS 洗涤。PBMCs 中的淋巴细胞通过前向散射 (FSC) 和侧向散射 (SSC) 进行划分。CD4⁺ T 细胞通过 CD3 和 CD4 染色进行划分。分离的 CD4⁺ T 细胞随后与 FITC 标记的抗 CD25 和抗 CD127 抗体孵育。 ³⁰ 所有实验均以三次重复进行。

2.15. Transwell assays 2.15. Transwell 实验

After transfection, 2 × 10⁵ cells were seeded into each well of the upper transwell chamber (8 pm pore size, Corning). A medium containing 10% FBS was added to the lower chamber. After incubation for 24 hours, cells on the upper side of the upper chamber were wiped off, and cells on the lower side of the upper chamber were fixed with 4% paraformaldehyde and stained with 5% crystal violet for 5 minutes. Finally, stained cells were counted under a microscope in five random visual fields. All experiments were performed in triplicates.
转染后，将 2 × 10⁵个细胞接种到上层透析室的每个孔中（孔径 8 pm，Corning）。在下层室中添加含有 10% FBS 的培养基。经过 24 小时的孵育后，擦去上层室上侧的细胞，用 4%多聚甲醛固定下层室上侧的细胞，并用 5%结晶紫染色 5 分钟。最后，在显微镜下在五个随机视野中计数染色细胞。所有实验均进行三次重复。

2.16. Wound‐healing assays
2.16. 创伤愈合实验

Cells were seeded into six‐well plates (2 × 10⁶/well) and incubated for 24 hours. Wounds were created by passing a plastic tip across the monolayer cells. The time of wound infliction was considered to be 0 hours, and wound closure was photographed 24 hours later using a microscope. All experiments were performed in triplicates.
细胞被接种到六孔板中（2 × 10⁶/孔），并孵育 24 小时。通过用塑料尖端划过单层细胞来创建伤口。伤口造成的时间被认为是 0 小时，伤口闭合的照片在 24 小时后使用显微镜拍摄。所有实验均进行三次重复。

2.17. Coculture of PBMCs and PCa cells
2.17. PBMCs 与前列腺癌细胞的共培养

Peripheral blood mononuclear cells obtained from Procell (Wuhan) were cultured for 72 hours for activation in RPMI‐1640 medium (Hyclone; GE Healthcare) with 10% FCS (Gibco; Thermo Fisher Scientific, Inc.), 2 mm l‐glutamine, 0.5% streptomycin and penicillin, 25 mm HEPES, 3 μG/mL anti‐CD28 antibody, 1 μG/mL anti‐CD3 antibody, and 100 u/mL IL‐2. PCa cells (PC3 or DU145) with or without HNRNPC interference were cocultured indirectly with activated PBMCs in six‐well transwell coculture plates (0.4 μm polyester film) for 48 hours, where PBMCs cells were planted in the upper layer and PCa cells were plated in the lower layer.
从 Procell（武汉）获得的外周血单核细胞在 RPMI‐1640 培养基（Hyclone；GE Healthcare）中培养 72 小时以进行激活，培养基中含有 10% FCS（Gibco；Thermo Fisher Scientific, Inc.）、2 mm l‐谷氨酰胺、0.5%链霉素和青霉素、25 mm HEPES、3 μG/mL 抗 CD28 抗体、1 μG/mL 抗 CD3 抗体和 100 u/mL IL‐2。带或不带HNRNPC干扰的 PCa 细胞（PC3 或 DU145）与激活的 PBMCs 在六孔转移培养板（0.4 μm 聚酯膜）中间接共培养 48 小时，其中 PBMCs 细胞种植在上层，PCa 细胞种植在下层。

2.18. Statistical analyses
2.18. 统计分析

All analyses were performed using R software (version 4.0.5). Differences between groups were evaluated using Wilcoxon rank‐sum tests for continuous data and Fisher's exact tests for categorical variables. Pearson's test was used for the correlation analysis. The R package “ConsensusClusterPlus” was used for unsupervised clustering. ssGSEA scores were calculated using the “GSVA” package. K‐M plots were constructed using the K‐M “survival” package. The immune score and the tumor purity were calculated with the “estimate” package based on gene expression levels. CIBERSORT and ImmuCellAI (http://bioinfo.life.hust.edu.cn/ImmuCellAI) were used to calculate the infiltration score, assess the response to immunotherapy, and estimate the abundance of immune cells. All analyses were two‐sided, and statistical significance was set at p < 0.05.
所有分析均使用 R 软件（版本 4.0.5）进行。组间差异使用 Wilcoxon 秩和检验评估连续数据，使用 Fisher 精确检验评估分类变量。相关性分析使用 Pearson 检验。R 包“ConsensusClusterPlus”用于无监督聚类。ssGSEA 分数使用“GSVA”包计算。K-M 图使用 K-M “survival”包构建。免疫评分和肿瘤纯度基于基因表达水平使用“estimate”包计算。CIBERSORT 和 ImmuCellAI（http://bioinfo.life.hust.edu.cn/ImmuCellAI）用于计算浸润评分，评估对免疫治疗的反应，并估计免疫细胞的丰度。所有分析均为双侧，统计显著性设定为 p < 0.05。

3.1. Identification of m6A‐dependent IMSs
3.1. m6A 依赖的 IMSs 的识别

We extracted the m6A regulatory gene (shown in Figure S1) expression profile data of 52 normal and 395 tumor tissues from TCGA. Eleven m6A genes were found to be differentially distributed between PCa and normal tissues with an FDR < 0.05, including RBM15B, IGF2BP2, FMR1, HNRNPA2B1, HNRNPC, METTL3, ZC3H13, YTHDF1, FTO, YTHDF2, and ALKBH5 (Figure 2A). Using unsupervised clustering based on 11 identified enzymes, PCa patients were separated into two clusters with different molecular and immune characteristics (Figure 2B). The baseline characteristics of the 203 group1 and 292 group2 patients are shown in Table S2. After excluding individuals with missing values, the heatmap is shown in Figure 2C, which shows the distributions of tumor stage, BCR condition, Gleason score, m6A gene expression, tumor purity, and immune score between subgroups. Group2 exhibited an abundance enrichment of m6A genes. Furthermore, patients in group2 had higher tumor purity and lower immune scores (Figure 2D,E). Therefore, we named the two subgroups immune microenvironment subtype 1 (IMS1) and IMS2 in the further study. Additionally, we found that patients with IMS2 had a more advanced tumor condition and poorer prognosis than those with IMS1 (Figure 2C,G,H). More importantly, IMS1 samples showed significantly higher scores for both stimulatory and inhibitory immune responses than IMS2 samples (p _all < 0.05, Figure 2F), indicating that IMS1 is an immune “hot” phenotype, while IMS2 is an immune “cold” phenotype. All above mentioned suggested that the enrichment of m6A gene may affect the reprogramming of tumor immune microenvironment, thus leading to different prognoses and survival outcomes.
我们提取了来自 TCGA 的 52 个正常组织和 395 个肿瘤组织的 m6A 调控基因（如图S1）表达谱数据。发现 11 个 m6A 基因在前列腺癌（PCa）和正常组织之间存在差异分布，FDR < 0.05，包括RBM15B、IGF2BP2、FMR1、HNRNPA2B1、HNRNPC、METTL3、ZC3H13、YTHDF1、FTO、YTHDF2和ALKBH5（图2A）。基于 11 个已识别酶的无监督聚类，PCa 患者被分为两个具有不同分子和免疫特征的簇（图2B）。203 名组 1 和 292 名组 2 患者的基线特征见表S2。在排除缺失值的个体后，热图如图2C所示，显示了亚组之间肿瘤分期、BCR 状态、Gleason 评分、m6A 基因表达、肿瘤纯度和免疫评分的分布。组 2 表现出 m6A 基因的丰度富集。 此外，组 2 的患者肿瘤纯度更高，免疫评分更低（图 2D,E）。因此，我们在进一步研究中将这两个亚组命名为免疫微环境亚型 1（IMS1）和 IMS2。此外，我们发现 IMS2 患者的肿瘤状况更为严重，预后也比 IMS1 患者差（图 2C,G,H）。更重要的是，IMS1 样本的刺激性和抑制性免疫反应评分显著高于 IMS2 样本（p_all < 0.05，图 2F），这表明 IMS1 是免疫“热”表型，而 IMS2 是免疫“冷”表型。以上所有结果表明，m6A 基因的富集可能影响肿瘤免疫微环境的重编程，从而导致不同的预后和生存结果。

3.2. IMSs exhibited different immune status on genomic and transcriptome levels
3.2. IMS 在基因组和转录组水平上表现出不同的免疫状态

The measures of DNA damage, including altered fraction, AS, HRD, ITH, and number of segments, were all significantly stronger in IMS2 than in IMS1, which represents a poorer prognosis of tumor patients (p _all < 0.05, Figure 3A–E). The mutation rates were also found to be significantly different between IMSs (p _all < 0.05, Figure 3F,G). Further comparison of the mutation spectra revealed several high‐frequency mutations. For example, LRP1B and RYR2 were specifically mutated in IMS1, while SPTA1, ATM, and CSMD3 were mutated in IMS2 (Figure S2A,B). Notable difference was not found in the percentage of immune subtypes (C1‐C4), neoantigen counts (both Indel and SNV), BCR/TCR diversity, and TMB between IMSs (Figures S3 and S4A).
DNA 损伤的测量，包括改变的比例、AS、HRD、ITH 和片段数量，在 IMS2 中均显著强于 IMS1，这代表了肿瘤患者较差的预后（p_all < 0.05，图3A–E）。突变率在 IMS 之间也发现显著差异（p_all < 0.05，图3F,G）。进一步比较突变谱揭示了几种高频突变。例如，LRP1B 和 RYR2 在 IMS1 中特异性突变，而 SPTA1、ATM 和 CSMD3 在 IMS2 中突变（图S2A,B）。在免疫亚型（C1-C4）、新抗原计数（包括 Indel 和 SNV）、BCR/TCR 多样性和 TMB 之间未发现显著差异（图S3和S4A）。

The expression of most ICD and ICP genes varied significantly between IMSs (Figure S3D,E), indicating a difference in the tumor immune status. Most importantly, well‐established immunotherapy markers showed obvious differences. CD274 (PD‐L1), ²¹ T‐cell immunoglobulin mucin‐3 (TIM‐3), ²² and CTLA4, ²³ which represent the most effective predictive biomarkers for checkpoint inhibitor–based immunotherapy, were expressed at higher levels in IMS2 (p _all < 0.05, Figure S2C). CXCL9, the ligand of CXCR3, was also expressed at higher levels in IMS2 cells (Figure S2C, p = 1.94 × 10⁻⁹). CXCL9 has been identified as a biomarker for sensitivity to PD‐1 blockade, and augmenting the intratumoral function of this CXCR3‐CXCL9 chemokine system could improve clinical outcomes. ²⁴ MEX3B was also significantly higher in IMS2 cells (p = 2.7 × 10⁻¹⁴), which can inhibit the killing effect by preventing T cells from recognizing tumor cells. ²⁵ Furthermore, tumors treated with PD‐1/PD‐L1–blocking antibodies developed drug resistance through the upregulation of CD38, ²⁶ which showed higher expression in IMS1 (p = 5.43 × 10^–3, Figure S2C).
大多数 ICD 和 ICP 基因的表达在 IMS 之间显著不同（图S3D,E），这表明肿瘤免疫状态存在差异。最重要的是，已确立的免疫治疗标志物显示出明显差异。CD274（PD-L1）， ²¹ T 细胞免疫球蛋白粘蛋白-3（TIM-3）， ²² 和 CTLA4， ²³ 作为基于检查点抑制剂的免疫治疗最有效的预测生物标志物，在 IMS2 中的表达水平更高（p_all < 0.05，图S2C）。CXCL9，CXCR3 的配体，在 IMS2 细胞中的表达水平也更高（图S2C，p = 1.94 × 10⁻⁹）。CXCL9 已被确定为对 PD-1 阻断敏感性的生物标志物，增强这一 CXCR3-CXCL9 趋化因子系统的肿瘤内功能可能改善临床结果。 ²⁴ MEX3B 在 IMS2 细胞中的表达也显著更高（p = 2.7 × 10⁻¹⁴），它可以通过防止 T 细胞识别肿瘤细胞来抑制杀伤效应。 ²⁵ 此外，接受 PD-1/PD-L1 阻断抗体治疗的肿瘤通过上调 CD38 发展出药物耐药性， ²⁶ 其在 IMS1 中的表达更高 (p = 5.43 × 10^–3, 图 S2C)。

3.3. Identification of HNRNPC as the hub gene classifying IMSs
3.3. 确定 HNRNPC 作为分类 IMS 的中心基因

To investigate the crosstalk between m6A modification and the immune microenvironment, we analyzed the immune correlation of 11 DEMGs. Correlation analysis identified six IRGs, HNRNPC, HNRNPA2B1, YTHDF1, YTHDF2, METTL3, and RBM15B, whose expression levels were significantly negatively correlated with the immune score (R = −0.19, −0.18, −0.15, −0.13, −0.28, and −0.23, respectively; p = 2.4 × 10⁻⁵, 5.3 × 10⁻⁵, 7.2 × 10⁻⁴, 4.0 × 10⁻³, 1.8 × 10⁻¹⁰, and 1.6 × 10⁻⁷, respectively; Figure 3H and Figure S4B–F) and positively correlated with tumor purity (R = 0.20, 0.19, 0.17, 0.16, 0.29, and 0.23, respectively; p = 4.8 × 10⁻⁶, 2.5 × 10⁻⁵, 1.3 × 10⁻⁴, 2.6 × 10⁻⁴, 4.0 × 10⁻¹¹, and 1.3 × 10⁻⁷, respectively; Figure S4G–L), suggesting that these genes may be associated with a low degree of immune invasion and tumor progression (Figure S5). In addition, PPI network analysis was performed to identify hub regulatory factors. As HNRNPC was associated with the most nodes, it was identified as a hub gene, which also showed association with the MHC module simultaneously (Figure 3I). The PPI network with the immunoinhibitory and immunostimulatory modules also confirmed the immune correlation of m6A regulatory factors (Figure S6). HNRNPC was also significantly upregulated in IMS2 (Figure 2C); therefore, we noted it as the representative classifying IMS in further studies.
为了研究 m6A 修饰与免疫微环境之间的相互作用，我们分析了 11 个差异表达基因（DEMGs）的免疫相关性。相关性分析确定了六个免疫相关基因（IRGs），HNRNPC、HNRNPA2B1、YTHDF1、YTHDF2、METTL3 和 RBM15B，其表达水平与免疫评分显著负相关（R = −0.19、−0.18、−0.15、−0.13、−0.28 和−0.23，分别；p = 2.4 × 10⁻⁵、5.3 × 10⁻⁵、7.2 × 10⁻⁴、4.0 × 10⁻³、1.8 × 10⁻¹⁰和 1.6 × 10⁻⁷，分别；图3H和图S4B–F）并与肿瘤纯度显著正相关（R = 0.20、0.19、0.17、0.16、0.29 和 0.23，分别；p = 4.8 × 10⁻⁶、2.5 × 10⁻⁵、1.3 × 10⁻⁴、2.6 × 10⁻⁴、4.0 × 10⁻¹¹和 1.3 × 10⁻⁷，分别；图S4G–L），这表明这些基因可能与低程度的免疫侵袭和肿瘤进展相关（图S5）。 此外，进行了 PPI 网络分析以识别中心调控因子。由于HNRNPC与最多节点相关，因此被确定为中心基因，同时也显示出与 MHC 模块的关联（图3I）。具有免疫抑制和免疫刺激模块的 PPI 网络也确认了 m6A 调控因子的免疫相关性（图S6）。HNRNPC在 IMS2 中也显著上调（图2C）；因此，我们在进一步研究中将其视为代表性分类 IMS。

3.4. HNRNPC expression associated with PCa immune microenvironment
3.4. HNRNPC 表达与前列腺癌免疫微环境相关

To investigate the effect of the hub m6A regulator HNRNPC on the tumor immune microenvironment of PCa, four GEO datasets were used for validation. Consistent results were obtained for the different datasets. In the GSE46602, GSE32571, and GSE60329 datasets, HNRNPC showed a negative correlation with immune score, but a positive correlation with tumor purity (Figure S7). In dataset GSE70768 with the largest sample size, the same result was obtained with statistical significance (R = −0.20 and 0.24, respectively; p = 5.3 × 10⁻³ and 5.9 × 10⁻⁴, respectively; Figure S7A). Additionally, the expression level of HNRNPC was negatively correlated with the infiltration score (R = −0.34; p = 8.7 × 10⁻⁷; Figure S7C). Meanwhile, we found that the expression of HNPNPC was significantly correlated with MEX3B (R = 0.40, p < 2.2 × 10⁻¹⁶; Figure 3J). Finally, the correlation between HNRNPC and the immune cells was investigated. A high abundance of natural regulatory T cells (nTreg), induced regulatory T cells (iTreg), T helper 1 cells (Th1), central memory cells, and monocytes could be identified in the HNRNPC low‐expression group (p _all < 0.05, Figure 4A). Overexpression of Treg cells is likely to be the reason for the tumor's immunosuppressive status. Meanwhile, the abundance of exhausted cells, natural killer T cells (NKT), and natural killer cells (NK) was significantly lower in the high‐expression group (p _all < 0.05, Figure 4A). To verify the relationship between HNRNPC expression and abundant of Treg, cytotoxic T cells, we performed a correlation analysis in the TCGA‐PRAD cohort, finding that the expression of HNRNPC was significantly positively correlated with Treg (iTreg and nTreg) scores, and negatively with cytotoxic T cell scores (R = 0.1, 0.31 and 0.11, respectively, p = 2.2 × 10⁻², 1.1 × 10⁻¹² and 1.1 × 10⁻², respectively; Figure 4B–D). Furthermore, HNRNPC was highly expressed in progressive PCa, such as higher pathologic T stage, higher pathologic N stage, and high Gleason score (Figure 4E–H) in the TCGA PRAD dataset. In addition, the OS of patients with high HNRNPC expression was poorer than that of patients with low expression (Figure 4I).
为了研究中心 m6A 调节因子HNRNPC对前列腺癌（PCa）肿瘤免疫微环境的影响，使用了四个 GEO 数据集进行验证。不同数据集得到了相似的结果。在GSE46602、GSE32571和GSE60329数据集中，HNRNPC与免疫评分呈负相关，但与肿瘤纯度呈正相关（图S7）。在样本量最大的GSE70768数据集中，得到了相同的结果，并具有统计学意义（R = −0.20 和 0.24；p = 5.3 × 10⁻³和 5.9 × 10⁻⁴；图S7A）。此外，HNRNPC的表达水平与浸润评分呈负相关（R = −0.34；p = 8.7 × 10⁻⁷；图S7C）。同时，我们发现 HNPNPC 的表达与 MEX3B 显著相关（R = 0.40，p < 2.2 × 10⁻¹⁶；图3J）。 最后，研究了HNRNPC与免疫细胞之间的相关性。在HNRNPC低表达组中，可以识别出高丰度的自然调节性 T 细胞（nTreg）、诱导性调节性 T 细胞（iTreg）、T 辅助细胞 1（Th1）、中心记忆细胞和单核细胞（p_all < 0.05，图4A）。Treg 细胞的过表达可能是肿瘤免疫抑制状态的原因。同时，高表达组中耗竭细胞、自然杀伤 T 细胞（NKT）和自然杀伤细胞（NK）的丰度显著降低（p_all < 0.05，图4A）。为了验证HNRNPC表达与 Treg、细胞毒性 T 细胞丰度之间的关系，我们在 TCGA-PRAD 队列中进行了相关性分析，发现HNRNPC的表达与 Treg（iTreg 和 nTreg）评分显著正相关，而与细胞毒性 T 细胞评分显著负相关（R = 0.1，0.31 和 0.11，分别为p = 2.2 × 10⁻²，1.1 × 10⁻¹²和 1。1 × 10⁻²，分别；图4B–D）。此外，HNRNPC 在进展性前列腺癌中高度表达，例如更高的病理 T 分期、更高的病理 N 分期和高 Gleason 评分（图4E–H）在 TCGA PRAD 数据集中。此外，高HNRNPC表达的患者的生存期比低表达患者差（图4I）。

3.5. Treg proportion in HNRNPC‐positive cells was significantly higher than in negative cells on the single‐cell level
3.5. HNRNPC 阳性细胞中的 Treg 比例在单细胞水平上显著高于阴性细胞。

To accurately map T cell status on the single‐cell level, we described the single‐cell transcriptome atlas of PCa. First, after data preprocessing, we obtained a total of 36,424 cells (Figure 5A,B and Figure S8A). We annotated nine cell types: luminal cells (AR), T cells (KRT3E), B cells (MS4A1), mast cells (KIT), monocytic cells (CD14), endothelial (CD34), fibroblast (ACAT2), basal (KRT19), and other epithelial cells (Figure S8B). Furthermore, we analyzed the correlation between the proportion of each cell type and HNRNPC‐positive cell rate, the result showed that HNRNPC‐positive cell rate was significantly positively correlated with epithelium cells, and negatively with T cells (Figure 5E). Through subanalyses on T cells, we got nine clusters including CD4_NaiveLike, CD8_EarlyActive, CD8_EffectorMemory, CD8_NaiveLike, CD8_Tex, CD8_Tpex, Tfh, Th1, and Treg. According to the expression of HNRNPC, we separated the cells into HNRNPC‐positive and ‐negative cells. The proportion of Treg cells in positive cells was much higher than that in HNRNPC‐negative cells. Furthermore, the number of naïve CD8 T cells and active CD8 T cells was lower in HNRNPC‐positive cells, which indicates that cells with high expression of HNRNPC were in a state of immunosuppression (Figure 5D–F).
为了准确映射单细胞水平的 T 细胞状态，我们描述了前列腺癌的单细胞转录组图谱。首先，在数据预处理后，我们获得了总共 36,424 个细胞（图 5A,B 和图 S8A）。我们注释了九种细胞类型：腔道细胞（AR）、T 细胞（KRT3E）、B 细胞（MS4A1）、肥大细胞（KIT）、单核细胞（CD14）、内皮细胞（CD34）、成纤维细胞（ACAT2）、基底细胞（KRT19）和其他上皮细胞（图 S8B）。此外，我们分析了每种细胞类型的比例与 HNRNPC 阳性细胞率之间的相关性，结果显示 HNRNPC 阳性细胞率与上皮细胞显著正相关，而与 T 细胞显著负相关（图 5E）。通过对 T 细胞的亚分析，我们得到了九个簇，包括 CD4_NaiveLike、CD8_EarlyActive、CD8_EffectorMemory、CD8_NaiveLike、CD8_Tex、CD8_Tpex、Tfh、Th1 和 Treg。根据 HNRNPC 的表达，我们将细胞分为 HNRNPC 阳性和阴性细胞。阳性细胞中 Treg 细胞的比例远高于 HNRNPC 阴性细胞中的比例。 此外，HNRNPC阳性细胞中天真 CD8 T 细胞和活跃 CD8 T 细胞的数量较低，这表明高表达HNRNPC的细胞处于免疫抑制状态（图5D–F）。

Furthermore, we explored specific cell‐cell communication in positive samples. According to the cutoff value of the positive rate of 70%, we divided 13 samples into two groups: positive group (five samples) and negative group (eight samples) (Figure S9A). The differentially enriched interaction between cell types is shown in the heatmap, which indicated that the cell‐cell interaction in the positive group was stronger than that in the negative group, especially between T cells and other cells (Figure S9B). Then, different signal flow analyses implied that a large number of signal flows were activated in the positive group, including SPP1, TGF‐β, which were associated with immunosuppression (Figure S9C). Finally, we discovered ligand‐receptor interaction specifically in the HNRNPC‐positive group. The epithelial cells in the HNRNPC‐positive group interacted with other types of cells through such ligand‐receptor pairs: MIF‐(CD74⁺CXCR4) and MIF‐(CD74⁺CD44) with T cells; VEGFR‐PDGFA (a tumor angiogenesis related interaction), and NAMPT‐(ITGA5⁺ITGB1) (an immunosuppression‐associated interaction) with endothelial cells, and JAG1‐NOTCH3 and PDGFA‐PDGFRB (two pairs associated with pericytes and formation of tumor‐associated fibroblasts) with fibroblast (Figure S9D).
此外，我们探讨了阳性样本中特定的细胞间通信。根据 70%的阳性率截止值，我们将 13 个样本分为两组：阳性组（五个样本）和阴性组（八个样本）（图 S9A）。细胞类型之间的差异富集相互作用在热图中显示，阳性组的细胞间相互作用强于阴性组，尤其是在 T 细胞与其他细胞之间（图 S9B）。然后，不同的信号流分析暗示阳性组中激活了大量信号流，包括与免疫抑制相关的 SPP1 和 TGF-β（图 S9C）。最后，我们在 HNRNPC-阳性组中特别发现了配体-受体相互作用。 在HNRNPC阳性组中，上皮细胞通过以下配体-受体对与其他类型的细胞相互作用：MIF-(CD74⁺CXCR4)和 MIF-(CD74⁺CD44)与 T 细胞；VEGFR-PDGFA（与肿瘤血管生成相关的相互作用），以及 NAMPT-(ITGA5⁺ITGB1)（与免疫抑制相关的相互作用）与内皮细胞，以及 JAG1-NOTCH3 和 PDGFA-PDGFRB（与周细胞和肿瘤相关成纤维细胞形成相关的两个配对）与成纤维细胞（图S9D）。

3.6. Knockdown of HNRNPC inhibited the proliferation and migration ability of PCa cell in vitro
3.6. HNRNPC 的敲除抑制了前列腺癌细胞在体外的增殖和迁移能力

We performed PCR analysis to confirm the expression levels of HNRNPC in PCa cell lines. Compared with the normal prostate epithelial cell line RWPE‐1, four PCa cell lines (C4‐2B, Lncap, DU145, and PC3) showed elevated HNRNPC expression (Figure 6A). To evaluate the oncogenic effect of HNRNPC in vitro, DU145 and PC3 cells with the highest HNRNPC levels were stably transfected with an shRNA targeting HNRNPC, whereas cells transfected with a scrambled vector were used as a negative control. Using shRNAs, the expression of HNRNPC was successfully knocked down in two PCa cell lines (DU145 and PC3) (Figure 6B, Figure S10A). Among the shRNAs, shRNA‐1 and shRNA‐2 were used for functional assays with the highest knockdown efficiency. We investigated the effect of HNRNPC on PCa cell proliferation. CCK‐8 assay demonstrated that knockdown of HNRNPC significantly decreased the proliferative ability of DU145 and PC3 cells (Figure 6C, Figure S10B). These results were further confirmed by EdU incorporation assays in both DU145 and PC3 cells (Figure 6D, Figure S10C). These results indicated that HNRNPC is required for PCa cell proliferation in vitro. Furthermore, we evaluated the effect of HNRNPC knockdown on apoptosis of PCa cells using TUNEL staining and flow cytometry. Our results showed that the apoptosis level of PCa cells was significantly decreased by HNRNPC downregulation (Figure 6 E‐F, Figure S10D,E). Transwell and wound‐healing assays were performed to detect the effects of HNRNPC on the migration and invasion of PCa cells. The results showed that HNRNPC could also promote malignancy in PCa cells, as evidenced by the decreased migration and invasion ability in DU145 and PC3 cells after knockdown of HNRNPC (Figure 6G, Figure S10F,G).
我们进行了 PCR 分析，以确认在 PCa 细胞系中HNRNPC的表达水平。与正常前列腺上皮细胞系 RWPE-1 相比，四个 PCa 细胞系（C4-2B、Lncap、DU145 和 PC3）显示出HNRNPC表达升高（图6A）。为了评估HNRNPC在体外的致癌效应，DU145 和 PC3 细胞（具有最高的HNRNPC水平）被稳定转染了针对HNRNPC的 shRNA，而转染了随机载体的细胞则作为阴性对照。使用 shRNA，成功在两个 PCa 细胞系（DU145 和 PC3）中敲低了HNRNPC的表达（图6B，图S10A）。在 shRNA 中，shRNA-1 和 shRNA-2 被用于功能实验，具有最高的敲低效率。我们研究了HNRNPC对 PCa 细胞增殖的影响。CCK-8 实验表明，敲低HNRNPC显著降低了 DU145 和 PC3 细胞的增殖能力（图6C，图S10B）。 这些结果通过在 DU145 和 PC3 细胞中进行 EdU 掺入实验进一步确认（图6D，图S10C）。这些结果表明HNRNPC在体外是前列腺癌细胞增殖所必需的。此外，我们使用 TUNEL 染色和流式细胞术评估了HNRNPC敲低对前列腺癌细胞凋亡的影响。我们的结果显示，前列腺癌细胞的凋亡水平因HNRNPC下调而显著降低（图6 E‐F，图S10D,E）。我们进行了 Transwell 和伤口愈合实验，以检测HNRNPC对前列腺癌细胞迁移和侵袭的影响。结果显示，HNRNPC也可以促进前列腺癌细胞的恶性程度，敲低HNRNPC后，DU145 和 PC3 细胞的迁移和侵袭能力均降低（图6G，图S10F,G）。

3.7. HNRNPC inhibits tumor immunity by elevating the activation of Treg cells and suppression of CD8 cells
3.7. HNRNPC 通过提高 Treg 细胞的活化和抑制 CD8 细胞来抑制肿瘤免疫

The above immune correlation analysis suggested that HNRNPC could mediate the activation of Treg cells. To determine whether HNRNPC‐mediated PCa cells induced activation of Tregs, we cocultured transfected PCa cells with PBMC T cells (Figure 7A). The expression of the Treg markers (CD25⁺CD127⁻) was significantly decreased in PBMC cells incubated with HNRNPC‐knockdown PCa cells compared with that in the NC group (Figure 7B,C), indicating the activation of Treg cells in HNRNPC low‐expression tumors. Furthermore, the number of effector CD8 T cells (CD8⁺CD107a⁺IFN‐γ⁺) was significantly increased in PBMC cells incubated with HNRNPC‐knockdown PCa cells compared with that in the NC group (Figure 7D,E), representing the suppression of CD8 cells.
上述免疫相关分析表明，HNRNPC可能介导 Treg 细胞的激活。为了确定HNRNPC‐介导的 PCa 细胞是否诱导 Tregs 的激活，我们将转染的 PCa 细胞与 PBMC T 细胞共同培养（图7A）。与 NC 组相比，PBMC 细胞中与HNRNPC敲低的 PCa 细胞共同培养后，Treg 标记物（CD25⁺CD127⁻）的表达显著降低（图7B,C），这表明在HNRNPC低表达肿瘤中 Treg 细胞的激活。此外，与 NC 组相比，PBMC 细胞中与HNRNPC敲低的 PCa 细胞共同培养后，效应 CD8 T 细胞（CD8⁺CD107a⁺IFN‐γ⁺）的数量显著增加（图7D,E），代表 CD8 细胞的抑制。