The collection and analysis of data

We collected gene expression data from a total of 492 prostate cancer (PRAD) patients and 52 normal prostate tissues, and their corresponding clinical information from the TCGA database (https://portal.gdc.cancer.gov/). Additionally, single-cell RNA sequencing (scRNA-seq) data (accession ID: GSE181294) were obtained from the GEO database (https://www.ncbi.nlm.nih.gov/geo/), including samples from 18 prostate tumors, 14 adjacent normal tissues, and 5 healthy prostate tissues. Cells containing fewer than 600 unique molecular identifiers (UMIs) were excluded as low-quality. To remove doublets, Scrublet was applied, and cells with doublet scores exceeding 0.4 were filtered out.

Single-cell data were further processed using Seurat v4. Principal component analysis (PCA) was conducted on individual cell samples, identifying the top 20 principal components (PCs). These components were then chosen for further study. The UMAP algorithm was employed to comprehensively analyze dimensionality reduction on the initial 20 PCs.

Identification of differentially expressed genes (DEGs)

Differential expression analysis comparing tumor and normal prostate tissues was performed using the “limma” R package [8]. Differentially expressed genes (DEGs) were identified with criteria of adj. P < 0.05 and |Log2FC| > 1. The results were visualized using volcano plots and the overlap between DEGs was displayed via Venn diagrams.

Screening key biomarkers

We focused on epitheial cells 2, distinguishing tumor, adjacent, and healthy tissue samples. Through differential gene analysis, we identified PRAD cancer cell-related genes with the standard: adj. P < 0.05 and |Log2FC| > 1. Next, we used the TCGA-PRAD data to perform differential analysis between cancer and adjacent tissues, and screened out differentially expressed genes using adj. P < 0.05 and |Log2FC| > 1 as thresholds. We intersected the differentially expressed genes with the same trend, and these genes were considered to be important genes associated with the occurrence of PRAD. Subsequently, LASSO regression (“glmnet” package) and multivariate Cox regression (“survival” package) were employed to identify prognostic biomarkers.

Survival analysis

Survival analysis was performed using the TCGA gene expression and clinical data on the GEPIA2 [9] web server (http://gepia2.cancer-pku.cn/).

Functional enrichment analyses

Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Hallmark pathway enrichment analyses were conducted using the R package clusterProfiler. Pathway enrichment results with p < 0.05 were considered significant, and Gene Set Enrichment Analysis (GSEA) was used to evaluate specific gene sets under different conditions.

Cell–cell communication analysis

Cellular communication was analyzed using the CellChat R toolkit [10], where ligand-receptor interactions were inferred from gene expression data to assess cell-cell communication.

Cell lines

293T (HEK 293T), 22RV1, RM-1, PC3 and C4-2 were obtained from ATCC. 293T and RM-1 were cultured in DMEM/high glucose medium (C11995500BT, Thermo Fisher Scientific) containing 10% FBS (A5669701, Thermo Fisher Scientific) plus 1% penicillin/streptomycin (E607011, Sangon Biotech (Shanghai) Co. Ltd.). 22RV1, PC3 and C4-2 were cultured in RPMI-1640 medium (C11875500BT, Thermo Fisher Scientific) containing 10% FBS plus 1% penicillin/streptomycin. All cells were maintained at 37°C incubator with 5% CO₂. Cells were used for experiments within 10–20 passages from thawing. All cells were authenticated via short tandem repeat testing. All cells were routinely tested for ensuring mycoplasma free.

Antibodies and reagents

Mouse anti TDP2 (sc-515179), mouse anti ERK 1/2 (sc-514302), mouse anti JNK (sc-7345), mouse anti JNK Phospho-Thr18/Tyr185 (sc-6254) were from Santa Cruz. Rabbit anti Erk1/2 Phospho-Thr202/Tyr204 (4370), rabbit anti p38 (8690), rabbit anti p38 Phospho-Thr180/Tyr182 (4511), rabbit anti CD8α (98941),rabbit anti CD3ε (78588), rabbit anti Granzyme B (44153) were from Cell Signaling Technology. Rabbit anti GAPDH (81640–5-RR) was from Proteintech. PE-CF594 rat anti-CD11b (562399), PE rat anti-CD86 (561963), Alexa Fluor 647 rat anti-CD206 (565250), BV421 rat anti- I-A/I-E (562564), APC-R700 rat anti-CD103 (565529), PE-Cy7 rat anti-CD8a (552877), BV786 hamster rat anti-CD69 (564683) and Purified rat anti-mouse CD16/CD32 (553141) were from BD Pharmingen. CD8⁺ T cell isolation kit (130-096-495) was from Miltenyibiotec. Red blood cell lysis buffer (abs9101) and LPS (abs42020800) was from ABsin. Mouse M-CSF Recombinant Protein (KPRT0174), Mouse GM-CSF Recombinant Protein (KPRT0153), Mouse IL-4 Recombinant Protein (KPRT0162) were from Genie.

Overexpression and depletion of genes in cell lines

Stable overexpression of TDP2 was accomplished in 22RV1, RM-1, PC3 and C4-2 cells Full-length expression cDNA of TDP2 (human and mouse) plasmids were purchased from Sino Biological Inc. TDP2(h) shRNA plasmid and TDP2(m) shRNA plasmid were purchased from Santa Cruz Biotechnology, Inc. The plasmids were co-transfected with psPAX2 and pMD2.G into 293T cell using Lipofectamine 2000. The supernatant containing packaged lentiviral particles was collected, centrifuged, mixed with polybrene, and added into 22RV1, RM-1, PC3 or C4-2 cells respectively. After 48 h of infection, the cells were selected in a medium containing 1 μg/ml puromycin for 14 days to generate the stable cell lines.

Westernblot analysis

Cells were lysed in RIPA buffer with protease inhibitors (Beyotime Biotechnology) containing proteinase inhibitor cocktail (Beyotime Biotechnology). as described before [11]. The protein concentration in the supernatant was determined using Bio-Rad protein assay reagent. Protein samples of cell lysates were separated by 10–12% SDS‒PAGE, and then transferred onto nitrocellulose membrane (Millipore) following standard procedures. Membranes were blocked with 5% BSA in TBST for 1 hour and subsequently incubated with indicated primary antibodies overnight at 4°C. After three washes with TBST, membranes were incubated with HRP-conjugated secondary antibodies for 1 hour at room temperature. The membranes were visualized using a ChemiScopeTouch imaging system. The intensity of proteins expression was quantified using Image J.

Cell proliferation assay

Cells from different experimental groups were plated in fresh 6-well plates at a density of 2 × 10⁵ cells per well. The cell numbers were subsequently quantified on days 2, 4, and 6 post-seeding to compare growth rates.

Colony formation assay

Approximately 1 × 10³ cells per group were seeded on 12-well plates in triplicate and incubated for 10–14 days with regular medium changes every three days to support colony growth. After the incubation period, colonies were fixed in absolute methanol for 30 min, and stained with 0.1% crystal violet for another 15 min. Subsequently, the plates were washed and examined for colony visualization.

Transwell migration assay

Migration capacity of prostate cancer (PC) cells was evaluated via the transwell assays with transwell compartments. Following transfection, 3 × 10⁴ 22RV1 cells or RM-1 cells were seeded in the upper chamber of transwell units (NEST) with 8 μm pore size polycarbonate filters under serum-free conditions. And the lower chamber was filled with 600 μL RPMI-1640 or DMEM/high glucose medium containing 10% FBS. After incubation for 24 hours under 37°C, the migrated cells were fixed with 4% paraformaldehyde and stained with 0.1% crystal violet solution. Imaging was done with an inverted microscope (Olympus) with a 20 × objective. Images of five fields per insert were captured. The migrated cells stained with crystal violet were quantified and compared.

Wound-healing assay

After transfection, the cells were cultured in complete medium in 6-well plates until reaching approximately 100% confluence. Subsequently, A vertical scratch was created using a 1000 μL pipette tip, followed by washing the wells with phosphate-buffered saline and replacing the medium with new serum-free medium. Images were captured at 0 h and 24 h post-scratching with an inverted microscope (Olympus) with a 5 × objective. Wound closure percentage was calculated using the formula: % wound closure = ((0 h wound area – 24 h wound area) × 100)/0 h wound area.

Transplant tumor formation assay

C57BL/6J mice (Male 4 weeks old) and Nude mice (Male 4 weeks old) were obtained from Hangzhou Medical College. RM-1 or RM-1 TDP2 KD cells (5 × 10⁵ cells suspended in 50 μl PBS) were injected into the right flank of 6-week-old male C57BL/6J mice or Nude mice to establish the subcutaneous implantation model. Tumor size was monitored by a caliper, and tumor volume was calculated using the formula: length × width² × 0.5. The ethical guidelines limited the tumor size to 2000 mm³ per mouse, and this threshold was strictly adhered to throughout the study. Cell implantation was performed under isoflurane anesthesia to minimize discomfort.

At the end of the experiment, mice were euthanized by gradual-fill CO₂ asphyxiation in accordance with institutional animal welfare protocols. Mice were placed in the euthanasia chamber before CO₂ administration. CO₂ was then introduced at a rate of approximately 30% of the chamber volume per minute. After confirming the cessation of respiration and heartbeat, CO₂ was turned off, and mice were observed for an additional two minutes to ensure death. Carcasses were subsequently removed for downstream experimental procedures. Upon sacrifice, the tumors were excised, weighed, and preserved in 10% formaldehyde for further analysis.

Animal welfare and humane endpoints

All animal experiments were approved by Animal Experimental Ethical Inspection of Hangzhou Special Service Sanatorium Center, ID of the approval: 20240121003. Animals were housed under specific pathogen-free (SPF) conditions with a 12 h light/dark cycle and ad libitum access to food and water.

Humane endpoints were defined and monitored throughout the experiment. Mice were observed at least twice daily for general health and tumor progression. Criteria for euthanasia included: (1) tumor volume exceeding 2000 mm³, (2) weight loss greater than 20% of baseline, (3) impaired mobility or inability to access food or water, (4) ulcerated or necrotic tumors, or (5) signs of severe distress such as hunched posture, labored breathing, or persistent lethargy. Once mice met endpoint criteria, euthanasia was performed within 6 hours using gradual-fill CO₂ asphyxiation followed by cervical dislocation, in accordance with AVMA guidelines.

In total, 8 mice were used in the transplantation experiments. The experiment lasted 17 days post-implantation. All animals were euthanized when tumors reached the size limit or when humane endpoint criteria were met. No animals were found dead before reaching the criteria. All procedures, including tumor implantation, monitoring, and euthanasia, were carried out by staff trained in animal handling and welfare to ensure the highest standards of care.

Immunohistochemistry (IHC)

Formalin-fixed paraffin-embedded mouse tumor tissue sections were analyzed by IHC for CD3,CD8 and Granzyme B.Following the manufacturer’s instructions, the slides with 4 μm-thick tissues,were baked for 1 hour at 68 °C, then deparaffinized. Antigen retrieval was performed using sodium citrate buffer (Solarbio). Samples were blocked in 3% BSA for 30 min at room temperature, incubated overnight with a primary antibody at 4°C. This was followed by incubate to a HRP-conjugated secondary antibody at room temperature for 40 min. Target proteins were visualized using a diaminobenzidine (DAB) Substrate Kit (Solarbio). Slides were counterstained with diluted hematoxylin for 3 min. Representative images of each tumor were captured using upright microscope (Olympus) with a 20 × objective. The immunohistochemical results were further quantified by Image J software.

In vitro co-culture assay

In brief, the co-culture system consists of RM-1(WT or TDP2 OE), mice BMDMs, mice BMDCs, or mice spleen-derived T lymphocytes. RM-1 cells from different experimental groups were seeded into 6-well plates at a density of 1 × 10⁵ per well and incubated 12 hours. To analyze the macrophage polarization, the isolated BMDMs were co-cultured with RM-1 WT cells or RM-1 TDP2 OE cells at the ratio of 1:1. After 48 hours incubation, the cells in the plate were collected and analyzed by flow cytometry. The BMDMs treated with LPS (0.5 μg/mL) for 48 hours were set as a positive control. The cells were incubated with CD11b, CD86, MHC II, and CD206 antibodies and the CD11b antibody was used to gate the BMDM group. To analyze the DC maturation, the isolated BMDCs were co-cultured with RM-1 WT cells or RM-1 TDP2 OE cells at the ratio of 1:1 for 48 hours. Then, the cells in the plate were analyzed by flow cytometry. The cells were incubated with CD11b and CD86 antibodies and the CD11b antibody was used to gate the BMDC group. The BMDCs treated with LPS (0.5 μg/mL) for 48 hours were set as positive reference. To analyze the CD8⁺ T cell maturation, a total of 2 × 10⁵ T lymphocytes were incubated with cancer cells for 48 hours with the existence of BMDMs and BMDCs. Then the cells were collected and incubated with CD8, CD103 and CD69 antibody to analyze the maturation.

The prostate TME characterized by single-cell transcriptomic analysis

Single-cell RNA-seq data from 18 human prostate tumors and 14 matched adjacent normal tissues, alongside 5 healthy prostate tissues, were processed. After quality control, 156,261 cells were retained for analysis, with an average of 4,223 cells per sample. Using PCA and UMAP, 16 distinct cell clusters were identified, with epithelial cells showing diverse expression patterns (Fig 1A and 1E). According to the cell source, we show cells from healthy samples, tumor samples, and adjacent samples (Fig 1A). Interestingly, epithelial cells were divided into two groups with different expressions. PRAD is a tumor type of epithelial carcinoma, so tumor cells exist in these two groups of epithelial cells. In addition, it is obvious that epithelial cells2 are significantly less in healthy samples. Fig 1B stacked graph more intuitively shows the proportion of different cell types in the three groups. We found that the proportion of macrophages was significantly different in tumor tissue and adjacent tissue (Fig 1C), but there was no significant difference between the healthy sample group and adjacent group. At the same time, the proportion of epithelial cells2 in healthy samples was significantly less than that in adjacent samples and tumor samples. We speculate that this may be a group of epithelial cells that are most directly affected by tumorigenesis, so it is also one of our subsequent focuses.

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Fig 1. Identification of key prognostic biomarkers in prostate cancer through scRNA-seq (GSE181294) and TCGA-PRAD Analysis.

A The cell type annotation of 156,261 cells using uniform manifold approximation and projection (UMAP) plots and split into individuals with 3 tissue origins. Colors indicate different cell types. B A stacked plot presenting the proportion of each cell type in the 4 tissue origins. Colors represent different cell types. Box plots showing the cell ratio of macrophages (C) and epithelial cells 2 (D) in the three groups. P values were determined by the student’s t-test, *P < 0.05, ns Non-significant. E Bubble diagram of marker genes in each cell type. F The volcano plots of TCGA-PRAD DEGs. G Results of LASSO regression analysis. H Results of multivariate Cox regression analysis.

https://doi.org/10.1371/journal.pone.0339607.g001

Key biomarker screening

To identify differentially expressed genes (DEGs) in PRAD samples relative to normal prostate tissues, we used the “limma” R package to identify genes with significant differences in expression with the criteria of adj. P < 0.05 and |Log2FC| > 1. We then used a volcano plot to visually display the DEGs (Fig 1F). To find the most critical genes in the development of the disease, we combined single-cell data for analysis. We focused on epithelial cells 2 and distinguished cells from tumor samples and cells from adjacent or healthy samples by source. Through differential gene analysis, we identified PRAD cancer cell-related genes with the criteria of adj. P < 0.05 and |Log2FC| > 1. We overlapped these DEGs with those with the same trend obtained from TCGA-PRAD, and these genes were considered to be important genes associated with the development of PRAD.

Combined with the prognostic data of TCGA-PRAD, we used Lasso regression (Fig 1G) and multivariate Cox regression to establish a prognostic prediction model (Fig 1G) to screen key biomarkers for prognosis. The importance of CNFN and TDP2 can be noted in both models. When the effects of these two genes on prognosis were examined separately, the Kaplan-Meier survival curve suggested that TDP2 had a stronger prognostic function (Fig 2A and 2B). Surprisingly, the feature plot in Fig 2C shows that TDP2 is specifically expressed in the epithelial cells 2 cluster, and not all epithelial cells 2 express TDP2. Therefore, the biological function of epithelial cells 2 with high expression of TDP2 deserves further exploration.

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Fig 2. Study on the mechanism of poor prognosis of tumor cells with high expression of TDP2.

The Kaplan-Meier survival curve of the CNFC (A) and TDP2 (B) in TCGA-PRAD. C UMAP visualization showing the expression of TDP2. D GO pathway enrichment analysis. E KEGG pathway enrichment analysis. F Hallmark pathway enrichment analysis. G, H, I Pathways enriched in TDP2 high cells in ssgsea analysis.

https://doi.org/10.1371/journal.pone.0339607.g002

Functional enrichment analysis of epithelial cells 2 with high expression of TDP2

We divided these cells into high TDP2 expression group and low TDP2 expression group based on the median TDP2 expression in epithelial cells 2. GO pathway enrichment analysis (Fig 2D) showed that the upregulated pathways associated with TDP2 high expression cells mainly involved immune response, cell proliferation and cell differentiation, which are closely related to the growth and metastasis of prostate cancer. KEGG pathway enrichment analysis (Fig 2E) further confirmed that TDP2 high expression cells were enriched in PPAR signaling pathway and fatty acid metabolism, suggesting that lipid metabolism plays an important role in the energy metabolism and survival of prostate cancer cells.

Hallmark pathway enrichment analysis (Fig 2F) revealed that classical signaling pathways associated with TDP2 high expression cells, such as TNFα signaling, KRAS signaling and hypoxia response, all play a driving role in the malignant transformation of the tumor microenvironment, which may promote tumor invasiveness and drug resistance.

The ssGSEA analysis (Fig 2G–2I) showed that TDP2-high-expressing cells were enriched in important biological processes such as negative regulation of inflammatory response (Fig 2G), extracellular matrix remodeling (Fig 2H), and macrophage differentiation (Fig 2I), emphasizing the close relationship between TDP2-high expression and immunosuppression, myeloid immune response, and mesenchymalization of the tumor microenvironment.

Overall, this part of the results reveals the different roles of prostate cancer epithelial cells with high and low TDP2 expression in tumor occurrence and development, especially their differences in immunosuppression, epithelial-mesenchymal transition (EMT), and myeloid immune response.

TDP2 promote tumor progression by affecting the phenotype of PC

To reveal the important effect of TDP2 in PC cell lines, we established cell lines with either overexpression (OE) or knockdown (KD) of TDP2 in human prostate cancer cell lines (22RV1, PC3, C4-2) and a mouse prostate cancer cell line (RM-1). After transfection, the TDP2 expression levels of the cells were measured by Western blot analysis (Figs 4A, 4C, S1A, S1B and S9). Thus, the results indicated that the TDP2 overexpression (OE) or knockdown (KD) prostate cancer cell line had been successfully established.

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Fig 3. TDP2 regulates proliferation and migration of prostate cancer cells.

Biological functions of TDP2 expression in 22RV1 and RM-1 cells. Cell growth curves (A, B) and colony formation assays (C, D) were performed to assess the proliferation of TDP2 KD or TDP2 OE transduced 22RV1 and RM-1 cells. Transwell assays in 22RV1 (E) and RM-1 (F) cells with or without TDP2 KD or OE was performed to assess the migration capacity. WT, wild type; TDP2 KD, TDP2 knockdown; TDP2 OE, TDP2 overexpression. Data were presented as means  ±  standard deviations. Error bars designate SD acquired from four independent experiments. P values were determined by the one-way ANOVA, *P  <  0.05, ns Non-significant.

https://doi.org/10.1371/journal.pone.0339607.g003

We further investigated the impact of TDP2 on the growth of prostate cancer through cell growth experiments. We observed that transfection with shRNA-TDP2 significantly suppressed the proliferation of the prostate cancer cell lines compared to that of the cells in the blank control group, and transfection with pLV3-TDP2 plasmids inconspicuously promoted the proliferation of the prostate cancer cell lines (Figs 3A, 3B, S1C and S1D). Consistently,the colony formation assay showed that knockdown or overexpression of TDP2 affect the total number of colonies generated of prostate cancer cells(Figs 3C, 3D and S1E–S1J).

TDP2 affect the adhesion ability of prostate cancer cells

To further investigate the impact of TDP2 on the biological functions of prostate cancer. We hypothesized that it might affect the adhesion ability of cells, thereby influencing the abilities of prostate cancer cells to migrate. Wound-healing and Transwell tests were used to assess the effects of TDP2 on prostate cancer cell migration. We observed that knocking down TDP2 significantly inhibited the migration ability of prostate cancer cells, while overexpression of TDP2 significantly enhanced the migration ability (Figs 3E, 3F, S2A and S2B).

TDP2 exerts biological functions through the MAPK pathway

Further explore the regulatory mechanisms of TDP2. Mitogen activating protein kinases (MAPKs) families, including c-Jun N terminal kinases (JNK), extracellular signal-regulated kinase (ERK), and MAPK p38 highly expressed in prostate cancer resulting in proliferation and invasion [12]. In the current study, JNK, p38, and ERK1/2 protein phosphorylation in prostate cancer cells 22RV1 and RM-1 were examined by western blotting. We observed that knockdown of TDP2 in 22RV1 and RM-1 significantly inhibited the phosphorylation level of p38, while overexpression of TDP2 sensibly promoted the overexpression of phosphorylated p38(Fig 4A–4D). But knockdown or overexpression of TDP2 has no effect on the phosphorylation of JNK and ERK1/2(Figs 4A–4D and S5–S9). The phosphorylation of p38 was inhibited by inhibiting p38 (S3A, S3B, S10 and S11 Figs). Some literature reported p38α regulates the activation of cyclooxygenase 2 (COX2), which has a proinflammatory activity that has significant effects on cancer progression, such as nonmelanoma skin cancer, breast cancer [13,14]. It indicates the potential key role of TDP2 in PC immunosuppressive system.

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Fig 4. TDP2 exerts biological functions through the MAPK pathway.

Western blot data demonstrating changes in protein concentration in 22RV1 cells transfected with shRNA-TDP2 or TDP2 (A). Quantification of protein concentration in 22RV1 cells (B). Western blot data demonstrating changes in protein concentration in RM-1 cells transfected with shRNA-TDP2 or TDP2 (C). Quantification of protein concentration in RM-1 cells (D). Error bars designate SD acquired from three independent experiments. P values were determined by the unpaired Student’s t-test, ns Non-significant, *P < 0.05, **P < 0.01, ***P < 0.001.

https://doi.org/10.1371/journal.pone.0339607.g004

TDP2 promotes PC tumorigenesis in immunocompetent mice

We hypothesize that targeting TDP2 may have a role in reshaping the tumor immune microenvironment. In animal models, we further explored the impact of TDP2 on anti-tumor immunity in prostate cancer. TDP2 WT and TDP2 KD RM-1 cells were subcutaneously injected into immunocompetent C57BL/6J mice and immunodeficient nude mice, with tumor growth progress monitored regularly (Fig 5A and 5D). The results showed that TDP2 KD significantly inhibited tumor growth in mice, especially in immunocompetent C57BL/6J mice (Fig 5B, 5C, 5E and 5F). Immunohistochemical analysis revealed that TDP2 KD remarkably increased CD3 + T cell, CD8 + T cell, GZMB+ cell infiltration (Figs 5G, 5H, S3A and S3B). These findings suggest that the tumor-promoting effect of TDP2 is immune-dependent, implying that TDP2 may play a crucial role in shaping the immunosuppressive microenvironment in renal cancer.

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Fig 5. TDP2 promotes tumor growth of RM-1 cell in vivo.

WT nude mice (n = 4) were Subcutaneously inoculated with WT RM-1 cells or TDP2 KD RM-1 cells (A). Quantification of tumor growth (B) and tumor weight (C). WT C57BL/6J mice (n = 4) were Subcutaneously inoculated with WT RM-1 cells or TDP2 KD RM-1 cells (D). Quantification of tumor growth (E) and tumor weight (F). Pathology studies show CD8 expression in the mice in different groups (G). CD8 positivity analyses in the RM-1 model (n = 4) (H). The bars represent 100 μm. Data were presented as means ± standard deviations. Error bars designate SD acquired from four independent experiments. P values were determined by the unpaired Student’s t-test, ns Non-significant, **P < 0.01, ***P < 0.001.

https://doi.org/10.1371/journal.pone.0339607.g005

Cellular crosstalk in epithelial cells that highly express TDP2

Previous results have demonstrated the effects of TDP2-overexpressing epithelial cells 2 on the tumor microenvironment. These effects are likely to function through crosstalk between TDP2-overexpressing epithelial cells and other immune cells. Cell-cell communication with CellChat revealed that TDP2 high-expressing epithelial cells had significantly increased interactions with a variety of immune cells, including myeloid cells, monocytes, macrophages, and T cells, indicating that TDP2 high expression plays an important role in regulating immune responses in the tumor microenvironment (Fig 6A and 6B). This is highly consistent with the results obtained in our pathway enrichment analysis. This further confirms that epithelial cells with high expression of TDP2 2 may lead to the formation of an immunosuppressive microenvironment and epithelial-mesenchymal transition through interaction with myeloid cells and fibroblasts, thereby leading to poor tumor prognosis.

In addition, we found that TDP2 high-expressing epithelial cells 2 may exert their effects through COLLAGEN, GALECTIN, MK, and OSM signaling pathways in tumor progression. COLLAGEN, as a key component of the extracellular matrix, promotes the transition from epithelial to mesenchymal cells and enhances cell migration and invasiveness in the tumor microenvironment [15]. The interaction between TDP2 high-expressing epithelial cells 2 and fibroblasts may further increase the synthesis and degradation of collagen, exacerbating the invasiveness of the tumor (Fig 6C and 6E). GALECTINS, by modulating cell adhesion, migration, and immune evasion, suppress immune cell activity and promote tumor cell survival and metastasis [16]. Activation of the MK signaling pathway promotes tumor cell proliferation and migration, particularly in the interaction between TDP2 high-expressing epithelial cells and myeloid cells and fibroblasts, driving remodeling of the tumor microenvironment and invasiveness (Fig 6D, 6F, 6G and 6I). OSM, as a cytokine, regulates immune evasion and cell migration, enhancing tumor invasiveness and possibly promoting EMT [17], thereby facilitating metastasis (Fig 6H and 6J).

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Fig 6. Cell communications analysis results.

A Circular plot of the number of cellular interactions between each cell type. B Circular plot of the number of cellular interactions between myeloid cells, fibroblasts, and other cell types. C, D, G, H Chord diagram showing all the significant interactions (L–R pairs) between different cell types. Heatmaps of COLLAGEN (E), GALECTIN (F), MK (I) and OSM (J) signals contributing mostly to outgoing or incoming signaling of certain cell groups.

https://doi.org/10.1371/journal.pone.0339607.g006

The expression of TDP2 in prostate cancer cell inhibits immune cells activation in vitro

In this part, we performed a series of experiments to investigate whether TDP2 could inhibit antitumor immunity. DC and macrophages are both important innate immune cells [18]. Typically, macrophages can be classified as M1 or M2 macrophages [19]. The M1 subtype is the strong killer of cancer cells, while the M2 subtype is beneficial to sustaining immune suppressiveness [20,21].

Firstly, we examined whether the overexpression of TDP2 in RM-1 cell could inhibit the M1-polarization of macrophages and the maturation of DCs. BMDMs and BMDCs were isolated from the bone marrow of mice in advance as previously described [22]. Lipopolysaccharide (LPS) can polarize macrophages and activate the immune response. Thus, treatments with LPS were conducted as a positive reference in the following experiments [23]. M1-like macrophage, M2-like macrophage, and mature DC were defined by CD86^high MHC II^high CD206^low, CD86^low MHC II^low CD206^high, and CD86^high, respectively. We observed that the overexpression of TDP2 in RM-1 cell could inhibit the M1-polarization of macrophages and the maturation of DCs (Fig 7A–7D). The impact of the expression of TDP2 in prostate cancer cell on T lymphocytes was studied subsequently. We concentrated on the CD8⁺ T cells which are recognized as the heart of adaptive immunity. We observed that the overexpression of TDP2 in RM-1 cell could inhibit the maturation of CD8⁺ T cells (Fig 7E and 7F). These results jointly revealed the expression of TDP2 in prostate cancer cell inhibits immune cells activation.

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Fig 7. In vitro immunosuppression of TDP2 overexpression.

The quantification of CD86⁺ macrophage (A), MHC II⁺ macrophage (B), and CD206⁺ macrophage (C) after co-culture in different groups. The quantification of mature DC cells after co-culture in various groups (D). The quantification of CD103⁺ CD8⁺ T cells (E) and CD69⁺ CD8⁺ T cells (F) after co-culture in different groups. Data were presented as means ± standard deviations. Error bars designate SD acquired from three independent experiments. P values were determined by the unpaired Student’s t-test, **P < 0.01, ****P < 0.0001.

https://doi.org/10.1371/journal.pone.0339607.g007