Monocyte differentiation dynamics and ligand-receptor interactions in peripheral blood of patients with prostate cancer and BPH: a comparative scRNA-seq analysis

Monocyte differentiation dynamics and ligand-receptor interactions in peripheral blood of patients with prostate cancer and BPH: a comparative scRNA-seq analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Monocyte differentiation dynamics and ligand-receptor interactions in peripheral blood of patients with prostate cancer and BPH: a comparative scRNA-seq analysis Kadriia Enikeeva, Vyacheslav Korobeynikov, Yuliya Sharifyanova, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7748159/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Monocytes critically shape immune responses in prostate diseases, yet their differentiation dynamics in prostate cancer (PCa) versus benign prostatic hyperplasia (BPH) remain unclear. We employed single-cell RNA sequencing to analyze peripheral blood mononuclear cells from four PCa patients (Gleason 3+3 to 3+4, stages T2N0M0–T3bN0M0) and three BPH patients. Unsupervised clustering identified 16 distinct cell populations, emphasizing classical (CD14++CD16−), intermediate (CD14++CD16+), and non-classical (CD14+CD16++) monocyte subsets. PCa samples showed greater monocyte heterogeneity with seven classical subsets versus six in BPH, reflecting increased malignant plasticity. Transcriptionally, PCa monocytes upregulated oncogenic MALAT1, metastasis-associated S100A8/S100A9 and VCAN, and immunosuppressive CD14/CD36, supporting angiogenesis and tumor growth. Conversely, BPH monocytes maintained protective functions through ALDH1A2, anti-proliferative ZFP36, and tissue homeostasis regulators TMEM176B, USP30-AS1, and TCOF1. Ligand–receptor analysis distinguished disease contexts: PCa monocytes formed restricted networks dominated by ANXA1–FPR1 and TGFβ1 signaling, fostering immunosuppression. BPH exhibited broader networks integrating pro-inflammatory chemokines (CCL3/CCL5–CCR1) and metabolic NAMPT–BSG pathways, consistent with controlled inflammation and repair. Our findings define distinct molecular programs of circulating monocytes in malignant versus benign prostate disease, providing potential biomarkers for differential diagnosis and immunomodulatory therapy targets. monocytes trajectories of differentiation prostate cancer benigh prostatic hyperplasia single cell sequencing Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Benign prostatic hyperplasia (BPH) and prostate cancer (PCa) are common diseases in men. Epidemiologic studies confirm that men with BPH have a 2-3 times increased risk of developing RPH [1]. Moreover, these diseases share a common polygenic background (genes PEX14, DPM3, GRHL1, MAT2A, ANO7) [2]. However, the direct causal relationship between them is disputed in some cases [3]. The microenvironment in both prostate cancer (PCa) and benign prostatic hyperplasia (BPH) is characterized by pronounced immunosuppressive properties [4], but the mechanisms of immunosuppression formation in these pathologies have fundamental differences. Monocytes, as key components of the tumor microenvironment, play a crucial role in modulating the local immune response [5]. Monocytes play a complex and dual role in antitumor defense and oncogenesis, contributing both to the suppression of tumor growth through the direct destruction of cancer cells and the activation of other immune components, and to tumor progression under certain conditions [6]. The most important aspect of their function is to replenish the pool of tumor-associated macrophages (TAMs), dendritic cells, and myeloid-derived suppressor cells (MDSCs), which is their key role in the nonspecific immune response [7]. These derivative cells actively regulate the interaction between immune-competent cells infiltrating the tumor, the tumor cells themselves, and other elements of the microenvironment. In addition, they directly influence tumor cell proliferation, angiogenesis [8] and metastatic spread, forming a pro- or anti-tumor balance depending on the context of the microenvironment and molecular signals [9]. Monocytes are a heterogeneous system consisting of three main subpopulations—classical (CD14++CD16-), non-classical (CD14+CD16++), and intermediate (CD14++CD16+), differing in the expression of surface markers, each of which can contribute to the maintenance and regulation of the antitumor immune response [10]. The functional roles of monocyte subpopulations remain poorly understood, with studies revealing significant overlap in functions [11]. However, the differential characteristics of these subpopulations are still being identified. A study by K. Wong et al. provides a detailed characterization of monocyte subpopulations: classical monocytes are characterized by high expression of genes encoding sensory receptors, including S100A12 and S100A8/9, which determines their key role in initiating and maintaining the inflammatory response. Intermediate monocytes demonstrate maximum levels of HLA-DR molecule expression, indicating their predominant involvement in antigen presentation processes. In turn, non-classical monocytes express genes that regulate cytoskeletal remodeling, ensuring their ability to patrol tissues and perform FcγR-dependent phagocytosis [12]. The limited data on the dynamics of monocyte differentiation in prostate cancer, as well as the lack of information on intercellular communication in circulating monocytes, significantly complicates the establishment of the exact mechanisms of immunopathogenesis of these conditions. An in-depth study of the subpopulation organization of monocytes and their differentiation trajectories in PCa and BPH allows us to identify specific patterns of monocyte communication and establish the spectrum of changes in the monocyte link in these diseases. This work aims to conduct a comparative analysis of intercellular interactions and differentiation trajectories of monocyte subpopulations using single-cell RNA sequencing (scRNA-seq) in samples of peripheral blood mononuclear cell (PBMC) from patients with PCa and BPH in order to identify key differences in the functional differentiation of monocyte subpopulations in these pathologies, determine their specific contribution to the immunopathogenesis of diseases, and characterize the molecular mechanisms of immunosuppressive microenvironment formation. 2. Results 2.1. Categorization of cells in the BPH and PCa groups Sequencing results revealed 16 cell clusters in both patient groups (Fig. 1): CD16 monocytes, CD14 monocytes, CD8 T cells, CD4 T cells, plasmacytoid dendritic cells, natural killer cells, CD4 T cells, double-negative T lymphocytes, cDC2 dendritic cells, naive B cells, gd T cells, invariant T cells, regulatory T cells, B memory cells, transitional B cells, platelets, as described in our previous publication [13]. We focused our attention on the monocyte cluster, as they account for about 10% of all circulating blood cells and participate in the implementation of innate immunity through phagocytosis, secretion of mediators, attraction of lymphocytes, etc [14]. As precursors of macrophages, they play an important role in regulating the immune response in the tumor microenvironment [15]. However, different subpopulations of monocytes are involved in the tumor process in different ways. In both groups, the monocyte cluster was divided into three subpopulations: classical (CD14++ CD16−), intermediate (CD14++ CD16+), and non-classical (CD14+ CD16++) (Fig. 2a,c). In the group of patients with PCa, the level of non-classical and intermediate monocytes exceeds that in the group of patients with BPH (Fig. 2b,d). The ratio of monocytes is an important indicator of antitumor immunity, as subpopulations have different effects on tumor progression and their properties may change under the influence of therapy. Classical monocytes are involved in processes such as phagocytosis, angiogenesis, extracellular matrix remodeling, and differentiation into macrophages and dendritic cells [16]. However, such macrophages often contribute to tumor progression [17]. As for non-classical monocytes, their function remains less studied, but it is known that they are capable of penetrating tissues, have anti-inflammatory potential, and can participate in the cleansing of tissues from cellular “debris,” pathogens, and even tumor cells [18]. Elevated levels of non-classical monocytes are associated with a more favorable prognosis in lung cancer and, in a mouse melanoma model, with less metastasis [19, 20]. However, with regard to prostate cancer, this topic requires further study. 2.2. Comparison of the trajectories of PCa and BPH monocyte development Analysis of the development trajectories of monocytes in patients with PCa and BPH using single-cell sequencing revealed both common structures and fundamental differences in the differentiation and expression of key genes in these clinical groups. In both groups, the differentiation of peripheral monocytes follows the classic sequence: classical → intermediate → non-classical monocytes. PCa is characterized by the formation of seven subsets of classical monocytes, one intermediate, and two types of non-classical cells, reflecting a high level of cellular heterogeneity (Fig. 3 A, B). In BPH, the number of subpopulations among classical monocytes was slightly lower (six), and among non-classical and intermediate monocytes, only one main profile was identified (Fig. 3 D, E). This may indicate an increase in the plasticity of monopoiesis and the activation of additional adaptive mechanisms in response to the malignant process. In classical monocytes in prostate cancer, the activation of a number of prognostically unfavorable genes and inflammatory signaling axes dominates (Fig. 3 C). MALAT1, an important long non-coding RNA, acts as a potent oncogene and marker of aggressive tumor progression, participates in the regulation of alternative splicing, and promotes tumor cell proliferation, and its overexpression correlates with an unfavorable prognosis [21–23]. The S100A8/S100A9 genes play a key role in the formation of a pro-inflammatory environment, support the processes of migration and invasion of tumor cells, and are recognized as new prognostic candidates [24]. CD14 and CD36 promote tumor vascularization and recruit immunosuppressive myeloid cells, which facilitates the formation of a microenvironment conducive to tumor growth [25, 26]. Metabolic reprogramming is supported by the expression of ACSL1 (an androgen-dependent enzyme) and the CSF3R receptor, which stimulates the proliferation and differentiation of myeloid cells [27, 28]. In contrast, in BPH, the expression of classical monocytes reflects the balance between regulatory and protective functions: there is increased expression of the enzyme ALDH1A2, which is responsible for the detoxification of aldehydes and the biosynthesis of retinoic acid, which is associated with the maintenance of antitumor immunity and tissue stability (Fig. 3 F) [29, 30]. The antiproliferative gene ZFP36, which inhibits the NF-κB pathway and is associated with a favorable clinical course in PC [31, 32]. TMEM176B, USP30-AS1, and TCOF1 support immune stability and metabolic homeostasis [33–35], while GAPDH and RAB11A play a role in cellular metabolism and regulation of intracellular flows [36, 37]. Non-classical monocytes in both groups are characterized by the expression of the key cell cycle suppressor CDKN1C (p57KIP2), as well as the suppressor gene MTSS1, which reduce the invasiveness and metastatic potential of tumors [38, 39]. However, in PCa, IFITM2/IFITM3, immune regulators that modulate cell adhesion and migration, are additionally activated, which contributes to the aggressiveness of the tumor process [40, 41]. For BPH, high expression of structural (ACTB) and immunoregulatory (FCER1G, COTL1) proteins, which is associated with maintaining homeostasis and actively controlling inflammatory processes [42, 43]. Intermediate monocytes in PCa retain a pronounced pro-inflammatory potential and role in antigen presentation, whereas in BPH, they predominantly express genes (RPS19, AIF1, LST1) associated with maintaining immune balance and tissue homeostasis. Taken together, the results of the analysis of monocyte development trajectories demonstrate that in PCa, the transcriptional landscape is restructured towards a pro-tumor, immunosuppressive, and metabolically active population that contributes to the maintenance of the tumor microenvironment and disease progression. In BPH, on the contrary, monocytes activate division suppressor genes, maintain inflammation control, and ensure the stability of tissue structures without signs of malignancy. These differences highlight the fundamental role of peripheral mononuclear cells in the pathogenesis of prostate diseases and open up opportunities for the identification of new biomarkers and targeted immunomodulatory strategies. 2.3. Ligand-receptor interaction in PBMCs of the study groups A comparison of ligand–receptor networks in peripheral monocytes in patients with prostate cancer (Fig. 4) and benign prostatic hyperplasia revealed clear subpopulation-specific differences in the number and type of interactions. Non-classical monocytes In prostate cancer, the main interactions are ANXA1–FPR1, LGALS9–CD44, LGALS9–TGFβR1_R2/TNFRSF10B, and TGFβ1–ACVR1B/TGFβR2. ANXA1 suppresses excessive inflammation through FPR1 desensitization and reduced monocyte adhesion [44], while LGALS9, by binding to CD44, modulates cell adhesion and survival [45]. These processes have been described in various immune cells. The TGFβ1 signal through the ACVR1B–TGFβR2 receptor complex activates Smad-dependent pathways, supporting immune homeostasis [46, 47]. In the BPH group, it is worth noting that the greatest interaction is observed between the LGALS9–PTPRC pathways, which may indicate an attempt to limit tissue damage caused by prolonged inflammation by suppressing B-cell activation. It is known that Gal-9 binding to CD45 (PTPRC) activates inhibitory signaling pathways via Lyn-CD22-SHP-1. Gal-9 acts as an inhibitor of signaling through the B-cell receptor, and its suppression may contribute to a decrease in the activation threshold of memory B cells [48]. Classical monocytes In prostate cancer patients, the classical monocyte subpopulation is also characterized by ANXA1–FPR1 and LGALS9–CD44 interactions, which are found in non-classical monocytes. GRN–SORT1, TGFβ1–(TGFβR1–TGFβR2) TGFβ1–(ACVR1B–TGFβR2) signals have also been identified. GRN–SORT1 are key regulators of inflammation, and SORT1 is one of the main modulators of progranulin suppression in blood serum [49, 50]. It has been proven that GRN–SORT1 ensures progranulin endocytosis and supports monocyte survival [51]. TNFRSF10B induces apoptosis of tumor cells [52]. The presence of functional TRAIL receptors (TRAIL-R1 and TRAIL-R2) on monocytes and macrophages makes these cells sensitive to TRAIL-mediated destruction. This differential susceptibility to TRAIL can be used to selectively target macrophages in tumors [53]. In BPH, an expanded repertoire is observed: in addition to the above axes, MIF–CD74–CXCR4 (proinflammatory activation of Akt pathways) [54], NAMPT–BSG (regulation of NAD+-dependent processes) [55], as well as additional ligand–receptor pairs TNF–TNFRSF1B and CCL3L1–CCR1. It is worth noting the interaction between CCL5 and CCR1. The interaction between CCR1 and CCL5 promotes the invasion of chemoresistant cell cultures and enhances the secretion of MMP 2 and 9 through the activation of ERK and Rac [56]. One of the most interesting interactions is that between PPIA and BSG (ERK-dependent phosphorylation of ERK1/2) [57]. Several studies have shown that increased activity of the interaction between BSG (CD147) and extracellular cyclophilin A (PPIA) may be associated with cancer progression. It has been shown that extracellular cyclophilin A stimulates the proliferation of lung cancer cell lines by inducing ERK1/2 signals [58]. Similar proliferative activity of cyclophilin A has been demonstrated in human pancreatic cancer cells, where cyclophilin A activated ERK1/2 and p38 pathways [59].The exact mechanisms of this proliferative activity and its role in cancer pathogenesis remain unclear and require further study. The presence of TNF–TNFRSF1B indicates a proinflammatory signal [60], while CCL3–CCR1 promotes chemotaxis and mobilization of monocytes from the bone marrow [61, 62]. Furthermore, there is evidence that CCL3–CCR1 signaling leads to monocyte recruitment, enhances M2 polarization of macrophages (an immunosuppressive phenotype), and thereby maintains a pro-inflammatory and proliferative environment conducive to tumor growth [63]. In PCa, intermediate monocytes are dominated by MIF–CD74–CXCR4 and MIF–CD44, proinflammatory and stress-responsive signaling axes, as confirmed by recent studies [64, 65]. Moderate LGALS9 and TGFβ1 signals likely play an important role in evading immune surveillance by cancer cells [66]. There are no direct descriptions of P4HB–SORT1 in recent publications, which highlights the relevance of further research into their relationship. However, these proteins influence tumor aggressiveness through their partially overlapping signaling cascades, mainly associated with the regulation of the immune response, proliferation, and apoptosis [67–69]. In BPH, intermediate monocytes demonstrate a broad profile: MIF–CD74–CXCR4, GRN–SORT1, TGFB1–receptor complexes, CCL3L1–CCR1, and CCL5–CCR1, indicating a combination of proinflammatory and restorative signals. In DGPZ, it is worth noting the interactions between MIF–CD74/CD44. Classical monocytes express CD74 and CD44, which allows them to actively respond to MIF (macrophage migration inhibitory factor). This interaction triggers pro-inflammatory signaling cascades (ERK/MAPK, PI3K/Akt), while blocking MIF either at the ligand (MIF) level or at the receptor level leads to a decrease in cell proliferation, MIF protein secretion, and invasion [70]. The literature notes an increase in MIF expression in prostate cancer compared to healthy prostate tissue or benign prostate epithelial cells (p < 0.01), but according to our analysis of intercellular communication, this pathway is more pronounced in the prostate hyperplasia group. A comparison of ligand-receptor networks demonstrates that in prostate cancer, monocytes are shifted toward immune suppression and homeostasis: a limited number of anti-inflammatory axes (ANXA1-FPR1, TGFβ1 receptors) suppress excessive inflammation and promote resolution. At the same time, GRN–SORT1 indicate a role in tumor cell apoptosis and microenvironment remodeling [71]. In contrast, in benign prostatic hyperplasia, monocytes form a balance between pro-inflammatory and restorative interactions. A broad repertoire of chemokines (CCL3/CCL5–CCR1), tumor necrosis factors (TNF–TNFRSF1B), and metabolic axes (NAMPT–BSG) corresponds to chronic inflammation and tissue hyperplasia, where simultaneous activation and control of the immune response is required [72–74]. Thus, prostate cancer is associated with a narrowly focused network of anti-inflammatory and remodeling signals in peripheral monocytes, whereas BPH is characterized by a plastic, multi-axial network of ligand-receptor interactions. These data open up prospects for targeted immunotherapy: in prostate cancer, blocking ANXA1–FPR1 or enhancing pro-inflammatory connections (e.g., MIF–CD74–CXCR4) may be effective, in BPH, it is advisable to modulate chemokine pathways (CCL3/CCL5–CCR1) to limit hyperplasia and chronic inflammation. 3. Conclusions This comparative single-cell RNA sequencing analysis of peripheral blood monocytes from patients with prostate cancer and benign prostatic hyperplasia revealed fundamental differences in monocyte differentiation trajectories and ligand-receptor interaction networks between these conditions. The study demonstrated that while both groups maintain the classical differentiation sequence (classical → intermediate → non-classical monocytes), prostate cancer exhibits increased cellular heterogeneity with seven classical monocyte subsets compared to six in BPH. This enhanced plasticity reflects adaptive mechanisms responding to the malignant process. Gene expression profiling revealed distinct functional orientations: prostate cancer monocytes displayed a pro-tumoral, immunosuppressive transcriptional landscape characterized by upregulation of oncogenes (MALAT1), metastasis-associated factors (S100A8/S100A9, VCAN), and immunosuppressive markers (CD14, CD36). These cells facilitate tumor progression through enhanced angiogenesis, myeloid cell recruitment, and metabolic reprogramming via ACSL1 and CSF3R pathways. In contrast, BPH monocytes maintained regulatory and protective functions through expression of detoxification enzymes (ALDH1A2), anti-proliferative genes (ZFP36), and homeostatic regulators (TMEM176B, USP30-AS1, TCOF1). This profile supports tissue stability and controlled inflammation without malignant transformation. Ligand-receptor interaction analysis further distinguished these conditions. Prostate cancer monocytes exhibited a narrow, anti-inflammatory network dominated by ANXA1–FPR1, TGFβ1 receptor complexes, and survival-promoting GRN–SORT1 interactions, creating an immunosuppressive microenvironment conducive to tumor growth. BPH monocytes demonstrated a broader, balanced network incorporating pro-inflammatory chemokines (CCL3/CCL5–CCR1), metabolic axes (NAMPT–BSG), and tissue remodeling factors, reflecting chronic inflammation with active repair mechanisms. These findings establish distinct molecular signatures for monocytes in malignant versus benign prostatic conditions, providing new insights into disease-specific immunopathogenesis. The identified differences in monocyte functionality and intercellular communication networks offer potential biomarkers for differential diagnosis and therapeutic targets for immunomodulatory interventions in prostate diseases. 4. Experimental Section 4 .1. Clinical samples The study was conducted in accordance with the principles of the Declaration of Helsinki, “Ethical Principles for Medical Research Involving Human Subjects” (as amended in 2013) and the regulatory documents “Rules of Good Clinical Practice in the Russian Federation,” approved by Order of the Ministry of Health of the Russian Federation No. 200n dated April 1, 2016. Written informed consent was obtained from all patients who voluntarily agreed to participate in the study. The study included 4 patients with prostate cancer and 3 patients with benign prostatic hyperplasia. All patients had histologically confirmed diagnoses. In the prostate cancer group, 3 patients had acinar adenocarcinoma and 1 patient had small acinar adenocarcinoma of the prostate. The patients had no acute pathologies, infectious diseases, or history of other malignant neoplasms other than prostate cancer. More detailed information is presented in Table 1. Table 1. Patient cohort details Case ID Age Histology Stage TNM Gleason Score PSA prior to surgery, ng/ml 1 68 acinar adenocarcinoma of the prostate stage 2 group 3 T2N0M0 3+4 8,64 2 71 small-acinar adenocarcinoma of the prostate stage 2 group 3 Т2с-Т3bN0M0 3+4 23,6 3 75 acinar adenocarcinoma of the prostate stage 2 group 3 Т2N0M0 3+3 5,95 F 62 acinar adenocarcinoma of the prostate stage 1 group 3 T2N0M0 3+4 4,59 B 66 BPH - - - 7,38 C 68 BPH - - - 3,98 D 59 BPH - - - 3,99 4.2. Isolation of peripheral blood mononuclear cells (PBMCs) Peripheral blood mononuclear cells were isolated by centrifuging whole blood diluted with phosphate-saline buffer (HiMedia, India) on a Ficoll gradient (Diam, Russia) with a density of 1.077 g/ml. After double washing with PBS, the total cell concentration and viability were determined using a BIO RAD TC20 automatic cell counter. The total number of live cells in the aliquot was up to 5 × 10^6 cells. Cell suspensions were frozen in 500 μl of RPMI medium (Servicebio, China), 400 μl of fetal bovine serum (#10500064, ThermoFisher Scientific, USA), and 100 μl of dimethyl sulfoxide (neoFroxx, Germany): first at −80 °C for 7 days, then transferred to liquid nitrogen for long-term storage at −196 °C (up to 6 months). To perform the experiment, cell suspensions selected from the biobank were thawed in a warm water bath. Test tubes with cells were stored at +4 °C until the preparation of libraries for single-cell sequencing. 4.3. Single-cell sequencing Libraries for single-cell sequencing (scRNA-seq) were prepared on the Chromium X platform using the 10× Genomics Chromium Next GEM Single Cell 3' Reagent Kit v3.1 (10× Genomics, USA). Quality control was performed on a TapeStation 4150 analyzer (Agilent Technologies, USA). The average number of cells in the sample was 2132 cells. Libraries were sequenced using Illumina NextSeq 2000 (Illumina, USA) with paired-end sequencing and double indexing in the following mode: 28 cycles, 10 cycles, 10 cycles, and 90 cycles for Read 1, i7 index, i5 index, and Read 2. 4.4. Single-cell RNA sequencing data analysis Counting matrices were obtained using Cell Ranger (version 7.1.0, 10x Genomics). Raw count data were imported into R (version 4.3.3) and analyzed using Seurat R (version 5.1.0). The effect of duplicate cells was taken into account using the scDblFinder program (version 1.16.0). Low-quality cells in each sample were identified and excluded from analysis based on thresholds for gene count and UMI, which were determined visually using VlnPlot. Cells with high mitochondrial content (> 10%) were also excluded. After quality control, all samples were combined into an integrated Seurat object. Gene expression counts were normalized using the Seurat SCTransform function. SCTransform also identified highly variable genes, which were used to obtain principal components (PC). Data integration of different samples was performed using the Harmony package. After integration, dimensionality reduction and cell clustering were performed using the RunUMAP, FindNeighbors (30 best PCA vectors), and FindClusters (resolution 0.3) functions. The resulting cell clusters were annotated using the Azimuth program (version 0.5.0). Cell interactions with receptors were profiled using ligand-receptor interaction analysis in CellChat (version 2.1.2). Trajectory inference was calculated using the MST method implemented in dyno (version 0.1.2). Declarations Author Contributions Acknowledgements Conceptualization the idea for the study, K. E.; developed the software, V. K.; methodology, Yu. Sh., performed the validation, D. G., P. Sh.; performed the formal analysis, E. A.; performed the investigation, D. G., P. Sh. and E. A.; performed the data curation, K. E., V. K.; writing—original draft preparation, L. K. D. G., P. Sh., K. E. and E. A.; writing—review and editing, K. E., Yu. Sh.; performed the visualization, V. K., K. E. and Yu. Sh., performed the supervision V. P.; performed the project administration, K. E., V. P., All authors have read and agreed to the published version of the manuscript. Funding The study was funded by BSMU Strategic Academic Leadership Program PRIORITY-2030 Conflict of Interest The authors declare no conflict of interest. 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1","display":"","copyAsset":false,"role":"figure","size":390565,"visible":true,"origin":"","legend":"\u003cp\u003eUMAP representations illustrating the normalized expression profiles of immune cell population–specific marker genes\u003cstrong\u003e \u003c/strong\u003ein patients with PCa and BPH \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003egroups [13].\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7748159/v1/65af01c514dc15ecef5dd5a2.jpg"},{"id":93543106,"identity":"047c629a-e4bc-482d-87be-bcebb7978b8f","added_by":"auto","created_at":"2025-10-15 02:41:45","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":135185,"visible":true,"origin":"","legend":"\u003cp\u003eMonocyte profile in the studied groups\u003cem\u003e\u003cstrong\u003e. \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e(A)\u003c/strong\u003e UMAP visualization of monocyte clustering in patients with PCa. Each subpopulation is labeled with the colors of the corresponding UMAP clusters\u003cstrong\u003e. (B)\u003c/strong\u003e Quantitative ratio of monocyte subpopulations in patients with PCa. \u003cstrong\u003e(C)\u003c/strong\u003e UMAP visualization of monocyte clustering in patients with BPH. Each subpopulation is marked with the colors of the corresponding UMAP clusters\u003cstrong\u003e. (D)\u003c/strong\u003e Quantitative ratio of monocyte subpopulations in patients with BPH.\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7748159/v1/4a7d0a3a42322fb8e798052a.jpg"},{"id":93543108,"identity":"63c7fcac-b1f3-44b3-bfb4-5ef27cc1e90b","added_by":"auto","created_at":"2025-10-15 02:41:50","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":572991,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of monocytes obtained from PBMCs of patients in the study groups\u003cstrong\u003e. (A), (B)\u003c/strong\u003eTrajectories of monocyte development in patients with PCa\u003cem\u003e\u003cstrong\u003e. \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e(\u003c/strong\u003eD\u003cstrong\u003e), (\u003c/strong\u003eE\u003cstrong\u003e)\u003c/strong\u003e Trajectories of monocyte development in patients with BPH. Clustering of monocyte subsets; \u003cstrong\u003e(\u003c/strong\u003eC\u003cstrong\u003e)\u003c/strong\u003e Heat map of differentially expressed genes (DEGs, logFC \u0026gt; 0.05) in clusters of classical, intermediate, and non-classical monocytes in the PC group\u003cstrong\u003e. (F)\u003c/strong\u003e Heat map of differentially expressed genes (DEGs, logFC \u0026gt; 0.05) in clusters of classical, intermediate, and non-classical monocytes in the BPH group.\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7748159/v1/9bfd75118ea59035bde8585b.jpg"},{"id":93543127,"identity":"6e11ed71-5b3d-4df7-92c7-fb17c96d5580","added_by":"auto","created_at":"2025-10-15 02:41:53","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":87585,"visible":true,"origin":"","legend":"\u003cp\u003eChord diagram showing interactions (L-R pairs) in classical, non-classical and intermediate monocytes in PCa and BPH.\u003c/p\u003e","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7748159/v1/615f31b10914b892499e8278.jpg"},{"id":94244602,"identity":"41e1fafd-bab6-428a-892a-5cc19796c2da","added_by":"auto","created_at":"2025-10-24 05:03:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1674768,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7748159/v1/12b026c8-42f1-4f33-8a5c-00ce7ebd8575.pdf"}],"financialInterests":"","formattedTitle":"Monocyte differentiation dynamics and ligand-receptor interactions in peripheral blood of patients with prostate cancer and BPH: a comparative scRNA-seq analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBenign prostatic hyperplasia (BPH) and prostate cancer (PCa) are common diseases in men. Epidemiologic studies confirm that men with BPH have a 2-3 times increased risk of developing RPH [1]. Moreover, these diseases share a common polygenic background (genes PEX14, DPM3, GRHL1, MAT2A, ANO7) [2]. However, the direct causal relationship between them is disputed in some cases [3]. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe microenvironment in both prostate cancer (PCa) and benign prostatic hyperplasia (BPH) is characterized by pronounced immunosuppressive properties [4], but the mechanisms of immunosuppression formation in these pathologies have fundamental differences. Monocytes, as key components of the tumor microenvironment, play a crucial role in modulating the local immune response [5].\u003c/p\u003e\n\u003cp\u003eMonocytes play a complex and dual role in antitumor defense and oncogenesis, contributing both to the suppression of tumor growth through the direct destruction of cancer cells and the activation of other immune components, and to tumor progression under certain conditions\u0026nbsp;[6]. The most important aspect of their function is to replenish the pool of tumor-associated macrophages (TAMs), dendritic cells, and myeloid-derived suppressor cells (MDSCs), which is their key role in the nonspecific immune response\u0026nbsp;[7]. These derivative cells actively regulate the interaction between immune-competent cells infiltrating the tumor, the tumor cells themselves, and other elements of the microenvironment. In addition, they directly influence tumor cell proliferation, angiogenesis\u0026nbsp;[8]\u0026nbsp;and metastatic spread, forming a pro- or anti-tumor balance depending on the context of the microenvironment and molecular signals\u0026nbsp;[9].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMonocytes are a heterogeneous system consisting of three main subpopulations—classical (CD14++CD16-), non-classical (CD14+CD16++), and intermediate (CD14++CD16+), differing in the expression of surface markers, each of which can contribute to the maintenance and regulation of the antitumor immune response\u0026nbsp;[10]. The functional roles of monocyte subpopulations remain poorly understood, with studies revealing significant overlap in functions\u0026nbsp;[11]. However, the differential characteristics of these subpopulations are still being identified. A study by K. Wong et al. provides a detailed characterization of monocyte subpopulations: classical monocytes are characterized by high expression of genes encoding sensory receptors, including S100A12 and S100A8/9, which determines their key role in initiating and maintaining the inflammatory response. Intermediate monocytes demonstrate maximum levels of HLA-DR molecule expression, indicating their predominant involvement in antigen presentation processes. In turn, non-classical monocytes express genes that regulate cytoskeletal remodeling, ensuring their ability to patrol tissues and perform FcγR-dependent phagocytosis\u0026nbsp;[12].\u003c/p\u003e\n\u003cp\u003eThe limited data on the dynamics of monocyte differentiation in prostate cancer, as well as the lack of information on intercellular communication in circulating monocytes, significantly complicates the establishment of the exact mechanisms of immunopathogenesis of these conditions. An in-depth study of the subpopulation organization of monocytes and their differentiation trajectories in PCa and BPH allows us to identify specific patterns of monocyte communication and establish the spectrum of changes in the monocyte link in these diseases.\u003c/p\u003e\n\u003cp\u003eThis work aims to conduct a comparative analysis of intercellular interactions and differentiation trajectories of monocyte subpopulations using single-cell RNA sequencing (scRNA-seq) in samples of peripheral blood mononuclear cell (PBMC) from patients with PCa and BPH in order to identify key differences in the functional differentiation of monocyte subpopulations in these pathologies, determine their specific contribution to the immunopathogenesis of diseases, and characterize the molecular mechanisms of immunosuppressive microenvironment formation.\u003c/p\u003e"},{"header":"2. Results ","content":"\u003cp\u003e\u003cem\u003e2.1. Categorization of cells in the BPH and PCa groups\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSequencing results revealed 16 cell clusters in both patient groups (Fig. 1): CD16 monocytes, CD14 monocytes, CD8 T cells, CD4 T cells, plasmacytoid dendritic cells, natural killer cells, CD4 T cells, double-negative T lymphocytes, cDC2 dendritic cells, naive B cells, gd T cells, invariant T cells, regulatory T cells, B memory cells, transitional B cells, platelets, as described in our previous publication\u0026nbsp;[13].\u003c/p\u003e\n\u003cp\u003eWe focused our attention on the monocyte cluster, as they account for about 10% of all circulating blood cells and participate in the implementation of innate immunity through phagocytosis, secretion of mediators, attraction of lymphocytes, etc [14]. \u0026nbsp;As precursors of macrophages, they play an important role in regulating the immune response in the tumor microenvironment [15]. However, different subpopulations of monocytes are involved in the tumor process in different ways. In both groups, the monocyte cluster was divided into three subpopulations: classical (CD14++ CD16\u0026minus;), intermediate (CD14++ CD16+), and non-classical (CD14+ CD16++) (Fig. 2a,c). In the group of patients with PCa, the level of non-classical and intermediate monocytes exceeds that in the group of patients with BPH (Fig. 2b,d). The ratio of monocytes is an important indicator of antitumor immunity, as subpopulations have different effects on tumor progression and their properties may change under the influence of therapy. Classical monocytes are involved in processes such as phagocytosis, angiogenesis, extracellular matrix remodeling, and differentiation into macrophages and dendritic cells [16]. \u0026nbsp;However, such macrophages often contribute to tumor progression [17]. \u0026nbsp;As for non-classical monocytes, their function remains less studied, but it is known that they are capable of penetrating tissues, have anti-inflammatory potential, and can participate in the cleansing of tissues from cellular \u0026ldquo;debris,\u0026rdquo; pathogens, and even tumor cells [18]. Elevated levels of non-classical monocytes are associated with a more favorable prognosis in lung cancer and, in a mouse melanoma model, with less metastasis [19, 20]. However, with regard to prostate cancer, this topic requires further study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.2. Comparison of the trajectories of PCa and BPH monocyte development\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAnalysis of the development trajectories of monocytes in patients with PCa and BPH using single-cell sequencing revealed both common structures and fundamental differences in the differentiation and expression of key genes in these clinical groups.\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn both groups, the differentiation of peripheral monocytes follows the classic sequence: classical \u0026rarr; intermediate \u0026rarr; non-classical monocytes. PCa is characterized by the formation of seven subsets of classical monocytes, one intermediate, and two types of non-classical cells, reflecting a high level of cellular heterogeneity (Fig. 3 A, B). In BPH, the number of subpopulations among classical monocytes was slightly lower (six), and among non-classical and intermediate monocytes, only one main profile was identified (Fig. 3 D, E). This may indicate an increase in the plasticity of monopoiesis and the activation of additional adaptive mechanisms in response to the malignant process.\u003c/p\u003e\n\u003cp\u003eIn classical monocytes in prostate cancer, the activation of a number of prognostically unfavorable genes and inflammatory signaling axes dominates\u0026nbsp;(Fig. 3 C). MALAT1, an important long non-coding RNA, acts as a potent oncogene and marker of aggressive tumor progression, participates in the regulation of alternative splicing, and promotes tumor cell proliferation, and its overexpression correlates with an unfavorable prognosis [21\u0026ndash;23]. The S100A8/S100A9 genes play a key role in the formation of a pro-inflammatory environment, support the processes of migration and invasion of tumor cells, and are recognized as new prognostic candidates [24]. CD14 and CD36 promote tumor vascularization and recruit immunosuppressive myeloid cells, which facilitates the formation of a microenvironment conducive to tumor growth [25, 26]. Metabolic reprogramming is supported by the expression of ACSL1 (an androgen-dependent enzyme) and the CSF3R receptor, which stimulates the proliferation and differentiation of myeloid cells [27, 28].\u003c/p\u003e\n\u003cp\u003eIn contrast, in BPH, the expression of classical monocytes reflects the balance between regulatory and protective functions: there is increased expression of the enzyme ALDH1A2, which is responsible for the detoxification of aldehydes and the biosynthesis of retinoic acid, which is associated with the maintenance of antitumor immunity and tissue stability\u0026nbsp;(Fig. 3 F)\u0026nbsp; [29, 30]. The antiproliferative gene ZFP36, which inhibits the NF-\u0026kappa;B pathway and is associated with a favorable clinical course in PC [31, 32]. TMEM176B, USP30-AS1, and TCOF1 support immune stability and metabolic homeostasis [33\u0026ndash;35], while GAPDH and RAB11A play a role in cellular metabolism and regulation of intracellular flows [36, 37].\u003c/p\u003e\n\u003cp\u003eNon-classical monocytes in both groups are characterized by the expression of the key cell cycle suppressor CDKN1C (p57KIP2), as well as the suppressor gene MTSS1, which reduce the invasiveness and metastatic potential of tumors [38, 39]. However, in PCa, IFITM2/IFITM3, immune regulators that modulate cell adhesion and migration, are additionally activated, which contributes to the aggressiveness of the tumor process [40, 41]. For BPH, high expression of structural (ACTB) and immunoregulatory (FCER1G, COTL1) proteins, which is associated with maintaining homeostasis and actively controlling inflammatory processes [42, 43].\u003c/p\u003e\n\u003cp\u003eIntermediate monocytes in PCa retain a pronounced pro-inflammatory potential and role in antigen presentation, whereas in BPH, they predominantly express genes (RPS19, AIF1, LST1) associated with maintaining immune balance and tissue homeostasis.\u003c/p\u003e\n\u003cp\u003eTaken together, the results of the analysis of monocyte development trajectories demonstrate that in PCa, the transcriptional landscape is restructured towards a pro-tumor, immunosuppressive, and metabolically active population that contributes to the maintenance of the tumor microenvironment and disease progression. In BPH, on the contrary, monocytes activate division suppressor genes, maintain inflammation control, and ensure the stability of tissue structures without signs of malignancy. These differences highlight the fundamental role of peripheral mononuclear cells in the pathogenesis of prostate diseases and open up opportunities for the identification of new biomarkers and targeted immunomodulatory strategies.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.3. Ligand-receptor interaction in PBMCs of the study groups\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA comparison of ligand\u0026ndash;receptor networks in peripheral monocytes in patients with prostate cancer (Fig. 4) and benign prostatic hyperplasia \u0026nbsp;revealed clear subpopulation-specific differences in the number and type of interactions.\u003c/p\u003e\n\u003cp\u003eNon-classical monocytes\u003c/p\u003e\n\u003cp\u003eIn prostate cancer, the main interactions are ANXA1\u0026ndash;FPR1, LGALS9\u0026ndash;CD44, LGALS9\u0026ndash;TGF\u0026beta;R1_R2/TNFRSF10B, and TGF\u0026beta;1\u0026ndash;ACVR1B/TGF\u0026beta;R2. ANXA1 suppresses excessive inflammation through FPR1 desensitization and reduced monocyte adhesion\u0026nbsp;[44], while LGALS9, by binding to CD44, modulates cell adhesion and survival\u0026nbsp;[45]. These processes have been described in various immune cells. The TGF\u0026beta;1 signal through the ACVR1B\u0026ndash;TGF\u0026beta;R2 receptor complex activates Smad-dependent pathways, supporting immune homeostasis\u0026nbsp;[46, 47].\u003c/p\u003e\n\u003cp\u003eIn the BPH group, it is worth noting that the greatest interaction is observed between the LGALS9\u0026ndash;PTPRC pathways, which may indicate an attempt to limit tissue damage caused by prolonged inflammation by suppressing B-cell activation. It is known that Gal-9 binding to CD45 (PTPRC) activates inhibitory signaling pathways via Lyn-CD22-SHP-1. Gal-9 acts as an inhibitor of signaling through the B-cell receptor, and its suppression may contribute to a decrease in the activation threshold of memory B cells\u0026nbsp;[48].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eClassical monocytes\u003c/p\u003e\n\u003cp\u003eIn prostate cancer patients, the classical monocyte subpopulation is also characterized by ANXA1\u0026ndash;FPR1 and LGALS9\u0026ndash;CD44 interactions, which are found in non-classical monocytes. GRN\u0026ndash;SORT1, TGF\u0026beta;1\u0026ndash;(TGF\u0026beta;R1\u0026ndash;TGF\u0026beta;R2) TGF\u0026beta;1\u0026ndash;(ACVR1B\u0026ndash;TGF\u0026beta;R2) signals have also been identified. GRN\u0026ndash;SORT1 are key regulators of inflammation, and SORT1 is one of the main modulators of progranulin suppression in blood serum\u0026nbsp;[49, 50]. It has been proven that GRN\u0026ndash;SORT1 ensures progranulin endocytosis and supports monocyte survival\u0026nbsp;[51]. TNFRSF10B induces apoptosis of tumor cells\u0026nbsp;[52]. The presence of functional TRAIL receptors (TRAIL-R1 and TRAIL-R2) on monocytes and macrophages makes these cells sensitive to TRAIL-mediated destruction. This differential susceptibility to TRAIL can be used to selectively target macrophages in tumors\u0026nbsp;[53]. In BPH, an expanded repertoire is observed: in addition to the above axes, MIF\u0026ndash;CD74\u0026ndash;CXCR4 (proinflammatory activation of Akt pathways)\u0026nbsp;[54], NAMPT\u0026ndash;BSG (regulation of NAD+-dependent processes)\u0026nbsp;[55], as well as additional ligand\u0026ndash;receptor pairs TNF\u0026ndash;TNFRSF1B and CCL3L1\u0026ndash;CCR1. It is worth noting the interaction between CCL5 and CCR1. The interaction between CCR1 and CCL5 promotes the invasion of chemoresistant cell cultures and enhances the secretion of MMP 2 and 9 through the activation of ERK and Rac\u0026nbsp;[56].\u003c/p\u003e\n\u003cp\u003eOne of the most interesting interactions is that between PPIA and BSG (ERK-dependent phosphorylation of ERK1/2)\u0026nbsp;[57]. Several studies have shown that increased activity of the interaction between BSG (CD147) and extracellular cyclophilin A (PPIA) may be associated with cancer progression. It has been shown that extracellular cyclophilin A stimulates the proliferation of lung cancer cell lines by inducing ERK1/2 signals\u0026nbsp;[58].\u003c/p\u003e\n\u003cp\u003eSimilar proliferative activity of cyclophilin A has been demonstrated in human pancreatic cancer cells, where cyclophilin A activated ERK1/2 and p38 pathways\u0026nbsp;[59].The exact mechanisms of this proliferative activity and its role in cancer pathogenesis remain unclear and require further study.\u003c/p\u003e\n\u003cp\u003eThe presence of TNF\u0026ndash;TNFRSF1B indicates a proinflammatory signal\u0026nbsp;[60], while CCL3\u0026ndash;CCR1 promotes chemotaxis and mobilization of monocytes from the bone marrow\u0026nbsp;[61, 62]. Furthermore, there is evidence that CCL3\u0026ndash;CCR1 signaling leads to monocyte recruitment, enhances M2 polarization of macrophages (an immunosuppressive phenotype), and thereby maintains a pro-inflammatory and proliferative environment conducive to tumor growth\u0026nbsp;[63].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn PCa, intermediate monocytes are dominated by MIF\u0026ndash;CD74\u0026ndash;CXCR4 and MIF\u0026ndash;CD44, proinflammatory and stress-responsive signaling axes, as confirmed by recent studies\u0026nbsp;[64, 65]. Moderate LGALS9 and TGF\u0026beta;1 signals likely play an important role in evading immune surveillance by cancer cells\u0026nbsp;[66]. There are no direct descriptions of P4HB\u0026ndash;SORT1 in recent publications, which highlights the relevance of further research into their relationship. However, these proteins influence tumor aggressiveness through their partially overlapping signaling cascades, mainly associated with the regulation of the immune response, proliferation, and apoptosis\u0026nbsp;[67\u0026ndash;69].\u0026nbsp;\u003cbr\u003eIn BPH, intermediate monocytes demonstrate a broad profile: MIF\u0026ndash;CD74\u0026ndash;CXCR4, GRN\u0026ndash;SORT1, TGFB1\u0026ndash;receptor complexes, CCL3L1\u0026ndash;CCR1, and CCL5\u0026ndash;CCR1, indicating a combination of proinflammatory and restorative signals. \u0026nbsp;In DGPZ, it is worth noting the interactions between MIF\u0026ndash;CD74/CD44. Classical monocytes express CD74 and CD44, which allows them to actively respond to MIF (macrophage migration inhibitory factor). This interaction triggers pro-inflammatory signaling cascades (ERK/MAPK, PI3K/Akt), while blocking MIF either at the ligand (MIF) level or at the receptor level leads to a decrease in cell proliferation, MIF protein secretion, and invasion\u0026nbsp;[70]. The literature notes an increase in MIF expression in prostate cancer compared to healthy prostate tissue or benign prostate epithelial cells (p \u0026lt; 0.01), but according to our analysis of intercellular communication, this pathway is more pronounced in the prostate hyperplasia group.\u003c/p\u003e\n\u003cp\u003eA comparison of ligand-receptor networks demonstrates that in prostate cancer, monocytes are shifted toward immune suppression and homeostasis: a limited number of anti-inflammatory axes (ANXA1-FPR1, TGF\u0026beta;1 receptors) suppress excessive inflammation and promote resolution. At the same time, GRN\u0026ndash;SORT1 indicate a role in tumor cell apoptosis and microenvironment remodeling\u0026nbsp;[71].\u003c/p\u003e\n\u003cp\u003eIn contrast, in benign prostatic hyperplasia, monocytes form a balance between pro-inflammatory and restorative interactions. A broad repertoire of chemokines (CCL3/CCL5\u0026ndash;CCR1), tumor necrosis factors (TNF\u0026ndash;TNFRSF1B), and metabolic axes (NAMPT\u0026ndash;BSG) corresponds to chronic inflammation and tissue hyperplasia, where simultaneous activation and control of the immune response is required\u0026nbsp;[72\u0026ndash;74].\u003c/p\u003e\n\u003cp\u003eThus, prostate cancer is associated with a narrowly focused network of anti-inflammatory and remodeling signals in peripheral monocytes, whereas BPH is characterized by a plastic, multi-axial network of ligand-receptor interactions. These data open up prospects for targeted immunotherapy: in prostate cancer, blocking ANXA1\u0026ndash;FPR1 or enhancing pro-inflammatory connections (e.g., MIF\u0026ndash;CD74\u0026ndash;CXCR4) may be effective, in BPH, it is advisable to modulate chemokine pathways (CCL3/CCL5\u0026ndash;CCR1) to limit hyperplasia and chronic inflammation.\u003c/p\u003e"},{"header":"3.\tConclusions","content":"\u003cp\u003eThis comparative single-cell RNA sequencing analysis of peripheral blood monocytes from patients with prostate cancer and benign prostatic hyperplasia revealed fundamental differences in monocyte differentiation trajectories and ligand-receptor interaction networks between these conditions.\u003c/p\u003e\n\u003cp\u003eThe study demonstrated that while both groups maintain the classical differentiation sequence (classical \u0026rarr; intermediate \u0026rarr; non-classical monocytes), prostate cancer exhibits increased cellular heterogeneity with seven classical monocyte subsets compared to six in BPH. This enhanced plasticity reflects adaptive mechanisms responding to the malignant process.\u003c/p\u003e\n\u003cp\u003eGene expression profiling revealed distinct functional orientations: prostate cancer monocytes displayed a pro-tumoral, immunosuppressive transcriptional landscape characterized by upregulation of oncogenes (MALAT1), metastasis-associated factors (S100A8/S100A9, VCAN), and immunosuppressive markers (CD14, CD36). These cells facilitate tumor progression through enhanced angiogenesis, myeloid cell recruitment, and metabolic reprogramming via ACSL1 and CSF3R pathways.\u003c/p\u003e\n\u003cp\u003eIn contrast, BPH monocytes maintained regulatory and protective functions through expression of detoxification enzymes (ALDH1A2), anti-proliferative genes (ZFP36), and homeostatic regulators (TMEM176B, USP30-AS1, TCOF1). This profile supports tissue stability and controlled inflammation without malignant transformation.\u003c/p\u003e\n\u003cp\u003eLigand-receptor interaction analysis further distinguished these conditions. Prostate cancer monocytes exhibited a narrow, anti-inflammatory network dominated by ANXA1\u0026ndash;FPR1, TGF\u0026beta;1 receptor complexes, and survival-promoting GRN\u0026ndash;SORT1 interactions, creating an immunosuppressive microenvironment conducive to tumor growth. BPH monocytes demonstrated a broader, balanced network incorporating pro-inflammatory chemokines (CCL3/CCL5\u0026ndash;CCR1), metabolic axes (NAMPT\u0026ndash;BSG), and tissue remodeling factors, reflecting chronic inflammation with active repair mechanisms.\u003c/p\u003e\n\u003cp\u003eThese findings establish distinct molecular signatures for monocytes in malignant versus benign prostatic conditions, providing new insights into disease-specific immunopathogenesis. The identified differences in monocyte functionality and intercellular communication networks offer potential biomarkers for differential diagnosis and therapeutic targets for immunomodulatory interventions in prostate diseases.\u003c/p\u003e"},{"header":"4.\tExperimental Section","content":"\u003cp\u003e\u003cem\u003e4\u003c/em\u003e\u003cem\u003e.1.\u003c/em\u003e\u003cem\u003eClinical samples\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the principles of the Declaration of Helsinki, \u0026ldquo;Ethical Principles for Medical Research Involving Human Subjects\u0026rdquo; (as amended in 2013) and the regulatory documents \u0026ldquo;Rules of Good Clinical Practice in the Russian Federation,\u0026rdquo; approved by Order of the Ministry of Health of the Russian Federation No. 200n dated April 1, 2016. Written informed consent was obtained from all patients who voluntarily agreed to participate in the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study included 4 patients with prostate cancer and 3 patients with benign prostatic hyperplasia. All patients had histologically confirmed diagnoses. In the prostate cancer group, 3 patients had acinar adenocarcinoma and 1 patient had small acinar adenocarcinoma of the prostate.\u003c/p\u003e\n\u003cp\u003eThe patients had no acute pathologies, infectious diseases, or history of other malignant neoplasms other than prostate cancer. More detailed information is presented in Table 1.\u003c/p\u003e\n\u003cp\u003eTable 1. Patient cohort details\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eCase ID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eHistology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003eStage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eTNM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;Gleason Score\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003ePSA prior to surgery, \u003cem\u003eng/ml\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eacinar adenocarcinoma of the prostate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003estage 2 group 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eT2N0M0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e3+4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e8,64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003esmall-acinar adenocarcinoma of the prostate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003estage 2 group 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eТ2с-Т3bN0M0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e3+4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e23,6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eacinar adenocarcinoma of the prostate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003estage 2 group 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eТ2N0M0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e3+3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e5,95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eF\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eacinar adenocarcinoma of the prostate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003estage 1 group 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eT2N0M0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e3+4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e4,59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eBPH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e7,38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eBPH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e3,98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eBPH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 65px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e3,99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e4.2. Isolation of peripheral blood mononuclear cells (PBMCs)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePeripheral blood mononuclear cells were isolated by centrifuging whole blood diluted with phosphate-saline buffer (HiMedia, India) on a Ficoll gradient (Diam, Russia) with a density of 1.077 g/ml. After double washing with PBS, the total cell concentration and viability were determined using a BIO RAD TC20 automatic cell counter. The total number of live cells in the aliquot was up to 5 \u0026times; 10^6 cells. Cell suspensions were frozen in 500 \u0026mu;l of RPMI medium (Servicebio, China), 400 \u0026mu;l of fetal bovine serum (#10500064, ThermoFisher Scientific, USA), and 100 \u0026mu;l of dimethyl sulfoxide (neoFroxx, Germany): first at \u0026minus;80 \u0026deg;C for 7 days, then transferred to liquid nitrogen for long-term storage at \u0026minus;196 \u0026deg;C (up to 6 months). To perform the experiment, cell suspensions selected from the biobank were thawed in a warm water bath. Test tubes with cells were stored at +4 \u0026deg;C until the preparation of libraries for single-cell sequencing.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e4.3. Single-cell sequencing\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eLibraries for single-cell sequencing (scRNA-seq) were prepared on the Chromium X platform using the 10\u0026times; Genomics Chromium Next GEM Single Cell 3\u0026apos; Reagent Kit v3.1 (10\u0026times; Genomics, USA). Quality control was performed on a TapeStation 4150 analyzer (Agilent Technologies, USA). The average number of cells in the sample was 2132 cells. Libraries were sequenced using Illumina NextSeq 2000 (Illumina, USA) with paired-end sequencing and double indexing in the following mode: 28 cycles, 10 cycles, 10 cycles, and 90 cycles for Read 1, i7 index, i5 index, and Read 2.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e4.4. Single-cell RNA sequencing data analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCounting matrices were obtained using Cell Ranger (version 7.1.0, 10x Genomics). Raw count data were imported into R (version 4.3.3) and analyzed using Seurat R (version 5.1.0). The effect of duplicate cells was taken into account using the scDblFinder program (version 1.16.0). Low-quality cells in each sample were identified and excluded from analysis based on thresholds for gene count and UMI, which were determined visually using VlnPlot. Cells with high mitochondrial content (\u0026gt; 10%) were also excluded. After quality control, all samples were combined into an integrated Seurat object. Gene expression counts were normalized using the Seurat SCTransform function. SCTransform also identified highly variable genes, which were used to obtain principal components (PC). Data integration of different samples was performed using the Harmony package. After integration, dimensionality reduction and cell clustering were performed using the RunUMAP, FindNeighbors (30 best PCA vectors), and FindClusters (resolution 0.3) functions. The resulting cell clusters were annotated using the Azimuth program (version 0.5.0). Cell interactions with receptors were profiled using ligand-receptor interaction analysis in CellChat (version 2.1.2). Trajectory inference was calculated using the MST method implemented in dyno (version 0.1.2).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eAuthor Contributions Acknowledgements\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization the idea for the study, K. E.; developed the software, V. K.; methodology, Yu. Sh., performed the validation, D. G., P. Sh.; performed the formal analysis, E. A.; performed the investigation, \u0026nbsp;D. G., P. Sh. and E. A.; performed the data curation, K. E., V. K.; writing—original draft preparation, L. K. D. G., P. Sh., K. E. and E. A.; writing—review and editing, K. E., Yu. Sh.; performed the visualization, V. K., K. E. and Yu. Sh., performed the supervision V. P.; performed the project administration, K. E., V. P.,\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll authors have read and agreed to the published version of the manuscript.\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe study was funded by BSMU Strategic Academic Leadership Program PRIORITY-2030\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConflict of Interest\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData Availability Statement\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEthical Statement\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the local ethical committee of Bashkir State University\u003c/p\u003e\n\u003cp\u003eOctober 13, 2023 (protocol #10). The study was conducted in accordance with the Declaration of Helsinki (1964, as amended in 1975 and 1983).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003e\u0026Oslash;rsted DD, Bojesen SE, Nielsen SF, Nordestgaard BG. Association of Clinical Benign Prostate Hyperplasia with Prostate Cancer Incidence and Mortality Revisited: A Nationwide Cohort Study of 3 009 258 Men. \u003cem\u003eEuropean Urology\u003c/em\u003e. 2011;60(4):691-698. doi:10.1016/j.eururo.2011.06.016\u003c/li\u003e\n\u003cli\u003eGlaser A, Shi Z, Wei J, et al. Shared Inherited Genetics of Benign Prostatic Hyperplasia and Prostate Cancer. \u003cem\u003eEuropean Urology Open Science\u003c/em\u003e. 2022;43:54-61. doi:10.1016/j.euros.2022.07.004\u003c/li\u003e\n\u003cli\u003eMeigs J, Barry M, Giovannucci E, Rimm E, Stampfer M, Kawachi I. 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We employed single-cell RNA sequencing to analyze peripheral blood mononuclear cells from four PCa patients (Gleason 3+3 to 3+4, stages T2N0M0–T3bN0M0) and three BPH patients. Unsupervised clustering identified 16 distinct cell populations, emphasizing classical (CD14++CD16−), intermediate (CD14++CD16+), and non-classical (CD14+CD16++) monocyte subsets.\nPCa samples showed greater monocyte heterogeneity with seven classical subsets versus six in BPH, reflecting increased malignant plasticity. Transcriptionally, PCa monocytes upregulated oncogenic MALAT1, metastasis-associated S100A8/S100A9 and VCAN, and immunosuppressive CD14/CD36, supporting angiogenesis and tumor growth. Conversely, BPH monocytes maintained protective functions through ALDH1A2, anti-proliferative ZFP36, and tissue homeostasis regulators TMEM176B, USP30-AS1, and TCOF1.\nLigand–receptor analysis distinguished disease contexts: PCa monocytes formed restricted networks dominated by ANXA1–FPR1 and TGFβ1 signaling, fostering immunosuppression. BPH exhibited broader networks integrating pro-inflammatory chemokines (CCL3/CCL5–CCR1) and metabolic NAMPT–BSG pathways, consistent with controlled inflammation and repair. Our findings define distinct molecular programs of circulating monocytes in malignant versus benign prostate disease, providing potential biomarkers for differential diagnosis and immunomodulatory therapy targets.","manuscriptTitle":"Monocyte differentiation dynamics and ligand-receptor interactions in peripheral blood of patients with prostate cancer and BPH: a comparative scRNA-seq analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-15 02:41:34","doi":"10.21203/rs.3.rs-7748159/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9ec948d7-0b93-4178-b576-b105df69416a","owner":[],"postedDate":"October 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-24T04:55:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-15 02:41:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7748159","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7748159","identity":"rs-7748159","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}