Single-cell analysis supports key cell subpopulations for drug resistance and metastasis in prostate cancer

Single-cell analysis supports key cell subpopulations for drug resistance and metastasis in prostate cancer | 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 Single-cell analysis supports key cell subpopulations for drug resistance and metastasis in prostate cancer Hongfei Zhao, Jiaxuan Li, Ting Yuan, Qi Sun, Guangkuo Wang, Han Fang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5029414/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 Although the long-term survival rate of localized prostate cnacer is high, prostate cancer is prone to drug resistance and metastasis after intensive multimodal therapy, significantly shortening patient survival. However, the mechanisms by which drug resistance and metastasis occur in prostate cancer are unknown, and there are no effective targeted drugs to treat metastatic prostate cancer. Therefore, the goal of this study was to construct a single-cell transcriptome atlas of prostate cancer by using single-cell RNA sequencing data through bioinformatics techniques, to explore the changes in abundance and function of different cell subpopulations during drug resistance and metastasis, and to speculate on the molecular pathways that may lead to drug resistance and metastasis. We analyzed the tumor cell subpopulation and predicted that the ASCL1 , RORB , RBP1 and CALML5 might be the genes responsible for the development of drug resistance and metastasis, providing potential targets for targeted prostate cancer therapy. In addition, we performed differentiation potential analysis, trajectory analysis, and intercellular communication analysis on tumor cell subpopulations, which identified key ligand-receptor interactions between tumor cells and stromal cells that lead to disease progression, such as CCL5-SDC1/4, ICOSLG-ICOS and CD24-SIGLEC10. Our analysis identified potential targets to treat resistance and metastasis in prostate cancer, suggesting key ligand-receptor interactions that might contribute to disease progression, and provided valuable insights into targeted therapy for prostate cancer. Prostate cancer Drug resistance Cancer metastasis Cellular communication Therapeutic target Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Prostate adenocarcinoma (PRAD) is the second most common malignancy in men and is one of the leading causes of cancer death worldwide (Pernar, Ebot, Wilson, & Mucci, 2018 ).Treatment options for primary prostate cancer include surgery, radiation therapy, and androgen deprivation therapy (ADT) (Rebello et al., 2021 ). Although most patients initially respond to ADT, they mostly progress to castration-resistant prostate cancer (CRPC), for which treatment options are limited to aggressive chemotherapy and palliative care, and these patients have a poor prognosis and low survival (Gleave, Bruchovsky, Moore, & Venner, 1999 ). Clinically, 95% of prostate cancer is adenocarcinoma, originating from the epithelial cells of the prostate (Vitkin, Nersesian, Siemens, & Koti, 2019 ). There is growing evidence that stromal cells make an important contribution to the development of drug resistance and metastasis after ADT in primary prostate cancer (Rycaj, Li, Zhou, Chen, & Tang, 2017 ). Pharmacological treatment of prostate cancer includes chemotherapy and anti-androgen medications. ADT is the cornerstone of prostate cancer treatment because the prostate gland is dependent on androgens for growth and development (Sharifi, Dahut, & Figg, 2008 ). The current standard of care for advanced prostate cancer includes gonadotropin-releasing hormone agonists (e.g., leuprolide), second-generation nonsteroidal androgen receptor (AR) antagonists (enzalutamide, apalutamide, and darolutamide), and the androgen biosynthesis inhibitor abiraterone (Desai, McManus, & Sharifi, 2021 ). Progression to CRPC is caused by a number of mechanisms, including, but not limited to, AR - dependent drug resistance mechanisms (Deng et al., 2022 ). Changes in cell lineages occur during prostate cancer progression, which is the process by which cancer cells change from one morphological and functional cell type to another in response to environmental stress (Ge et al., 2020 ). Alterations in cell lineages contribute to the development of drug resistance and metastasis during prostate cancer treatment; for example, in the context of ADT, alterations in cell lineages can shift the cell phenotype from an ARdependent adenocarcinoma to a phenotype with significantly different epigenetic features, including low AR sensitivity, and a more aggressive phenotype, among others (Ku et al., 2017 ). Among these, neuroendocrine prostate cancer (NEPC) is one of the most aggressive subtypes (Wang et al., 2021 ). In this study, we used published prostate cancer single-cell RNA sequencing (scRNA-seq) data to construct a single-cell atlas of PRAD and to explore the changes in the abundance and function of different cell subpopulations during disease progression. We also analyzed the intrinsic and microenvironmental factors in cancer cells that might contribute to drug resistance and metastasis in prostate cancer. PRAD has a complex tumor microenvironment (TME), in which stromal cells support tumor cell survival through various intercellular communication pathways (Sfanos, Yegnasubramanian, Nelson, & De Marzo, 2018 ). Among these stromal cells, macrophages and T cells play a crucial role in promoting tumor progression (Larionova et al., 2020 ). We applied functional enrichment analysis to identify signaling pathways that might be associated with PRAD. We use trajectory analysis to track the dynamic changes and differentiation trajectory of tumor cells. We also aimed to explore cellular communication to identify potential targets in tumor cell-stromal cell interactions. Existing studies have focused only on the functional and phenotypic evolutionary outcomes of subpopulations of prostate cancer during disease progression, without exploring the molecular mechanisms leading to their evolution. Our findings might help to determine the disease mechanisms and provide scientific theoretical guidance and a research basis for better intervention and treatment of prostate cancer. Results Single cell transcriptome mapping of prostate cancer ScRNA-seq data from 41 samples were subjected to quality control, finally yielding 135,436 cells, which were clustered to yield 61 cell clusters (Figure 1A), and annotated with 13 cell types, including tumor cells (PRAD), epithelial cells (Ep), fibroblasts (Fib), smooth muscle cells (SMC), endothelial cells (EN), B cells (B), CD4 + T cells (CD4 + T), CD8 + T cells (CD8 + T), natural killer T cells(NKT), Naive T cells (Naive.T), plasmacytoid dendritic cells (pDC), mast cells (Mast), and macrophages (Mac) (Figure 1B). The marker genes expressed by different cell clusters are shown in a bubble diagram (Figure 1C). We divided the samples into four groups: The Control group for paraneoplastic tissue, the PRAD group for primary prostate cancer, the Resistant group for prostate cancer tissue that recurred after drug treatment, and the MPRAD group for prostate cancer metastases that recurred after drug treatment. We then analyzed the changes in abundance of each cell cluster during drug resistance and progression of prostate cancer. By comparison, we found that the abundances of Naive.T, CD8+T, B, and NKT immune cells were decreased, and the abundance of fibroblasts was increased, in the Resistant and MPRAD groups compared with those in the PRAD group (Figure 1D). This result suggested that immune cell infiltration was reduced in prostate cancer drug resistance and metastatic progression, whereas the abundance of fibroblasts, which contribute to disease progression, was increased. The LAIR2 gene of CD4 + T cells is associated tightly with prostate cancer drug resistance We further re-clustered CD4 + T cells to obtain six cell subpopulations (Figure 2A). The different cell subpopulations were distinguished from each other by specific markers, and we defined the subpopulation of cells expressing FOXP3 as the regulatory T cell (Treg) cell subpopulation (Figure 2B). The Treg_LAIR2 and CD4 + T_MINOS1 subpopulations were significantly more abundant in the Resistant group compared with those in the PRAD group; however, in the MPRAD group, significant increases in abundance were observed for the Treg_APOE and Treg_IGLC3 subpopulations (Figure 2C). This suggested that different cellular ecologies created different dominant subpopulations, and in the MPRAD group, the vast majority of CD+4 cells were Tregs, implying a more suppressive immune microenvironment. Furthermore, enrichment analysis revealed that the Treg_LAIR2 and Treg_APOE subpopulations were significantly associated with the oxidative phosphorylation pathway, which supported the Tregs’ suppressive function and stability (Figure 2D). Moreover, through intercellular communication analysis we found that the CD86 (B7-2) ligands secreted by macrophages could bind to CTLA4 receptors on the surface of Treg cells in the Treg_LAIR2 subpopulation, whereas the TNFSF9 ligands secreted by CD8 + T cells could bind to TNFRSF9 receptors on the surface of Treg cells in the Treg_LAIR2 subpopulation (Figure 2E). These cellular communication networks reshape the tumor microenvironment and promote the development of PRAD. M2_SPP1 and M2_FOLR2 are key TAM subpopulations in the tumor immune microenvironment Tumor associated macrophages (TAMs) are macrophages that infiltrate tumor tissues and exert immunosuppressive effects to promote tumor progression in multiple ways. We further divided Mac cells into nine cell subpopulations (Figure 3A), named according to their specifically expressed markers. We then defined the M2 cell subpopulations based on the relative expression of CD86 and CD163, in which the M2_SPP1 and M2_FOLR2 subpopulations with higher CD163 expression were M2 macrophages (Figure 3B). The M2_SPP1 subpopulation was significantly more abundant in prostate cancer progression, and a new cell subpopulation, M2_FOLR2, emerged in MPRAD (Figure 3C), and both subpopulations were specifically enriched (more abundant) in the MPRAD group; therefore, we defined these types of macrophages as TAMs. Intercellular communication existed between macrophages of the M2_FOLR2 subpopulation and multiple immune cells (Figure 3D). For example, the LGALS9 ligand secreted by M2_FOLR2 cells bound to the CTLA4 receptor on APOE Treg cells and IGLC3 Treg cells, exerting immunosuppressive effects. Trajectory analysis showed that M2_FOLR2 cells were positioned later on the differentiation trajectory than M2_SPP1 cells; therefore, we hypothesized that a transformation from M2_SPP1 cells to M2_FOLR2 cells had occurred, further promoting tumor development (Figure 3E, F). Multiple signaling pathways that contribute to prostate cancer resistance and metastasis are enriched in the EN-THY1 subpopulation To explore the changes in endothelial cells, we performed a subpopulation analysis of the endothelial cells and obtained seven subpopulations expressing different specific markers (Figures S1A, S1B). A new subpopulation, EN-THY1, was identified in the MPRAD group, which had the highest abundance in the metastasis group (Figure S1C). The EN-THY1 subpopulation was significantly enriched in genes related to the local adhesion pathway, which might contribute to the colonization of prostate cancer tumor cells via bloodstream metastasis. In addition, we found that this subpopulation had high expression of genes in leukocyte transendothelial migration pathway and regulation of actin cytoskeleton pathway, they all played an important role in the intravasation of tumor cells into the circulatory system and extravasation into local tissues. (Figure S1D). RBP1 and RORB genes are promising therapeutic targets for resistant prostate cancer We further divided the tumor cells into eleven cell subpopulations (Figure 4A), and each subpopulation was represented by characteristic genes (Figure 4B). We observed that the ecological composition of tumor cells becomes progressively more complex as the disease progresses. The cell subpopulation PRAD_NME2 showed significantly decreased abundance in the Resistant and MPRAD groups (Figure 4C). In addition, RBP1 tumor cells were specifically present in the Resistant group, and we further found that RBP1 was highly expressed in tumor cells that were treated with anti-androgen drugs. RBP1 was also highly expressed in the liver metastasis subpopulation of prostate cancer. Therefore, we hypothesized that the RBP1 gene contributes to antiandrogenic resistance in prostate cancer and prostate cancer liver metastasis. Besides, a new cell subpopulation, PRAD_RORB, in the Resistant group was mainly composed of tumor cells that were resistant to doxorubicin treatment (Figure 4D). This implies that RORB is involved in the resistance of tumor cells to doxycycline. PRAD_ASCL1 is important for drug resistance in tumor cells The PRAD_ASCL1 subpopulation had the highest potential for tumor cell differentiation in the Resistant group (Figure 5A), as well as being the terminal subpopulation for tumor cell differentiation (Figure 5B). This might reflect the fact that as the disease progresses, the surviving tumor cells approached tumor stem cell status to a greater extent and therefore had a higher differentiation potential. It was also possible that during progression, the tumor cells depolarized and regained their high differentiation potential. Enrichment analyses showed that PRAD_ASCL1 was significantly involved in the spliceosome, cell cycle, and oxidative phosphorylation pathways (Figure 5C). Abnormal splicing in prostate cancer can lead to genetic mutations that activate cancer-related signaling pathways. Small molecules that target components of the spliceosome, such as the spliceosome inhibitor E7107, have exhibited antitumor effects in prostate cancer cells and the prostate cancer cell line PC3 (Zhang et al., 2020). Aberrant cell cycle progression is one of the fundamental mechanisms of tumorigenesis (J. Liu, Peng, & Wei, 2022). Oxidative phosphorylation leads to increased reactive oxygen species production and hypoxia leads to increased tumor cell drug-resistance (Gilkes, Semenza, & Wirtz, 2014). These analyses suggested that PRAD_ASCL1 tumor cells play a crucial role in prostate cancer resistance. We then explored the mechanisms that might lead to drug resistance of tumor cells through cellular communication. The PRAD_ASCL1 subpopulation has the closest communication between tumor cells and stromal cells, and tumor cells in the PRAD_ASCL1 subpopulation can interact with T cells and macrophages through the ICOSLG-ICOS and CD24-SIGLEC10 ligand-receptor pairs, respectively, to build a tumor immunosuppressive microenvironment. Other subpopulations interacted with immune cells mainly through the CD24-SIGLEC10 ligand-receptor pair (Figure 5D). In addition, T cells interacted with multiple subpopulations of tumor cells in the Resistant group via the CCL5-SDC1/4 ligand-receptor pair in an intercellular interaction that might function to promote migration of resistant tumor cells (Figure 5E). Tumor cells can interact with NRP1, NRP2, and FLT1 receptors on endothelial cells via the ligand PGF to promote vascular endothelium proliferation. In turn, endothelial cells can interact with tumor cells through the ligand receptor pair HBEGF-CD9 to promote tumor cell proliferation (Figure 5F). This analysis confirmed that tumor cells and endothelial cells of this subpopulation interact to promote each other's proliferation. Interestingly, tumor cells of the PRAD_CALML5 subpopulation in the MPRAD group expressed NGF , which encodes a secreted growth factor that might interact with the intracellular protein encoded by the NGFRAP1 gene in PRAD_ASCL1 tumor cells in the Resistant group, functioning as a promoter of proliferation and metastasis of resistant tumor cells (Figure 5F). Subsequently, we converted the transcriptomic data of tumor cells in the Resistant group into pseudo-bulk data and predicted sensitive drugs in the different subpopulations using the oncoppredict R package. Our analysis revealed that tumor cells in the PRAD_ASCL1 subpopulation were relatively sensitive to KRAS G12C Inhibitor class drugs (Supplementary Table 2). PRAD_CALML5 is a key subpopulation of the MPRAD group In the MPRAD group, PRAD_CALML5 was the subpopulation with the highest potential for tumor cell differentiation (Figure 6A), as well as being the terminal subpopulation for tumor cell differentiation (Figure 5B). Enrichment analyses showed that like the PRAD_ASCL1 subpopulation, PRAD_CALML5 was also significantly involved in the spliceosome, cell cycle, and oxidative phosphorylation pathways (Figure 5C). These analyses suggested that PRAD_CALML5 tumor cells play a crucial role in prostate cancer metastasis. Cellular communication analysis revealed that PRAD_CALML5 tumor cells exert tumor immune suppressive functions with M2_SPP1 macrophages via CD24-SIGLEC10 ligand-receptor interactions (Figure 6A). Similar to the Resistant group, T cells in the MPRAD group could also interact with tumor cells via the CCL5-SDC1 ligand-receptor pair, which might play a role in regulating tumor cell migration (Figure 6B). PRAD_CALML5 tumor cells interacted with EN_THY1 and EN_C11or158 endothelial cells via IGF1-INSR ligand-receptor interactions to promote neovascularization (Figure 5C). Moreover, CTHRC1 -associated cancer-associated fibroblasts (CAFs) interacted with PRAD_CALML5 tumor cells via the TIMP1-CD63 ligand‑receptor interaction, thereby promoting prostate cancer progression (Figure 6D). Discussion In this study, we used published prostate cancer scRNA-seq data to construct a single-cell transcriptome atlas of prostate cancer and explored the changes in the abundance and function of different cell subpopulations during drug resistance and metastasis in prostate cancer. The results suggested that the ecological composition of each cell subpopulation becomes more complex in the context of drug treatment. The cellular phenotype of the abundance-dominant subpopulations in different cell subpopulations facilitates drug resistance and metastasis of tumor cells. Here, we focus our analysis on CD4 + T cells, macrophages, endothelial cells, and tumor cells. Immune cells, an important component of the TME, directly influence disease progression (Galluzzi, Humeau, Buque, Zitvogel, & Kroemer, 2020 ). CD4 + T cells assist B cells in antibody production, activate phagocytosis of macrophages, and recruit other immune cells to infected or inflamed areas of the body. Naive CD4 + T cells are activated to differentiate into specific subtypes after interacting with antigen-MHC complexes, including helper T cells, Tregs, and follicular helper T cells (S. Liu et al., 2020 ). Among them, Tregs play an immunosuppressive function in tumor immunity by suppressing the activation and proliferation of effector cells, such as monocytes, macrophages, NK cells, and APCs, as well as inducing apoptosis through various mechanisms (Radej, Szewc, & Maciejewski, 2022 ). LAIR2 was predicted to be a biomarker of T-cell depletion in the TME of cholangiocarcinoma and is a biomarker for immune infiltration assessment in cholangiocarcinoma (Z. Chen et al., 2021 ). In our study, the abundance of the Treg_LAIR2 subpopulation increased significantly in the Resistant group. Macrophages and CD8 + T cells interact with the Treg_LAIR2 subpopulation via CD86CTLA4 and TNFSF9TNFRSF9 ligand-receptor pairs, respectively, and these immune checkpoints contribute to the immune escape of tumor cells (Etxeberria, Glez-Vaz, Teijeira, & Melero, 2020 ; Pai et al., 2019 ).Therefore, we hypothesized that the increased abundance of the CD4 + T cell subpopulation expressing LAIR2 might be associated with drug resistance in prostate cancer. The abundance of the M2-type macrophage subpopulation also increased significantly during disease progression and participated in the formation of a tumor immunosuppressive microenvironment together with Tregs (C. Liu et al., 2019 ). In addition, M2_SPP1 and M2_FOLR2 are key TAM subpopulations in the immune microenvironment. Previous studies have confirmed that SPP1 + TAMs have immunosuppressive effects and are associated with the invasive metastasis of tumor cells (Wei et al., 2021 ). In hepatocellular carcinoma, TAMs expressing FOLR2 colocalize with Tregs and exert immunosuppressive effects (Sharma et al., 2020 ). Herein, intercellular communication analysis also showed that FOLR2-expressing TAMs communicate with Tregs in the metastatic group, which contributes to drug resistance and metastasis of prostate cancer. Our analysis showed that the TAM subpopulations M2_SPP1 and M2_FOLR2 were enriched in the MPRAD group, suggesting their involvement in the formation of a tumor immunosuppressive microenvironment, thereby contributing to the development of drug resistance and metastasis in prostate cancer. Tumor growth and metastasis require neovascular support, and endothelial cells are a major part of neovascularization (Lugano, Ramachandran, & Dimberg, 2020 ).Tumor cells complete hematogenous metastasis within the microvasculature of specific organs through retention, adhesion, and growth into the extravascular parenchymal environment (Sobierajska, Ciszewski, Sacewicz-Hofman, & Niewiarowska, 2020 ).The new subpopulation, EN-THY1, had the highest abundance in the endothelial cell ecology of the MPRAD group, and the GO and KEGG pathway-based enrichment analysis revealed that this subpopulation had high expression of genes in the local adhesion pathway, the adhesion molecules expressed by endothelial cells could enable tumor cells that have already entered the bloodstream to adhere to endothelial cells, possibly contributing to the colonization of prostate cancer tumor cells via bloodstream metastasis (Zhao & Guan, 2011 ).In addition, this subpopulation was significantly enriched in genes related to the leukocyte transendothelial migration pathway. This pathway was generally activated in cancer progression (Enarsson, Lundin, Johnsson, Brezicka, & Quiding-Jarbrink, 2007 ). Activation of the pathway disrupted the tightness and integrity of the endothelial cell junctions, inflammatory cytokines produced by leukocytes contribute to cancer cell extravasation as well (Reymond, d'Agua, & Ridley, 2013 ). The RBP1 gene was highly expressed in tumor cells of the PRAD_RBP1 subpopulation, which comprised anti-androgen-treated tumor cells. In addition, tumor cells in a subpopulation of prostate cancer liver metastases also showed high expression of the RBP1 gene. RBP1 increases the proliferation and aggressiveness of the invasive class IV human prostate adenocarcinoma cell line PC-3 (Napoli, 2017 ). Therefore, we hypothesized that the RBP1 gene contributes to antiandrogenic resistance in prostate cancer and prostate cancer liver metastasis. The PRAD_RORB subpopulation is mainly composed of tumor cells resistant to doxorubicin treatment, and previous studies have shown that RORB is a clock-related gene that is associated with low survival in prostate cancer (Yue et al., 2021 ). Our analysis also demonstrated high expression of RORB in doxorubicin-resistant tumor cells. Therefore, we hypothesized that RORB is a potential therapeutic target in resistant prostate cancer. As the disease progresses, the ecological composition of tumor cells becomes progressively more complex. The cell subpopulation PRAD_NME2 showed significantly decreased abundance in the Resistant and MPRAD groups, which led us to speculate that the tumor cell subpopulation PRAD_NME2 is a class of drug therapy-sensitive tumor cells. In addition, the abundance of cell subpopulations with high expression of the NME2 was higher in the EN and Mac ecologies of the primary foci, while their abundance was significantly decreased in both the Resistant and MPRAD groups. We also analyzed intergroup differences in NME2 gene expression, revealing that its expression was significantly lower in prostate cancer resistant primary and metastatic focal tissues compared with that in tumor primary and paracancerous tissues. NME2 is a metastasis suppressor gene (Chang et al., 2015 ) that is associated with telomere ends and telomerase, reducing telomerase activity within cells (Kar et al., 2012 ). NME2 has different effects on different types of cancer cells and their invasion or metastasis (Y. F. Liu et al., 2015 ). The NME2 gene is expected to be a relevant target or biological marker for future prostate cancer drug therapy. We analyzed the differentiation potential of tumor cell subpopulations, and in general, tumor cell subpopulations in the Resistance group had a higher cell differentiation potential compared with those in the MPRAD group. In addition, we tracked the dynamic changes and differentiation trajectories of tumor cells using trajectory analysis. Both the PRAD_ASCL1 and PRAD_CALML5 subpopulations were terminal subpopulations of tumor cell differentiation, and were the subpopulations with the highest differentiation potentials in the Resistant and MPRAD groups. Previous studies have shown that the stronger the differentiation potential of cancer cells, the stronger the drug resistance (Pattabiraman & Weinberg, 2014 ). The PRAD_ASCL1 subpopulation, as a subpopulation with high specificity and the highest tumor cell stemness in the Resistant group, might be important for drug resistance in tumor cells. Tumor cells in the PRAD_ASCL1 subpopulation highly express the ASCL1 gene, which is a marker gene for neuroendocrine prostate cancer, one of the most aggressive subtypes of prostate cancer (Dong et al., 2020 ). To this end, we further investigated the remodeling effect of the PRAD_ASCL1 subpopulation of tumor cells on stromal cells and predicted sensitive drugs for treatment against this subpopulation. Through intercellular communication analysis we found that tumor cells of the PRAD_ASCL1 subpopulation could interact with T cells and macrophages via ICOSLG-ICOS and CD24-SIGLEC10 ligand-receptor pairs, respectively, to construct a tumor immunosuppressive microenvironment (Barkal et al., 2019 ). In addition, T cells can interact with the PRAD_ASCL1 subpopulation of tumor cells via the CCL5-SDC1/4 ligand-receptor pair. Some in vitro experiments have demonstrated that T cells can promote the migration of pancreatic ductal adenocarcinoma tumor cells via CCL5-SDC1 receptor-ligand interactions. (K. Chen et al., 2022 ) Therefore, we speculated that CCL5-SDC4 ligand-receptor intercellular interactions between T cells and tumor cells in prostate cancer might also function to promote the migration of resistant tumor cells; however, further experimental validation of our speculation is required. This subpopulation of tumor cells also interacted with endothelial cells via the ligandreceptor pairs PGF-NRP1, PGF-NRP2, PGF-FLT1, and HBEGF-CD9 to promote each other's proliferation. Subsequently, we found that tumor cells in the PRAD_ASCL1 subpopulation were most sensitive to the KRAS G12C inhibitor class of drugs according to the drug response prediction analysis. KRAS is the most frequently mutated oncogene in humans (Huang, Guo, Wang, & Fu, 2021 ), and the hallmark cancers for KRAS mutations are pancreatic, colorectal, lung, and genitourinary cancers (Timar & Kashofer, 2020 ), in which the gene is involved in multiple tumor-related signaling pathways (Hallin et al., 2020 ). Previous experiments have validated the therapeutic effects of such drugs in non-small cell lung cancer and colorectal cancer (Hallin et al., 2020 ). Our analysis predicted that KRAS G12C inhibitor class drugs would be expected to be potential agents to treat neuroendocrine prostate cancer. The PRAD_CALML5 subpopulation, as the terminal subpopulation for tumor cell differentiation, and the subpopulation with the highest differentiation potential of tumor cells in the MPRAD group, is important for the proliferation and migration of tumor cells. Therefore, we used intercellular communication analysis to predict how it creates a microenvironment conducive to tumor cell metastasis. We constructed an extensive regulatory network of intercellular communication between tumor cells and stromal cells, and found that tumor cells of the PRAD_CALML5 subpopulation communicate with M2-type macrophages in the metastasis group via the ligand-receptor pair CD24-SIGLEC10, with T cells via the ligandreceptor pair CCL5-SDC1/4, and with EN cells via the ligand-receptor pair IGF1- INSR for intercellular communication, promoting proliferation resistance and metastasis of tumor cells. In addition, we identified for the first time that CTHRC1-related CAFs can interact with PRAD_CALML5 tumor cells via the TIMP1-CD63 ligand-receptor pair to promote drug resistance and metastasis in prostate cancer (Li, Zhang, Wang, & Jia, 2022 ). Overall, we identified, through intercellular communication analysis, key ligand receptors, such as CD24-SIGLEC10 andCCL5-SDC1/4, between tumor cells and stromal cells that affect the development of drug resistance and metastasis in prostate cancer. Prostate cancer tumor cells expressing CD24 interact with macrophage-indicated sialic acid-binding Ig-like lectin 10 ( SIGLEC10 ) receptors, which might contribute to the immune escape function of prostate cancer tumor cells and help build an immunosuppressive microenvironment for prostate cancer resistance and metastasis. CD24 also has potential as a therapeutic target in prostate cancer (Panagiotou, Syrigos, Charpidou, Kotteas, & Vathiotis, 2022 ). In addition, we speculated that CCL5-SDC1/4 ligand-receptor celltocell interactions between T cells and tumor cells in prostate cancer might function to promote the migration of resistant tumor cells; however, further experimental validation of this speculation is needed. Although this study reports novel findings, it has some limitations. First, the samples included in this study were relatively small, and the analytical results obtained need to be further validated in a larger sample. Second, although the mechanisms derived in this study are based on a bioinformatic analysis approach, they have not been validated by molecular and cellular experiments. Therefore, we plan to further expand the samples in future studies and use molecular and cellular experiments to validate the findings. In conclusion, our study provides theoretical support for changes in the abundance and function of different cell subpopulations during the progression of prostate cancer resistance and metastasis, thus identifying potential targets for treatment of prostate cancer resistance and metastasis, suggesting key ligand-receptor interactions that might contribute to disease progression, and providing valuable insights for targeted therapy of prostate cancer. Materials and methods Data sources We collected scRNA-seq data from a total of 41 samples, including 11 PRAD primary focal tissues, 11 paracancerous tissues (Control), 6 resistant primary focal tissues (Resistant), and 13 resistant metastatic (MPRAD) focal tissues. The collected data were based on 10x Genomics scRNA-seq, and were all available in the Gene Expression Omnibus (GEO) databases: GSE193337, GSE137829, GSE185344, GSE210358 (clinical information for the samples is available in Supplementary Table 1). ScRNA-seq data processing ScRNA-seq data were processed for quality control, filtering out the 1% of cells with the highest and lowest feature numbers of expression, as well as cells with more than 10% mitochondrial gene expression. After quality control, the data were integrated and analyzed based on standardization using SCTransform in Seurat R package (Hao et al., 2021). UMAP visualization and cell type annotation Dimensionality reduction and clustering analysis of single cells was implemented based on the Seurat R package (Hao et al., 2021). We then analyzed single cells using the default parameters, constructing single cell profiles, and visualizing clusters of cells using dimensionality reduction with uniform manifold approximation and projection (UMAP) plots. Clusters were associated with cell types based on differential expression scores of marker genes for: CD8 + T cells ( CD8A , CD8B , CD3D , CD3E , and CD3G ); CD4 + T cells ( CD4, CD3D , CD3E , and CD3G ); Naive T cells ( CD247 , CD3D , CD3E , and CD3G ); natural killer T cells ( CD3E , CD247 , and CD160 ); B cells ( CD79A , CD79B , and CD19 ); mast cells ( FCER1A and KIT ), macrophages ( CD14 , CD68 , HLA-DRB1 , and PTPRC ); plasmacytoid dendritic cells ( CLEC4C , IL3RA , and PTPRC ), fibroblasts ( ACTA2 , PDGFRB , and COL1A1 ), smooth muscle cells ( DES and ACTA2 ); endothelial cells ( ENG , PECAM1 , VWF , and CDH5 ); and tumor cells ( KRT18 , SOX2 , SCHLAP1 , CDKN2A , AMACR , AR , and KLK3 (Chan et al., 2022)). Functional enrichment analysis Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the clusterProfiler R package (Yu, Wang, Han, & He, 2012) to determine the potential function of the molecular pathways active in each cell subpopulation. P < 0.05 was considered significant. Trajectory analysis The Monocle3 R package (Trapnell et al., 2014) with default parameters was used to perform single-cell trajectory analysis of tumor cells, depicting a single-cell atlas of the proposed temporal trajectory of cell clusters. Cell communication analysis We mapped receptor-ligand pairs using iTALK (v0.1.0) (doi:https://doi.org/10.1101/507871) using the default parameters (https://github.com/Coolgenome/iTALK). The visualization of ligand-receptor pairs in iTALK was divided into four groups, including cytokines, growth factors, immune checkpoints and the other, to discover important cellular communication events that influence disease progression. Predicting cell differentiation potential Based on the CytoTRACE R package (v0.1.0) (Gulati et al., 2020), which predicts the relative differentiation status of cells based on single-cell RNA sequencing data without any a priori information. Inferring intercellular activity and relative intercellular differentiation status based on single-cell counting matrices. Drug prediction The expression matrix of single cells was converted into a standard pseudo-bulk expression matrix. Then, the oncoppredict R package used the expression matrix to predict sensitive drugs for tumor cells in the Resistant group (Maeser, Gruener, & Huang, 2021). Abbreviations PRAD Prostate adenocarcinoma ADT Androgen deprivation therapy CRPC Castration-resistant prostate cancer AR Androgen receptor NEPC Neuroendocrine prostate cancer scRNA-seq Single-cell RNA sequencing TME Tumor microenvironment EP Epithelial cells Fib Fibroblasts SMC Smooth muscle cells EN Endothelial cells B B cells CD4 + T CD4 + T cells CD8 + T CD8 + T cells NKT Natural killer T cells Naive.T Naive T cells pDC Plasmacytoid dendritic cells Mast Mast cells Mac Macrophages TAMs Tumor associated macrophages CAFs Cancer-associated fibroblasts GEO Gene Expression Omnibus UMAP Uniform manifold approximation and projection GO Gene Ontology KEGG Kyoto Encyclopedia of Genes and Genomes Declarations Ethics statement The study protocol was approved by the Ethics Committee of Yantai Yuhuangding Hospital, and the patients provided their informed consent for study participation. All methods were carried out in accordance with the Declaration of Helsinki. CRediT authorship contribution statement All authors contributed to the study conception and design. Bioinformatics analysis were performed by HZ, JL and TY. The first draft of the manuscript was written by HZ, and all authors commented on previous versions of the manuscript. All authors contributed to the article and approved the submitted version. Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Data Availability Statement The single-cell RNA sequencing data used in the article are deposited in the NCBI Gene Expression Omnibus (GEO) databases under accession numbers: GSE193337, GSE137829, GSE185344, GSE210358. Acknowledgments We are indebted and thankful to all participants for their valuable contributions. 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Focal adhesion kinase and its signaling pathways in cell migration and angiogenesis. Adv Drug Deliv Rev, 63 (8), 610-615. doi:10.1016/j.addr.2010.11.001 Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1Clinicalinformation.xlsx SupplementaryTable2Drugresponseprediction.xlsx SupplementalFigure.jpg Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5029414","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":361153775,"identity":"3030a6fa-dba9-4a70-894e-c10985352c66","order_by":0,"name":"Hongfei Zhao","email":"","orcid":"","institution":"Yuhuangding Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hongfei","middleName":"","lastName":"Zhao","suffix":""},{"id":361153776,"identity":"5939ec3b-3277-4af0-850b-1c5684779881","order_by":1,"name":"Jiaxuan Li","email":"","orcid":"","institution":"Yuhuangding Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jiaxuan","middleName":"","lastName":"Li","suffix":""},{"id":361153777,"identity":"031849e8-1b6a-461a-b3bf-b65ef5052d2f","order_by":2,"name":"Ting Yuan","email":"","orcid":"","institution":"Yuhuangding Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ting","middleName":"","lastName":"Yuan","suffix":""},{"id":361153778,"identity":"5ae421f8-0d30-4af9-b217-d60fdc9c55d3","order_by":3,"name":"Qi Sun","email":"","orcid":"","institution":"Yuhuangding Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qi","middleName":"","lastName":"Sun","suffix":""},{"id":361153783,"identity":"99beed9e-d0df-44c4-b982-6198e525fd7d","order_by":4,"name":"Guangkuo Wang","email":"","orcid":"","institution":"Yuhuangding Hospital","correspondingAuthor":false,"prefix":"","firstName":"Guangkuo","middleName":"","lastName":"Wang","suffix":""},{"id":361153784,"identity":"99c6bfdf-b809-45f6-a364-f7bff2eca341","order_by":5,"name":"Han Fang","email":"","orcid":"","institution":"Yuhuangding Hospital","correspondingAuthor":false,"prefix":"","firstName":"Han","middleName":"","lastName":"Fang","suffix":""},{"id":361153785,"identity":"1345df9f-6f38-439e-a867-f20fe8469e38","order_by":6,"name":"Huifang Liu","email":"","orcid":"","institution":"The 2nd Medical College of Binzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Huifang","middleName":"","lastName":"Liu","suffix":""},{"id":361153787,"identity":"85427830-279e-40fd-ae5d-5d7f96d961cf","order_by":7,"name":"Yao Wang","email":"","orcid":"","institution":"Yuhuangding Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yao","middleName":"","lastName":"Wang","suffix":""},{"id":361153788,"identity":"fe553ea7-373c-4627-8392-c4baf68c973c","order_by":8,"name":"Xicheng Song","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYDCCAwwMzGAGe2Pjww+kaeE53GwsQZoWifQ2AR5idPDdPmP4ueCPTZ585MM2BgkGOzndBgJaJM+lJUvPbEsrNryd2PaggCHZ2OwAAS0GZ5iPMfM2HE7cODux3UCC4UDiNsJaGNuYef4Atcw82CbBQ5wWoC08bIcT50swEqlF8gxbsjRvW1riBp5EYCAbEOEXvjM8hp95/tgkzm8//vDhhwo7OYJaEC4EqzQgVjkIyDeQonoUjIJRMApGFAAAS25DJSseG0QAAAAASUVORK5CYII=","orcid":"","institution":"Yuhuangding Hospital","correspondingAuthor":true,"prefix":"","firstName":"Xicheng","middleName":"","lastName":"Song","suffix":""}],"badges":[],"createdAt":"2024-09-04 07:17:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5029414/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5029414/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67127253,"identity":"cd6f4933-8c30-4a2c-a0bf-b30e515233a0","added_by":"auto","created_at":"2024-10-21 12:09:50","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":7811603,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruct a panoramic single-cell atlas of PRAD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e. Prostate cancer single cell data downscaled and clustered to obtain 61 cell clusters.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e. Global PRAD tumor ecological map showing the different cell types.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e. Expression of marker genes for cellular annotation (size: -log10 (adjusted p-value); statistical test method: Wilcoxon rank sum).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD\u003c/strong\u003e. The difference in composition of 13 cell types between the control, PRAD, Resistant, and MPRAD groups.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-5029414/v1/a27f9f4e12fa162c991d686a.png"},{"id":67127252,"identity":"214dcc11-a939-410f-8a91-9988cec60c76","added_by":"auto","created_at":"2024-10-21 12:09:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":9619576,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-cell atlas of CD4+ T cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e. CD4+ T cells re-clustered.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e. Violin diagram showing specific markers between different cell subpopulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e. Changes in the abundance of CD4\u003csup\u003e+ \u003c/sup\u003eT cell subpopulations during the progression of PRAD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD\u003c/strong\u003e. The KEGG pathway enrichment analysis of CD4\u003csup\u003e+\u003c/sup\u003e T cell subpopulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eE\u003c/strong\u003e. Cellular communication diagram demonstrating the interaction between Treg_LAIR1 subpopulation and other cell subpopulations.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-5029414/v1/34047d67af8edab6b7282027.png"},{"id":67127254,"identity":"8485e188-9ac2-419a-a1f4-5d7b309e685c","added_by":"auto","created_at":"2024-10-21 12:09:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":14382234,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-cell atlas of Mac cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e. Mac cells are further divided into 9 cell subpopulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e. Violin diagram showing the specific markers between different cell subpopulations of Mac cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e. Changes in the abundance of Mac cell subpopulations during the progression of PRAD. \u003cstrong\u003eD\u003c/strong\u003e. Cellular communication between macrophages of M2_FOLR2 subpopulation and multiple immune cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eE, F\u003c/strong\u003e. Trajectory analysis of Mac subpopulations.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-5029414/v1/36cea4842d5efc6034f2dce4.png"},{"id":67127256,"identity":"a4f18812-d168-441a-9ddf-c7ad54a8af8e","added_by":"auto","created_at":"2024-10-21 12:09:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":5319483,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-cell atlas of tumor cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e. Tumor cells were further divided into 11 cell subpopulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e. Violin diagram demonstrating specific markers between different cell subpopulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e. Changes in the abundance of tumor cell subpopulations during the progression of PRAD. \u003cstrong\u003eD\u003c/strong\u003e. \u003cem\u003eRBP1\u003c/em\u003e may be a resistance gene for antiandrogenic therapy in prostate cancer; \u003cem\u003eORB\u003c/em\u003e gene is a potential therapeutic target for Resistant prostate cancer.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-5029414/v1/241bb6584810f1890dd1e7c2.png"},{"id":67127258,"identity":"743d7758-706c-42f2-ac70-e986bd1d2179","added_by":"auto","created_at":"2024-10-21 12:09:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":25450916,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of PRAD_ASCL1 subpopulation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA. \u003c/strong\u003eAnalysis of differentiation potential of tumor cell subpopulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB. \u003c/strong\u003eTrajectory analysis of tumor cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC. \u003c/strong\u003eThe KEGG pathway enrichment analysis of tumor cell subpopulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD. \u003c/strong\u003eTumor cells in the PRAD_ASCL1 subpopulation can interact with T cells and macrophages through ICOSLG-ICOS and CD24-SIGLEC10 ligand receptors, to build a tumor immunosuppressive microenvironment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eE. \u003c/strong\u003eT cells interacted with multiple subpopulations of tumor cells in the Resistant group via CCL5-SDC1/4 ligand.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e. Tumor cells and endothelial cells interacted to promote each other's proliferation.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-5029414/v1/1b1458c013c69f347a4fcc6f.png"},{"id":67127257,"identity":"7c6177da-2bb1-453e-9de8-4fea7d6597a5","added_by":"auto","created_at":"2024-10-21 12:09:51","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":16073286,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of PRAD_CALML5 subpopulation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA. \u003c/strong\u003eTumor cells of PRAD_CALML5 interact with macrophages of M2_SPP1 via CD24-SIGLEC10 ligand-receptor interaction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB.\u003c/strong\u003e T cells interact with multiple tumor cell subsets in the metastasis group via CCL5-SDC1/4 ligand-receptors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC. \u003c/strong\u003eCommunication between tumor cells and endothelial cells in PRAD_CALML5.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD\u003c/strong\u003e. CTHRC1-associated CAF interacts with tumor cells of PRAD_CALML5 via TIMP1-CD63 ligand-receptor.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-5029414/v1/a16137e6f7d222a4f229692c.png"},{"id":70015375,"identity":"88b2cb00-2c5a-46e2-b15e-4a8fe6a64aa2","added_by":"auto","created_at":"2024-11-27 13:47:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":85644389,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5029414/v1/a549dc54-4440-4729-8652-70603db76436.pdf"},{"id":67127250,"identity":"330d6604-d98b-4dcb-87de-d6469efe8d20","added_by":"auto","created_at":"2024-10-21 12:09:50","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15915,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1Clinicalinformation.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5029414/v1/d8e48708392a39a30912469b.xlsx"},{"id":67127504,"identity":"62b70212-9dbf-4960-9e30-ea8c222b8615","added_by":"auto","created_at":"2024-10-21 12:17:50","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":42036,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2Drugresponseprediction.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5029414/v1/8a17853dba653bccc0771273.xlsx"},{"id":67127505,"identity":"e074f343-0ec1-4f0a-9bea-a8ade4d853f3","added_by":"auto","created_at":"2024-10-21 12:17:51","extension":"jpg","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1731546,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalFigure.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5029414/v1/86c00730343e5bc09d7b2c0e.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Single-cell analysis supports key cell subpopulations for drug resistance and metastasis in prostate cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eProstate adenocarcinoma (PRAD) is the second most common malignancy in men and is one of the leading causes of cancer death worldwide (Pernar, Ebot, Wilson, \u0026amp; Mucci, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).Treatment options for primary prostate cancer include surgery, radiation therapy, and androgen deprivation therapy (ADT) (Rebello et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Although most patients initially respond to ADT, they mostly progress to castration-resistant prostate cancer (CRPC), for which treatment options are limited to aggressive chemotherapy and palliative care, and these patients have a poor prognosis and low survival (Gleave, Bruchovsky, Moore, \u0026amp; Venner, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). Clinically, 95% of prostate cancer is adenocarcinoma, originating from the epithelial cells of the prostate (Vitkin, Nersesian, Siemens, \u0026amp; Koti, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). There is growing evidence that stromal cells make an important contribution to the development of drug resistance and metastasis after ADT in primary prostate cancer (Rycaj, Li, Zhou, Chen, \u0026amp; Tang, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePharmacological treatment of prostate cancer includes chemotherapy and anti-androgen medications. ADT is the cornerstone of prostate cancer treatment because the prostate gland is dependent on androgens for growth and development (Sharifi, Dahut, \u0026amp; Figg, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The current standard of care for advanced prostate cancer includes gonadotropin-releasing hormone agonists (e.g., leuprolide), second-generation nonsteroidal androgen receptor (AR) antagonists (enzalutamide, apalutamide, and darolutamide), and the androgen biosynthesis inhibitor abiraterone (Desai, McManus, \u0026amp; Sharifi, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Progression to CRPC is caused by a number of mechanisms, including, but not limited to, AR\u003cem\u003e-\u003c/em\u003edependent drug resistance mechanisms (Deng et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eChanges in cell lineages occur during prostate cancer progression, which is the process by which cancer cells change from one morphological and functional cell type to another in response to environmental stress (Ge et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Alterations in cell lineages contribute to the development of drug resistance and metastasis during prostate cancer treatment; for example, in the context of ADT, alterations in cell lineages can shift the cell phenotype from an ARdependent adenocarcinoma to a phenotype with significantly different epigenetic features, including low AR sensitivity, and a more aggressive phenotype, among others (Ku et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Among these, neuroendocrine prostate cancer (NEPC) is one of the most aggressive subtypes (Wang et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, we used published prostate cancer single-cell RNA sequencing (scRNA-seq) data to construct a single-cell atlas of PRAD and to explore the changes in the abundance and function of different cell subpopulations during disease progression. We also analyzed the intrinsic and microenvironmental factors in cancer cells that might contribute to drug resistance and metastasis in prostate cancer. PRAD has a complex tumor microenvironment (TME), in which stromal cells support tumor cell survival through various intercellular communication pathways (Sfanos, Yegnasubramanian, Nelson, \u0026amp; De Marzo, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Among these stromal cells, macrophages and T cells play a crucial role in promoting tumor progression (Larionova et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). We applied functional enrichment analysis to identify signaling pathways that might be associated with PRAD. We use trajectory analysis to track the dynamic changes and differentiation trajectory of tumor cells. We also aimed to explore cellular communication to identify potential targets in tumor cell-stromal cell interactions. Existing studies have focused only on the functional and phenotypic evolutionary outcomes of subpopulations of prostate cancer during disease progression, without exploring the molecular mechanisms leading to their evolution. Our findings might help to determine the disease mechanisms and provide scientific theoretical guidance and a research basis for better intervention and treatment of prostate cancer.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eSingle cell transcriptome mapping\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;of prostate cancer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eScRNA-seq data from 41 samples were subjected to quality control, finally yielding 135,436 cells, which were clustered to yield 61 cell clusters (Figure 1A), and annotated with 13 cell types, including tumor cells (PRAD), epithelial cells (Ep), fibroblasts (Fib), smooth muscle cells (SMC), endothelial cells (EN), B cells (B), CD4\u003csup\u003e+\u0026nbsp;\u003c/sup\u003eT cells (CD4\u003csup\u003e+\u0026nbsp;\u003c/sup\u003eT), CD8\u003csup\u003e+\u0026nbsp;\u003c/sup\u003eT cells (CD8\u003csup\u003e+\u0026nbsp;\u003c/sup\u003eT), natural killer T cells(NKT), Naive T cells (Naive.T), plasmacytoid dendritic cells (pDC), mast cells (Mast), and macrophages (Mac) (Figure 1B). The marker genes expressed by different cell clusters are shown in a bubble diagram (Figure 1C). We divided the samples into four groups: The Control group for paraneoplastic tissue, the PRAD group for primary prostate cancer, the Resistant group for prostate cancer tissue that recurred after drug treatment, and the MPRAD group for prostate cancer metastases that recurred after drug treatment. We then analyzed the changes in abundance of each cell cluster during drug resistance and progression of prostate cancer. By comparison, we found that the abundances of Naive.T, CD8+T, B, and NKT immune cells were decreased, and the abundance of fibroblasts was increased, in the Resistant and MPRAD groups compared with those in the PRAD group (Figure 1D). This result suggested that immune cell infiltration was reduced in prostate cancer drug resistance and metastatic progression, whereas the abundance of fibroblasts, which contribute to disease progression, was increased.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe \u003cem\u003eLAIR2\u0026nbsp;\u003c/em\u003egene of CD4\u003csup\u003e+\u0026nbsp;\u003c/sup\u003eT cells is associated tightly with prostate cancer drug resistance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe further re-clustered CD4\u003csup\u003e+\u003c/sup\u003e T cells to obtain six cell subpopulations (Figure 2A). The different cell subpopulations were distinguished from each other by specific markers, and we defined the subpopulation of cells expressing \u003cem\u003eFOXP3\u003c/em\u003e as the regulatory T cell (Treg) cell subpopulation (Figure 2B). The Treg_LAIR2 and CD4\u003csup\u003e+\u003c/sup\u003eT_MINOS1 subpopulations were significantly more abundant in the Resistant group compared with those in the PRAD group; however, in the MPRAD group, significant increases in abundance were observed for the Treg_APOE and Treg_IGLC3 subpopulations (Figure 2C). This suggested that different cellular ecologies created different dominant subpopulations, and in the MPRAD group, the vast majority of CD+4 cells were Tregs, implying a more suppressive immune microenvironment. Furthermore, enrichment analysis revealed that the Treg_LAIR2 and Treg_APOE subpopulations were significantly associated with the oxidative phosphorylation pathway, which supported the Tregs\u0026rsquo; suppressive function and stability (Figure 2D). Moreover, through intercellular communication analysis we found that the CD86 (B7-2) ligands secreted by macrophages could bind to CTLA4 receptors on the surface of Treg cells in the Treg_LAIR2 subpopulation, whereas the TNFSF9 ligands secreted by CD8\u003csup\u003e+\u0026nbsp;\u003c/sup\u003eT cells could bind to TNFRSF9 receptors on the surface of Treg cells in the Treg_LAIR2 subpopulation (Figure 2E). These cellular communication networks reshape the tumor microenvironment and promote the development of PRAD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eM2_SPP1 and M2_FOLR2 are key TAM subpopulations in the tumor immune microenvironment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTumor associated macrophages (TAMs) are macrophages that infiltrate tumor tissues and exert immunosuppressive effects to promote tumor progression in multiple ways. We further divided Mac cells into nine cell subpopulations (Figure 3A), named according to their specifically expressed markers. We then defined the M2 cell subpopulations based on the relative expression of CD86 and CD163, in which the M2_SPP1 and M2_FOLR2 subpopulations with higher CD163 expression were M2 macrophages (Figure 3B). The M2_SPP1 subpopulation was significantly more abundant in prostate cancer progression, and a new cell subpopulation, M2_FOLR2, emerged in MPRAD (Figure 3C), and both subpopulations were specifically enriched (more abundant) in the MPRAD group; therefore, we defined these types of macrophages as TAMs. Intercellular communication existed between macrophages of the M2_FOLR2 subpopulation and multiple immune cells (Figure 3D). For example, the LGALS9 ligand secreted by M2_FOLR2 cells bound to the CTLA4 receptor on APOE Treg cells and IGLC3 Treg cells, exerting immunosuppressive effects.\u0026nbsp;Trajectory\u0026nbsp;analysis showed that M2_FOLR2 cells were positioned later on the differentiation trajectory than M2_SPP1 cells; therefore, we hypothesized that a transformation from M2_SPP1 cells to M2_FOLR2 cells had occurred, further promoting tumor development (Figure 3E, F).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultiple signaling pathways that contribute to prostate cancer resistance and metastasis are enriched in the EN-THY1 subpopulation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the changes in endothelial cells, we performed a subpopulation analysis of the endothelial cells and obtained seven subpopulations expressing different specific markers (Figures S1A, S1B). A new subpopulation, EN-THY1, was identified in the MPRAD group, which had the highest abundance in the metastasis group (Figure S1C). The EN-THY1 subpopulation was significantly enriched in genes related to the local adhesion pathway, which might contribute to the colonization of prostate cancer tumor cells via bloodstream metastasis. In addition, we found that this subpopulation had high expression of genes in leukocyte transendothelial migration pathway and regulation of actin cytoskeleton pathway, they all played an important role in the intravasation of tumor cells into the circulatory system and extravasation into local tissues. \u0026nbsp;(Figure S1D).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eRBP1\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;and \u003cem\u003eRORB\u003c/em\u003e genes are promising therapeutic targets for resistant prostate cancer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe further divided the tumor cells into eleven cell subpopulations (Figure 4A), and each subpopulation was represented by characteristic genes (Figure 4B). We observed that the ecological composition of tumor cells becomes progressively more complex as the disease progresses. The cell subpopulation PRAD_NME2 showed significantly decreased abundance in the Resistant and MPRAD groups (Figure 4C). In addition, \u003cem\u003eRBP1\u003c/em\u003e tumor cells were specifically present in the Resistant group, and we further found that \u003cem\u003eRBP1\u0026nbsp;\u003c/em\u003ewas highly expressed in tumor cells that were treated with anti-androgen drugs. \u003cem\u003eRBP1\u0026nbsp;\u003c/em\u003ewas also highly expressed in the liver metastasis subpopulation of prostate cancer. Therefore, we hypothesized that the \u003cem\u003eRBP1\u003c/em\u003e gene contributes to antiandrogenic resistance in prostate cancer and prostate cancer liver metastasis. Besides, a new cell subpopulation, PRAD_RORB, in the Resistant group was mainly composed of tumor cells that were resistant to doxorubicin treatment (Figure 4D). This implies that RORB is involved in the resistance of tumor cells to doxycycline.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePRAD_ASCL1 is important for drug resistance in tumor cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe PRAD_ASCL1 subpopulation had the highest potential for tumor cell differentiation in the Resistant group (Figure 5A), as well as being the terminal subpopulation for tumor cell differentiation (Figure 5B). This might reflect the fact that as the disease progresses, the surviving tumor cells approached tumor stem cell status to a greater extent and therefore had a higher differentiation potential. It was also possible that during progression, the tumor cells depolarized and regained their high differentiation potential. Enrichment analyses showed that PRAD_ASCL1 was significantly involved in the spliceosome, cell cycle, and oxidative phosphorylation pathways (Figure 5C). Abnormal splicing in prostate cancer can lead to genetic mutations that activate cancer-related signaling pathways. Small molecules that target components of the spliceosome, such as the spliceosome inhibitor E7107, have exhibited antitumor effects in prostate cancer cells and the prostate cancer cell line PC3 (Zhang et al., 2020). Aberrant cell cycle progression is one of the fundamental mechanisms of tumorigenesis (J. Liu, Peng, \u0026amp; Wei, 2022). Oxidative phosphorylation leads to increased reactive oxygen species production and hypoxia leads to increased tumor cell drug-resistance (Gilkes, Semenza, \u0026amp; Wirtz, 2014). These analyses suggested that PRAD_ASCL1 tumor cells play a crucial role in prostate cancer resistance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe then explored the mechanisms that might lead to drug resistance of tumor cells through cellular communication. The PRAD_ASCL1 subpopulation has the closest communication between tumor cells and stromal cells, and tumor cells in the PRAD_ASCL1 subpopulation can interact with T cells and macrophages through the ICOSLG-ICOS and CD24-SIGLEC10 ligand-receptor pairs, respectively, to build a tumor immunosuppressive microenvironment. Other subpopulations interacted with immune cells mainly through the CD24-SIGLEC10 ligand-receptor pair (Figure 5D). In addition, T cells interacted with multiple subpopulations of tumor cells in the Resistant group via the CCL5-SDC1/4 ligand-receptor pair in an intercellular interaction that might function to promote migration of resistant tumor cells (Figure 5E). Tumor cells can interact with NRP1, NRP2, and FLT1 receptors on endothelial cells via the ligand PGF to promote vascular endothelium proliferation. In turn, endothelial cells can interact with tumor cells through the ligand receptor pair HBEGF-CD9 to promote tumor cell proliferation (Figure 5F). This analysis confirmed that tumor cells and endothelial cells of this subpopulation interact to promote each other\u0026apos;s proliferation. Interestingly, tumor cells of the PRAD_CALML5 subpopulation in the MPRAD group expressed \u003cem\u003eNGF\u003c/em\u003e, which encodes a secreted growth factor that might interact with the intracellular protein encoded by the \u003cem\u003eNGFRAP1\u003c/em\u003e gene in PRAD_ASCL1 tumor cells in the Resistant group, functioning as a promoter of proliferation and metastasis of resistant tumor cells (Figure 5F).\u003c/p\u003e\n\u003cp\u003eSubsequently, we converted the transcriptomic data of tumor cells in the Resistant group into pseudo-bulk data and predicted sensitive drugs in the different subpopulations using the oncoppredict R package. Our analysis revealed that tumor cells in the PRAD_ASCL1 subpopulation were relatively sensitive to\u0026nbsp;KRAS\u003csup\u003eG12C\u003c/sup\u003e Inhibitor class drugs (Supplementary Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePRAD_CALML5 is a key\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003esubpopulation\u003c/strong\u003e \u003cstrong\u003eof the MPRAD group\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the MPRAD group, PRAD_CALML5 was the subpopulation with the highest potential for tumor cell differentiation (Figure 6A), as well as being the terminal subpopulation for tumor cell differentiation (Figure 5B). Enrichment analyses showed that like the PRAD_ASCL1 subpopulation, PRAD_CALML5 was also significantly involved in the spliceosome, cell cycle, and oxidative phosphorylation pathways (Figure 5C). These analyses suggested that PRAD_CALML5 tumor cells play a crucial role in prostate cancer metastasis. Cellular communication analysis revealed that PRAD_CALML5 tumor cells exert tumor immune suppressive functions with M2_SPP1 macrophages via CD24-SIGLEC10 ligand-receptor interactions (Figure 6A). Similar to the Resistant group, T cells in the MPRAD group could also interact with tumor cells via the CCL5-SDC1 ligand-receptor pair, which might play a role in regulating tumor cell migration (Figure 6B). PRAD_CALML5 tumor cells interacted with EN_THY1 and EN_C11or158 endothelial cells via IGF1-INSR ligand-receptor interactions to promote neovascularization (Figure 5C). Moreover, \u003cem\u003eCTHRC1\u003c/em\u003e-associated cancer-associated fibroblasts (CAFs) interacted with PRAD_CALML5 tumor cells via the TIMP1-CD63 ligand‑receptor interaction, thereby promoting prostate cancer progression (Figure 6D).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we used published prostate cancer scRNA-seq data to construct a single-cell transcriptome atlas of prostate cancer and explored the changes in the abundance and function of different cell subpopulations during drug resistance and metastasis in prostate cancer. The results suggested that the ecological composition of each cell subpopulation becomes more complex in the context of drug treatment. The cellular phenotype of the abundance-dominant subpopulations in different cell subpopulations facilitates drug resistance and metastasis of tumor cells. Here, we focus our analysis on CD4\u003csup\u003e+\u003c/sup\u003e T cells, macrophages, endothelial cells, and tumor cells.\u003c/p\u003e \u003cp\u003eImmune cells, an important component of the TME, directly influence disease progression (Galluzzi, Humeau, Buque, Zitvogel, \u0026amp; Kroemer, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). CD4\u003csup\u003e+\u003c/sup\u003e T cells assist B cells in antibody production, activate phagocytosis of macrophages, and recruit other immune cells to infected or inflamed areas of the body. Naive CD4\u003csup\u003e+\u003c/sup\u003e T cells are activated to differentiate into specific subtypes after interacting with antigen-MHC complexes, including helper T cells, Tregs, and follicular helper T cells (S. Liu et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Among them, Tregs play an immunosuppressive function in tumor immunity by suppressing the activation and proliferation of effector cells, such as monocytes, macrophages, NK cells, and APCs, as well as inducing apoptosis through various mechanisms (Radej, Szewc, \u0026amp; Maciejewski, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). \u003cem\u003eLAIR2\u003c/em\u003e was predicted to be a biomarker of T-cell depletion in the TME of cholangiocarcinoma and is a biomarker for immune infiltration assessment in cholangiocarcinoma (Z. Chen et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In our study, the abundance of the Treg_LAIR2 subpopulation increased significantly in the Resistant group. Macrophages and CD8\u003csup\u003e+\u003c/sup\u003e T cells interact with the Treg_LAIR2 subpopulation via CD86CTLA4 and TNFSF9TNFRSF9 ligand-receptor pairs, respectively, and these immune checkpoints contribute to the immune escape of tumor cells (Etxeberria, Glez-Vaz, Teijeira, \u0026amp; Melero, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Pai et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).Therefore, we hypothesized that the increased abundance of the CD4\u003csup\u003e+\u003c/sup\u003e T cell subpopulation expressing \u003cem\u003eLAIR2\u003c/em\u003e might be associated with drug resistance in prostate cancer.\u003c/p\u003e \u003cp\u003eThe abundance of the M2-type macrophage subpopulation also increased significantly during disease progression and participated in the formation of a tumor immunosuppressive microenvironment together with Tregs (C. Liu et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In addition, M2_SPP1 and M2_FOLR2 are key TAM subpopulations in the immune microenvironment. Previous studies have confirmed that \u003cem\u003eSPP1\u003c/em\u003e\u0026thinsp;+\u0026thinsp;TAMs have immunosuppressive effects and are associated with the invasive metastasis of tumor cells (Wei et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In hepatocellular carcinoma, TAMs expressing \u003cem\u003eFOLR2\u003c/em\u003e colocalize with Tregs and exert immunosuppressive effects (Sharma et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Herein, intercellular communication analysis also showed that FOLR2-expressing TAMs communicate with Tregs in the metastatic group, which contributes to drug resistance and metastasis of prostate cancer. Our analysis showed that the TAM subpopulations M2_SPP1 and M2_FOLR2 were enriched in the MPRAD group, suggesting their involvement in the formation of a tumor immunosuppressive microenvironment, thereby contributing to the development of drug resistance and metastasis in prostate cancer.\u003c/p\u003e \u003cp\u003eTumor growth and metastasis require neovascular support, and endothelial cells are a major part of neovascularization (Lugano, Ramachandran, \u0026amp; Dimberg, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).Tumor cells complete hematogenous metastasis within the microvasculature of specific organs through retention, adhesion, and growth into the extravascular parenchymal environment (Sobierajska, Ciszewski, Sacewicz-Hofman, \u0026amp; Niewiarowska, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).The new subpopulation, EN-THY1, had the highest abundance in the endothelial cell ecology of the MPRAD group, and the GO and KEGG pathway-based enrichment analysis revealed that this subpopulation had high expression of genes in the local adhesion pathway, the adhesion molecules expressed by endothelial cells could enable tumor cells that have already entered the bloodstream to adhere to endothelial cells, possibly contributing to the colonization of prostate cancer tumor cells via bloodstream metastasis (Zhao \u0026amp; Guan, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).In addition, this subpopulation was significantly enriched in genes related to the leukocyte transendothelial migration pathway. This pathway was generally activated in cancer progression (Enarsson, Lundin, Johnsson, Brezicka, \u0026amp; Quiding-Jarbrink, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Activation of the pathway disrupted the tightness and integrity of the endothelial cell junctions, inflammatory cytokines produced by leukocytes contribute to cancer cell extravasation as well (Reymond, d'Agua, \u0026amp; Ridley, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eRBP1\u003c/em\u003e gene was highly expressed in tumor cells of the PRAD_RBP1 subpopulation, which comprised anti-androgen-treated tumor cells. In addition, tumor cells in a subpopulation of prostate cancer liver metastases also showed high expression of the \u003cem\u003eRBP1\u003c/em\u003e gene. RBP1 increases the proliferation and aggressiveness of the invasive class IV human prostate adenocarcinoma cell line PC-3 (Napoli, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Therefore, we hypothesized that the \u003cem\u003eRBP1\u003c/em\u003e gene contributes to antiandrogenic resistance in prostate cancer and prostate cancer liver metastasis. The PRAD_RORB subpopulation is mainly composed of tumor cells resistant to doxorubicin treatment, and previous studies have shown that \u003cem\u003eRORB\u003c/em\u003e is a clock-related gene that is associated with low survival in prostate cancer (Yue et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Our analysis also demonstrated high expression of \u003cem\u003eRORB\u003c/em\u003e in doxorubicin-resistant tumor cells. Therefore, we hypothesized that RORB is a potential therapeutic target in resistant prostate cancer.\u003c/p\u003e \u003cp\u003eAs the disease progresses, the ecological composition of tumor cells becomes progressively more complex. The cell subpopulation PRAD_NME2 showed significantly decreased abundance in the Resistant and MPRAD groups, which led us to speculate that the tumor cell subpopulation PRAD_NME2 is a class of drug therapy-sensitive tumor cells. In addition, the abundance of cell subpopulations with high expression of the \u003cem\u003eNME2\u003c/em\u003e was higher in the EN and Mac ecologies of the primary foci, while their abundance was significantly decreased in both the Resistant and MPRAD groups. We also analyzed intergroup differences in \u003cem\u003eNME2\u003c/em\u003e gene expression, revealing that its expression was significantly lower in prostate cancer resistant primary and metastatic focal tissues compared with that in tumor primary and paracancerous tissues. \u003cem\u003eNME2\u003c/em\u003e is a metastasis suppressor gene (Chang et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) that is associated with telomere ends and telomerase, reducing telomerase activity within cells (Kar et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). NME2 has different effects on different types of cancer cells and their invasion or metastasis (Y. F. Liu et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The \u003cem\u003eNME2\u003c/em\u003e gene is expected to be a relevant target or biological marker for future prostate cancer drug therapy.\u003c/p\u003e \u003cp\u003eWe analyzed the differentiation potential of tumor cell subpopulations, and in general, tumor cell subpopulations in the Resistance group had a higher cell differentiation potential compared with those in the MPRAD group. In addition, we tracked the dynamic changes and differentiation trajectories of tumor cells using trajectory analysis. Both the PRAD_ASCL1 and PRAD_CALML5 subpopulations were terminal subpopulations of tumor cell differentiation, and were the subpopulations with the highest differentiation potentials in the Resistant and MPRAD groups. Previous studies have shown that the stronger the differentiation potential of cancer cells, the stronger the drug resistance (Pattabiraman \u0026amp; Weinberg, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The PRAD_ASCL1 subpopulation, as a subpopulation with high specificity and the highest tumor cell stemness in the Resistant group, might be important for drug resistance in tumor cells. Tumor cells in the PRAD_ASCL1 subpopulation highly express the \u003cem\u003eASCL1\u003c/em\u003e gene, which is a marker gene for neuroendocrine prostate cancer, one of the most aggressive subtypes of prostate cancer (Dong et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). To this end, we further investigated the remodeling effect of the PRAD_ASCL1 subpopulation of tumor cells on stromal cells and predicted sensitive drugs for treatment against this subpopulation. Through intercellular communication analysis we found that tumor cells of the PRAD_ASCL1 subpopulation could interact with T cells and macrophages via ICOSLG-ICOS and CD24-SIGLEC10 ligand-receptor pairs, respectively, to construct a tumor immunosuppressive microenvironment (Barkal et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In addition, T cells can interact with the PRAD_ASCL1 subpopulation of tumor cells via the CCL5-SDC1/4 ligand-receptor pair. Some \u003cem\u003ein vitro\u003c/em\u003e experiments have demonstrated that T cells can promote the migration of pancreatic ductal adenocarcinoma tumor cells via CCL5-SDC1 receptor-ligand interactions. (K. Chen et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) Therefore, we speculated that CCL5-SDC4 ligand-receptor intercellular interactions between T cells and tumor cells in prostate cancer might also function to promote the migration of resistant tumor cells; however, further experimental validation of our speculation is required. This subpopulation of tumor cells also interacted with endothelial cells via the ligandreceptor pairs PGF-NRP1, PGF-NRP2, PGF-FLT1, and HBEGF-CD9 to promote each other's proliferation. Subsequently, we found that tumor cells in the PRAD_ASCL1 subpopulation were most sensitive to the KRAS\u003csup\u003eG12C\u003c/sup\u003e inhibitor class of drugs according to the drug response prediction analysis. \u003cem\u003eKRAS\u003c/em\u003e is the most frequently mutated oncogene in humans (Huang, Guo, Wang, \u0026amp; Fu, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and the hallmark cancers for \u003cem\u003eKRAS\u003c/em\u003e mutations are pancreatic, colorectal, lung, and genitourinary cancers (Timar \u0026amp; Kashofer, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), in which the gene is involved in multiple tumor-related signaling pathways (Hallin et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Previous experiments have validated the therapeutic effects of such drugs in non-small cell lung cancer and colorectal cancer (Hallin et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Our analysis predicted that KRAS\u003csup\u003eG12C\u003c/sup\u003e inhibitor class drugs would be expected to be potential agents to treat neuroendocrine prostate cancer.\u003c/p\u003e \u003cp\u003eThe PRAD_CALML5 subpopulation, as the terminal subpopulation for tumor cell differentiation, and the subpopulation with the highest differentiation potential of tumor cells in the MPRAD group, is important for the proliferation and migration of tumor cells. Therefore, we used intercellular communication analysis to predict how it creates a microenvironment conducive to tumor cell metastasis. We constructed an extensive regulatory network of intercellular communication between tumor cells and stromal cells, and found that tumor cells of the PRAD_CALML5 subpopulation communicate with M2-type macrophages in the metastasis group via the ligand-receptor pair CD24-SIGLEC10, with T cells via the ligandreceptor pair CCL5-SDC1/4, and with EN cells via the ligand-receptor pair IGF1- INSR for intercellular communication, promoting proliferation resistance and metastasis of tumor cells. In addition, we identified for the first time that CTHRC1-related CAFs can interact with PRAD_CALML5 tumor cells via the TIMP1-CD63 ligand-receptor pair to promote drug resistance and metastasis in prostate cancer (Li, Zhang, Wang, \u0026amp; Jia, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOverall, we identified, through intercellular communication analysis, key ligand receptors, such as CD24-SIGLEC10 andCCL5-SDC1/4, between tumor cells and stromal cells that affect the development of drug resistance and metastasis in prostate cancer. Prostate cancer tumor cells expressing CD24 interact with macrophage-indicated sialic acid-binding Ig-like lectin 10 (\u003cem\u003eSIGLEC10\u003c/em\u003e) receptors, which might contribute to the immune escape function of prostate cancer tumor cells and help build an immunosuppressive microenvironment for prostate cancer resistance and metastasis. CD24 also has potential as a therapeutic target in prostate cancer (Panagiotou, Syrigos, Charpidou, Kotteas, \u0026amp; Vathiotis, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In addition, we speculated that CCL5-SDC1/4 ligand-receptor celltocell interactions between T cells and tumor cells in prostate cancer might function to promote the migration of resistant tumor cells; however, further experimental validation of this speculation is needed.\u003c/p\u003e \u003cp\u003eAlthough this study reports novel findings, it has some limitations. First, the samples included in this study were relatively small, and the analytical results obtained need to be further validated in a larger sample. Second, although the mechanisms derived in this study are based on a bioinformatic analysis approach, they have not been validated by molecular and cellular experiments. Therefore, we plan to further expand the samples in future studies and use molecular and cellular experiments to validate the findings.\u003c/p\u003e \u003cp\u003eIn conclusion, our study provides theoretical support for changes in the abundance and function of different cell subpopulations during the progression of prostate cancer resistance and metastasis, thus identifying potential targets for treatment of prostate cancer resistance and metastasis, suggesting key ligand-receptor interactions that might contribute to disease progression, and providing valuable insights for targeted therapy of prostate cancer.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003eData sources \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe collected scRNA-seq data from a total of 41 samples, including 11 PRAD primary focal tissues, 11 paracancerous tissues (Control), 6 resistant primary focal tissues (Resistant), and 13 resistant metastatic (MPRAD) focal tissues. The collected data were based on 10x Genomics scRNA-seq, and were all available in the Gene Expression Omnibus (GEO) databases: GSE193337, GSE137829, GSE185344, GSE210358 (clinical information for the samples is available in Supplementary Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eScRNA-seq data processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eScRNA-seq data were processed for quality control, filtering out the 1% of cells with the highest and lowest feature numbers of expression, as well as cells with more than 10% mitochondrial gene expression. After quality control, the data were integrated and analyzed based on standardization using SCTransform in Seurat R package (Hao et al., 2021).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUMAP visualization and cell type annotation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDimensionality reduction and clustering analysis of single cells was implemented based on the Seurat R package (Hao et al., 2021). We then analyzed single cells using the default parameters, constructing single cell profiles, and visualizing clusters of cells using dimensionality reduction with uniform manifold approximation and projection (UMAP) plots. Clusters were associated with cell types based on differential expression scores of marker genes for: CD8\u003csup\u003e+ \u003c/sup\u003eT cells (\u003cem\u003eCD8A\u003c/em\u003e, \u003cem\u003eCD8B\u003c/em\u003e, \u003cem\u003eCD3D\u003c/em\u003e, \u003cem\u003eCD3E\u003c/em\u003e, and \u003cem\u003eCD3G\u003c/em\u003e); CD4\u003csup\u003e+\u003c/sup\u003eT cells (\u003cem\u003eCD4, CD3D\u003c/em\u003e, \u003cem\u003eCD3E\u003c/em\u003e, and \u003cem\u003eCD3G\u003c/em\u003e); Naive T cells (\u003cem\u003eCD247\u003c/em\u003e, \u003cem\u003eCD3D\u003c/em\u003e, \u003cem\u003eCD3E\u003c/em\u003e, and \u003cem\u003eCD3G\u003c/em\u003e); natural killer T cells (\u003cem\u003eCD3E\u003c/em\u003e, \u003cem\u003eCD247\u003c/em\u003e, and \u003cem\u003eCD160\u003c/em\u003e); B cells (\u003cem\u003eCD79A\u003c/em\u003e,\u003cem\u003e CD79B\u003c/em\u003e, and \u003cem\u003eCD19\u003c/em\u003e); mast cells (\u003cem\u003eFCER1A\u003c/em\u003e and \u003cem\u003eKIT\u003c/em\u003e), macrophages (\u003cem\u003eCD14\u003c/em\u003e, \u003cem\u003eCD68\u003c/em\u003e, \u003cem\u003eHLA-DRB1\u003c/em\u003e, and\u003cem\u003e PTPRC\u003c/em\u003e); plasmacytoid dendritic cells (\u003cem\u003eCLEC4C\u003c/em\u003e,\u003cem\u003e IL3RA\u003c/em\u003e, and \u003cem\u003ePTPRC\u003c/em\u003e), fibroblasts (\u003cem\u003eACTA2\u003c/em\u003e, \u003cem\u003ePDGFRB\u003c/em\u003e, and\u003cem\u003e COL1A1\u003c/em\u003e), smooth muscle cells (\u003cem\u003eDES\u003c/em\u003e and \u003cem\u003eACTA2\u003c/em\u003e); endothelial cells (\u003cem\u003eENG\u003c/em\u003e, \u003cem\u003ePECAM1\u003c/em\u003e, \u003cem\u003eVWF\u003c/em\u003e,\u003cem\u003e \u003c/em\u003eand \u003cem\u003eCDH5\u003c/em\u003e); and tumor cells (\u003cem\u003eKRT18\u003c/em\u003e,\u003cem\u003e SOX2\u003c/em\u003e, \u003cem\u003eSCHLAP1\u003c/em\u003e, \u003cem\u003eCDKN2A\u003c/em\u003e, \u003cem\u003eAMACR\u003c/em\u003e,\u003cem\u003e AR\u003c/em\u003e,\u003cem\u003e \u003c/em\u003eand \u003cem\u003eKLK3\u003c/em\u003e (Chan et al., 2022)).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional enrichment analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the clusterProfiler R package (Yu, Wang, Han, \u0026amp; He, 2012) to determine the potential function of the molecular pathways active in each cell subpopulation. P \u0026lt; 0.05 was considered significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrajectory analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Monocle3 R package (Trapnell et al., 2014) with default parameters was used to perform single-cell trajectory analysis of tumor cells, depicting a single-cell atlas of the proposed temporal trajectory of cell clusters.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell communication analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe mapped receptor-ligand pairs using iTALK (v0.1.0) (doi:https://doi.org/10.1101/507871) using the default parameters (https://github.com/Coolgenome/iTALK). The visualization of ligand-receptor pairs in iTALK was divided into four groups, including cytokines, growth factors, immune checkpoints and the other, to discover important cellular communication events that influence disease progression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePredicting cell differentiation potential\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the CytoTRACE R package (v0.1.0) (Gulati et al., 2020), which predicts the relative differentiation status of cells based on single-cell RNA sequencing data without any a priori information. Inferring intercellular activity and relative intercellular differentiation status based on single-cell counting matrices.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDrug prediction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe expression matrix of single cells was converted into a standard pseudo-bulk expression matrix. Then, the oncoppredict R package used the expression matrix to predict sensitive drugs for tumor cells in the Resistant group (Maeser, Gruener, \u0026amp; Huang, 2021).\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ePRAD\u0026nbsp;\u0026nbsp;Prostate adenocarcinoma\u003c/p\u003e\n\u003cp\u003eADT\u0026nbsp; Androgen deprivation therapy\u003c/p\u003e\n\u003cp\u003eCRPC\u0026nbsp; Castration-resistant prostate cancer\u003c/p\u003e\n\u003cp\u003eAR\u0026nbsp; Androgen receptor\u003c/p\u003e\n\u003cp\u003eNEPC\u0026nbsp; Neuroendocrine prostate cancer\u003c/p\u003e\n\u003cp\u003escRNA-seq\u0026nbsp; Single-cell RNA sequencing\u003c/p\u003e\n\u003cp\u003eTME\u0026nbsp; Tumor microenvironment\u003c/p\u003e\n\u003cp\u003eEP\u0026nbsp; Epithelial cells\u003c/p\u003e\n\u003cp\u003eFib\u0026nbsp; Fibroblasts\u003c/p\u003e\n\u003cp\u003eSMC\u0026nbsp; Smooth muscle cells\u003c/p\u003e\n\u003cp\u003eEN\u0026nbsp;\u0026nbsp;Endothelial cells\u003c/p\u003e\n\u003cp\u003eB\u0026nbsp;\u0026nbsp;B cells\u003c/p\u003e\n\u003cp\u003eCD4\u003csup\u003e+\u003c/sup\u003eT\u0026nbsp;\u0026nbsp;CD4\u003csup\u003e+\u003c/sup\u003eT cells\u003c/p\u003e\n\u003cp\u003eCD8\u003csup\u003e+\u003c/sup\u003eT\u0026nbsp;\u0026nbsp;CD8\u003csup\u003e+\u0026nbsp;\u003c/sup\u003eT cells\u003c/p\u003e\n\u003cp\u003eNKT\u0026nbsp; Natural killer T cells\u003c/p\u003e\n\u003cp\u003eNaive.T\u0026nbsp;\u0026nbsp;Naive T cells\u003c/p\u003e\n\u003cp\u003epDC \u0026nbsp;Plasmacytoid dendritic cells\u003c/p\u003e\n\u003cp\u003eMast\u0026nbsp; Mast cells\u003c/p\u003e\n\u003cp\u003eMac\u0026nbsp; Macrophages\u003c/p\u003e\n\u003cp\u003eTAMs\u0026nbsp;\u0026nbsp;Tumor associated macrophages\u003c/p\u003e\n\u003cp\u003eCAFs\u0026nbsp; Cancer-associated fibroblasts\u003c/p\u003e\n\u003cp\u003eGEO\u0026nbsp;\u0026nbsp;Gene Expression Omnibus\u003c/p\u003e\n\u003cp\u003eUMAP\u0026nbsp; Uniform manifold approximation and projection\u003c/p\u003e\n\u003cp\u003eGO\u0026nbsp;\u0026nbsp;Gene Ontology\u003c/p\u003e\n\u003cp\u003eKEGG \u0026nbsp;Kyoto Encyclopedia of Genes and Genomes\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the Ethics Committee of Yantai Yuhuangding Hospital, and the patients provided their informed consent for study participation. All methods were carried out in accordance with the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Bioinformatics analysis were performed by\u0026nbsp;HZ, JL\u0026nbsp;and\u0026nbsp;TY. The first draft of the manuscript was written by\u0026nbsp;HZ, and all authors commented on previous versions of the manuscript. All authors contributed to the article and approved the submitted version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;single-cell\u0026nbsp;RNA sequencing\u0026nbsp;data used in the article are deposited in the NCBI Gene Expression Omnibus (GEO) databases under accession numbers: GSE193337, GSE137829, GSE185344, GSE210358.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are indebted and thankful to all participants for their valuable contributions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by the Key Research and Development Program of Shandong Province (Major Science and Technology Innovation Project), 2022CXPT023, Taishan Scholars Project (ts20190991).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBarkal, A. 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However, the mechanisms by which drug resistance and metastasis occur in prostate cancer are unknown, and there are no effective targeted drugs to treat metastatic prostate cancer. Therefore, the goal of this study was to construct a single-cell transcriptome atlas of prostate cancer by using single-cell RNA sequencing data through bioinformatics techniques, to explore the changes in abundance and function of different cell subpopulations during drug resistance and metastasis, and to speculate on the molecular pathways that may lead to drug resistance and metastasis. We analyzed the tumor cell subpopulation and predicted that the \u003cem\u003eASCL1\u003c/em\u003e, \u003cem\u003eRORB\u003c/em\u003e, \u003cem\u003eRBP1\u003c/em\u003e and \u003cem\u003eCALML5\u003c/em\u003e might be the genes responsible for the development of drug resistance and metastasis, providing potential targets for targeted prostate cancer therapy. In addition, we performed differentiation potential analysis, trajectory analysis, and intercellular communication analysis on tumor cell subpopulations, which identified key ligand-receptor interactions between tumor cells and stromal cells that lead to disease progression, such as CCL5-SDC1/4, ICOSLG-ICOS and CD24-SIGLEC10. Our analysis identified potential targets to treat resistance and metastasis in prostate cancer, suggesting key ligand-receptor interactions that might contribute to disease progression, and provided valuable insights into targeted therapy for prostate cancer.\u003c/p\u003e","manuscriptTitle":"Single-cell analysis supports key cell subpopulations for drug resistance and metastasis in prostate cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-21 12:09:45","doi":"10.21203/rs.3.rs-5029414/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":"d15b2d85-d457-4b80-89bc-2f578117974a","owner":[],"postedDate":"October 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-27T13:38:48+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-21 12:09:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5029414","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5029414","identity":"rs-5029414","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}