Mendelian randomization analysis integrating GWAS and eQTL data identified potential regulatory genes associated with prostate cancer in neural cells

Mendelian randomization analysis integrating GWAS and eQTL data identified potential regulatory genes associated with prostate cancer in neural cells | 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 Mendelian randomization analysis integrating GWAS and eQTL data identified potential regulatory genes associated with prostate cancer in neural cells Jiahao Guo, Hao Xie, Quanting Yin, Changming Dong, Yufan Yang, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6747184/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Objective: Although studies have suggested a potential link between the nervous system and prostate cancer, the underlying regulatory mechanisms remain unclear. Therefore, it is crucial to identify the genes involved in regulating prostate cancer within the nervous system. Methods: We utilized eQTL data from eight neural cell types as exposure factors and GWAS data for prostate cancer as outcome events. Mendelian randomization (MR) analyses were performed to identify causative genes associated with prostate, bladder, and renal cancers in Astrocytes, Endothelial cells, Excitatory neurons, Inhibitory neurons, Microglia, Oligodendrocytes, OPCs/COPs, and Pericytes. Bladder and renal cancers were used as controls. Sensitivity analyses (heterogeneity, pleiotropy, and leave-one-out tests) were conducted to ensure reliability. Results: In astrocytes, seven positive genes were identified as being causally related to prostate cancer: KANSL1 , AC005670.2 , ARL17B , LRRC37A2 , LRRC37A , MAPT , and LINC02210 . In Endothelial cells, Inhibitory neuron and Microglia, three genes (LRRC37A2, ARL17B，and KANSL1) were identified as risk genes that are associated with prostate cancer. Four protective genes were identified in excitatory neurons, including LRRC37A2, ARL17B, KANSL1 and LINC02210. In oligodendrocytes, eight genes were identified, with LRRC37A2 , ARL17B , and KANSL1 acting as protective factors, while OR2L13 , OR2L3 , OR2L5 , OR2L2 , and OR2M4 were identified as risk factors. Additionally, sensitivity analyses showed no heterogeneity or horizontal pleiotropy in the MR results, confirming their reliability and stability. In addition, no positive genes were found in bladder cancer and renal cancer. Conclusion: Our study highlights the role of the nervous system, particularly astrocytes, in regulating prostate cancer. We identified three genes, with LRRC37A2, ARL17B, and KANSL1 emerging as key protective factors. These findings provide potential targets for prostate cancer diagnosis and treatment. Prostate cancer neural cell eQTL Mendelian randomization causal relationship Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Tumors of the urinary system can develop in various organs, including the kidneys, prostate, bladder, urethra, and more [ 1 ] . These tumors generally occur after the age of 40 and are about twice as common in men as in women. Prostate cancer (PCa) represents an epithelial malignant tumor originating in the prostate. The incidence rate is low before the age of 55, gradually increases after 55, with the peak age range being 70–80 years old. As the most common malignant tumor of the male reproductive system, PCa is the second leading cause of cancer-related deaths among men. [ 2 – 3 ] . In recent years, the incidence of major urinary system tumors in China has been steadily increasing, with a significantly higher prevalence in men compared to women. Thus, identifying key genes that regulate the development of these tumors is critical. Genome-wide association studies (GWAS) have identified several important genetic variants associated with urinary tumors [ 4 ] . However, the functional genes that play a definitive role in the development of urinary system tumors remain largely unknown. Studies have indicated that the nervous system plays a significant role in cancer, particularly PCa [ 5 ] . Research has further demonstrated that the nervous system contributes to organogenesis, maintains homeostasis during development, and interacts with the immune system and stromal cells in the tumor microenvironment through nerve fibers. These interactions have been observed in various malignant tumors. For example, research has demonstrated the interaction between astrocytes and PCa cells originating from different metastatic sites [ 6 ] . Additionally, endothelial cells can function as precursors to osteoblasts in bones affected by metastatic PCa. Other findings indicate that certain microglial components in mice possess antioxidant properties that combat PCa cells [ 7 ] . Furthermore, studies suggest that enhancing specific gene activity in astrocytes may offer therapeutic benefits for treating metastatic bladder cancer [ 8 ] . These findings underscore the crucial involvement of nerve cells in the development of urinary system tumors. However, the key genes that regulate tumor growth within nerve cells are not yet fully understood. Mendelian randomization (MR) is a powerful approach used to determine the causal relationships between modifiable exposures or risk factors and clinically relevant outcomes [ 9 ] . It has been widely applied to discover new therapeutic targets by integrating data from disease GWASs and expression quantitative trait loci (eQTL) studies [ 10 ] . Traditional observational studies often face challenges such as confounding factors and reverse causality, which can affect their ability to infer causality. MR, by using genetic variations as instrumental variables (IVs), helps overcome these issues by detecting and quantifying causal relationships more reliably. In recent years, many researchers have applied MR analysis to urinary system diseases. For instance, studies have shown that the expression level of the mitochondrial-related gene NSUN4 is positively correlated with the risk of PCa [ 11 ] . Other research has identified CD4 on monocytes and FSC-A on plasmacytoid dendritic cells as protective factors against PCa [ 12 ] . Furthermore, HES4 has been identified as an independent prognostic factor for bladder cancer outcomes [ 13 ] . In this study, we utilized GWAS and eQTL data to identify genes within eight different types of nerve cells that exhibit a causal relationship with urinary system tumors, including bladder, prostate, and kidney cancer, through two-sample MR analysis. By doing so, this research aims to uncover the potential mechanisms by which these genes contribute to the pathogenesis of urinary system tumors, providing new theoretical insights that could enhance the diagnosis and treatment of patients. 2. Materials and methods 2.1 Data Collection Exposure Factors : We obtained eQTL summary statistics for eight types of neurons (Astrocytes, Endothelial cells, Excitatory neurons, Inhibitory neurons, Microglia, Oligodendrocytes, OPCs/COPs, and Pericytes). These data are available on Zenodo at https://doi.org/10.5281/zenodo.5543734[14] . Excitatory neurons contain 2,725 eQTLs, Oligodendrocytes contain 1,903 eQTLs, Astrocytes contain 976 eQTLs, Inhibitory neurons contain 831 eQTLs, OPCs contain 591 eQTLs, Microglia contain 444 eQTLs, Endothelial cells contain 100 eQTLs, and Pericytes contain 16 eQTLs. Outcome Data The data for prostate cancer, bladder cancer and renal cancer.were obtained from the GWAS Catalog database, comprising 140,254 samples (61,106 control samples and 79,148 case samples) of European ethnicity. The UKB-B-8193 bladder cancer dataset includes 46,293,33 samples (46,183,26 control samples and 1,101 case samples) with 9,518,867 SNPs. Additionally, the dataset iu-b-4874 includes 373,295 samples (3,720,161 control samples and 1,279 case samples) with 9,904,926 SNPs. The GWAS data for renal cancer were also sourced from the GWAS Catalog, including 555 samples (210 control samples and 340 case samples). 2.2 MR Analysis We conducted Mendelian Randomization (MR) analysis using the "TwoSampleMR" R package, with eQTL data from eight types of neural cells as the exposure and prostate and bladder cancer as the outcomes. To generate instrumental variables (IVs), we selected SNPs with a significance level of p < 5×10^-8 and an F-statistic of ≥ 10 to ensure strong correlation with the exposure factor. SNPs with an FDR < 0.05 and located within ± 100 kb of the transcription start site (TSS) of each gene were included. We also performed linkage disequilibrium (LD) analysis on the SNPs from each eQTL using European samples from the 1000 Genomes Project (r² < 0.01, kb = 10,000). Five primary methods were employed for MR analysis: the inverse-variance weighted (IVW) method, weighted mode, MR-Egger regression, weighted median estimator (WME), and simple mode. The IVW method was used as the primary adfghpproach to estimate causal effects, while the other four methods served to validate the reliability and stability of the results. 2.3 Sensitivity analysis To assess sensitivity, we used the TwoSampleMR package to conduct MR Pleiotropy Residuals, detecting horizontal pleiotropy (p < 0.05) and removing outlier SNPs. Horizontal pleiotropy was further evaluated using the intercept of the MR-Egger method. Cochran's Q test was applied to analyze heterogeneity among the instrumental variables, and depending on the degree of heterogeneity, either a fixed-effect or random-effect model was employed for further analysis. Additionally, we performed leave-one-out analysis to determine whether the significant associations between exposure and outcomes were driven by any single SNP. 3. Results 3.1 Study overview We first obtained GWAS datasets for prostate, bladder, and kidney cancers from the GWAS Catalog database. Additionally, eQTL data for eight types of neurons (Astrocytes, Endothelial cells, Excitatory neurons, Inhibitory neurons, Microglia, Oligodendrocytes, OPCs/COPs, and Pericytes) were collected. The eQTL data were used as exposure factors, and prostate, bladder, and kidney cancers were treated as outcomes for the MR analysis. Positive genes were identified based on causal inference, and sensitivity analyses were conducted to evaluate the robustness of the MR results (Table 1 ). Figure 1 provides a analysis flow of this study. Table 1 Data summary Traits ID Sample size Ancestry prostatic cancer categorical-20001-both_sexes-1034 140254(Control :61106; Case:79148) European bladder cancer ukb-b-8193 462933(Control :4618326; Case:1101) European ieu-b-4874 462933(Control :372016; Case:1279) European renal cancer categorical-20001-1034 555 samples (Control: 210 ; Case:340) European Eight kinds of neurons (Astrocytes, Endothelial.cells, Excitatory.neurons, Inhibitory.neurons, Microglia, Oligodendrocytes, OPCs/COPs and Pericytes) ) eQTL https://doi.org/10.5281/zenodo.5543734 3.2 MR analysis of nerve cell eQTL and prostate cancer As shown in Fig. 2 , seven protective genes were identified in astrocytes that exhibited a causal relationship with prostate cancer, including KANSL1, AC005670.2, ARL17B, LRRC37A2, LRRC37A, MAPT, and LINC02210 (all with OR < 1). In endothelial cells, three protective genes (OR < 1) were identified: LRRC37A2, ARL17B, and KANSL1 (Fig. 3 ). In excitatory neurons, four positive protective genes (including LRRC37A2, ARL17B, KANSL1, and LINC02210) were associated with prostate cancer (Fig. 4 ). Inhibitory neurons revealed three protective genes: LRRC37A2, ARL17B, and KANSL1 (OR < 1, Fig. 5 ). Figure 6 demonstrated that microglia harbored three protective genes (OR 1), and the remaining three (LRRC37A2, ARL17B, and KANSL1) were protective factors (OR < 1) (Fig. 7 ). Notably, LRRC37A2, ARL17B, and KANSL1 were identified as shared genes across six neuron types (Fig. 8 ). No positive genes were found to have a causal relationship with prostate cancer in OPCs or Pericytes. The MR Scatter diagram of positive genes selected from Astrocytes, Endothelial.cells, Excitatory.neurons, Inhibitory.neurons, Microglia and Oligodendrocytes was shown in supplementary Fig. 1–6 . 3.3 Sensitivity analysis To evaluate the reliability of the MR results, we conducted several sensitivity analyses. First, a heterogeneity test was performed ( Supplemental Table 1 ), revealing that the heterogeneity p-values for the positive genes identified in astrocytes, endothelial cells, excitatory neurons, inhibitory neurons, microglia, and oligodendrocytes were all greater than 0.05. This indicates that there was no significant heterogeneity in these findings. Next, we conducted a horizontal pleiotropy test ( Supplemental Table 1 ), and the p-values for all positive genes were above 0.05, indicating no horizontal pleiotropy effects or confounding factors, thereby ensuring the robustness of the results. Lastly, we performed a leave-one-out sensitivity analysis ( Supplementary Figs. 7–12 ), demonstrating that excluding individual SNPs did not significantly affect the outcome, further confirming the stability and reliability of the MR results. 3.4 MR analysis of nerve cell eQTL and bladder cancer, renal cancer We conducted MR analysis using eQTLs from eight neuronal types as exposure factors and bladder cancer as the outcome. The causal relationship was evaluated using MR Egger, Weighted Median, IVW, Simple Mode, and Weighted Mode. For dataset iue-b-4874, no causal genes were identified for bladder cancer in any neuron type (Astrocytes, Endothelial cells, Excitatory neurons, Inhibitory neurons, Microglia, Oligodendrocytes, OPCs/COPs, or Pericytes) (p > 0.05, Supplemental Table 2 ). Similarly, for dataset ukb-b-8193, no causal genes were identified in any neuron type (p > 0.05, Supplemental Table 2 ). Both the heterogeneity test and horizontal pleiotropy test yielded p-values greater than 0.05, indicating no significant heterogeneity or pleiotropy effects ( Supplemental Table 2 ), further supporting the reliability of the MR analysis results.We also conducted MR analysis using eQTLs from the same eight neuronal types as exposure factors, with renal cancer as the outcome. The causal relationships were again determined using MR Egger, Weighted Median, IVW, Simple Mode, and Weighted Mode. The results ( Supplemental Table 2 ) showed no causal genes for renal cancer across all neuron types (Astrocytes, Endothelial cells, Excitatory neurons, Inhibitory neurons, Microglia, Oligodendrocytes, OPCs/COPs, or Pericytes) (p > 0.05). Both heterogeneity and horizontal pleiotropy tests also yielded p-values greater than 0.05, indicating no significant heterogeneity or pleiotropy ( Supplemental Table 2 ), reinforcing the reliability of the MR results. 4. Discussion Urinary system tumors pose significant threats to human health, including serious damage to organ function, cancer-related pain, infections, general weakness, psychological impacts, and sexual dysfunction. These tumors not only contribute to mortality but also severely impact patients' quality of life, placing a significant burden on both individuals and their families. Therefore, early detection and timely treatment are crucial to alleviating these negative effects. Recent studies highlight the crucial role of nerve cells in urinary system tumor development, where the nervous system significantly contributes to tumor growth, invasion, metastasis, and symptom regulation by influencing the tumor microenvironment through the release of neurotransmitters, nutrients, and signaling molecules [ 15 – 16 ] .Targeting the nervous system could open up new therapeutic opportunities for treating these cancers. Our research findings suggest that the nervous system is indeed associated with prostate cancer, but not with renal or bladder cancer. Research on the innervation of the prostate, benign prostatic hyperplasia (BPH), and prostate cancer has demonstrated that the prostate is richly innervated, primarily by sympathetic, parasympathetic, and sensory nerves [ 17 ] . These nerves originate from the inferior hypogastric plexus and spread around and throughout the prostate gland. Studies have indicated that nerve density is considerably higher in healthy prostate tissue and in BPH, whereas it significantly decreases in prostate cancer, especially in high-grade cases. This decline in nerve density may result from several factors, such as expansive tumor growth, hypoxic conditions in the tumor microenvironment, and reduced nerve proliferation activity. We further confirmed this through MR imaging, finding that there are indeed many nerve cells associated with PCa, which are considered a protective factor in prostate cancer, consistent with the aforementioned literature and further supporting the validity of our results.Meanwhile, based on the results, we did not find any connection between nerve cells and kidney cancer or bladder cancer. Among these nerve cell types, astrocytes appear to be the most closely associated with PCa, as suggested by the MR results, which indicate the highest level of gene expression in this cell type. A study explored interactions between astrocytes and PCa cells, focusing on their role in early brain metastasis [ 6 ] . The brain-derived prostate cancer cell line DU145 shows increased invasiveness through interactions with astrocytes. DU145 cells enhance proliferation and inhibit apoptosis via the extracellular matrix (ECM) produced by astrocytes. Astrocytes promote DU145 cell proliferation and migration by secreting ECM and soluble factors. In the presence of astrocytes, DU145 cells exhibit features of epithelial-mesenchymal transition (EMT), including upregulation of mesenchymal markers like vimentin and N-cadherin, and downregulation of epithelial markers like E-cadherin. Astrocyte-secreted TGF-β is crucial for inducing EMT in DU145 cells, and neutralizing TGF-β significantly reduces the expression of EMT-related genes, thereby limiting their invasiveness. In conclusion, this study highlights the critical role of astrocytes in promoting prostate cancer progression and brain metastasis, emphasizing their impact on tumor proliferation, migration, and EMT. These findings align closely with the results of our research. Astrocytes, the most abundant glial cells in the central nervous system, play critical roles not only in maintaining brain homeostasis, supporting neurons, and regulating the blood-brain barrier but also in cancer progression, particularly in metastasis [ 18 ] . Prostate cancers, common malignancies in males, often metastasize to the bones, lymph nodes, and central nervous system (including the brain and spinal cord) in advanced stages. Astrocytes are pivotal in this metastatic process. Additionally, other types of nerve cells are also involved in shaping the progression of PCa. Cancer cells adhere to the vascular endothelium by interacting with adhesion molecules, such as integrins, on endothelial cells—a key step in metastasis. Furthermore, neurotransmitters released by excitatory neurons, especially glutamate, may influence the prostate cancer microenvironment [ 19 ] , while inhibitory neurons release GABA, affecting prostate cancer cell behavior. Microglia, the immune cells of the central nervous system, also play a significant role in both bladder and prostate cancer development. Given the close involvement of these nerve cells in the progression of urinary system tumors, we employed MR analysis to identify genes with significant causal relationships to bladder, prostate, and renal cancers across these eight nerve cell types. In this study, we analyzed the causal relationship between the eQTLs of eight types of nerve cells and both bladder, prostate cancer and renal cancer using MR. We identified three regulatory genes—LRRC37A2, ARL17B, and KANSL1—that demonstrate protective effects against prostate cancer. Increased expression levels of these genes correlate with a reduced risk of prostate cancer, pointing toward promising new directions for targeted prostate cancer therapies. The LRRC37A2 gene is located in the 17q21.31 region of the chromosome, which harbors several other genes associated with leucine repeats. Members of the LRRC gene family typically contain leucine-rich repeat (LRR) domains, enabling them to play essential roles in intercellular and intracellular signaling. Wisnieski F. et al. found that decreased mRNA levels of LRRC37A2 were associated with poorly differentiated and undifferentiated gastric cancers [ 20 ] . Additionally, studies by Malarstig A. et al. indicated that LRRC37A2 may play a causal role in breast cancer risk [ 21 ] . ARL17B (ADP-ribosylation factor-like protein 17B), part of the small GTPase family, is involved in several cellular processes, including cytoskeletal organization, membrane transport, and cell proliferation. Caibo Ning et al. identified the ARL17B gene as playing a crucial role in the nervous system, with strong links to several neuropsychiatric disorders, including Parkinson's and Alzheimer's diseases. This association is driven by ARL17B's influence on hippocampal volume and neural signaling. The gene is situated in the key 17q21.31 region, known for its involvement in hippocampal development and various neurodegenerative diseases. Multiple genes in this region collaboratively participate in neural development and contribute to the onset of neurodegenerative disorders. Functional annotation analyses indicate that ARL17B may regulate neuronal development, survival, and signaling pathways such as the Wnt and Hippo pathways, which are essential for neuronal growth, differentiation, and survival. These genetic associations and functional roles position ARL17B as a potentially pivotal gene in the understanding of neurological diseases [ 22 ] . Rushing BR et al. identified ARL17B as a gene involved in protein transport, distinguishing tumor-sensitive from resistant cells, and potentially influencing the metabolic characteristics of cancer cells, thereby altering their response to chemotherapy. A study provides new insights into the role of the ARL17B gene in DNA damage-induced apoptosis and its potential link to cancer development. Specifically, ARL17B was identified as an exposure expression quantitative trait locus (e 2 QTL) in response to ultraviolet (UVC) radiation, suggesting that genetic variability in ARL17B may modulate the cellular response to DNA damage, a key factor in cancer progression. The association of ARL17B e 2 QTL with breast cancer risk variants, as identified by genome-wide association studies (GWAS), highlights the significance of ARL17B in oncogenic processes [ 23 ] . KANSL1 (KAT8 regulatory NSL complex subunit 1) encodes a protein that is part of the NSL (nonspecific lethal) complex, which plays a role in chromatin modification, particularly in the acetylation of histone H4 [ 24 ] . The NSL complex is important for regulating gene expression, cell cycle progression, and cell proliferation. Fejzo MS et al. found that KANSL1 is a biomarker and potential therapeutic target in epithelial ovarian cancer through its involvement in immune response and HDAC inhibition [ 25 ] . A research identified KANSL1 fusion as a characteristic of potentially aggressive uterine sarcomas [ 26 ] . Currently, there are few studies linking KANSL1 to prostate cancer. However, because the NSL complex is critical for cell proliferation, and excessive proliferation is a hallmark of prostate cancer, KANSL1 may influence prostate cancer pathology by affecting cell proliferation signaling pathways and cell cycle regulation. Notably, our study is the first to establish a connection between LRRC37A2, ARL17B, and KANSL1 and prostate cancer, marking a novel and significant finding. These genes have the potential to serve as promising targets for future prostate cancer therapies. In summary, Our research confirms that the nervous system (cells) is related to prostate cancer.Our study identified LRRC37A2, ARL17B, and KANSL1 as protective regulatory genes closely related to prostate cancer development. These genes may play crucial roles in the initiation and progression of the disease. These genes may play critical roles in the initiation and progression of the disease. Future studies will further clarify their specific biological functions and explore their potential applications in prostate cancer treatment. Declarations Funding Sources This study was funded by the Taishan Scholar Program of Shandong Province (no. Tsqn202103198). Ethics approval and consent to participate Not applicable Consent for publication Not applicable Conflict of Interest Disclosures None Limitations of the Study This study has several limitations. First, the dataset used is relatively small, which may affect the generalizability of the findings. Second, no independent validation dataset was employed, and therefore, the results should be interpreted with caution. Further validation using larger and more diverse datasets is necessary to confirm the robustness and reproducibility of the findings. Data Availability Statement The eQTL summary statistics for eight neural cell types (Astrocytes, Endothelial cells, Excitatory neurons, Inhibitory neurons, Microglia, Oligodendrocytes, OPCs/COPs, and Pericytes) are publicly available from Zenodo at https://doi.org/10.5281/zenodo.5543734. GWAS summary statistics for prostate cancer, bladder cancer, and renal cancer were obtained from the GWAS Catalog (https://www.ebi.ac.uk/gwas/) under accession numbers UKB-B-8193 and iu-b-4874. All data used in this study are publicly available and fully referenced within the manuscript. Authors' contributions CL and FX conceived the study. 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Fejzo MS, Chen HW, Anderson L, McDermott MS, Karlan B, Konecny GE, Slamon DJ. Analysis in epithelial ovarian cancer identifies KANSL1 as a biomarker and target gene for immune response and HDAC inhibition. Gynecol Oncol. 2021 Feb;160(2):539-546. doi: 10.1016/j.ygyno.2020.11.008. Epub 2020 Nov 20. PMID: 33229045. Agaimy A, Clarke BA, Kolin DL, Lee CH, Lee JC, McCluggage WG, Pöschke P, Stoehr R, Swanson D, Turashvili G, Beckmann MW, Hartmann A, Antonescu CR, Dickson BC. Recurrent KAT6B/A::KANSL1 Fusions Characterize a Potentially Aggressive Uterine Sarcoma Morphologically Overlapping With Low-grade Endometrial Stromal Sarcoma. Am J Surg Pathol. 2022 Sep 1;46(9):1298- 1308. doi: 10.1097/PAS.0000000000001915. Epub 2022 May 17. PMID: 35575789; PMCID: PMC9388494. Additional Declarations No competing interests reported. Supplementary Files SupplementalTable1.xlsx SupplementalTable2.xlsx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 28 Aug, 2025 Reviews received at journal 23 Jul, 2025 Reviews received at journal 19 Jul, 2025 Reviewers agreed at journal 16 Jul, 2025 Reviewers agreed at journal 16 Jul, 2025 Reviewers agreed at journal 16 Jul, 2025 Reviewers invited by journal 16 Jul, 2025 Editor assigned by journal 16 Jul, 2025 Editor invited by journal 15 Jul, 2025 Submission checks completed at journal 09 Jun, 2025 First submitted to journal 09 Jun, 2025 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. 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05:23:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6747184/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6747184/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87190357,"identity":"ef86a205-c018-4d86-9671-2d089f718e6f","added_by":"auto","created_at":"2025-07-21 11:09:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":146347,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of this study\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6747184/v1/f3b469e892ef1f144e647d4b.png"},{"id":87190852,"identity":"e81cd487-d1f5-4ed7-b071-24d25f40c32d","added_by":"auto","created_at":"2025-07-21 11:17:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":290888,"visible":true,"origin":"","legend":"\u003cp\u003eMR analysis forest diagram of Astrocytes and prostate cancer.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6747184/v1/59066d45054480238262603a.png"},{"id":87189191,"identity":"621edd41-6663-40ac-bd7d-4e66def2bb1d","added_by":"auto","created_at":"2025-07-21 11:01:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":166192,"visible":true,"origin":"","legend":"\u003cp\u003eMR analysis forest diagram of Endothelial.cells and prostate cancer.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6747184/v1/c03e75f760ae55bad7e2455f.png"},{"id":87190854,"identity":"6ef4bbc2-ede1-4b4f-b8f4-f6067f0ea5ce","added_by":"auto","created_at":"2025-07-21 11:17:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":240080,"visible":true,"origin":"","legend":"\u003cp\u003eMR analysis forest diagram of Excitatory.neurons and prostate cancer.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6747184/v1/3a897a727513171c50a0d339.png"},{"id":87190362,"identity":"c9ec62eb-e08b-44ad-8b5c-243c71ec67c8","added_by":"auto","created_at":"2025-07-21 11:09:02","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":188631,"visible":true,"origin":"","legend":"\u003cp\u003eMR analysis forest diagram of Inhibitory.neurons and prostate cancer.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6747184/v1/76bd86c04a2283d2b3a2933b.png"},{"id":87189193,"identity":"6285d59a-ad1e-491a-be96-c36fd76f419b","added_by":"auto","created_at":"2025-07-21 11:01:02","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":187816,"visible":true,"origin":"","legend":"\u003cp\u003eMR analysis forest diagram of Microglia and prostate cancer.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6747184/v1/c8593b31b529be2329da38ef.png"},{"id":87189200,"identity":"a22ace65-9eaa-4d4a-adac-a6fed19f1a33","added_by":"auto","created_at":"2025-07-21 11:01:02","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":311091,"visible":true,"origin":"","legend":"\u003cp\u003eMR analysis forest diagram of Oligodendrocytes and prostate cancer.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6747184/v1/7cbef24bd5005bf00838095e.png"},{"id":87189196,"identity":"2810bd44-ff0a-4349-8554-d442b95348cc","added_by":"auto","created_at":"2025-07-21 11:01:02","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":38990,"visible":true,"origin":"","legend":"\u003cp\u003eUpset plot of shared genes in 6 types of neural cells.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6747184/v1/9d190202084998bbdeac1cf8.png"},{"id":87191897,"identity":"a77de1e6-d4aa-4141-90d7-3dbacf207af9","added_by":"auto","created_at":"2025-07-21 11:33:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2270010,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6747184/v1/60bfc58e-0f1e-4b3e-982a-b1175a3f963e.pdf"},{"id":87189189,"identity":"442dbdb1-5684-4dfb-ba11-26c3fb8e9e41","added_by":"auto","created_at":"2025-07-21 11:01:02","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":29650,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6747184/v1/f4fe128c08d9641de6528a45.xlsx"},{"id":87189195,"identity":"cb2ce4d1-180d-4654-b884-335ce36c8b2e","added_by":"auto","created_at":"2025-07-21 11:01:02","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1712847,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalTable2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6747184/v1/c7672c231f9ab448a2b523f4.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mendelian randomization analysis integrating GWAS and eQTL data identified potential regulatory genes associated with prostate cancer in neural cells","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eTumors of the urinary system can develop in various organs, including the kidneys, prostate, bladder, urethra, and more \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. These tumors generally occur after the age of 40 and are about twice as common in men as in women. Prostate cancer (PCa) represents an epithelial malignant tumor originating in the prostate. The incidence rate is low before the age of 55, gradually increases after 55, with the peak age range being 70\u0026ndash;80 years old. As the most common malignant tumor of the male reproductive system, PCa is the second leading cause of cancer-related deaths among men.\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. In recent years, the incidence of major urinary system tumors in China has been steadily increasing, with a significantly higher prevalence in men compared to women. Thus, identifying key genes that regulate the development of these tumors is critical. Genome-wide association studies (GWAS) have identified several important genetic variants associated with urinary tumors \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. However, the functional genes that play a definitive role in the development of urinary system tumors remain largely unknown.\u003c/p\u003e\u003cp\u003eStudies have indicated that the nervous system plays a significant role in cancer, particularly PCa \u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. Research has further demonstrated that the nervous system contributes to organogenesis, maintains homeostasis during development, and interacts with the immune system and stromal cells in the tumor microenvironment through nerve fibers. These interactions have been observed in various malignant tumors. For example, research has demonstrated the interaction between astrocytes and PCa cells originating from different metastatic sites \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Additionally, endothelial cells can function as precursors to osteoblasts in bones affected by metastatic PCa. Other findings indicate that certain microglial components in mice possess antioxidant properties that combat PCa cells \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Furthermore, studies suggest that enhancing specific gene activity in astrocytes may offer therapeutic benefits for treating metastatic bladder cancer \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. These findings underscore the crucial involvement of nerve cells in the development of urinary system tumors. However, the key genes that regulate tumor growth within nerve cells are not yet fully understood.\u003c/p\u003e\u003cp\u003eMendelian randomization (MR) is a powerful approach used to determine the causal relationships between modifiable exposures or risk factors and clinically relevant outcomes \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. It has been widely applied to discover new therapeutic targets by integrating data from disease GWASs and expression quantitative trait loci (eQTL) studies \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. Traditional observational studies often face challenges such as confounding factors and reverse causality, which can affect their ability to infer causality. MR, by using genetic variations as instrumental variables (IVs), helps overcome these issues by detecting and quantifying causal relationships more reliably. In recent years, many researchers have applied MR analysis to urinary system diseases. For instance, studies have shown that the expression level of the mitochondrial-related gene NSUN4 is positively correlated with the risk of PCa \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Other research has identified CD4 on monocytes and FSC-A on plasmacytoid dendritic cells as protective factors against PCa \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Furthermore, HES4 has been identified as an independent prognostic factor for bladder cancer outcomes \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn this study, we utilized GWAS and eQTL data to identify genes within eight different types of nerve cells that exhibit a causal relationship with urinary system tumors, including bladder, prostate, and kidney cancer, through two-sample MR analysis. By doing so, this research aims to uncover the potential mechanisms by which these genes contribute to the pathogenesis of urinary system tumors, providing new theoretical insights that could enhance the diagnosis and treatment of patients.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Data Collection\u003c/h2\u003e\u003cp\u003e\u003cb\u003eExposure Factors\u003c/b\u003e: We obtained eQTL summary statistics for eight types of neurons (Astrocytes, Endothelial cells, Excitatory neurons, Inhibitory neurons, Microglia, Oligodendrocytes, OPCs/COPs, and Pericytes). These data are available on Zenodo at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5281/zenodo.5543734[14]\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.5543734[14]\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Excitatory neurons contain 2,725 eQTLs, Oligodendrocytes contain 1,903 eQTLs, Astrocytes contain 976 eQTLs, Inhibitory neurons contain 831 eQTLs, OPCs contain 591 eQTLs, Microglia contain 444 eQTLs, Endothelial cells contain 100 eQTLs, and Pericytes contain 16 eQTLs.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eOutcome Data\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe data for prostate cancer, bladder cancer and renal cancer.were obtained from the GWAS Catalog database, comprising 140,254 samples (61,106 control samples and 79,148 case samples) of European ethnicity. The UKB-B-8193 bladder cancer dataset includes 46,293,33 samples (46,183,26 control samples and 1,101 case samples) with 9,518,867 SNPs. Additionally, the dataset iu-b-4874 includes 373,295 samples (3,720,161 control samples and 1,279 case samples) with 9,904,926 SNPs. The GWAS data for renal cancer were also sourced from the GWAS Catalog, including 555 samples (210 control samples and 340 case samples).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 MR Analysis\u003c/h2\u003e\u003cp\u003eWe conducted Mendelian Randomization (MR) analysis using the \"TwoSampleMR\" R package, with eQTL data from eight types of neural cells as the exposure and prostate and bladder cancer as the outcomes. To generate instrumental variables (IVs), we selected SNPs with a significance level of p \u0026lt; 5×10^-8 and an F-statistic of ≥ 10 to ensure strong correlation with the exposure factor. SNPs with an FDR \u0026lt; 0.05 and located within ± 100 kb of the transcription start site (TSS) of each gene were included. We also performed linkage disequilibrium (LD) analysis on the SNPs from each eQTL using European samples from the 1000 Genomes Project (r² \u0026lt; 0.01, kb = 10,000).\u003c/p\u003e\u003cp\u003eFive primary methods were employed for MR analysis: the inverse-variance weighted (IVW) method, weighted mode, MR-Egger regression, weighted median estimator (WME), and simple mode. The IVW method was used as the primary adfghpproach to estimate causal effects, while the other four methods served to validate the reliability and stability of the results.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Sensitivity analysis\u003c/h2\u003e\u003cp\u003eTo assess sensitivity, we used the TwoSampleMR package to conduct MR Pleiotropy Residuals, detecting horizontal pleiotropy (p \u0026lt; 0.05) and removing outlier SNPs. Horizontal pleiotropy was further evaluated using the intercept of the MR-Egger method. Cochran's Q test was applied to analyze heterogeneity among the instrumental variables, and depending on the degree of heterogeneity, either a fixed-effect or random-effect model was employed for further analysis. Additionally, we performed leave-one-out analysis to determine whether the significant associations between exposure and outcomes were driven by any single SNP.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003ch2\u003e3.1 Study overview\u003c/h2\u003e\u003cp\u003eWe first obtained GWAS datasets for prostate, bladder, and kidney cancers from the GWAS Catalog database. Additionally, eQTL data for eight types of neurons (Astrocytes, Endothelial cells, Excitatory neurons, Inhibitory neurons, Microglia, Oligodendrocytes, OPCs/COPs, and Pericytes) were collected. The eQTL data were used as exposure factors, and prostate, bladder, and kidney cancers were treated as outcomes for the MR analysis. Positive genes were identified based on causal inference, and sensitivity analyses were conducted to evaluate the robustness of the MR results (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides a analysis flow of this study.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eData summary\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTraits\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSample size\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAncestry\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eprostatic cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecategorical-20001-both_sexes-1034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e140254(Control :61106; Case:79148)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEuropean\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003ebladder cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eukb-b-8193\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e462933(Control :4618326; Case:1101)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEuropean\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eieu-b-4874\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e462933(Control :372016; Case:1279)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEuropean\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003erenal cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ecategorical-20001-1034\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e555 samples (Control: 210 ; Case:340)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eEuropean\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEight kinds of neurons (Astrocytes, Endothelial.cells, Excitatory.neurons, Inhibitory.neurons, Microglia, Oligodendrocytes, OPCs/COPs and Pericytes) ) eQTL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5281/zenodo.5543734\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.5543734\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003ch2\u003e3.2 MR analysis of nerve cell eQTL and prostate cancer\u003c/h2\u003e\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, seven protective genes were identified in astrocytes that exhibited a causal relationship with prostate cancer, including KANSL1, AC005670.2, ARL17B, LRRC37A2, LRRC37A, MAPT, and LINC02210 (all with OR \u0026lt; 1). In endothelial cells, three protective genes (OR \u0026lt; 1) were identified: LRRC37A2, ARL17B, and KANSL1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In excitatory neurons, four positive protective genes (including LRRC37A2, ARL17B, KANSL1, and LINC02210) were associated with prostate cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Inhibitory neurons revealed three protective genes: LRRC37A2, ARL17B, and KANSL1 (OR \u0026lt; 1, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e demonstrated that microglia harbored three protective genes (OR \u0026lt; 1), including LRRC37A2, ARL17B, and KANSL1, while oligodendrocytes exhibited eight positive genes, of which five (OR2L13, OR2L3, OR2L5, OR2L2, and OR2M4) were risk factors (OR \u0026gt; 1), and the remaining three (LRRC37A2, ARL17B, and KANSL1) were protective factors (OR \u0026lt; 1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Notably, LRRC37A2, ARL17B, and KANSL1 were identified as shared genes across six neuron types (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). No positive genes were found to have a causal relationship with prostate cancer in OPCs or Pericytes. The MR Scatter diagram of positive genes selected from Astrocytes, Endothelial.cells, Excitatory.neurons, Inhibitory.neurons, Microglia and Oligodendrocytes was shown in \u003cb\u003esupplementary Fig.\u0026nbsp;1–6\u003c/b\u003e.\u003c/p\u003e\u003ch2\u003e3.3 Sensitivity analysis\u003c/h2\u003e\u003cp\u003eTo evaluate the reliability of the MR results, we conducted several sensitivity analyses. First, a heterogeneity test was performed (\u003cb\u003eSupplemental Table\u0026nbsp;1\u003c/b\u003e), revealing that the heterogeneity p-values for the positive genes identified in astrocytes, endothelial cells, excitatory neurons, inhibitory neurons, microglia, and oligodendrocytes were all greater than 0.05. This indicates that there was no significant heterogeneity in these findings. Next, we conducted a horizontal pleiotropy test (\u003cb\u003eSupplemental Table\u0026nbsp;1\u003c/b\u003e), and the p-values for all positive genes were above 0.05, indicating no horizontal pleiotropy effects or confounding factors, thereby ensuring the robustness of the results. Lastly, we performed a leave-one-out sensitivity analysis (\u003cb\u003eSupplementary Figs.\u0026nbsp;7–12\u003c/b\u003e), demonstrating that excluding individual SNPs did not significantly affect the outcome, further confirming the stability and reliability of the MR results.\u003c/p\u003e\u003ch2\u003e3.4 MR analysis of nerve cell eQTL and bladder cancer, renal cancer\u003c/h2\u003e\u003cp\u003eWe conducted MR analysis using eQTLs from eight neuronal types as exposure factors and bladder cancer as the outcome. The causal relationship was evaluated using MR Egger, Weighted Median, IVW, Simple Mode, and Weighted Mode. For dataset iue-b-4874, no causal genes were identified for bladder cancer in any neuron type (Astrocytes, Endothelial cells, Excitatory neurons, Inhibitory neurons, Microglia, Oligodendrocytes, OPCs/COPs, or Pericytes) (p \u0026gt; 0.05, \u003cb\u003eSupplemental Table\u0026nbsp;2\u003c/b\u003e).\u003c/p\u003e\u003cp\u003eSimilarly, for dataset ukb-b-8193, no causal genes were identified in any neuron type (p \u0026gt; 0.05, \u003cb\u003eSupplemental Table\u0026nbsp;2\u003c/b\u003e). Both the heterogeneity test and horizontal pleiotropy test yielded p-values greater than 0.05, indicating no significant heterogeneity or pleiotropy effects (\u003cb\u003eSupplemental Table\u0026nbsp;2\u003c/b\u003e), further supporting the reliability of the MR analysis results.We also conducted MR analysis using eQTLs from the same eight neuronal types as exposure factors, with renal cancer as the outcome. The causal relationships were again determined using MR Egger, Weighted Median, IVW, Simple Mode, and Weighted Mode. The results (\u003cb\u003eSupplemental Table\u0026nbsp;2\u003c/b\u003e) showed no causal genes for renal cancer across all neuron types (Astrocytes, Endothelial cells, Excitatory neurons, Inhibitory neurons, Microglia, Oligodendrocytes, OPCs/COPs, or Pericytes) (p \u0026gt; 0.05).\u003c/p\u003e\u003cp\u003eBoth heterogeneity and horizontal pleiotropy tests also yielded p-values greater than 0.05, indicating no significant heterogeneity or pleiotropy (\u003cb\u003eSupplemental Table\u0026nbsp;2\u003c/b\u003e), reinforcing the reliability of the MR results.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eUrinary system tumors pose significant threats to human health, including serious damage to organ function, cancer-related pain, infections, general weakness, psychological impacts, and sexual dysfunction. These tumors not only contribute to mortality but also severely impact patients' quality of life, placing a significant burden on both individuals and their families. Therefore, early detection and timely treatment are crucial to alleviating these negative effects. Recent studies highlight the crucial role of nerve cells in urinary system tumor development, where the nervous system significantly contributes to tumor growth, invasion, metastasis, and symptom regulation by influencing the tumor microenvironment through the release of neurotransmitters, nutrients, and signaling molecules \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e.Targeting the nervous system could open up new therapeutic opportunities for treating these cancers.\u003c/p\u003e\u003cp\u003eOur research findings suggest that the nervous system is indeed associated with prostate cancer, but not with renal or bladder cancer. Research on the innervation of the prostate, benign prostatic hyperplasia (BPH), and prostate cancer has demonstrated that the prostate is richly innervated, primarily by sympathetic, parasympathetic, and sensory nerves\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. These nerves originate from the inferior hypogastric plexus and spread around and throughout the prostate gland. Studies have indicated that nerve density is considerably higher in healthy prostate tissue and in BPH, whereas it significantly decreases in prostate cancer, especially in high-grade cases. This decline in nerve density may result from several factors, such as expansive tumor growth, hypoxic conditions in the tumor microenvironment, and reduced nerve proliferation activity. We further confirmed this through MR imaging, finding that there are indeed many nerve cells associated with PCa, which are considered a protective factor in prostate cancer, consistent with the aforementioned literature and further supporting the validity of our results.Meanwhile, based on the results, we did not find any connection between nerve cells and kidney cancer or bladder cancer.\u003c/p\u003e\u003cp\u003eAmong these nerve cell types, astrocytes appear to be the most closely associated with PCa, as suggested by the MR results, which indicate the highest level of gene expression in this cell type. A study explored interactions between astrocytes and PCa cells, focusing on their role in early brain metastasis \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. The brain-derived prostate cancer cell line DU145 shows increased invasiveness through interactions with astrocytes. DU145 cells enhance proliferation and inhibit apoptosis via the extracellular matrix (ECM) produced by astrocytes. Astrocytes promote DU145 cell proliferation and migration by secreting ECM and soluble factors. In the presence of astrocytes, DU145 cells exhibit features of epithelial-mesenchymal transition (EMT), including upregulation of mesenchymal markers like vimentin and N-cadherin, and downregulation of epithelial markers like E-cadherin. Astrocyte-secreted TGF-β is crucial for inducing EMT in DU145 cells, and neutralizing TGF-β significantly reduces the expression of EMT-related genes, thereby limiting their invasiveness. In conclusion, this study highlights the critical role of astrocytes in promoting prostate cancer progression and brain metastasis, emphasizing their impact on tumor proliferation, migration, and EMT. These findings align closely with the results of our research. Astrocytes, the most abundant glial cells in the central nervous system, play critical roles not only in maintaining brain homeostasis, supporting neurons, and regulating the blood-brain barrier but also in cancer progression, particularly in metastasis \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Prostate cancers, common malignancies in males, often metastasize to the bones, lymph nodes, and central nervous system (including the brain and spinal cord) in advanced stages. Astrocytes are pivotal in this metastatic process.\u003c/p\u003e\u003cp\u003eAdditionally, other types of nerve cells are also involved in shaping the progression of PCa. Cancer cells adhere to the vascular endothelium by interacting with adhesion molecules, such as integrins, on endothelial cells\u0026mdash;a key step in metastasis. Furthermore, neurotransmitters released by excitatory neurons, especially glutamate, may influence the prostate cancer microenvironment \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e, while inhibitory neurons release GABA, affecting prostate cancer cell behavior. Microglia, the immune cells of the central nervous system, also play a significant role in both bladder and prostate cancer development. Given the close involvement of these nerve cells in the progression of urinary system tumors, we employed MR analysis to identify genes with significant causal relationships to bladder, prostate, and renal cancers across these eight nerve cell types.\u003c/p\u003e\u003cp\u003eIn this study, we analyzed the causal relationship between the eQTLs of eight types of nerve cells and both bladder, prostate cancer and renal cancer using MR. We identified three regulatory genes\u0026mdash;LRRC37A2, ARL17B, and KANSL1\u0026mdash;that demonstrate protective effects against prostate cancer. Increased expression levels of these genes correlate with a reduced risk of prostate cancer, pointing toward promising new directions for targeted prostate cancer therapies. The LRRC37A2 gene is located in the 17q21.31 region of the chromosome, which harbors several other genes associated with leucine repeats. Members of the LRRC gene family typically contain leucine-rich repeat (LRR) domains, enabling them to play essential roles in intercellular and intracellular signaling. Wisnieski F. et al. found that decreased mRNA levels of LRRC37A2 were associated with poorly differentiated and undifferentiated gastric cancers \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Additionally, studies by Malarstig A. et al. indicated that LRRC37A2 may play a causal role in breast cancer risk \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eARL17B (ADP-ribosylation factor-like protein 17B), part of the small GTPase family, is involved in several cellular processes, including cytoskeletal organization, membrane transport, and cell proliferation. Caibo Ning et al. identified the ARL17B gene as playing a crucial role in the nervous system, with strong links to several neuropsychiatric disorders, including Parkinson's and Alzheimer's diseases. This association is driven by ARL17B's influence on hippocampal volume and neural signaling. The gene is situated in the key 17q21.31 region, known for its involvement in hippocampal development and various neurodegenerative diseases. Multiple genes in this region collaboratively participate in neural development and contribute to the onset of neurodegenerative disorders. Functional annotation analyses indicate that ARL17B may regulate neuronal development, survival, and signaling pathways such as the Wnt and Hippo pathways, which are essential for neuronal growth, differentiation, and survival. These genetic associations and functional roles position ARL17B as a potentially pivotal gene in the understanding of neurological diseases\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Rushing BR et al. identified ARL17B as a gene involved in protein transport, distinguishing tumor-sensitive from resistant cells, and potentially influencing the metabolic characteristics of cancer cells, thereby altering their response to chemotherapy. A study provides new insights into the role of the ARL17B gene in DNA damage-induced apoptosis and its potential link to cancer development. Specifically, ARL17B was identified as an exposure expression quantitative trait locus (e\u003csup\u003e2\u003c/sup\u003eQTL) in response to ultraviolet (UVC) radiation, suggesting that genetic variability in ARL17B may modulate the cellular response to DNA damage, a key factor in cancer progression. The association of ARL17B e\u003csup\u003e2\u003c/sup\u003eQTL with breast cancer risk variants, as identified by genome-wide association studies (GWAS), highlights the significance of ARL17B in oncogenic processes\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eKANSL1 (KAT8 regulatory NSL complex subunit 1) encodes a protein that is part of the NSL (nonspecific lethal) complex, which plays a role in chromatin modification, particularly in the acetylation of histone H4 \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. The NSL complex is important for regulating gene expression, cell cycle progression, and cell proliferation. Fejzo MS et al. found that KANSL1 is a biomarker and potential therapeutic target in epithelial ovarian cancer through its involvement in immune response and HDAC inhibition \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. A research identified KANSL1 fusion as a characteristic of potentially aggressive uterine sarcomas \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Currently, there are few studies linking KANSL1 to prostate cancer. However, because the NSL complex is critical for cell proliferation, and excessive proliferation is a hallmark of prostate cancer, KANSL1 may influence prostate cancer pathology by affecting cell proliferation signaling pathways and cell cycle regulation. Notably, our study is the first to establish a connection between LRRC37A2, ARL17B, and KANSL1 and prostate cancer, marking a novel and significant finding. These genes have the potential to serve as promising targets for future prostate cancer therapies.\u003c/p\u003e\u003cp\u003eIn summary, Our research confirms that the nervous system (cells) is related to prostate cancer.Our study identified LRRC37A2, ARL17B, and KANSL1 as protective regulatory genes closely related to prostate cancer development. These genes may play crucial roles in the initiation and progression of the disease. These genes may play critical roles in the initiation and progression of the disease. Future studies will further clarify their specific biological functions and explore their potential applications in prostate cancer treatment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding Sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the Taishan Scholar Program of Shandong Province (no. Tsqn202103198).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest Disclosures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations of the Study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has several limitations. First, the dataset used is relatively small, which may affect the generalizability of the findings. Second, no independent validation dataset was employed, and therefore, the results should be interpreted with caution. Further validation using larger and more diverse datasets is necessary to confirm the robustness and reproducibility of the findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe eQTL summary statistics for eight neural cell types (Astrocytes, Endothelial cells, Excitatory neurons, Inhibitory neurons, Microglia, Oligodendrocytes, OPCs/COPs, and Pericytes) are publicly available from Zenodo at https://doi.org/10.5281/zenodo.5543734. GWAS summary statistics for prostate cancer, bladder cancer, and renal cancer were obtained from the GWAS Catalog (https://www.ebi.ac.uk/gwas/) under accession numbers UKB-B-8193 and iu-b-4874. All data used in this study are publicly available and fully referenced within the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCL and FX conceived the study. JG and HX conducted and performed data analysis, and JG wrote the manuscript and prepared the figures.QY, CD, YY, JC, JY and XL reviewed the manuscript. All authors read and approved the final version of the manuscript for publication. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBurgess KE, DeRegis CJ. Urologic Oncology. Vet Clin North Am Small Anim Pract. 2019 Mar;49(2):311-323. doi: 10.1016/j.cvsm.2018.11.006. Epub 2019 Jan 8. PMID: 30635132.\u003c/li\u003e\n\u003cli\u003eWang G, Zhao D, Spring DJ, DePinho RA. Genetics and biology of prostate cancer. Genes Dev. 2018 Sep 1;32(17-18):1105-1140. doi: 10.1101/gad.315739.118. 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PMID: 33229045.\u003c/li\u003e\n\u003cli\u003eAgaimy A, Clarke BA, Kolin DL, Lee CH, Lee JC, McCluggage WG, P\u0026ouml;schke P, Stoehr R, Swanson D, Turashvili G, Beckmann MW, Hartmann A, Antonescu CR, Dickson BC. Recurrent KAT6B/A::KANSL1 Fusions Characterize a Potentially Aggressive Uterine Sarcoma Morphologically Overlapping With Low-grade Endometrial Stromal Sarcoma. Am J Surg Pathol. 2022 Sep 1;46(9):1298- 1308. doi: 10.1097/PAS.0000000000001915. Epub 2022 May 17. PMID: 35575789; PMCID: PMC9388494.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Prostate cancer, neural cell, eQTL, Mendelian randomization, causal relationship","lastPublishedDoi":"10.21203/rs.3.rs-6747184/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6747184/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eAlthough studies have suggested a potential link between the nervous system and prostate cancer, the underlying regulatory mechanisms remain unclear. Therefore, it is crucial to identify the genes involved in regulating prostate cancer within the nervous system.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eWe utilized eQTL data from eight neural cell types as exposure factors and GWAS data for prostate cancer as outcome events. Mendelian randomization (MR) analyses were performed to identify causative genes associated with prostate, bladder, and renal cancers in Astrocytes, Endothelial cells, Excitatory neurons, Inhibitory neurons, Microglia, Oligodendrocytes, OPCs/COPs, and Pericytes. Bladder and renal cancers were used as controls. Sensitivity analyses (heterogeneity, pleiotropy, and leave-one-out tests) were conducted to ensure reliability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eIn astrocytes, seven positive genes were identified as being causally related to prostate cancer: \u003cstrong\u003eKANSL1\u003c/strong\u003e, \u003cstrong\u003eAC005670.2\u003c/strong\u003e, \u003cstrong\u003eARL17B\u003c/strong\u003e, \u003cstrong\u003eLRRC37A2\u003c/strong\u003e, \u003cstrong\u003eLRRC37A\u003c/strong\u003e, \u003cstrong\u003eMAPT\u003c/strong\u003e, and \u003cstrong\u003eLINC02210\u003c/strong\u003e. In\u003c/p\u003e\n\u003cp\u003eEndothelial cells, Inhibitory neuron and Microglia, three genes (LRRC37A2, ARL17B，and KANSL1) were identified as risk genes that are associated with prostate cancer. Four protective genes were identified in excitatory neurons, including LRRC37A2, ARL17B, KANSL1 and LINC02210. In oligodendrocytes, eight genes were identified, with \u003cstrong\u003eLRRC37A2\u003c/strong\u003e, \u003cstrong\u003eARL17B\u003c/strong\u003e, and \u003cstrong\u003eKANSL1\u003c/strong\u003e acting as protective factors, while \u003cstrong\u003eOR2L13\u003c/strong\u003e, \u003cstrong\u003eOR2L3\u003c/strong\u003e, \u003cstrong\u003eOR2L5\u003c/strong\u003e, \u003cstrong\u003eOR2L2\u003c/strong\u003e, and \u003cstrong\u003eOR2M4\u003c/strong\u003e were identified as risk factors. Additionally, sensitivity analyses showed no heterogeneity or horizontal pleiotropy in the MR results, confirming their reliability and stability. In addition, no positive genes were found in bladder cancer and renal cancer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Our study highlights the role of the nervous system, particularly astrocytes, in regulating prostate cancer. We identified three genes, with LRRC37A2, ARL17B, and KANSL1 emerging as key protective factors. These findings provide potential targets for prostate cancer diagnosis and treatment.\u003c/p\u003e","manuscriptTitle":"Mendelian randomization analysis integrating GWAS and eQTL data identified potential regulatory genes associated with prostate cancer in neural cells","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-21 11:00:57","doi":"10.21203/rs.3.rs-6747184/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-28T09:28:12+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-23T16:15:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-19T22:09:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"223578826159174486030965846364442271813","date":"2025-07-17T02:58:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"253681843687700646038562673958569825682","date":"2025-07-17T02:47:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"228692153176500296926738368051689566881","date":"2025-07-17T02:45:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-17T02:44:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-17T02:32:22+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-07-15T08:41:46+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-09T13:32:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2025-06-09T13:28:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cf9f5326-3b34-4bb8-b7d6-35aa87f7d4cf","owner":[],"postedDate":"July 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-09-22T05:53:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-21 11:00:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6747184","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6747184","identity":"rs-6747184","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}