Identifying potential therapeutic targets for prostate cancer with mediating role in tumor immunity

Identifying potential therapeutic targets for prostate cancer with mediating role in tumor immunity | 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 Identifying potential therapeutic targets for prostate cancer with mediating role in tumor immunity Zhechun Wu, Sihan Li, Yuqing Li, Zhizhi Wang, Wei Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5697374/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 Background Prostate cancer (PCa) is a leading malignancy with a rising global incidence, posing significant challenges in treatment. The immunosuppressive tumor microenvironment (TME) in CRPC is a major barrier to effective immunotherapy. Identifying therapeutic targets that modulate the immune response within TME is crucial for advancing PCa treatment. Materials and Methods In this study, we employed Mendelian randomization (MR) to investigate the causal relationships between gene expression of blood proteins and PCa risk. We utilized cis-eQTL data from the eQTLGen Consortium and immune cell phenotype data from the NHGRI-EBI GWAS Catalog. Our analysis included discovery and validation cohorts, totaling over 800,000 individuals. Colocalization analysis was performed to confirm the genetic associations, and mediation MR analysis was used to explore the mediating role of proteins in tumor immunity. Drug prediction and molecular docking were applied to assess the potential of identified targets as druggable candidates. Results Our MR analysis identified 557 proteins associated with PCa in the discovery cohort, with 86 proteins remaining significant in an independent validation cohort. Mediation analysis revealed nine proteins that mediated the impact of immune cells on PCa. Colocalization analysis confirmed the causality of five proteins, which were further supported by phenome-wide association studies (PheWAS) and protein-protein interaction (PPI) networks. Molecular docking demonstrated strong binding affinity of potential drugs to these targets. Conclusions This study identified five drug targets in prostate cancer that modulate the tumor immune response. These targets may expedite drug development and personalize medicine, potentially enhancing treatment efficacy and reducing side effects. Prostate cancer Drug target Tumor immunity Mendelian randomization Genetics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1 Introduction Prostate cancer (PCa) is among the most prevalent malignancies around the world, contributing significantly to the overall cancer mortality rate. 1 , 2 The incidence of prostate cancer is anticipated to escalate from approximately 1.4 million cases annually in 2020 to an estimated 2.9 million cases by 2040. Concurrently, the annual mortality rate due to prostate cancer is estimated to surge by 85% over the next two decades, rising from 375,000 deaths in 2020 to nearly 700,000 by 2040. 3 Despite significant advancements in the screening and treatment of prostate cancer, there persist numerous challenges. Castration-resistant prostate cancer (CRPC) is one of the major challenges in treatment, and nearly all patients receiving androgen deprivation therapy (ADT) will eventually develop CRPC. 4 The restricted efficacy of immunotherapy in patients with CRPC may be associated with its capacity to foster an immunosuppressive tumor microenvironment (TME). 5 In addition, the treatment of prostate cancer also faces barriers such as drug resistance, the impact of side effects on quality of life, and the complexity of early diagnosis and treatment options. 6 , 7 Within the TME, immune cells exert a significant influence on tumor growth and metastasis. Tumor infiltrating lymphocytes (TILs) are heterogeneous lymphocytes present in TME and participate in anti-tumor immune response, 8 which can inhibit tumor growth by recognizing and destroying tumor cells, but in some cases may also be used by tumor cells to promote tumor progression. 9 Tumor cells regulate the immune microenvironment through various mechanisms to achieve immune escape. For example, tumor-associated macrophages (TAMs) can promote tumor growth by secreting anti-inflammatory cytokines and inhibitory molecules that inhibit T cell activity. 10 In addition, tumor cells can also inhibit the function of T cells by up-regulating the expression of PD-L1 and evade immune surveillance. 11 , 12 Currently, the therapeutic targets for prostate cancer mainly center on the androgen receptor (AR) signaling pathway, the development of novel anti-androgen drugs, and immune checkpoint inhibitors. 13 Notably, new drugs like abiraterone and enzalutamide have been employed in the treatment of CRPC, demonstrating notable efficacy. 14 Despite the identification of numerous therapeutic targets, there remains a pressing need to discover novel targets that significantly influence the modulation of the tumor immune microenvironment. In this context, immune cells such as TAMs, regulatory T cells (Tregs), and myeloid-derived suppressor cells (MDSCs) have been implicated in facilitating immune evasion in prostate cancer. 15 Targeting these cell populations or their associated signaling pathways could potentially yield innovative therapeutic strategies for the management of prostate cancer. Recent studies have discovered several promising drug targets playing an important role in the TME. For instance, combining CDK4/6 inhibitors with immune checkpoint inhibitors is proved to enhance immune responses and increase lymphocyte infiltration within the tumor in prostate cancer treatment. 16 , 17 Furthermore, the overexpression of the histone methyltransferase EZH2 is the primary cause of the increased genomic distribution of H3K27me3 in metastatic prostate cancer, rendering EZH2 a valuable drug target. 13 , 18 These findings suggest that by targeting key genes and proteins in specific immune microenvironments, new therapeutic options may be available for prostate cancer patients. GWAS is a widely used method to identify genetic variations (such as single nucleotide polymorphisms, SNPs) in the genome that are associated with a specific disease or trait. By analyzing genomic data from a large number of patients, GWAS was able to reveal genetic loci associated with prostate cancer risk, providing clues for subsequent functional studies. Mendelian randomization (MR) analyses use genetic variation as an instrumental variable to assess the causal relationship between exposure factors and the outcome. This method can effectively reduce the influence of confounding factors and reverse causality, and improve the accuracy of causal inference. The main objective of this study is to identify potential therapeutic targets for prostate cancer with mediation effects in tumor immunity. By identifying these targets, this study will provide a theoretical basis for the development of new immunotherapy strategies, which are expected to improve the efficacy of prostate cancer treatment and improve patient survival and quality of life. 2 Materials and Methods 2.1 Exposure data The eQTLs data were sourced from eQTLGen Consortium ( https://eqtlgen.org/ ). This dataset encompassed 16987 genes along with 31684 cis-eQTLs identified in blood samples predominantly from healthy European individuals. 19 On Sept. 14, 2024, the complete set of cis-eQTLs data and allele frequency statistics were retrieved from the eQTLGen Consortium. The data of immune cell phenotype ranging from GCST0001391 to GCST0002121 were retrieved from NHGRI-EBI GWAS Catalog. 20 The datasets encompassed 731 immune cell phenotypes, including Treg cells, mature T cells, B cells, natural killer cells, monocytes, and myeloid cells. 2.2 Outcome data GWAS data for the PCa discovery cohort were downloaded from NHGRI-EBI GWAS Catalog 21 on July 26, 2024, for GCST90274713, the study conducted by Wang A et al in 2023. 22 Their research amassed a total of 122188 cases of European ancestry and 604640 controls, employing genome-wide genotyping arrays for genetic analysis. The discovery ancestry was recruited from various European nations to ensure diversity. For consistency with the exposure data, to maintain consistency with the exposure data, GWAS data for the PCa validation cohort, comprising 17,258 cases and 143,624 controls, were sourced from FinnGen Release 11 ( https://www.fnngen.fi/) , 23 which was released in June 24, 2024. The FinnGen study, a collaborative genomics project, has analyzed over 500,000 Finnish biobank samples to link genetic variations with health outcomes, enhancing our understanding of disease mechanisms and predispositions. 2.3 Mendelian randomization analysis For MR analysis, the R package TwoSampleMR (version 0.5.6) was employed. 24 The principal method to assess the causal effects of exposure on outcome was the inverse variance weighted (IVW). The two-step MR approach was applied to explore potential therapeutic target proteins and their impact on tumor immunity. The product of coefficients method was utilized to estimate indirect effects, with the mediated proportion determined by the ratio of the indirect effect to the total effect. Standard errors for these indirect effects were calculated using the delta method. 25 , 26 To validate IVW results, we employed four additional MR analysis methods: MR Egger, weighted median, simple mode, and weighted mode. Heterogeneity was evaluated through the Cochran Q statistic and I 2 value, while the MR-Egger regression intercept test was applied to detect horizontal pleiotropy. Any significant pleiotropy (P < 0.05) led to the exclusion of the respective results. The mediated proportion is referred to as a "mediation effect" when it intensifies the association between the predictor and the outcome, and as a "suppression effect" when it diminishes or inverts this association, suggesting a deleterious impact on the outcome that could conceal or modify the direct effect of the predictor. 27 2.4 Colocalization analysis For genes that demonstrated significance across both cohorts, a colocalization analysis of PCa risk was conducted using the R package coloc. 28 This analysis utilized SNPs harmonized by the TwoSampleMR package with default prior probabilities: p1 = 1E − 4, p2 = 1E − 4, and p12 = 1E − 5. These probabilities pertain to the likelihood that a SNP in the test region is substantially linked to gene expression, PCa risk, or both. The posterior probabilities from the colocalization analysis were categorized under one of five hypotheses: PPH0, indicating no association of SNPs with either trait; PPH1, association with gene expression but not PCa risk; PPH2, association with PCa risk but not gene expression; PPH3, association with both PCa risk and gene expression driven by different SNPs; and PPH4, association with both PCa risk and gene expression driven by common SNPs. The significance threshold for colocalization was established at PPH4 > 0.85, with genes that met this criterion being considered as potential drug target genes. 2.5 Phenome‑wide association analysis To further assess the horizontal pleiotropy of potential drug targets and their potential side effects, a phenome-wide association study (PheWAS) was conducted using the AstraZeneca PheWAS Portal ( https://azpheWAS.com/) . 29 This study leveraged a high-quality subset of approximately 470000 exome-sequenced participants from the UK Biobank, who were predominantly unrelated and of European ancestry (n = 419391). The association between protein-coding variants and approximately 10000 binary and 3500 continuous phenotypes was evaluated through variant-level and gene-level PheWAS. Various correction methods were employed, and the significance threshold was established at 2E − 9, aligning with the standard set by the AstraZeneca PheWAS Portal, to minimize the likelihood of false-positive results. 2.6 Enrichment analysis To delve into the functional properties and biological implications of the prospective therapeutic target genes identified, enrichment analyses using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were conducted with the R package clusterProfiler. 30 The GO analysis encompasses three domains: biological process (BP), molecular function (MF), and cellular component (CC). Meanwhile, the KEGG pathway analysis offers insights into metabolic pathways. 2.7 Protein interaction network construction Protein–protein interaction (PPI) networks provide a detailed perspective on the intricate interactions among proteins inside cells. Through the analysis of these networks, we can reveal the underlying mechanisms of cellular processes, signal transduction, and disease-related pathways. In this research, the PPI network was constructed using STRING database ( https://cn.string-db.org/ ), setting a confidence score threshold of 0.4 for the minimum interaction score, with all other parameters set to their default values. 31 The PPI outcomes were visually represented using Cytoscape (V3.10.2). 32 GeneMANIA ( https://genemania.org/ ) was also employed for PPI analysis [3]. 33 2.8 Candidate drug prediction To see if our target genes can be drug targets, this study will adopt the Drug Signatures Database (DSigDB) 34 on Enrichr web server ( https://maayanlab.cloud/Enrichr/ ) 35 for drug prediction. DSigDB is a specialized database that connects drugs and other chemicals to their target genes. It currently comprises 22527 gene sets, consisting of 17389 unique compounds covering 19531 genes. 2.9 Molecular docking To gain insights into how drug candidates interact with their target genes and to evaluate the druggability of these targets, this study conducted atomic-level molecular docking to assess the binding energy and interaction patterns. Molecular docking simulations are instrumental in analyzing the binding affinity and interaction modes between ligands and their drug targets. By pinpointing ligands with strong binding affinity and favorable interactions, we can select priority drug targets for experimental validation and refine the design of potential drugs. For this study, AutoDock Vina 1.5.7 ( http://autodock.scripps.edu/) , 36 a computational protein-ligand docking tool, was employed to carry out molecular docking for the most significant drugs and the proteins encoded by the target genes. The structural data for the drugs were retrieved from the PubChem Compound Database ( https://pubchem.ncbi.nlm.nih.gov/) , 37 with the respective compound IDs presented in Table 4. The protein structures were sourced from the Protein Data Bank (PDB, http://www.rcsb.org/ ), and their corresponding PDB IDs are detailed in Table 4. For the top significant three proteins and five drugs, we derived their final structures by first stripping water molecules from both protein and ligand files and then appending polar hydrogen atoms. We positioned grid boxes to cover the protein's structural domains, ensuring ample space for molecular flexibility. The grid was set with a spacing of 0.05 nm and a dimension of 40Å × 40Å × 40Å to define the docking pocket. Autodock Vina 1.5.7 visualized the entire molecular docking procedure. All statistical analyses were performed using R (version 4.4.1, R Core Team) and RStudio (version 2023.06.2 Build 524, Posit Software, PBC). A flow chart summarizing the study is depicted in Fig. 1 . 3 Results 3.1 Associations of proteins with PCa during the discovery phase As illustrated in Fig. 2 and Fig. 3 , in the discovery cohort, the IVW method analyses revealed causal relationships between 557 proteins and PCa, while these results were generally supported by other supplementary MR methods, including MR Egger and the weighted median method, in terms of both the direction of causal estimation and the magnitude of the causal effect, suggesting that the findings are reliable and trustworthy. The effect estimates of these proteins on PCa measured by different MR methods are presented in Table S1 . To explore whether SNPs linked to these proteins might contribute to PCa via alternative pathways, we conducted additional horizontal pleiotropic analyses, the results of which are detailed in Table S2. The proteins DSE, IL1R2, VAMP8, IFI6, and TRIM10 failed the horizontal pleiotropy test (P < 0.05) and were excluded from further analyses. The heterogeneity test revealed significant heterogeneity among the SNPs for 196 proteins (P < 0.05), as indicated in Table S3. 3.2 Validation phase 86 proteins remain significant in an independent PCa cohort During the validation, this study utilized GWAS data from the Finnish FinnGen database, which comprised 17258 cases and 143624 controls of European ancestry. The MR analysis was conducted identically to the methods employed in the discovery cohort. Employing the IVW approach, the genetically predicted expression of 64 proteins was successfully replicated (P < 0.05), establishing a causal link to PCa risk, as illustrated in Fig. 4 . The effect estimates of proteins on PCa, assessed by various MR methods, are outlined in Table S4. The horizontal pleiotropy test did not identify significant pleiotropy for any of the proteins in question (all P > 0.05), as detailed in Table S5. In the heterogeneity test, the proteins LAMC1, AK9, and STMN3 exhibited inter-SNP heterogeneity (P < 0.05), as reported in Table S6. 3.3 Mediation MR analysis discovered 9 mediating proteins in tumor immunity To elucidate the impact of the identified proteins on the immune response to PCa, we conducted mediation MR analyses using the PCa dataset from the discovery cohort. After assessing for heterogeneity and horizontal pleiotropic effects, we selected reliable SNPs with an MR Egger test P value over 0.05. Fig. S1 demonstrates that 39 immune cell types were associated with PCa, with comprehensive results from MR analyses, pleiotropy, and heterogeneity analyses presented in Tables S7-S9. Subsequently, 32 causal associations were identified between these immune cells and the proteins, with detailed MR analysis results provided in Table S10. Ultimately, mediation analysis uncovered 20 instances where 9 of the previously identified proteins mediated the influence of immune cells on PCa, as depicted in Fig. 5 . Specifically, IFITM4P was found to mediate the effects of immune cells on PCa in five distinct phenotypes: CD27 on unsw mem, CD8 on EM CD8br, HLA DR on CD14 + CD16 − monocyte, HLA DR on CD14 + monocyte, and HLA DR on DC, with mediated proportions of -0.16%, 3.03%, -10.74%, -11.1%, -5.38% respectively. PBX2 mediated four immune cells’ effects on PCa, including CD45RA − CD4+ %T cell, CD64 on CD14 − CD16−, HVEM on CD8br, and SSC − A on HLA DR + T cell, with mediated proportions of 8.48%, -0.13%, 0.09%, -11.44% respectively. Additionally, SUPT4H1 was identified as mediating 2.69% of the total impact of Naive CD8br %T cell on PCa. 3.4 Enrichment analysis As shown in Fig. 6 , the most significant pathways in the BP category were linked to vesicular transport and membrane targeting, particularly highlighting post-Golgi vesicle-mediated transport, vitamin K and fat-soluble vitamin metabolic process, and localization to membrane establishment of protein. Additionally, there was a notable focus on the regulation of pseudopodium assembly and organization. In the CC category, the gene enrichment was also notably associated with components of the endomembrane system and vesicular transport processes, including phagocytic vesicle, COPII-coated ER to Golgi transport vesicle and endoplasmic reticulum exit site, reflecting the cell's intricate mechanisms for material transport and membrane trafficking. Moreover, in terms of MF, the identified genes were enriched for roles in metabolic activities such as NADP binding, proline-rich region binding, oxidoreductase activity, NAD binding, GTPase activator activity and GDP phosphatase activity, as well as RNA methyltransferase activity and damaged DNA binding. As shown in Fig. 7 , the top three pathways analysed by KEGG enrichment are DNA validation, Mismatch repair and Thyroid cancer, which partially supports the results of the GO analysis. 3.5 Colocalization analysis Co-localization analyses can confirm whether associated exposures and outcomes are influenced by the same causal SNP, 28 and proteins undergo MR and co-localization analyses are more likely to be drug targets. 38 Therefore, this study conducted a colocalization analysis on the 86 genes proposed in previous analyses with PCa data of the discovery cohort. As detailed in Table 1 , out of the 86 proteins analyzed, five (SUPT4H1, NOSIP, MYO5C, RFC5, and CNEP1R1) showed strong evidence of colocalization with PCa (PP.H4 > 0.85), suggesting their potential as drug target candidates. Table 1 Colocalization results of eQTLs for top 10 genes with associated SNPs. PP.H0–PP.H4 represent the posterior probabilities of different hypotheses. PP.H4 > 0.85 represents a strong colocalization between gene expression and prostate cancer risk. Gene PP.H0.abf PP.H1.abf PP.H2.abf PP.H3.abf PP.H4.abf SUPT4H1 9.40E-95 0.0041568 1.88E-94 0.0073213 0.988522 NOSIP 1.25E-95 2.73E-07 6.11E-91 0.0123935 0.9876062 MYO5C 1.07E-293 0.000441 3.66E-293 0.0140649 0.9854941 RFC5 5.30E-110 7.51E-05 2.68E-107 0.0369585 0.9629664 CNEP1R1 2.84E-34 0.0697275 1.56E-34 0.0375201 0.8927524 IGHV3-20 1.19E-27 0.1605989 7.43E-28 0.0994936 0.7399075 BLTP3A 0 3.80E-09 0 0.2684241 0.7315759 OGFRL1 8.34E-293 0.0036662 6.44E-293 0.2823716 0.7139622 REELD1 0 0.0401158 0 0.33286 0.6270243 CSK 0 0.2430919 0 0.1779487 0.5789594 3.6 PheWAS To investigate the potential influences of the five potential drug target genes and uncover any hidden pleiotropy via the MR-Egger intercept test, we examined their potential influence on additional traits with PheWAS. As depicted in Figs. S3–S12, none of the five drug target genes exhibited significant associations with other traits at the gene level (P < 5E − 8 for genomic association). This suggests minimal likelihood of substantial side effects from drugs targeting these genes, which further ensure the robustness of the study results. 3.7 PPI networks To explore the mechanisms and pathways of the five drug target genes, we uploaded them to the STRING database to generate an interaction network, which was then visualized in Cytoscape. Figure 8 depicts the interactions among the five drug targets and other proteins, forming a PPI network comprising 35 nodes and 332 edges. For the PPI network constructed using GeneMANIA, the five drug targets were complemented by an additional 20 genes that may interact with them, totaling 484 interaction links (Fig. 9 ). The interactions within this network were categorized as: physical interactions (77.64%), co-expression (8.01%), predicted (5.37%), co-localization (3.63%), genetic interactions (2.87%), pathway (1.88%) and shared protein domains (0.60%). The network functional analysis did not detect any significant functional correlations between the drug targets and the associated genes. 3.8 Candidate drug prediction To find interventional drugs for the identified genes, we utilized the DSigDB database. As presented in Table 2 , the top 10 potential chemical compounds were listed based on adjusted P-values. The results showed that captopril (captopril HL60 DOWN, PubChem ID: 44093) and dexibuprofen (dexibuprofen PC3 DOWN, PubChem ID: 39912) were the two most significant drugs, linked to RFC5 and SUPT4H1, RFC5 and NOSIP respectively. Notably, acetaminophen (acetaminophen CTD 00005295, PubChem ID: 1983) exhibited interactions with the most genes including RFC5, CNEP1R1, MYO5C and NOSIP. Table 2 Candidate drug predicted using DsigDB. Drug P value Adjusted P value Genes captopril HL60 DOWN 0.000706 0.059562 RFC5; SUPT4H1 dexibuprofen PC3 DOWN 0.001168 0.059562 RFC5; NOSIP amikacin PC3 DOWN 0.002705 0.070715 RFC5; NOSIP diltiazem PC3 DOWN 0.003185 0.070715 RFC5; NOSIP CUPRIC OXIDE CTD 00001471 0.003703 0.070715 MYO5C; SUPT4H1 paclitaxel PC3 DOWN 0.004160 0.070715 RFC5; NOSIP acepromazine HL60 DOWN 0.005986 0.084121 RFC5 chlorhexidine PC3 DOWN 0.006745 0.084121 RFC5; MYO5C acetaminophen CTD 00005295 0.007617 0.084121 RFC5; CNEP1R1; MYO5C; NOSIP harmol PC3 DOWN 0.008968 0.084121 SUPT4H1 3.9 Molecular docking After identifying potential drug candidates, we performed molecular docking to assess their binding affinity and druggability. Autodock Vina v.1.5.7 was used to analyze the top five candidates' binding sites and interactions with the proteins from the target genes. Valid docking outcomes and the binding energy for each interaction for three proteins in complex with the drugs are presented in Table 3 and Fig. 10 . Each drug candidate formed visible hydrogen bonds and strong electrostatic interactions with its protein target. Moreover, the binding pocket of each target was effectively occupied by the five drug candidates. Notably, the combination of RFC5 and paclitaxel displayed the lowest binding energy (− 9.3 kcal/mol), suggesting exceptionally stable binding. Table 3 Docking results of available proteins with small molecules. Genes PDB ID Drug PubChem ID Binding energy (kcal/mol) RFC5 8UNJ captopril 44093 -4.8 SUPT4H1 3H7H captopril 44093 -4.1 RFC5 8UNJ dexibuprofen 39912 -4.8 NOSIP Q9Y314 dexibuprofen 39912 -4.6 RFC5 8UNJ amikacin 37768 -7.1 NOSIP Q9Y314 amikacin 37768 -6.0 RFC5 8UNJ diltiazem 39186 -6.9 NOSIP Q9Y314 diltiazem 39186 -5.5 RFC5 8UNJ paclitaxel 36314 -9.3 NOSIP Q9Y314 paclitaxel 36314 -7.7 4 Discussion This study identified five potential therapeutic targets for prostate cancer: SUPT4H1, NOSIP, MYO5C, RFC5 and CNEP1R1. We used multiple MR methods to identify genes with potential causal associations with PCa. In addition, enrichment analysis and PPI network analysis were performed in this study to understand the biological significance of these drug targets. Then, a co-localization analysis was performed to confirm the revealed genetic associations. Furthermore, a mediation MR analysis was conducted to assess the potential mediating role of these proteins in tumor immunity. In consideration of the possible pleiotropy of target genes and potential drug side effects, we also conducted phenome-wide association analysis. Finally, prediction and molecular docking proved the medicinal value of these drug target genes. Prostate cancer is the second most common malignancy in men and has a complex etiology involving genetic predisposition, environmental exposures and inflammatory processes. 39 The role of immune cells in the tumor microenvironment is critical, influencing tumor growth, invasion and metastasis. 40 , 41 Existing studies have shown that protein-coding genes such as HNRNPL, HOXB13, PTEN and BRCA2 are strongly associated with prostate cancer development and could be potentioal drug targets. 42 – 44 However, the complex interplay between drug target genes and the immune microenvironment in prostate cancer is not fully understood. SUPT4H1 is essential in the transcriptional function of RNA polymerase II, supporting the enzyme's movement along the DNA strand. 45 In gene expression, SUPT4H1 often forms a complex SPT4/SPT5 with SUPT5H to regulate chromatin elasticity. 46 Recent studies have shown that SUPT4H1 is a target for immunotherapy in breast cancer. 47 Moreover, our study specified SUPT4H1 exerting a mediating effect on Naïve CD8 T cell’s impact on PCa, which is a novel finding. In addition, knockdown of SUPT4H1 with CRISPR/Cas9 technique was proved to promote the differentiation and functional recovery of neural cells. 48 NOSIP interacts with nitric oxide synthase (NOS) to regulate nitric oxide (NO) production, which plays an important role in vasodilation, immune response, and neurotransmission. 49 , 50 Recent research indicates that NOSIP could be an oncogene in hepatocellular carcinoma progression, with quercetin potentially inhibiting NOSIP to treat HCC. 51 MYO5C, a non-muscle myosin motor protein, plays a crucial role in the regulation of intracellular trafficking and cytoskeletal dynamics, which are related with the migration and invasiveness of tumor cells. 52 , 53 Recent research has revealed that targeted degradation agents can bind with and promote the degradation of MYC protein through a process mediated by E3 ligases and a proteasome system, 54 highlighting the potential of MYO5C as a druggable target. RFC5 is a subunit of RFC that plays a vital role in the processes of DNA replication and repair, by forming an ATP-dependent clamp loader with other RFC subunits and helping with fixation of the PCNA ring protein to DNA, 55 which functions as a molecular platform for DNA replication, repair, and chromatin assembly. 56 It has been demonstrated that aberrant expression of RFC5 may result in errors during DNA replication and repair, thereby promoting the development of tumors, such as lung cancer. 57 , 58 CNEP1R1 is involved in nuclear membrane biosynthesis and remodeling, regulation of cell cycle processes, and maintenance of cell membrane integrity and dynamic homeostasis, and plays a critical role in cellular lipid metabolism and the dynamics of cell membrane structure. 59 , 60 It activates CTDNEP1, which dephosphorylates and activates LPIN1, a key enzyme in lipid synthesis, thereby affecting lipid production and degradation. 61 It has been found that dysregulation of CNEP1R1 may lead to disturbances in cellular lipid metabolism and has been associated with the progression of several metabolic diseases, such as fatty liver and obesity, as well as cancer. 62 These results demonstrate a strong correlation between the proposed drug targets and PCa, highlighting their significant therapeutic potential and providing a promising basis for targeted PCa treatment strategies. Limitations While the study provides valuable insights, there are some limitations that need to be considered. The MR analysis provides important clues to understanding causality, but it is based on the assumption of low-dose drug exposure and a linear relationship between exposure and outcome, which may not fully mimic the real-world setting of a clinical trial, which typically evaluates high-dose drugs over a short period of time. As a result, MR results may not accurately reflect effect sizes observed in clinical practice and may not fully predict the true effects of drugs. The diversity of the study cohorts is also a limiting factor. Although the eQTLs analysis included individuals of diverse ancestry, the PCa study population was restricted to European ancestry. This homogeneity of population background may introduce a potential bias in MR effect estimates due to differences in genetic background and linkage disequilibrium patterns. Extending these findings to individuals of other ethnicities requires further research and validation to ensure their broader applicability. In addition, although researchers attempt to minimize bias, MR analyses may still be affected by unmeasured factors or pleiotropy, which may affect the accuracy of the results. It is important to recognize these limitations and their potential impact on study conclusions. Finally, this study focused primarily on cis-eQTLs and their association with prostate cancer, while ignoring the role of other regulatory components and environmental factors such as non-coding RNAs, epigenetic modifications, and gene-gene interactions, as well as environmental factors like diet, lifestyle, and exposure to carcinogens, in the complexity of the disease. These factors may have an important impact on disease initiation and progression, and therefore future studies need to consider these factors to gain a more comprehensive understanding of the disease. Suggestions for furthur investigations Given the limitations of this study, the following recommendations are intended to guide future research in this critical area. First, future studies may consider increasing the sample size and including populations from diverse geographic, racial, and ethnic backgrounds to increase the generalizability and transferability of the findings. This integrated approach will allow for a deeper understanding of the relationship between MR-observed drug effects and clinical outcomes, which can then be translated into medical practice with real-world applications. Second, when interpreting the results of enrichment analysis, researchers should exercise caution and consider alternative methods to minimize bias. Because enrichment analysis relies on predefined sets of genes or pathways, future studies could explore a wider range of biological networks and interactions to more comprehensively capture biological mechanisms. Third, future research could improve the accuracy of molecular docking by prioritizing the collection of high-quality experimental data and the development of docking algorithms to better simulate conformational changes of proteins. This will optimize target selection and bring predicted interactions closer to actual molecular behavior. Fourth, advances in the development of in vitro models that better reflect in vivo conditions will make drug target screening and early validation more representative. Advances in drug bioavailability and pharmacokinetic studies will further reduce potential discrepancies between experimental and clinical results. Finally, further experimental validation and rigorous clinical studies are needed to confirm the therapeutic potential of targets identified by Mendelian randomization, enrichment analysis and molecular docking, and to assess their safety and efficacy in real-world applications. 5 Conclusions In summary, this study identified five potential drug targets in PCa by MR analysis, which were significant in two independent cohorts and validated by co-localization analysis. Some of the targets served as mediators in modulating the tumor immune response. Drug prediction and molecular docking further confirmed the medicinal value of these targets, which is expected to reduce drug development costs and promote personalized medicine. This study emphasizes the potential importance of these targets in PCa therapy, facilitating more targeted and efficient treatment strategies, which in turn improves treatment efficacy and reduces side effects. Future studies and clinical trials could explore the clinical application value of these targets in depth. Abbreviations PCa Prostate cancer TME Tumor microenvironment MR Mendelian randomization NHANES National health and nutrition examination survey IVW Inverse-variance weighted OR Odds ratio CI Confidence interval GWAS Genome-wide association study SNPs Single nucleotide polymorphisms GO Gene ontology KEGG Kyoto encyclopedia of genes and genomes BP Biological process MF Molecular function CC Cellular component Declarations Acknowledgments We sincerely thank all the projects (eQTLGen consortium, the FinnGen team and MRCIEU) who participated in this study. Authors' contributions Conceptualization, Zhechun Wu, Yuqing Li and Wei Wang; Formal analysis, Zhechun Wu and Sihan Li; Methodology, Zhizhi Wang and Wei Wang; Project administration, Wei Wang; Supervision, Yuqing Li and Wei Wang; Validation, Zhechun Wu and Sihan Li; Visualization, Zhechun Wu and Zhizhi Wang; Writing – original draft, Zhechun Wu and Sihan Li; Writing – review & editing, Sihan Li and Yuqing Li. Funding None. Availability of data and materials The GWAS data of prostate cancer and immune cell phenotypes were retrieved from NHGRI-EBI GWAS Catalog (https://www.ebi.ac.uk/gwas/studies/GCST90274714/ for prostate cancer, and https://www.ebi.ac.uk/gwas/studies/GCST0001391-GCST0002121/ for immune cell phenotypes). The GWAS data for the prostate cancer validation cohort were sourced from FinnGen Release 11 (https://www.fnngen.fi/). The eQTLs data were sourced from eQTLGen Consortium (https://eqtlgen.org/). Ethics approval and consent to participate Data used in the MR study were derived from existing studies, obviating the need for additional ethical approval. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Declaration of generative AI and AI-assisted technologies in the writing process During the preparation of this work the authors used ChatGPT 4o in order to assist with translation and language refinement. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication. References Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin . 2021;71(3):209-249. doi:10.3322/caac.21660 Culp MB, Soerjomataram I, Efstathiou JA, Bray F, Jemal A. Recent Global Patterns in Prostate Cancer Incidence and Mortality Rates. 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Results of Mendelian randomization of the discovery cohort. Table S2. Results of MR Egger intercept horizontal pleiotropy test for the discovery cohort. Table S3. Results of heterogeneity test for the discovery cohort. Table S4. Results of Mendelian randomization of the validation cohort. Table S5. Results of MR Egger intercept horizontal pleiotropy test for the validation cohort. Table S6. Results of heterogeneity test for the validation cohort. Table S7. Results of Mendelian randomization of immune cells' effect on PCa. Table S8.Results of MR Egger intercept horizontal pleiotropy test for immune cells' effect on PCa. Table S9. Results of heterogeneity test for immune cells' effect on PCa. Table S10. Results of Mendelian randomization of immune cells' effect on druggable genes. Figure S1. Forest plot for immune cells' effect on PCa. Figure S2. Forest plot for immune cells' effect on druggable genes. Figure S3-S12. Results of PheWAS analysis for 5 genes. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 28 Jan, 2025 Reviews received at journal 27 Jan, 2025 Reviews received at journal 26 Jan, 2025 Reviewers agreed at journal 21 Jan, 2025 Reviewers agreed at journal 20 Jan, 2025 Reviewers agreed at journal 20 Jan, 2025 Reviewers agreed at journal 12 Jan, 2025 Reviewers invited by journal 09 Jan, 2025 Editor assigned by journal 02 Jan, 2025 Submission checks completed at journal 31 Dec, 2024 First submitted to journal 23 Dec, 2024 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. 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(B) Plot illustrating number of association and mediated proportion of each protein.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5697374/v1/2b8ea6d9fba5d8780fa23772.png"},{"id":72800752,"identity":"ff7d42ff-73af-4e0f-9444-610f5c04dcea","added_by":"auto","created_at":"2025-01-02 09:25:56","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":238966,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGO enrichment results of three ontologies.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-5697374/v1/cc1a3964f2bdeb5be43075c8.png"},{"id":72800731,"identity":"ff8f6eb3-759a-410d-be52-ceddde7ca945","added_by":"auto","created_at":"2025-01-02 09:25:55","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":159758,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKEGG enrichment results.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-5697374/v1/bb2f29bb8324fdca4c287f1d.png"},{"id":72801155,"identity":"f2e1e994-6f24-4d6a-a74b-866b6c44ba56","added_by":"auto","created_at":"2025-01-02 09:33:56","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":436760,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePPI network built with STRING.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-5697374/v1/bb5a06a14ad2cbe7607be801.png"},{"id":72800750,"identity":"7c37da38-d721-44c5-b46b-57e0d9e3b434","added_by":"auto","created_at":"2025-01-02 09:25:56","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":242236,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePPI network built with GeneMANIA.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-5697374/v1/746de31b5b3a629d46cf91e4.png"},{"id":72800746,"identity":"3ab71f85-3c1e-404e-8d2a-467df65ce5c4","added_by":"auto","created_at":"2025-01-02 09:25:56","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":952722,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDocking results of available small molecule proteins. \u003c/strong\u003e(A) RFC5 docking captopril, (B) SUPT4H1 docking captopril, (C) RFC5 docking dexibuprofen, (D) NOSIP docking dexibuprofen, (E) RFC5 docking amikacin, (F) NOSIP docking amikacin, (G) RFC5 docking diltiazem, (H) NOSIP docking diltiazem, (I) RFC5 docking paclitaxel, (J) NOSIP docking paclitaxel.\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-5697374/v1/3db671fa537aa83cee83f38a.png"},{"id":72802731,"identity":"fdc029fb-41ec-4935-baf3-c9102b9368f8","added_by":"auto","created_at":"2025-01-02 09:49:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4332918,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5697374/v1/95ec6790-5435-4336-80bd-bc1e3b477ee7.pdf"},{"id":72801156,"identity":"54e3458e-752d-4242-9dac-84bb6b072e3b","added_by":"auto","created_at":"2025-01-02 09:33:56","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3554781,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Material 1\u003c/p\u003e\n\u003cp\u003eFile format: .pdf\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable S1. \u003c/strong\u003eResults of Mendelian randomization of the discovery cohort. \u003cstrong\u003eTable S2. \u003c/strong\u003eResults of MR Egger intercept horizontal pleiotropy test for the discovery cohort.\u003cstrong\u003e Table S3. \u003c/strong\u003eResults of heterogeneity test for the discovery cohort. \u003cstrong\u003eTable S4. \u003c/strong\u003eResults of Mendelian randomization of the validation cohort. \u003cstrong\u003eTable S5. \u003c/strong\u003eResults of MR Egger intercept horizontal pleiotropy test for the validation cohort. \u003cstrong\u003eTable S6. \u003c/strong\u003eResults of heterogeneity test for the validation cohort. \u003cstrong\u003eTable S7. \u003c/strong\u003eResults of Mendelian randomization of immune cells' effect on PCa.\u003cstrong\u003e Table S8.\u003c/strong\u003eResults of MR Egger intercept horizontal pleiotropy test for immune cells' effect on PCa.\u003cstrong\u003e Table S9. \u003c/strong\u003eResults of heterogeneity test for immune cells' effect on PCa.\u003cstrong\u003e Table S10. \u003c/strong\u003eResults of Mendelian randomization of immune cells' effect on druggable genes. \u003cstrong\u003eFigure S1. \u003c/strong\u003eForest plot for immune cells' effect on PCa. \u003cstrong\u003eFigure S2. \u003c/strong\u003eForest plot for immune cells' effect on druggable genes. \u003cstrong\u003eFigure S3-S12.\u003c/strong\u003e Results of PheWAS analysis for 5 genes.\u003c/p\u003e","description":"","filename":"SupplementaryMaterial1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5697374/v1/1f100a61501b0ed85f1dc152.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identifying potential therapeutic targets for prostate cancer with mediating role in tumor immunity","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eProstate cancer (PCa) is among the most prevalent malignancies around the world, contributing significantly to the overall cancer mortality rate.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e The incidence of prostate cancer is anticipated to escalate from approximately 1.4\u0026nbsp;million cases annually in 2020 to an estimated 2.9\u0026nbsp;million cases by 2040. Concurrently, the annual mortality rate due to prostate cancer is estimated to surge by 85% over the next two decades, rising from 375,000 deaths in 2020 to nearly 700,000 by 2040.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Despite significant advancements in the screening and treatment of prostate cancer, there persist numerous challenges. Castration-resistant prostate cancer (CRPC) is one of the major challenges in treatment, and nearly all patients receiving androgen deprivation therapy (ADT) will eventually develop CRPC.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e The restricted efficacy of immunotherapy in patients with CRPC may be associated with its capacity to foster an immunosuppressive tumor microenvironment (TME).\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e In addition, the treatment of prostate cancer also faces barriers such as drug resistance, the impact of side effects on quality of life, and the complexity of early diagnosis and treatment options.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eWithin the TME, immune cells exert a significant influence on tumor growth and metastasis. Tumor infiltrating lymphocytes (TILs) are heterogeneous lymphocytes present in TME and participate in anti-tumor immune response,\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e which can inhibit tumor growth by recognizing and destroying tumor cells, but in some cases may also be used by tumor cells to promote tumor progression.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e Tumor cells regulate the immune microenvironment through various mechanisms to achieve immune escape. For example, tumor-associated macrophages (TAMs) can promote tumor growth by secreting anti-inflammatory cytokines and inhibitory molecules that inhibit T cell activity.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e In addition, tumor cells can also inhibit the function of T cells by up-regulating the expression of PD-L1 and evade immune surveillance.\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eCurrently, the therapeutic targets for prostate cancer mainly center on the androgen receptor (AR) signaling pathway, the development of novel anti-androgen drugs, and immune checkpoint inhibitors.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e Notably, new drugs like abiraterone and enzalutamide have been employed in the treatment of CRPC, demonstrating notable efficacy.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e Despite the identification of numerous therapeutic targets, there remains a pressing need to discover novel targets that significantly influence the modulation of the tumor immune microenvironment. In this context, immune cells such as TAMs, regulatory T cells (Tregs), and myeloid-derived suppressor cells (MDSCs) have been implicated in facilitating immune evasion in prostate cancer.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e Targeting these cell populations or their associated signaling pathways could potentially yield innovative therapeutic strategies for the management of prostate cancer.\u003c/p\u003e \u003cp\u003eRecent studies have discovered several promising drug targets playing an important role in the TME. For instance, combining CDK4/6 inhibitors with immune checkpoint inhibitors is proved to enhance immune responses and increase lymphocyte infiltration within the tumor in prostate cancer treatment.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e Furthermore, the overexpression of the histone methyltransferase EZH2 is the primary cause of the increased genomic distribution of H3K27me3 in metastatic prostate cancer, rendering EZH2 a valuable drug target.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e These findings suggest that by targeting key genes and proteins in specific immune microenvironments, new therapeutic options may be available for prostate cancer patients.\u003c/p\u003e \u003cp\u003eGWAS is a widely used method to identify genetic variations (such as single nucleotide polymorphisms, SNPs) in the genome that are associated with a specific disease or trait. By analyzing genomic data from a large number of patients, GWAS was able to reveal genetic loci associated with prostate cancer risk, providing clues for subsequent functional studies. Mendelian randomization (MR) analyses use genetic variation as an instrumental variable to assess the causal relationship between exposure factors and the outcome. This method can effectively reduce the influence of confounding factors and reverse causality, and improve the accuracy of causal inference.\u003c/p\u003e \u003cp\u003eThe main objective of this study is to identify potential therapeutic targets for prostate cancer with mediation effects in tumor immunity. By identifying these targets, this study will provide a theoretical basis for the development of new immunotherapy strategies, which are expected to improve the efficacy of prostate cancer treatment and improve patient survival and quality of life.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Exposure data\u003c/h2\u003e \u003cp\u003eThe eQTLs data were sourced from eQTLGen Consortium (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://eqtlgen.org/\u003c/span\u003e\u003cspan address=\"https://eqtlgen.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). This dataset encompassed 16987 genes along with 31684 cis-eQTLs identified in blood samples predominantly from healthy European individuals.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e On Sept. 14, 2024, the complete set of cis-eQTLs data and allele frequency statistics were retrieved from the eQTLGen Consortium. The data of immune cell phenotype ranging from GCST0001391 to GCST0002121 were retrieved from NHGRI-EBI GWAS Catalog.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e The datasets encompassed 731 immune cell phenotypes, including Treg cells, mature T cells, B cells, natural killer cells, monocytes, and myeloid cells.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Outcome data\u003c/h2\u003e \u003cp\u003eGWAS data for the PCa discovery cohort were downloaded from NHGRI-EBI GWAS Catalog\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e on July 26, 2024, for GCST90274713, the study conducted by Wang A et al in 2023.\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e Their research amassed a total of 122188 cases of European ancestry and 604640 controls, employing genome-wide genotyping arrays for genetic analysis. The discovery ancestry was recruited from various European nations to ensure diversity. For consistency with the exposure data, to maintain consistency with the exposure data, GWAS data for the PCa validation cohort, comprising 17,258 cases and 143,624 controls, were sourced from FinnGen Release 11 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fnngen.fi/)\u003c/span\u003e\u003cspan address=\"https://www.fnngen.fi/)\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e,\u003csup\u003e23\u003c/sup\u003e which was released in June 24, 2024. The FinnGen study, a collaborative genomics project, has analyzed over 500,000 Finnish biobank samples to link genetic variations with health outcomes, enhancing our understanding of disease mechanisms and predispositions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Mendelian randomization analysis\u003c/h2\u003e \u003cp\u003eFor MR analysis, the R package TwoSampleMR (version 0.5.6) was employed.\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e The principal method to assess the causal effects of exposure on outcome was the inverse variance weighted (IVW). The two-step MR approach was applied to explore potential therapeutic target proteins and their impact on tumor immunity. The product of coefficients method was utilized to estimate indirect effects, with the mediated proportion determined by the ratio of the indirect effect to the total effect. Standard errors for these indirect effects were calculated using the delta method.\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e To validate IVW results, we employed four additional MR analysis methods: MR Egger, weighted median, simple mode, and weighted mode. Heterogeneity was evaluated through the Cochran Q statistic and I\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e value, while the MR-Egger regression intercept test was applied to detect horizontal pleiotropy. Any significant pleiotropy (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) led to the exclusion of the respective results. The mediated proportion is referred to as a \"mediation effect\" when it intensifies the association between the predictor and the outcome, and as a \"suppression effect\" when it diminishes or inverts this association, suggesting a deleterious impact on the outcome that could conceal or modify the direct effect of the predictor.\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Colocalization analysis\u003c/h2\u003e \u003cp\u003eFor genes that demonstrated significance across both cohorts, a colocalization analysis of PCa risk was conducted using the R package coloc.\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e This analysis utilized SNPs harmonized by the TwoSampleMR package with default prior probabilities: p1\u0026thinsp;=\u0026thinsp;1E\u0026thinsp;\u0026minus;\u0026thinsp;4, p2\u0026thinsp;=\u0026thinsp;1E\u0026thinsp;\u0026minus;\u0026thinsp;4, and p12\u0026thinsp;=\u0026thinsp;1E\u0026thinsp;\u0026minus;\u0026thinsp;5. These probabilities pertain to the likelihood that a SNP in the test region is substantially linked to gene expression, PCa risk, or both. The posterior probabilities from the colocalization analysis were categorized under one of five hypotheses: PPH0, indicating no association of SNPs with either trait; PPH1, association with gene expression but not PCa risk; PPH2, association with PCa risk but not gene expression; PPH3, association with both PCa risk and gene expression driven by different SNPs; and PPH4, association with both PCa risk and gene expression driven by common SNPs. The significance threshold for colocalization was established at PPH4\u0026thinsp;\u0026gt;\u0026thinsp;0.85, with genes that met this criterion being considered as potential drug target genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Phenome‑wide association analysis\u003c/h2\u003e \u003cp\u003eTo further assess the horizontal pleiotropy of potential drug targets and their potential side effects, a phenome-wide association study (PheWAS) was conducted using the AstraZeneca PheWAS Portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://azpheWAS.com/)\u003c/span\u003e\u003cspan address=\"https://azpheWAS.com/)\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003csup\u003e29\u003c/sup\u003e This study leveraged a high-quality subset of approximately 470000 exome-sequenced participants from the UK Biobank, who were predominantly unrelated and of European ancestry (n\u0026thinsp;=\u0026thinsp;419391). The association between protein-coding variants and approximately 10000 binary and 3500 continuous phenotypes was evaluated through variant-level and gene-level PheWAS. Various correction methods were employed, and the significance threshold was established at 2E\u0026thinsp;\u0026minus;\u0026thinsp;9, aligning with the standard set by the AstraZeneca PheWAS Portal, to minimize the likelihood of false-positive results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Enrichment analysis\u003c/h2\u003e \u003cp\u003eTo delve into the functional properties and biological implications of the prospective therapeutic target genes identified, enrichment analyses using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were conducted with the R package clusterProfiler.\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e The GO analysis encompasses three domains: biological process (BP), molecular function (MF), and cellular component (CC). Meanwhile, the KEGG pathway analysis offers insights into metabolic pathways.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Protein interaction network construction\u003c/h2\u003e \u003cp\u003eProtein\u0026ndash;protein interaction (PPI) networks provide a detailed perspective on the intricate interactions among proteins inside cells. Through the analysis of these networks, we can reveal the underlying mechanisms of cellular processes, signal transduction, and disease-related pathways. In this research, the PPI network was constructed using STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cn.string-db.org/\u003c/span\u003e\u003cspan address=\"https://cn.string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), setting a confidence score threshold of 0.4 for the minimum interaction score, with all other parameters set to their default values.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e The PPI outcomes were visually represented using Cytoscape (V3.10.2).\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e GeneMANIA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://genemania.org/\u003c/span\u003e\u003cspan address=\"https://genemania.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was also employed for PPI analysis [3].\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Candidate drug prediction\u003c/h2\u003e \u003cp\u003eTo see if our target genes can be drug targets, this study will adopt the Drug Signatures Database (DSigDB)\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e on Enrichr web server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://maayanlab.cloud/Enrichr/\u003c/span\u003e\u003cspan address=\"https://maayanlab.cloud/Enrichr/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e35\u003c/sup\u003e for drug prediction. DSigDB is a specialized database that connects drugs and other chemicals to their target genes. It currently comprises 22527 gene sets, consisting of 17389 unique compounds covering 19531 genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Molecular docking\u003c/h2\u003e \u003cp\u003eTo gain insights into how drug candidates interact with their target genes and to evaluate the druggability of these targets, this study conducted atomic-level molecular docking to assess the binding energy and interaction patterns. Molecular docking simulations are instrumental in analyzing the binding affinity and interaction modes between ligands and their drug targets. By pinpointing ligands with strong binding affinity and favorable interactions, we can select priority drug targets for experimental validation and refine the design of potential drugs. For this study, AutoDock Vina 1.5.7 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://autodock.scripps.edu/)\u003c/span\u003e\u003cspan address=\"http://autodock.scripps.edu/)\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e,\u003csup\u003e36\u003c/sup\u003e a computational protein-ligand docking tool, was employed to carry out molecular docking for the most significant drugs and the proteins encoded by the target genes. The structural data for the drugs were retrieved from the PubChem Compound Database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/)\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/)\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e,\u003csup\u003e37\u003c/sup\u003e with the respective compound IDs presented in Table\u0026nbsp;4. The protein structures were sourced from the Protein Data Bank (PDB, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.rcsb.org/\u003c/span\u003e\u003cspan address=\"http://www.rcsb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and their corresponding PDB IDs are detailed in Table\u0026nbsp;4. For the top significant three proteins and five drugs, we derived their final structures by first stripping water molecules from both protein and ligand files and then appending polar hydrogen atoms. We positioned grid boxes to cover the protein's structural domains, ensuring ample space for molecular flexibility. The grid was set with a spacing of 0.05 nm and a dimension of 40\u0026Aring; \u0026times; 40\u0026Aring; \u0026times; 40\u0026Aring; to define the docking pocket. Autodock Vina 1.5.7 visualized the entire molecular docking procedure.\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed using R (version 4.4.1, R Core Team) and RStudio (version 2023.06.2 Build 524, Posit Software, PBC). A flow chart summarizing the study is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Associations of proteins with PCa during the discovery phase\u003c/h2\u003e \u003cp\u003eAs illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, in the discovery cohort, the IVW method analyses revealed causal relationships between 557 proteins and PCa, while these results were generally supported by other supplementary MR methods, including MR Egger and the weighted median method, in terms of both the direction of causal estimation and the magnitude of the causal effect, suggesting that the findings are reliable and trustworthy. The effect estimates of these proteins on PCa measured by different MR methods are presented in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. To explore whether SNPs linked to these proteins might contribute to PCa via alternative pathways, we conducted additional horizontal pleiotropic analyses, the results of which are detailed in Table S2. The proteins DSE, IL1R2, VAMP8, IFI6, and TRIM10 failed the horizontal pleiotropy test (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and were excluded from further analyses. The heterogeneity test revealed significant heterogeneity among the SNPs for 196 proteins (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), as indicated in Table S3.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Validation phase 86 proteins remain significant in an independent PCa cohort\u003c/h2\u003e \u003cp\u003eDuring the validation, this study utilized GWAS data from the Finnish FinnGen database, which comprised 17258 cases and 143624 controls of European ancestry. The MR analysis was conducted identically to the methods employed in the discovery cohort. Employing the IVW approach, the genetically predicted expression of 64 proteins was successfully replicated (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), establishing a causal link to PCa risk, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The effect estimates of proteins on PCa, assessed by various MR methods, are outlined in Table S4. The horizontal pleiotropy test did not identify significant pleiotropy for any of the proteins in question (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), as detailed in Table S5. In the heterogeneity test, the proteins LAMC1, AK9, and STMN3 exhibited inter-SNP heterogeneity (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), as reported in Table S6.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Mediation MR analysis discovered 9 mediating proteins in tumor immunity\u003c/h2\u003e \u003cp\u003eTo elucidate the impact of the identified proteins on the immune response to PCa, we conducted mediation MR analyses using the PCa dataset from the discovery cohort. After assessing for heterogeneity and horizontal pleiotropic effects, we selected reliable SNPs with an MR Egger test P value over 0.05. Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e demonstrates that 39 immune cell types were associated with PCa, with comprehensive results from MR analyses, pleiotropy, and heterogeneity analyses presented in Tables S7-S9. Subsequently, 32 causal associations were identified between these immune cells and the proteins, with detailed MR analysis results provided in Table S10. Ultimately, mediation analysis uncovered 20 instances where 9 of the previously identified proteins mediated the influence of immune cells on PCa, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Specifically, IFITM4P was found to mediate the effects of immune cells on PCa in five distinct phenotypes: CD27 on unsw mem, CD8 on EM CD8br, HLA DR on CD14\u0026thinsp;+\u0026thinsp;CD16\u0026thinsp;\u0026minus;\u0026thinsp;monocyte, HLA DR on CD14\u0026thinsp;+\u0026thinsp;monocyte, and HLA DR on DC, with mediated proportions of -0.16%, 3.03%, -10.74%, -11.1%, -5.38% respectively. PBX2 mediated four immune cells\u0026rsquo; effects on PCa, including CD45RA\u0026thinsp;\u0026minus;\u0026thinsp;CD4+ %T cell, CD64 on CD14\u0026thinsp;\u0026minus;\u0026thinsp;CD16\u0026minus;, HVEM on CD8br, and SSC\u0026thinsp;\u0026minus;\u0026thinsp;A on HLA DR\u0026thinsp;+\u0026thinsp;T cell, with mediated proportions of 8.48%, -0.13%, 0.09%, -11.44% respectively. Additionally, SUPT4H1 was identified as mediating 2.69% of the total impact of Naive CD8br %T cell on PCa.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Enrichment analysis\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the most significant pathways in the BP category were linked to vesicular transport and membrane targeting, particularly highlighting post-Golgi vesicle-mediated transport, vitamin K and fat-soluble vitamin metabolic process, and localization to membrane establishment of protein. Additionally, there was a notable focus on the regulation of pseudopodium assembly and organization. In the CC category, the gene enrichment was also notably associated with components of the endomembrane system and vesicular transport processes, including phagocytic vesicle, COPII-coated ER to Golgi transport vesicle and endoplasmic reticulum exit site, reflecting the cell's intricate mechanisms for material transport and membrane trafficking. Moreover, in terms of MF, the identified genes were enriched for roles in metabolic activities such as NADP binding, proline-rich region binding, oxidoreductase activity, NAD binding, GTPase activator activity and GDP phosphatase activity, as well as RNA methyltransferase activity and damaged DNA binding. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, the top three pathways analysed by KEGG enrichment are DNA validation, Mismatch repair and Thyroid cancer, which partially supports the results of the GO analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Colocalization analysis\u003c/h2\u003e \u003cp\u003eCo-localization analyses can confirm whether associated exposures and outcomes are influenced by the same causal SNP,\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e and proteins undergo MR and co-localization analyses are more likely to be drug targets.\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e Therefore, this study conducted a colocalization analysis on the 86 genes proposed in previous analyses with PCa data of the discovery cohort. As detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, out of the 86 proteins analyzed, five (SUPT4H1, NOSIP, MYO5C, RFC5, and CNEP1R1) showed strong evidence of colocalization with PCa (PP.H4\u0026thinsp;\u0026gt;\u0026thinsp;0.85), suggesting their potential as drug target candidates.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\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\u003e\u003cb\u003eColocalization results of eQTLs for top 10 genes with associated SNPs.\u003c/b\u003e PP.H0\u0026ndash;PP.H4 represent the posterior probabilities of different hypotheses. PP.H4\u0026thinsp;\u0026gt;\u0026thinsp;0.85 represents a strong colocalization between gene expression and prostate cancer risk.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePP.H0.abf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePP.H1.abf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePP.H2.abf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePP.H3.abf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePP.H4.abf\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUPT4H1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.40E-95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0041568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.88E-94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0073213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.988522\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNOSIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.25E-95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.73E-07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.11E-91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0123935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9876062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMYO5C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.07E-293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.66E-293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0140649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9854941\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRFC5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.30E-110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.51E-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.68E-107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0369585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9629664\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCNEP1R1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.84E-34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0697275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.56E-34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0375201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.8927524\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIGHV3-20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.19E-27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1605989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.43E-28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0994936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.7399075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBLTP3A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.80E-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2684241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.7315759\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOGFRL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.34E-293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0036662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.44E-293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2823716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.7139622\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREELD1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0401158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.33286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.6270243\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.2430919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1779487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.5789594\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.6 PheWAS\u003c/h2\u003e \u003cp\u003eTo investigate the potential influences of the five potential drug target genes and uncover any hidden pleiotropy via the MR-Egger intercept test, we examined their potential influence on additional traits with PheWAS. As depicted in Figs. S3\u0026ndash;S12, none of the five drug target genes exhibited significant associations with other traits at the gene level (P\u0026thinsp;\u0026lt;\u0026thinsp;5E\u0026thinsp;\u0026minus;\u0026thinsp;8 for genomic association). This suggests minimal likelihood of substantial side effects from drugs targeting these genes, which further ensure the robustness of the study results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.7 PPI networks\u003c/h2\u003e \u003cp\u003eTo explore the mechanisms and pathways of the five drug target genes, we uploaded them to the STRING database to generate an interaction network, which was then visualized in Cytoscape. Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e depicts the interactions among the five drug targets and other proteins, forming a PPI network comprising 35 nodes and 332 edges. For the PPI network constructed using GeneMANIA, the five drug targets were complemented by an additional 20 genes that may interact with them, totaling 484 interaction links (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). The interactions within this network were categorized as: physical interactions (77.64%), co-expression (8.01%), predicted (5.37%), co-localization (3.63%), genetic interactions (2.87%), pathway (1.88%) and shared protein domains (0.60%). The network functional analysis did not detect any significant functional correlations between the drug targets and the associated genes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Candidate drug prediction\u003c/h2\u003e \u003cp\u003eTo find interventional drugs for the identified genes, we utilized the DSigDB database. As presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the top 10 potential chemical compounds were listed based on adjusted P-values. The results showed that captopril (captopril HL60 DOWN, PubChem ID: 44093) and dexibuprofen (dexibuprofen PC3 DOWN, PubChem ID: 39912) were the two most significant drugs, linked to RFC5 and SUPT4H1, RFC5 and NOSIP respectively. Notably, acetaminophen (acetaminophen CTD 00005295, PubChem ID: 1983) exhibited interactions with the most genes including RFC5, CNEP1R1, MYO5C and NOSIP.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCandidate drug predicted using DsigDB.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrug\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdjusted P value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGenes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecaptopril HL60 DOWN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.059562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRFC5; SUPT4H1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edexibuprofen PC3 DOWN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.001168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.059562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRFC5; NOSIP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eamikacin PC3 DOWN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.002705\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.070715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRFC5; NOSIP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ediltiazem PC3 DOWN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.003185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.070715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRFC5; NOSIP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCUPRIC OXIDE CTD 00001471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.003703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.070715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMYO5C; SUPT4H1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epaclitaxel PC3 DOWN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.004160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.070715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRFC5; NOSIP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eacepromazine HL60 DOWN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.005986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.084121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRFC5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echlorhexidine PC3 DOWN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.006745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.084121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRFC5; MYO5C\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eacetaminophen CTD 00005295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.007617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.084121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRFC5; CNEP1R1; MYO5C; NOSIP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eharmol PC3 DOWN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.008968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.084121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSUPT4H1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.9 Molecular docking\u003c/h2\u003e \u003cp\u003eAfter identifying potential drug candidates, we performed molecular docking to assess their binding affinity and druggability. Autodock Vina v.1.5.7 was used to analyze the top five candidates' binding sites and interactions with the proteins from the target genes. Valid docking outcomes and the binding energy for each interaction for three proteins in complex with the drugs are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e. Each drug candidate formed visible hydrogen bonds and strong electrostatic interactions with its protein target. Moreover, the binding pocket of each target was effectively occupied by the five drug candidates. Notably, the combination of RFC5 and paclitaxel displayed the lowest binding energy (\u0026minus;\u0026thinsp;9.3 kcal/mol), suggesting exceptionally stable binding.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDocking results of available proteins with small molecules.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePDB ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDrug\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePubChem ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBinding energy (kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRFC5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8UNJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecaptopril\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUPT4H1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3H7H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecaptopril\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRFC5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8UNJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003edexibuprofen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNOSIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ9Y314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003edexibuprofen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRFC5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8UNJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eamikacin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-7.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNOSIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ9Y314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eamikacin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-6.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRFC5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8UNJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ediltiazem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-6.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNOSIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ9Y314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ediltiazem\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-5.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRFC5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8UNJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003epaclitaxel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-9.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNOSIP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ9Y314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003epaclitaxel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36314\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-7.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis study identified five potential therapeutic targets for prostate cancer: SUPT4H1, NOSIP, MYO5C, RFC5 and CNEP1R1. We used multiple MR methods to identify genes with potential causal associations with PCa. In addition, enrichment analysis and PPI network analysis were performed in this study to understand the biological significance of these drug targets. Then, a co-localization analysis was performed to confirm the revealed genetic associations. Furthermore, a mediation MR analysis was conducted to assess the potential mediating role of these proteins in tumor immunity. In consideration of the possible pleiotropy of target genes and potential drug side effects, we also conducted phenome-wide association analysis. Finally, prediction and molecular docking proved the medicinal value of these drug target genes.\u003c/p\u003e \u003cp\u003eProstate cancer is the second most common malignancy in men and has a complex etiology involving genetic predisposition, environmental exposures and inflammatory processes.\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e The role of immune cells in the tumor microenvironment is critical, influencing tumor growth, invasion and metastasis.\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e Existing studies have shown that protein-coding genes such as HNRNPL, HOXB13, PTEN and BRCA2 are strongly associated with prostate cancer development and could be potentioal drug targets.\u003csup\u003e\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e However, the complex interplay between drug target genes and the immune microenvironment in prostate cancer is not fully understood.\u003c/p\u003e \u003cp\u003eSUPT4H1 is essential in the transcriptional function of RNA polymerase II, supporting the enzyme's movement along the DNA strand.\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e In gene expression, SUPT4H1 often forms a complex SPT4/SPT5 with SUPT5H to regulate chromatin elasticity.\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e Recent studies have shown that SUPT4H1 is a target for immunotherapy in breast cancer.\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e Moreover, our study specified SUPT4H1 exerting a mediating effect on Na\u0026iuml;ve CD8 T cell\u0026rsquo;s impact on PCa, which is a novel finding. In addition, knockdown of SUPT4H1 with CRISPR/Cas9 technique was proved to promote the differentiation and functional recovery of neural cells.\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e NOSIP interacts with nitric oxide synthase (NOS) to regulate nitric oxide (NO) production, which plays an important role in vasodilation, immune response, and neurotransmission.\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e Recent research indicates that NOSIP could be an oncogene in hepatocellular carcinoma progression, with quercetin potentially inhibiting NOSIP to treat HCC.\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e MYO5C, a non-muscle myosin motor protein, plays a crucial role in the regulation of intracellular trafficking and cytoskeletal dynamics, which are related with the migration and invasiveness of tumor cells.\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e Recent research has revealed that targeted degradation agents can bind with and promote the degradation of MYC protein through a process mediated by E3 ligases and a proteasome system,\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e highlighting the potential of MYO5C as a druggable target. RFC5 is a subunit of RFC that plays a vital role in the processes of DNA replication and repair, by forming an ATP-dependent clamp loader with other RFC subunits and helping with fixation of the PCNA ring protein to DNA,\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e which functions as a molecular platform for DNA replication, repair, and chromatin assembly.\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e It has been demonstrated that aberrant expression of RFC5 may result in errors during DNA replication and repair, thereby promoting the development of tumors, such as lung cancer.\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e,\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e CNEP1R1 is involved in nuclear membrane biosynthesis and remodeling, regulation of cell cycle processes, and maintenance of cell membrane integrity and dynamic homeostasis, and plays a critical role in cellular lipid metabolism and the dynamics of cell membrane structure.\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e It activates CTDNEP1, which dephosphorylates and activates LPIN1, a key enzyme in lipid synthesis, thereby affecting lipid production and degradation.\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e It has been found that dysregulation of CNEP1R1 may lead to disturbances in cellular lipid metabolism and has been associated with the progression of several metabolic diseases, such as fatty liver and obesity, as well as cancer.\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003eThese results demonstrate a strong correlation between the proposed drug targets and PCa, highlighting their significant therapeutic potential and providing a promising basis for targeted PCa treatment strategies.\u003c/p\u003e \u003cp\u003e \u003cb\u003eLimitations\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWhile the study provides valuable insights, there are some limitations that need to be considered. The MR analysis provides important clues to understanding causality, but it is based on the assumption of low-dose drug exposure and a linear relationship between exposure and outcome, which may not fully mimic the real-world setting of a clinical trial, which typically evaluates high-dose drugs over a short period of time. As a result, MR results may not accurately reflect effect sizes observed in clinical practice and may not fully predict the true effects of drugs.\u003c/p\u003e \u003cp\u003eThe diversity of the study cohorts is also a limiting factor. Although the eQTLs analysis included individuals of diverse ancestry, the PCa study population was restricted to European ancestry. This homogeneity of population background may introduce a potential bias in MR effect estimates due to differences in genetic background and linkage disequilibrium patterns. Extending these findings to individuals of other ethnicities requires further research and validation to ensure their broader applicability.\u003c/p\u003e \u003cp\u003eIn addition, although researchers attempt to minimize bias, MR analyses may still be affected by unmeasured factors or pleiotropy, which may affect the accuracy of the results. It is important to recognize these limitations and their potential impact on study conclusions.\u003c/p\u003e \u003cp\u003eFinally, this study focused primarily on cis-eQTLs and their association with prostate cancer, while ignoring the role of other regulatory components and environmental factors such as non-coding RNAs, epigenetic modifications, and gene-gene interactions, as well as environmental factors like diet, lifestyle, and exposure to carcinogens, in the complexity of the disease. These factors may have an important impact on disease initiation and progression, and therefore future studies need to consider these factors to gain a more comprehensive understanding of the disease.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSuggestions for furthur investigations\u003c/b\u003e \u003c/p\u003e \u003cp\u003eGiven the limitations of this study, the following recommendations are intended to guide future research in this critical area.\u003c/p\u003e \u003cp\u003eFirst, future studies may consider increasing the sample size and including populations from diverse geographic, racial, and ethnic backgrounds to increase the generalizability and transferability of the findings. This integrated approach will allow for a deeper understanding of the relationship between MR-observed drug effects and clinical outcomes, which can then be translated into medical practice with real-world applications.\u003c/p\u003e \u003cp\u003eSecond, when interpreting the results of enrichment analysis, researchers should exercise caution and consider alternative methods to minimize bias. Because enrichment analysis relies on predefined sets of genes or pathways, future studies could explore a wider range of biological networks and interactions to more comprehensively capture biological mechanisms.\u003c/p\u003e \u003cp\u003eThird, future research could improve the accuracy of molecular docking by prioritizing the collection of high-quality experimental data and the development of docking algorithms to better simulate conformational changes of proteins. This will optimize target selection and bring predicted interactions closer to actual molecular behavior.\u003c/p\u003e \u003cp\u003eFourth, advances in the development of in vitro models that better reflect in vivo conditions will make drug target screening and early validation more representative. Advances in drug bioavailability and pharmacokinetic studies will further reduce potential discrepancies between experimental and clinical results.\u003c/p\u003e \u003cp\u003eFinally, further experimental validation and rigorous clinical studies are needed to confirm the therapeutic potential of targets identified by Mendelian randomization, enrichment analysis and molecular docking, and to assess their safety and efficacy in real-world applications.\u003c/p\u003e"},{"header":"5 Conclusions","content":"\u003cp\u003eIn summary, this study identified five potential drug targets in PCa by MR analysis, which were significant in two independent cohorts and validated by co-localization analysis. Some of the targets served as mediators in modulating the tumor immune response. Drug prediction and molecular docking further confirmed the medicinal value of these targets, which is expected to reduce drug development costs and promote personalized medicine. This study emphasizes the potential importance of these targets in PCa therapy, facilitating more targeted and efficient treatment strategies, which in turn improves treatment efficacy and reduces side effects. Future studies and clinical trials could explore the clinical application value of these targets in depth.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ePCa Prostate cancer\u003c/p\u003e\n\u003cp\u003eTME Tumor microenvironment\u003c/p\u003e\n\u003cp\u003eMR Mendelian randomization \u003c/p\u003e\n\u003cp\u003eNHANES National health and nutrition examination survey \u003c/p\u003e\n\u003cp\u003eIVW Inverse-variance weighted\u003c/p\u003e\n\u003cp\u003eOR Odds ratio \u003c/p\u003e\n\u003cp\u003eCI Confidence interval \u003c/p\u003e\n\u003cp\u003eGWAS Genome-wide association study\u003c/p\u003e\n\u003cp\u003eSNPs Single nucleotide polymorphisms\u003c/p\u003e\n\u003cp\u003eGO Gene ontology\u003c/p\u003e\n\u003cp\u003eKEGG Kyoto encyclopedia of genes and genomes\u003c/p\u003e\n\u003cp\u003eBP Biological process\u003c/p\u003e\n\u003cp\u003eMF Molecular function\u003c/p\u003e\n\u003cp\u003eCC Cellular component\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch5\u003eAcknowledgments\u003c/h5\u003e\n\u003cp\u003eWe sincerely thank all the projects (eQTLGen consortium, the FinnGen team and MRCIEU) who participated in this study.\u003c/p\u003e\n\u003ch5\u003eAuthors\u0026apos; contributions\u003c/h5\u003e\n\u003cp\u003eConceptualization, Zhechun Wu, Yuqing Li and Wei Wang; Formal analysis, Zhechun Wu and Sihan Li; Methodology, Zhizhi Wang and Wei Wang; Project administration, Wei Wang; Supervision, Yuqing Li and Wei Wang; Validation, Zhechun Wu and Sihan Li; Visualization, Zhechun Wu and Zhizhi Wang; Writing \u0026ndash; original draft, Zhechun Wu and Sihan Li; Writing \u0026ndash; review \u0026amp; editing, Sihan Li and Yuqing Li.\u003c/p\u003e\n\u003ch5\u003eFunding\u003c/h5\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003ch5\u003eAvailability of data and materials\u003c/h5\u003e\n\u003cp\u003eThe GWAS data of prostate cancer and immune cell phenotypes were retrieved from NHGRI-EBI GWAS Catalog (https://www.ebi.ac.uk/gwas/studies/GCST90274714/ for prostate cancer, and https://www.ebi.ac.uk/gwas/studies/GCST0001391-GCST0002121/ for immune cell phenotypes). The GWAS data for the prostate cancer validation cohort were sourced from FinnGen Release 11 (https://www.fnngen.fi/). The eQTLs data were sourced from eQTLGen Consortium (https://eqtlgen.org/). \u003c/p\u003e\n\u003ch5\u003eEthics approval and consent to participate\u003c/h5\u003e\n\u003cp\u003eData used in the MR study were derived from existing studies, obviating the need for additional ethical approval.\u003c/p\u003e\n\u003ch5\u003eConsent for publication\u003c/h5\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch5\u003eCompeting interests\u003c/h5\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003ch5\u003eDeclaration of generative AI and AI-assisted technologies in the writing process\u003c/h5\u003e\n\u003cp\u003eDuring the preparation of this work the authors used ChatGPT 4o in order to assist with translation and language refinement. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. \u003cem\u003eCA Cancer J Clin\u003c/em\u003e. 2021;71(3):209-249. doi:10.3322/caac.21660\u003c/li\u003e\n\u003cli\u003eCulp MB, Soerjomataram I, Efstathiou JA, Bray F, Jemal A. Recent Global Patterns in Prostate Cancer Incidence and Mortality Rates. \u003cem\u003eEur Urol\u003c/em\u003e. 2020;77(1):38-52. doi:10.1016/j.eururo.2019.08.005\u003c/li\u003e\n\u003cli\u003eJames ND, Tannock I, N\u0026rsquo;Dow J, et al. 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Extracellular vesicles in metabolic disease. \u003cem\u003eDiabetologia\u003c/em\u003e. 2019;62(12):2179-2187. doi:10.1007/s00125-019-05014-5\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, Drug target, Tumor immunity, Mendelian randomization, Genetics","lastPublishedDoi":"10.21203/rs.3.rs-5697374/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5697374/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eProstate cancer (PCa) is a leading malignancy with a rising global incidence, posing significant challenges in treatment. The immunosuppressive tumor microenvironment (TME) in CRPC is a major barrier to effective immunotherapy. Identifying therapeutic targets that modulate the immune response within TME is crucial for advancing PCa treatment.\u003c/p\u003e\u003ch2\u003eMaterials and Methods\u003c/h2\u003e \u003cp\u003eIn this study, we employed Mendelian randomization (MR) to investigate the causal relationships between gene expression of blood proteins and PCa risk. We utilized cis-eQTL data from the eQTLGen Consortium and immune cell phenotype data from the NHGRI-EBI GWAS Catalog. Our analysis included discovery and validation cohorts, totaling over 800,000 individuals. Colocalization analysis was performed to confirm the genetic associations, and mediation MR analysis was used to explore the mediating role of proteins in tumor immunity. Drug prediction and molecular docking were applied to assess the potential of identified targets as druggable candidates.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOur MR analysis identified 557 proteins associated with PCa in the discovery cohort, with 86 proteins remaining significant in an independent validation cohort. Mediation analysis revealed nine proteins that mediated the impact of immune cells on PCa. Colocalization analysis confirmed the causality of five proteins, which were further supported by phenome-wide association studies (PheWAS) and protein-protein interaction (PPI) networks. Molecular docking demonstrated strong binding affinity of potential drugs to these targets.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study identified five drug targets in prostate cancer that modulate the tumor immune response. These targets may expedite drug development and personalize medicine, potentially enhancing treatment efficacy and reducing side effects.\u003c/p\u003e","manuscriptTitle":"Identifying potential therapeutic targets for prostate cancer with mediating role in tumor immunity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-02 09:25:50","doi":"10.21203/rs.3.rs-5697374/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-01-28T10:57:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-27T12:41:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-26T16:29:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"283324649073856509891621016355149518388","date":"2025-01-21T11:28:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"66190335873800594625552641180947438485","date":"2025-01-21T01:38:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"110972840926774591722508529829268826166","date":"2025-01-20T17:42:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"101383289427176390020968569328269563675","date":"2025-01-12T10:39:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-01-09T07:08:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-01-02T05:58:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-12-31T13:36:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2024-12-23T07:14:47+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":"7aaec4a9-b180-4db8-b514-d22c88b9735d","owner":[],"postedDate":"January 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[{"value":"featured","date":"2025-01-02 18:29:31"}],"updatedAt":"2025-06-12T12:53:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-02 09:25:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5697374","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5697374","identity":"rs-5697374","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}