Integrated multi-omics analyses reveal causal insights into the molecular landscape of urologic cancers

Integrated multi-omics analyses reveal causal insights into the molecular landscape of urologic cancers | 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 Integrated multi-omics analyses reveal causal insights into the molecular landscape of urologic cancers Chongjun Xiang, Xiaoyu Yang, Shuang Wu, Zhe Han, Hongjie He, Yue Li, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6043857/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Urologic cancers continue to present an ongoing challenge to human health, underscoring the necessity for a systematic exploration of their underlying mechanisms. Hence, we employed a multi-omics Mendelian randomization (MR) approach to infer potential pathogenic genes and unravel the associated mechanisms. Methods Genome-wide association study (GWAS) summary statistics on urologic cancers, along with expression quantitative trait loci (QTLs), DNA methylation QTLs, and protein QTLs were employed for summary data-based MR (SMR), colocalization, and transcriptome-wide association study (TWAS) analysis to identify putative causal genes. Additionally, metabolites GWAS summary statistics from both blood and urine were analyzed by two-sample MR method to uncover potential relationships with the risk of urologic cancers. Results A total of 15 genes from blood were identified related to prostate cancer (PCa) risk using SMR, colocalization, and TWAS analysis, with C10orf32, MARVELD1, UHRF1BP1, and POLI were being replicated in tissue SMR analysis. Additionally, 15 genes were prioritized as potential causal genes with their methylation regulatory, encompassing both well-established genes (NSUN4, FAAH, and SIK2) as well as novel candidates, such as HAAO, and UHRF1BP1. Furthermore, four metabolites, including phenyllactate, N-lactoyl phenylalanine, indolelactate, and adenosine 3',5'-cyclic monophosphate, were positively associated with PCa risk. For bladder cancer, its risk was associated with the expression variation of GSTM1, BTNL8, and BTNL9 and PSCA methylation. Conclusion This integrative approach not only enhances our understanding of the complex molecular landscape underlying urologic cancers but also opens up new avenues for future precision medicine and personalized treatment strategies. Urologic cancer Mendelian randomization Integrated omics Colocalization TWAS Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction The global burden and influence of cancer, specifically urologic cancers, continue to deepen, challenging the world-wide health sector's resilience. From 1990 to 2013, the incidence of urologic cancers, which include prostate, bladder, and kidney cancer, rose by 2.5 times, resulting in a 1.6-fold increase in mortality(Dy et al. 2017 ). The GLOBOCAN indicates that over two million individuals were diagnosed with a urologic malignancy in 2020, leading to approximately 800,000 deaths(Sung et al. 2021 ). Various factors can exert an impact on the progression of urological cancers, encompassing environmental factors as well as genetic or hereditary factors. Smoking, for instance, represents a significant influencing environmental factor, contributing for nearly half of bladder cancer (BCa) cases(Freedman et al. 2011 ). Meanwhile, owing to the familial hereditary nature of prostate cancer (PCa), germline mutations are believed to predispose individuals to early onset or metastatic PCa(Rebello et al. 2021 ), marked by a molecular enrichment of HRAS mutations(Carrot-Zhang et al. 2020 ; Delon et al. 2022 ). Furthermore, the risk of kidney cancer (KC) is heightened in individuals affected by autosomal dominant hereditary cancer syndromes due to germline mutations in VHL , FH , MET , and FLCN genes. Genome-wide association studies (GWAS) effectively exploit the genetic correlations with traits based on single nucleotide polymorphisms (SNPs)(Deng et al. 2021 ). These studies have resulted in identifying hundreds of susceptibility loci for urologic cancers(Conti et al. 2021 ; Purdue et al. 2011 ; Schumacher et al. 2018 ). Taking the susceptibility locus rs17632542 in KLK3, which encodes prostate-specific antigen (PSA), as an example, it has been demonstrated influencing the PCa risk(Koutros et al. 2023 ) and widely used for PCa diagnosis in clinical settings(Kote-Jarai et al. 2011 ). Additionally, urinalysis is commonly employed in urological evaluations(Pernar et al. 2018 ). Due to the accessibility for measurement and minimal invasiveness, these tests provide clinically insights into human health and illness associated risk factors(Dinges et al. 2019 ; Schmidt et al. 2020 ). However, the findings derived from certain studies exhibited incongruity, and one plausible explanation for this inconsistency could be attributed to the unaccounted influence of confounding variables. A comprehensive assessment of gene and metabolite associations in urologic cancers would therefore prove beneficial. Mendelian randomization (MR) is a methodological approach that adroitly applies SNPs as instrumental variables to infer potential causal relationships between exposures and outcomes. By harnessing the principles of genetic inheritance, MR emerges as an innovative instrument in epidemiology that can effectively attenuate influences from confounding variables unobserved in observational studies(Davies et al. 2018 ). Notably, this promising approach lends itself to conducting two types of MR analysis: Two-sample MR and Summary-data-based MR (SMR). Embarking on these analytic methodologies facilitates the exploration of novel cancer associations and guides us towards unlocking deeper understandings of the biological mechanisms associated with carcinogenesis, thus shedding light on potential therapeutic targets and prevention strategies. In our study, we leverage these advanced analytical paradigms, employing a multi-omics data with MR to investigate causal effects in urologic cancers. GWAS summary statistics were integrated with DNA methylation (DNAm) quantitative trait locus (QTL), gene expression QTL (eQTL), protein QTL (pQTL) in blood, and tissue eQTL to identify potential gene-cancer interactions, followed by colocalization and transcriptome-wide association study (TWAS) analyses. Subsequently, a bidirectional two-sample MR method was used to explore the casual relationships between blood and urine metabolites and urologic cancers. 2. Materials and Methods 2.1. Study Population and Data Acquisition Our study population was retrieved from databases including the eQTLGen consortium(Võsa et al. 2021 ), the Genotype-Tissue Expression (GTEx) project(Lonsdale J 2013), the IEU OpenGWAS project(Lyon et al. 2021 ), and the GWAS catalog(Sollis et al. 2023 ). Blood DNAm QTL (mQTL) was used and derived from McRae et al.'s study, a meta-analysis of two European cohorts: the Brisbane Systems Genetics Study (n = 614) and the Lothian Birth Cohorts (n = 1366). The eQTL was obtained from the eQTLGen consortium where the information about trait-associated SNPs from 31684 individuals was stored. The pQTL garnered from plasma and cerebrospinal fluid were integrated from several studies by Yang et al., Suhre et al.(Suhre et al. 2017 ; Yang et al. 2021 ), Sun et al.(Sun et al. 2018 ), Yao et al.(Yao et al. 2018 ), Emilsson et al.(Emilsson et al. 2018 ), Folkersen et al.(Folkersen et al. 2017 ), and Benjamin et al(Sun et al. 2023 ). Tissue eQTL (kidney cortex and prostate eQTL) and an additional set of whole blood eQTL were sourced from GTEx (n = 860). Only cis -QTL were included for further analysis. A total of 2697 sets of GWAS summary statistics of blood and urine metabolites were taken from the GWAS catalog (GCST90264176-GCST90266872). Urological cancer GWAS summary statistics were acquired from the IEU OpenGWAS project (BCa: ukb-b-8193; PCa: ieu-b-85) and the GWAS catalog (KC: GCST008225). The subjects in our study comprised individuals of European ancestry. 2.2. SMR Analysis 2.2.1. File Preparation According to the analysis requirement, a flist file was prepared using pQTL data and Genome Reference Consortium Human Build 37 (GRCh37) gene information from the ensemble database as needed. We then created a corresponding BESD format file (a file format to store the QTL summary data in binary format). Both BCa and PCa GWAS summary statistic datasets were initially converted into plain text using R packages of VariantAnnotation (v 1.44.1), gwasglue (v 0.0.0.9000), and dplyr (v 1.1.2). 2.2.2. Formal Analysis The SMR tool could determine the extent of SNP effects on phenotypes influenced by molecular traits like DNAm, gene and protein expression. A four-step SMR was performed as follows: 1). SNPs as instruments, with blood gene expression as exposure, and cancers as outcome; 2). SNPs as instruments, with blood DNAm as exposure, and cancers as outcome; 3). SNPs as instruments, with blood DNAm as exposure, and blood gene expression as outcome where only significant signals from step 1 were considered for inclusion; 4). SNPs as instruments, with blood protein as exposure, and cancers as outcome. The 1000 Genomes Project European dataset was used as a reference for linkage disequilibrium (LD) calculation. False Discovery Rate (FDR) was used to adjust the resultant data. Signals were selected per these criteria: an FDR value of SMR 0.01. 2.3 Two-Sample MR Analysis 2.3.1. Formal Analysis The two-sample MR was employed to estimate the influence of blood and urine metabolites on urological cancer risk, with SNPs with p-values below 5 x 10 − 8 were considered. Relevant R packages mainly included TwoSampleMR (v 0.5.7), plinkbinr (v 0.0.0.9000), and ieugwasr (v 0.1.5). In order to account for LD, we eliminated SNPs with r 2 > 0.01 within a 1000kb range (clump_r 2 = 0.01, clump_kb = 1000). Moreover, a bidirectional MR analysis was conducted to explore potential reverse causality between metabolites and cancers. 2.3.2. Result interpretation Five methods were employed for the two-sample MR analysis to estimate the effect: MR-Egger, Weighted median, Inverse variance weighted (IVW), Simple mode, and Weighted mode. When multiple genetic instruments of exposure were available (nSNP > = 2), the IVW method was selected as the primary result. In cases where only one genetic instrument was available (nSNP = 1), the Wald ratio was automatically used. A significance threshold of an FDR value of 0.05 was considered to prioritize the results for further analysis. 2.4 Colocalization analysis The Bayesian testing was performed using the coloc package (v 5.2.3) to estimate the posterior probability of shared causal genetic variant between blood/tissue gene expression and PCa or BCa. For the significant genes identified in the SMR, a colocalization result was considered supportive when the posterior probability for shared causal variants (PP.H4) was equal to or greater than 0.8. 2.5 Transcriptome-Wide Association Studies (TWAS) TWAS has emerged as a prominent methodology for elucidating the intricate relationships between genetically regulated genes and phenotypic traits. This approach has contributed significantly to enhancing our comprehension of the intricate mechanisms. Here. the TWAS related to urologic cancers were obtained from the TWAS Atlas(Lu et al. 2023 ). 2.6 Drug repositioning analyses Target drugs have the potential to interact with and modulate the activity of specific genes which in turn influence the underlying biological processes related to a particular disease. The databases Drugbank(Wishart et al. 2018 ), Therapeutic Target Database (TTD)(Zhou et al. 2024 ), and ChEMBL(Zdrazil et al. 2024 ) were used to identify drugs associated with the genes of interests. These databases provide valuable insights into the therapeutic targets and drug interactions, aiding in the exploration of potential pharmacological interventions for diseases associated with specific genes. 3. Results The data collection and detailed workflow designed for this study are shown in Table 1 and Fig. 1 , respectively, which provide a comprehensive workflow including data collection and analysis procedures. Initially, the associations between blood cis -eQTL, cis -mQTL, and cis -pQTL and urological cancers were scrutinized employing a four-step SMR approach, followed by tissue cis -eQTL SMR. Subsequently, we ascertained urine and blood metabolite statistics and scrutinized their associations with urologic cancers utilizing a two-sample MR methodology. The candidate causal genes were further explored by colocalization, TWAS, and drug repositioning analyses. Table 1 The data resources of different omics. Data type Data resource Blood mQTL McRae et al.’s study Blood eQTL eQTLGen GTEx pQTL Yang et al. [21], Suhre et al. [22], Sun et al. [23], Yao et al. [24], Emilsson et al. [25], Folkersen et al. [26], Benjamin et al [27]. Tissue eQTL GTEx Metabolites GWAS catalog (GCST90264176 - GCST90266872) Cancer IEU OpenGWAS (PCa: ieu-b-85, BCa: ukb-b-8193, KC: GCST008225) 3.1 PCa was associated with multi-omics outcomes Firstly, we embarked on an integrated analysis for PCa employing multi-omics data through a four-step SMR methodology, as delineated in the Methods section. Upon analyzing cis -eQTL from eQTLGen, 234 significant genes were identified (FDR 0) and 115 negatively associated with PCa (b_SMR < 0) ( Supplementary data 1 ). A secondary exploration of whole blood cis -eQTL from GTEx yielded 121 significant genes (FDR < 0.05, Supplementary data 1 ). Of these, 65 overlapped in both aforementioned cis -eQTL analyses, with only one gene ( MDM4 ) producing divergent results (Fig. 2 ). Additionally, the prostate tissue cis -eQTL analysis from GTEx implicated 74 significant genes (FDR < 0.05, Supplementary data 1 ), and 16 genes recurred across all three analyses. These included eight with positive roles ( RP11-6L6.2, PSORS1C3, CYP21A1P, UHRF1BP1, MARVELD1, C10orf32, WFDC3, DNTTIP1 ) and eight with negative effects ( RAB7L1, AC079922.3, ALS2CR12, STK19P, L3MBTL3, SF3B2, ZDHHC7, POLI ) (Fig. 2 ). We further probed potential associated drugs within three databases - Drugbank, TTD, ChEMBL - and found 10 targetable genes (Table 2 ), with SIK2 , CASP8 , and FAAH appeared in two database and had several targeted therapies. Table 2 Target drugs for 65 genes in eQTL SMR results. Database Gene Drug relations Drugbank SIK2 Fostamatinib Therapeutic Target Database (TTD) CASP8① Amooranin SIK2 GRN-300 ZNF217 ISIS 114527 FAAH Thiopental ASRGL1 Calaspargase pegol PYGB PMID26666989-Compound-Figure9middle11 L3MBTL3 UNC1215 LPAR2 NAEPA SF3B2 H3B-8800 ChEMBL LAMC1 Ocriplasmin FAAH Acetaminophen, JNJ-42165279, PF-04457845 CASP8 Nivocasan, Emricasan CASP8 gene was both identified in TWAS results and target drugs. The colocalization results for 65 genes indicated that 45 genes likely contained a shared causal variant (PP.H4 > 0.5), with 26 genes presented robust association with PCa (PP.H4 > 0.8), while 14 genes were associated with PCa but shared different causal variants (PP.H3 > 0.5) (Fig. 3 ). Of the 26 genes, 15 genes showed a significant correlation with PCa in TWAS results (Fig. 4 , Supplementary data 1 ). For instance, CASP8 , encoding a caspase protein, was associated with PCa risk and found higher expressed in the serum of high-grade PCa patients(Liu et al. 2021 ). This was in align with our blood SMR analysis result (b_SMR = 0.202). Promoters or enhancers often harbor disease-associated target genes regulated by DNAm(Wu et al. 2018 ). In the blood, candidate causal genes for cancers and their possible underlying epigenetic mechanism of gene regulation were in need of investigation. Therefore, cis -eQTL and cis -mQTL SMR analyses was done and only the significant results were interpreted as suggestive causal genes. In concrete, 1655 DNAm probes were identified at the FDR-significant level of 0.05, including 862 with positive roles and 793 antagonizing PCa ( Supplementary data 1 ). Further integration of overlapped 45 genes from cis -eQTL and cis -mQTL putative causal results implied 85 DNAm probes of 30 genes contributed to their expression levels (Fig. 5 ). Moreover, 15 genes were also replicated in GTEx blood cis -eQTL analysis including 8 positively associated with PCa: NSUN4, FAAH, ECHDC2, SLC39A1, ZBTB38, PBX2, SIK2, GATAD2A , and 7 negatively associated with PCa: LAMC1, HAAO, TRIM26, TTC16, HKDC1, TRIM8, PYGB , suggesting that these 15 genes were likely to be influenced by DNAm, which in turn affected PCa. Proteomic research had the potential to enhance our understanding of molecular mechanisms and identify potential therapeutic targets(Finan et al. 2017 ). Therefore, we conducted SMR analysis to investigate the potential association between cis -pQTL and PCa. However, no significant results were found under FDR 0.01. Nevertheless, when excluding the HEIDI test which was used to assess the heterogeneity, the most significant results was KLK3 (PSA, b_SMR = 2.884, Supplementary data 1 ), which was already used in the clinical for PCa detection or diagnosis. We then employed two-sample MR analysis using 2697 sets of metabolite data as exposure. Four urinary metabolites (phenyllactate (PLA): OR = 1.165, N-lactoyl phenylalanine: OR = 1.185, indolelactate: OR = 1.149, adenosine 3',5'-cyclic monophosphate (cAMP): OR = 1.290) (Table 3 ) were identified as being associated with PCa. While PCa served as the exposure, no significant association observed. Table 3 Two-sample MR results for metabolites in PCa. metabolite(s) nSNP OR beta se p value phenyllactate (PLA) 3 1.165 0.153 2.560 X 10 − 2 2.540 X 10 − 9 N-lactoyl phenylalanine 6 1.185 0.170 3.887 X 10 − 2 1.270 X 10 − 5 indolelactate 7 1.149 0.139 3.506 X 10 − 2 7.170 X 10 − 5 adenosine 3',5'-cyclic monophosphate (cAMP) 8 1.290 0.254 6.312 X 10 − 2 5.600 X 10 − 5 * The IVW method was employed and metabolites were as exposure in these results. 3.2 BCa was associated with the expression of GSTM1, BTNL8, BTNL9 and PSCA methylation At a significance level of FDR < 0.05, we identified three signals within cis -mQTL analysis (Fig. 6 , Supplementary data 2 ). Notably, the significant DNAm site cg13446199 was located within the body of PSCA (Fig. 6 A), manifesting a positive influence on BCa (b_SMR = 5.934 X 10 − 4 ). PSCA was first marked as a prostate-specific cell-surface antigen with high expression in urological cancers(Wang et al. 2009 ). However, no association between the PSCA expression level and BCa was discovered in our study. The other two substantial DNAm sites were cg04035553 and cg27028750, both showing positive effects (b_SMR = 8.028 X 10 − 4 and b_SMR = 7.739 X 10 − 4 respectively). However, the cg04035553 was located approximately 4 kb upstream from the transcription start site of PSCA . The cg27028750 was not mapped to any recognized gene or CpG island and located in the intergenic region between CLPTM1L and SLC6A3 genes (Fig. 6 B). In terms of cis -eQTL analysis, three genes ( GSTM1 , BTNL8 , and BTNL9 ) were identified and were all strongly associated with BCa according to the colocalization results (Fig. 3 ). GSTM1 , encoding the anti-carcinogenic enzyme glutathione S-transferase M1, was reported to play a protective role in BCa among both smokers and non-smokers(Bell et al. 1993 ; García-Closas et al. 2005 ). Our study corroborates these findings, denoting GSTM1 as protective against BCa (b_SMR = -9.103 X 10 − 4 ). Furthermore, 16 target drugs for GSTM1 were indicated - approved or experimental according to Drugbank, including Busulfan, Cisplatin, Carboplatin, Oxaliplatin et al. The genes BTNL8 and BTNL9 belong to the immunoglobulin superfamily and specifically, BTNL8 was implicated in T cell regulation(Blazquez et al. 2018 ). Both genes were found to play negative roles ( BTNL8 : b_SMR = -9.312 X 10 − 4 , BTNL9 : b_SMR = -1.654 X 10 − 3 ) in our study. 3.3 No association was identified between KC and multi-omics analysis Initially, we identified 4428 signals in cis -mQTL and 824 signals in cis -eQTL (p_SMR < 0.05) ( Supplementary data 3 ). However, following FDR < 0.05, none of these signals sustained significant levels. Distinct causal associations were absent within integrated cis -pQTL, whole blood cis -eQTL, or kidney cortex cis -eQTL. The use of at least two SNPs as instrumental variables allowed us to observe that 32 types of metabolites − 18 from plasma and 14 from urine - reached significance in terms of p-values with the IVW analyses ( Supplementary data 3 ). 4. Discussion The increasing prevalence of cancer necessitates a heightened focus on patient treatment and care. Our study delved into the correlation between multi-omics data and the risk of urologic cancers through MR analysis. This comprehensive approach enables a thorough exploration of the intricate interplay among genetic variations, gene expression, protein levels, and metabolites in the context of urologic cancers. Our findings not only enhance comprehension of the complex molecular landscape in these diseases but also pave the way for future precision medicine and personalized treatment strategies. Blood-based biomarkers are being explored to diagnose and monitor PCa progression(Kohaar et al. 2019 ). In other terms, blood may be utilized as a valuable proxy to characterize genetic effects on gene expression and understand the intricate etiology of PCa. In our study, 15 putative causal genes were detected through genetically epigenomic and transcriptomic regulation analysis, with genes such as NSUN4, FAAH , and SIK2 which have already been reported positively associated with PCa risk. Consistent with our results, NSUN4 methylation and its expression level, as well as susceptibility loci were all related to PCa risk(Kar et al. 2016 ). FAAH has been reported involving in prostate tumorigenesis(Endsley et al. 2008 ) and SIK2 was highly expressed in aggressive PCa patients’ blood which may serve as a potential blood marker for PCa diagnosis(Bon et al. 2015 ), Our results further indicated that the DNAm level of FAAH and SIK2 had an impact on PCa perhaps resulting in an increased risk associated with gene expression. In addition, we identified several target drugs for FAAH and SIK2. These drugs, such as GRN-300 and Acetaminophen have been previously utilized or investigated in ovarian cancer, non-small cell lung cancer, lymphoma, glioma, and breast cancer treatment. Therefore, it is plausible that suppressing these two targets with these drugs and disrupting DNAm could be a potential therapeutic approach for PCa. Considering genes whose PP.H4 fell between 0.5 and 0.8 could be regarded as a moderate indication of colocalization, HAAO and GATAD2A were identified causally associated with PCa occurrence or progression. The HAAO gene was frequently found to be hypermethylated in PCa; however, the association with PCa risk remains controversial(Li et al. 2021 ; Mahapatra et al. 2012 ). In our study, we found HAAO had a protective effect against PCa. Specially, DNAm site cg09480054 could promote the occurrence of PCa by inhibiting HAAO expression, suggesting an intricate but vital role of epigenetic factors. Apart from genes mentioned above, which exhibited varying associations with PCa, additional genes ECHDC2, TRIM26, PBX2, HKDC1 , and TRIM8 , although not previously reported in relation to PCa, had demonstrated significance in other types of cancers(Alholle et al. 2013 ; Caratozzolo et al. 2017 ; Lin et al. 2021 ; Wang et al. 2023 ; Xia et al. 2023 ). Taking TRIM8 as an example, TRIM8 was reported to be involved in multiple immune pathways and exert a dual action in various cancers(Caratozzolo, Marzano, Mastropasqua, Sbisà and Tullo 2017 ; Seong et al. 2021 ). Based on our results, TRIM8 demonstrated a protective effect against PCa no matter in DNAm level or expression level. The observed association at the expression level was determined to be mediated by diverse genetic variants (PP.H3 = 0.926). Moreover, its significant relationship was further confirmed by TWAS. By incorporating tissue analysis, we were able to identify both overlapping and unique findings compared to blood analysis. This suggests that the potential influence of gene expression in blood and prostate could lead to varying effects on PCa. Among the shared results, there were also several genes that have been previously established as being associated with cancers. In concrete, the expression of MARVELD1 was found to be decreased in approximately half of PCa patients. In other cancer types, it was hypermethylated and underwent epigenetic inactivation by CpG methylation(Wang, Li, Han, Hu, Yue, Yu, Zhang, He, Zheng, Shi, Fu and Wu 2009). A positive relationship between MARVELD1 expression and PCa was found (b_SMR = 0.112) in tissue SMR analysis but no significant association was observed in blood DNAm SMR results. One potential explanation could be that the DNAm data utilized derived from blood rather than tissue. Therefore, it is necessary to validate these results using data obtained from tissue in future research. The elevated expression of UHRF1BP1 may function as a tumor suppressor, impeding cell growth. However, conflicting findings from other studies have reported its potential cancer-promoting effects(Dan et al. 2021 ; El Baroudi et al. 2017 ). We found that both blood and tissue SMR results showed that UHRF1BP1 gene was correlated with increased PCa risk and TWAS results also confirmed this association. Tissue gene expression analysis has been demonstrated to clarify distinct biological molecular mechanisms(Yang et al. 2013 ). These observations highlighted the essential nature of tissue analysis alongside blood analysis. Certain genes, such as C10orf32 , were identified in both analyses, while others were exclusive to tissue analysis. For instance, the PPP1R14A gene, although absent in the blood analysis results, emerged as the most significant tissue-specific signal (b_SMR = 0.478) and it was reported as a target of PCa risk loci or microRNAs in previous studies(Emami et al. 2019 ). Given the diverse impact of gene expression across different tissues on PCa, conducting integrated multi-omics analysis from multiple relevant tissues to identify risk factor becomes imperative. Given the advantages of convenience, cost-effectiveness, and minimal invasiveness, metabolite assessments in blood and urine tests presented indicators of considerable interest. In our study, we prioritized four specific metabolites in urine – cAMP, PLA, N-lactoyl phenylalanine, and indolelactate - all of which showed positive correlation with PCa. cAMP, extensively studied before, could influence the release of neurotransmitters and hormones, neurodegeneration, and tumor growth besides regulating pro- and anti-inflammatory responses(Bergantin 2021 ). Notably, PLA, previously reported as a potential metabolic biomarker for ovarian cancer, oral squamous cell carcinoma, and cervical cancer(Li et al. 2019 ) which could serve as a promising subject for future cancer research. Less was known about N-lactoyl phenylalanine, a blood-borne signaling metabolite induced by exercise stimulation and linked with suppressing feeding and obesity(Li et al. 2022 ). No prior associations between N-lactoyl phenylalanine and cancers have been documented, yet our observations unveiled a significantly positive connection with PCa (OR = 1.185). Indolelactate, hinted at reducing inflammation and insulin resistance through aryl hydrocarbon receptor activation, presents contradictory evidence - an association with type 2 diabetes was reported elsewhere(Qi et al. 2022 ). Our investigation contradicted a previous serum metabolomic profiling study involving 146 participants where indolelactate was found downregulated in PCa(Khan et al. 2019 ). This discrepancy might be due to the blood data deficiency of Indolelactate, different sample types (serum versus urine) or different population across studies. The positive associations exhibited by these four metabolites with PCa underscored their potential utility in cancer detection or screening. Our investigation into BCa led to the identification of three significant genes and DNAm sites. GSTM1 was recognized for its protective influence on BCa in both smokers and non-smokers(Bell, Taylor, Paulson, Robertson, Mohler and Lucier 1993 ; García-Closas, Malats, Silverman, Dosemeci, Kogevinas, Hein, Tardón, Serra, Carrato, García-Closas, Lloreta, Castaño-Vinyals, Yeager, Welch, Chanock, Chatterjee, Wacholder, Samanic, Torà, Fernández, Real and Rothman 2005), a finding that our study echoed (b_SMR = -9.103 X 10 − 4 ). BTNL8 and BTNL9 are members of the immunoglobulin superfamily which involve in T-cell regulation(Blazquez, Benyamine, Pasero and Olive 2018 ). Prior studies reported downregulated BTNL8 expression in colon tumors(Blazquez, Benyamine, Pasero and Olive 2018 ) and decreased BTNL9 expression in osteosarcoma, colon cancer, lung adenocarcinoma, and breast cancer(Mo et al. 2021 ). In alignment with these observations, our results revealed negative correlations between both BTNL8 (b_SMR = -9.312 X 10 − 4 ) and BTNL9 (b_SMR = -1.654 X 10 − 3 ) gene expressions and BCa risk. PSCA stood out due to the positive association of the DNAm site cg13446199, but no additional associations were identified between the expression of PSCA and BCa. However, it was observed that hypomethylation of PSCA could potentially influence prostate carcinogenesis by mediating the effect of DEHP in rats(Xia et al. 2018 ). In addition, the expression of PSCA was up-regulated in numerous cancers, including PCa and BCa, with genetic variants in PSCA flagged as risk factors for BCa. Specifically, rs2294008 and rs2978974 have been implicated in modulating BCa susceptibility through influencing PSCA gene expression and interaction with regulatory elements(Furukawa et al. 2020 ). There were also some limitations in our study. First, we didn’t find any significant associations with KC. Although studies have shown an association between KC risk and certain mutations in blood DNA, it was important to note that not all previous blood-based findings in other studies were replicated(Chow et al. 2010 ). Additionally, the tissue analysis specifically focused on kidney cortex eQTL and other renal regions were not analyzed. Second, despite we identified some metabolites associated with PCa risk, considering the involvement of kidney in urine formation, future research necessitates a more comprehensive exploration of metabolism-related data to unveil novel findings. Third, no association was discerned with cis -pQTL. One potential explanation for such outcomes could be our exclusive usage of cis -pQTL, whereas combinatorial employment of cis - and trans -pQTL might have led to diverse results(Xu et al. 2023 ). Fourth, the effects of some genes on PCa were contrary to previous reports such as ZBTB38( de Dieuleveult et al. 2020 ; Ding et al. 2021 ; Hotta et al. 2018 ), SLC39A1( Wang et al. 2020 ), LAMC1( Nishikawa et al. 2014 ; Pasqualini et al. 2015 ), PYGB( Wang et al. 2018 ), and POLI( Mancuso et al. 2018 ; Sakiyama et al. 2005 ; Yang et al. 2004 ; Yuan et al. 2013 ). The discrepancy might be attributed to the different study objects, their diverse genetic backgrounds and the neglect of gene-gene interaction. In conclusion, our study utilized a diverse range of multi-omics data, enabling us to comprehensively investigate the intricate interplay between genetic variations, gene expression, protein levels, and metabolites in the context of urologic cancers. This integrative approach not only enhanced our understanding of the complex molecular landscape underlying these diseases but also opened up new avenues for precision medicine and personalized treatment strategies. Furthermore, the inclusion of both blood and tissue samples in our analysis allowed for a comprehensive assessment of the systemic and local factors contributing to urologic cancers. Declarations Author contributions Conceptualization: F.X., C.L., G.T.; Data curation: C.X., X.Y., S.W., Y.L., X.C., Z.H., J.M.; Formal analysis: C.X., X.Y. ; Investigation: C.X., X.Y., F.X.; Methodology: C.X., X.Y., Z.H., H.H., F.X.; Software: C.X., X.Y., F.X.; Supervision: G.T., F.X., C.L.; Visualization: C.X.; Writing – original draft: C.X., X.Y., F.X., C.L.; Writing – review & editing: C.X., X.Y., F.X., G.T., C.L. All authors read and approved the finalmanuscript. Funding This study was funded by Taishan Scholar Program (Tsqn202103198), Taishan Scholars Construction Engineering, Major Basic Research Project of Shandong Provincial Natural Science Foundation (ZR2019ZD27), Key research and development program of shandong province (2023CXPTO12), Shandong Province Higher Educational Youth Innovation Science and Technology Program (2019KJE013), and Binzhou Medical University Research Start-up Fund (50012305190). Clinical trial number not applicable. Ethics, Consent to Participate, and Consent to Publish declarations not applicable. Data Availability Statement The source of data used in our study were described in the Materials section and all data analyzed were included in the supplementary files. Conflicts of Interest The authors declare that they have no competing interests. References Alholle A, Brini AT, Gharanei S, et al. Functional epigenetic approach identifies frequently methylated genes in Ewing sarcoma. 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Supplementary Files Supplementarydata1TablesS1.xls Supplementarydata1TablesS2.xls Supplementarydata1TablesS3.xls Supplementarydata1TablesS4.xls Supplementarydata1TablesS5.xls Supplementarydata1TablesS6.xls Supplementarydata1TablesS7.xls Supplementarydata2TablesS8.xls Supplementarydata2TablesS9.xls Supplementarydata3TablesS10.xls Supplementarydata3TablesS11.xls Supplementarydata3TablesS12.xls Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6043857","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":420277680,"identity":"24ddde30-1608-4e02-98e2-e71a6af08ece","order_by":0,"name":"Chongjun Xiang","email":"","orcid":"","institution":"The 2nd Medical College of Binzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chongjun","middleName":"","lastName":"Xiang","suffix":""},{"id":420277681,"identity":"3a19cb0e-7bc6-467c-915b-2cb81e747cec","order_by":1,"name":"Xiaoyu Yang","email":"","orcid":"","institution":"Binzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyu","middleName":"","lastName":"Yang","suffix":""},{"id":420277682,"identity":"77b1d711-a9a7-4755-aef2-88ea0ac25293","order_by":2,"name":"Shuang Wu","email":"","orcid":"","institution":"Department of Urology, the Affiliated Yantai Yuhuangding Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Shuang","middleName":"","lastName":"Wu","suffix":""},{"id":420277683,"identity":"646e3b79-1641-4b42-9c20-d029743caede","order_by":3,"name":"Zhe Han","email":"","orcid":"","institution":"Binzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhe","middleName":"","lastName":"Han","suffix":""},{"id":420277684,"identity":"a131b3e3-f9ba-43dd-afbe-2b4fa32a2f85","order_by":4,"name":"Hongjie He","email":"","orcid":"","institution":"Binzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hongjie","middleName":"","lastName":"He","suffix":""},{"id":420277685,"identity":"9e3bb6fd-c65a-4399-a43e-ea682e26ddd6","order_by":5,"name":"Yue Li","email":"","orcid":"","institution":"The 2nd Medical College of Binzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Li","suffix":""},{"id":420277686,"identity":"c608d29f-b565-47ec-bdc5-0cac08687687","order_by":6,"name":"Xin Cui","email":"","orcid":"","institution":"School of Clinical Medicine, Weifang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Cui","suffix":""},{"id":420277687,"identity":"7aa0f18e-4404-43a5-8319-f3d50e8a91ed","order_by":7,"name":"Jia Mi","email":"","orcid":"","institution":"Binzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"Mi","suffix":""},{"id":420277688,"identity":"7dc9ca7e-fc32-44a0-a442-beaeb5fff80a","order_by":8,"name":"Geng Tian","email":"","orcid":"","institution":"Binzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Geng","middleName":"","lastName":"Tian","suffix":""},{"id":420277689,"identity":"23505f33-57bd-4569-a1bb-b8a615bb1c7d","order_by":9,"name":"Fuyi Xu","email":"","orcid":"","institution":"Binzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Fuyi","middleName":"","lastName":"Xu","suffix":""},{"id":420277690,"identity":"7f155943-b644-4994-815e-803e6e9c7b8b","order_by":10,"name":"Yanping Zhu","email":"","orcid":"","institution":"Binzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yanping","middleName":"","lastName":"Zhu","suffix":""},{"id":420277691,"identity":"544baa9f-90d4-4073-a886-fd75cd4e9b43","order_by":11,"name":"Chunhua Lin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYDACCQYGZhDNx8zA+ICBDcRMIFILGzMDswGJWoBIgigt/LN7zB4XttkxsLHzHqt4U3aYgZ89x4Dh5w48ltw5Y248sy0Z6DC+tJtzzh1mkOx5Y8DYewa3FgOJHDNp3jZmoBYes9u8bYcZDG7kGDAzthHUUg/WUgzSYk+klsNgLcxgWyQIaJG4kVYmzXPuOA9Qi7HknHPpPBJnnhUc7MWjhX9G8jZpnrJqOX7+M4Yf3pRZy/G3J2988BOPFhjggZFgxgHCGlA1joJRMApGwShABQBG8j4zAgwZJwAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Urology, the Affiliated Yantai Yuhuangding Hospital of Qingdao University","correspondingAuthor":true,"prefix":"","firstName":"Chunhua","middleName":"","lastName":"Lin","suffix":""}],"badges":[],"createdAt":"2025-02-17 02:53:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6043857/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6043857/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":77261685,"identity":"fca1c22b-3ca6-456e-a6cd-f082a317c847","added_by":"auto","created_at":"2025-02-26 19:30:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":244194,"visible":true,"origin":"","legend":"\u003cp\u003eThe schematic workflow of our research. The study cohort comprised three distinct urologic malignancies, specifically kidney cancer, bladder cancer, and prostate cancer. Research data employed in our study were derived from comprehensive analysis of blood, tissue, and urine samples. SMR, two-sample MR and colocalization emerged as the primary analytical approaches in our investigation.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6043857/v1/b1fdc86f0585cdd6e42b4a5c.png"},{"id":77261683,"identity":"c73d1366-1733-46de-9ccf-d503933bce4f","added_by":"auto","created_at":"2025-02-26 19:30:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":154466,"visible":true,"origin":"","legend":"\u003cp\u003eResults of blood and tissue eQTL SMR analysis. The Y-axis represents the 65 genes that were identified to have overlapping eQTL SMR results in both eQTLGen and GTEx whole blood datasets. The X-axis represents the beta values of each gene in the three analyses. The color of each plot corresponds to the negative log10-transformed p values obtained from the SMR analysis. The left panel displays the SMR results from eQTLGen dataset, while the middle and right panels show the SMR results from GTEx whole blood and GTEx tissue, respectively.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6043857/v1/84765b5584a39e37d0abe618.png"},{"id":77261796,"identity":"21806b78-a407-4b5d-a65f-2abae0c23862","added_by":"auto","created_at":"2025-02-26 19:38:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":169918,"visible":true,"origin":"","legend":"\u003cp\u003eThe colocalization results of 68 genes obtained through the analysis of eQTL and GWAS in PCa and BCa. 65 genes in PCa were denoted by blue dots while the remaining three genes for BCa were marked by red triangles.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6043857/v1/148a192d2abeecb756af8b02.png"},{"id":77262208,"identity":"59ae8337-bc75-4ea8-866a-628b46a5b00e","added_by":"auto","created_at":"2025-02-26 19:46:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":87621,"visible":true,"origin":"","legend":"\u003cp\u003eTWAS analysis results for genes with PP.H4 \u0026gt; 0.8 in PCa. The three bar groups represented tissues under investigation, including prostate (light blue), blood (light yellow), and whole blood (pink). The length of each bar represented the negative log10-transformed p values associated of the corresponding gene.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6043857/v1/f84ad681f2af5e66c2ecdec8.png"},{"id":77261705,"identity":"4a3dccbf-6298-4b24-80a2-5910e99cfa6b","added_by":"auto","created_at":"2025-02-26 19:30:11","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":238532,"visible":true,"origin":"","legend":"\u003cp\u003eCircos plot illustrating the results of the SMR analysis. The height of the lines corresponds to the negative log10-transformed p-values for the eQTL analysis (inner blue ring), mQTL analysis (middle orange ring), and the mQTL-eQTL analysis (purple ring). The plot only includes significant signals and the top SNP for each gene. The cytoband region presents 30 genes that exhibit significant signals in all three analyses.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6043857/v1/5bfa1fb53d43248b1047437d.png"},{"id":77261694,"identity":"e76a87f4-0152-4a5e-ae47-c058385351c3","added_by":"auto","created_at":"2025-02-26 19:30:10","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":273845,"visible":true,"origin":"","legend":"\u003cp\u003eResults of significant eQTL and mQTL signals in BCa. (\u003cstrong\u003eA\u003c/strong\u003e) The significant signals of eQTL and mQTL in BCa. (\u003cstrong\u003eB\u003c/strong\u003e) Locus plot illustrating the mQTL signals in BCa.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6043857/v1/14d5a703d41d9e3adb5b658d.png"},{"id":77455642,"identity":"3d319588-02c6-435d-9e0e-b59f12abdafb","added_by":"auto","created_at":"2025-02-28 20:31:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2197205,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6043857/v1/4526aade-6d21-4964-8010-ad6e00ee8e07.pdf"},{"id":77261692,"identity":"e8e4e751-334d-4896-b68c-cbfd4e3308d8","added_by":"auto","created_at":"2025-02-26 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19:30:13","extension":"xls","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":6632960,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydata3TablesS11.xls","url":"https://assets-eu.researchsquare.com/files/rs-6043857/v1/5368a1a32921138a98eb01d4.xls"},{"id":77261806,"identity":"fb9e8f4e-8607-471d-b229-b867b30ef11c","added_by":"auto","created_at":"2025-02-26 19:38:11","extension":"xls","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":171008,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarydata3TablesS12.xls","url":"https://assets-eu.researchsquare.com/files/rs-6043857/v1/e2aae4f3728d6f5db514dd5c.xls"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrated multi-omics analyses reveal causal insights into the molecular landscape of urologic cancers","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe global burden and influence of cancer, specifically urologic cancers, continue to deepen, challenging the world-wide health sector's resilience. From 1990 to 2013, the incidence of urologic cancers, which include prostate, bladder, and kidney cancer, rose by 2.5 times, resulting in a 1.6-fold increase in mortality(Dy et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The GLOBOCAN indicates that over two million individuals were diagnosed with a urologic malignancy in 2020, leading to approximately 800,000 deaths(Sung et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eVarious factors can exert an impact on the progression of urological cancers, encompassing environmental factors as well as genetic or hereditary factors. Smoking, for instance, represents a significant influencing environmental factor, contributing for nearly half of bladder cancer (BCa) cases(Freedman et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Meanwhile, owing to the familial hereditary nature of prostate cancer (PCa), germline mutations are believed to predispose individuals to early onset or metastatic PCa(Rebello et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), marked by a molecular enrichment of HRAS mutations(Carrot-Zhang et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Delon et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Furthermore, the risk of kidney cancer (KC) is heightened in individuals affected by autosomal dominant hereditary cancer syndromes due to germline mutations in \u003cem\u003eVHL\u003c/em\u003e, \u003cem\u003eFH\u003c/em\u003e, \u003cem\u003eMET\u003c/em\u003e, and \u003cem\u003eFLCN\u003c/em\u003e genes.\u003c/p\u003e \u003cp\u003eGenome-wide association studies (GWAS) effectively exploit the genetic correlations with traits based on single nucleotide polymorphisms (SNPs)(Deng et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These studies have resulted in identifying hundreds of susceptibility loci for urologic cancers(Conti et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Purdue et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Schumacher et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Taking the susceptibility locus rs17632542 in KLK3, which encodes prostate-specific antigen (PSA), as an example, it has been demonstrated influencing the PCa risk(Koutros et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and widely used for PCa diagnosis in clinical settings(Kote-Jarai et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Additionally, urinalysis is commonly employed in urological evaluations(Pernar et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Due to the accessibility for measurement and minimal invasiveness, these tests provide clinically insights into human health and illness associated risk factors(Dinges et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Schmidt et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, the findings derived from certain studies exhibited incongruity, and one plausible explanation for this inconsistency could be attributed to the unaccounted influence of confounding variables. A comprehensive assessment of gene and metabolite associations in urologic cancers would therefore prove beneficial.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) is a methodological approach that adroitly applies SNPs as instrumental variables to infer potential causal relationships between exposures and outcomes. By harnessing the principles of genetic inheritance, MR emerges as an innovative instrument in epidemiology that can effectively attenuate influences from confounding variables unobserved in observational studies(Davies et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Notably, this promising approach lends itself to conducting two types of MR analysis: Two-sample MR and Summary-data-based MR (SMR). Embarking on these analytic methodologies facilitates the exploration of novel cancer associations and guides us towards unlocking deeper understandings of the biological mechanisms associated with carcinogenesis, thus shedding light on potential therapeutic targets and prevention strategies.\u003c/p\u003e \u003cp\u003eIn our study, we leverage these advanced analytical paradigms, employing a multi-omics data with MR to investigate causal effects in urologic cancers. GWAS summary statistics were integrated with DNA methylation (DNAm) quantitative trait locus (QTL), gene expression QTL (eQTL), protein QTL (pQTL) in blood, and tissue eQTL to identify potential gene-cancer interactions, followed by colocalization and transcriptome-wide association study (TWAS) analyses. Subsequently, a bidirectional two-sample MR method was used to explore the casual relationships between blood and urine metabolites and urologic cancers.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study Population and Data Acquisition\u003c/h2\u003e \u003cp\u003eOur study population was retrieved from databases including the eQTLGen consortium(V\u0026otilde;sa et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), the Genotype-Tissue Expression (GTEx) project(Lonsdale J 2013), the IEU OpenGWAS project(Lyon et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and the GWAS catalog(Sollis et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Blood DNAm QTL (mQTL) was used and derived from McRae et al.'s study, a meta-analysis of two European cohorts: the Brisbane Systems Genetics Study (n\u0026thinsp;=\u0026thinsp;614) and the Lothian Birth Cohorts (n\u0026thinsp;=\u0026thinsp;1366). The eQTL was obtained from the eQTLGen consortium where the information about trait-associated SNPs from 31684 individuals was stored. The pQTL garnered from plasma and cerebrospinal fluid were integrated from several studies by Yang et al., Suhre et al.(Suhre et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), Sun et al.(Sun et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), Yao et al.(Yao et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), Emilsson et al.(Emilsson et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), Folkersen et al.(Folkersen et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), and Benjamin et al(Sun et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Tissue eQTL (kidney cortex and prostate eQTL) and an additional set of whole blood eQTL were sourced from GTEx (n\u0026thinsp;=\u0026thinsp;860). Only \u003cem\u003ecis\u003c/em\u003e-QTL were included for further analysis.\u003c/p\u003e \u003cp\u003eA total of 2697 sets of GWAS summary statistics of blood and urine metabolites were taken from the GWAS catalog (GCST90264176-GCST90266872). Urological cancer GWAS summary statistics were acquired from the IEU OpenGWAS project (BCa: ukb-b-8193; PCa: ieu-b-85) and the GWAS catalog (KC: GCST008225). The subjects in our study comprised individuals of European ancestry.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. SMR Analysis\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. File Preparation\u003c/h2\u003e \u003cp\u003eAccording to the analysis requirement, a flist file was prepared using pQTL data and Genome Reference Consortium Human Build 37 (GRCh37) gene information from the ensemble database as needed. We then created a corresponding BESD format file (a file format to store the QTL summary data in binary format). Both BCa and PCa GWAS summary statistic datasets were initially converted into plain text using R packages of VariantAnnotation (v 1.44.1), gwasglue (v 0.0.0.9000), and dplyr (v 1.1.2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. Formal Analysis\u003c/h2\u003e \u003cp\u003eThe SMR tool could determine the extent of SNP effects on phenotypes influenced by molecular traits like DNAm, gene and protein expression. A four-step SMR was performed as follows: 1). SNPs as instruments, with blood gene expression as exposure, and cancers as outcome; 2). SNPs as instruments, with blood DNAm as exposure, and cancers as outcome; 3). SNPs as instruments, with blood DNAm as exposure, and blood gene expression as outcome where only significant signals from step 1 were considered for inclusion; 4). SNPs as instruments, with blood protein as exposure, and cancers as outcome. The 1000 Genomes Project European dataset was used as a reference for linkage disequilibrium (LD) calculation.\u003c/p\u003e \u003cp\u003eFalse Discovery Rate (FDR) was used to adjust the resultant data. Signals were selected per these criteria: an FDR value of SMR\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and a p value in the Heterogeneity in Dependent Instruments (HEIDI) test\u0026thinsp;\u0026gt;\u0026thinsp;0.01.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Two-Sample MR Analysis\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1. Formal Analysis\u003c/h2\u003e \u003cp\u003eThe two-sample MR was employed to estimate the influence of blood and urine metabolites on urological cancer risk, with SNPs with p-values below 5 x 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e were considered. Relevant R packages mainly included TwoSampleMR (v 0.5.7), plinkbinr (v 0.0.0.9000), and ieugwasr (v 0.1.5). In order to account for LD, we eliminated SNPs with r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.01 within a 1000kb range (clump_r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.01, clump_kb\u0026thinsp;=\u0026thinsp;1000). Moreover, a bidirectional MR analysis was conducted to explore potential reverse causality between metabolites and cancers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2. Result interpretation\u003c/h2\u003e \u003cp\u003eFive methods were employed for the two-sample MR analysis to estimate the effect: MR-Egger, Weighted median, Inverse variance weighted (IVW), Simple mode, and Weighted mode. When multiple genetic instruments of exposure were available (nSNP\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;2), the IVW method was selected as the primary result. In cases where only one genetic instrument was available (nSNP\u0026thinsp;=\u0026thinsp;1), the Wald ratio was automatically used. A significance threshold of an FDR value of 0.05 was considered to prioritize the results for further analysis.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Colocalization analysis\u003c/h2\u003e \u003cp\u003eThe Bayesian testing was performed using the coloc package (v 5.2.3) to estimate the posterior probability of shared causal genetic variant between blood/tissue gene expression and PCa or BCa. For the significant genes identified in the SMR, a colocalization result was considered supportive when the posterior probability for shared causal variants (PP.H4) was equal to or greater than 0.8.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Transcriptome-Wide Association Studies (TWAS)\u003c/h2\u003e \u003cp\u003eTWAS has emerged as a prominent methodology for elucidating the intricate relationships between genetically regulated genes and phenotypic traits. This approach has contributed significantly to enhancing our comprehension of the intricate mechanisms. Here. the TWAS related to urologic cancers were obtained from the TWAS Atlas(Lu et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Drug repositioning analyses\u003c/h2\u003e \u003cp\u003eTarget drugs have the potential to interact with and modulate the activity of specific genes which in turn influence the underlying biological processes related to a particular disease. The databases Drugbank(Wishart et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), Therapeutic Target Database (TTD)(Zhou et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and ChEMBL(Zdrazil et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) were used to identify drugs associated with the genes of interests. These databases provide valuable insights into the therapeutic targets and drug interactions, aiding in the exploration of potential pharmacological interventions for diseases associated with specific genes.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eThe data collection and detailed workflow designed for this study are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, respectively, which provide a comprehensive workflow including data collection and analysis procedures. Initially, the associations between blood \u003cem\u003ecis\u003c/em\u003e-eQTL, \u003cem\u003ecis\u003c/em\u003e-mQTL, and \u003cem\u003ecis\u003c/em\u003e-pQTL and urological cancers were scrutinized employing a four-step SMR approach, followed by tissue \u003cem\u003ecis\u003c/em\u003e-eQTL SMR. Subsequently, we ascertained urine and blood metabolite statistics and scrutinized their associations with urologic cancers utilizing a two-sample MR methodology. The candidate causal genes were further explored by colocalization, TWAS, and drug repositioning analyses.\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\u003eThe data resources of different omics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eData resource\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood mQTL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMcRae et al.\u0026rsquo;s study\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBlood eQTL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eeQTLGen\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGTEx\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epQTL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYang et al. [21], Suhre et al. [22], Sun et al. [23],\u003c/p\u003e \u003cp\u003eYao et al. [24], Emilsson et al. [25], Folkersen et al. [26],\u003c/p\u003e \u003cp\u003eBenjamin et al [27].\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTissue eQTL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGTEx\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolites\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGWAS catalog\u003c/p\u003e \u003cp\u003e(GCST90264176 - GCST90266872)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIEU OpenGWAS\u003c/p\u003e \u003cp\u003e(PCa: ieu-b-85, BCa: ukb-b-8193, KC: GCST008225)\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 \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1 PCa was associated with multi-omics outcomes\u003c/h2\u003e \u003cp\u003eFirstly, we embarked on an integrated analysis for PCa employing multi-omics data through a four-step SMR methodology, as delineated in the Methods section. Upon analyzing \u003cem\u003ecis\u003c/em\u003e-eQTL from eQTLGen, 234 significant genes were identified (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05), including 119 positively associated with PCa (b_SMR\u0026thinsp;\u0026gt;\u0026thinsp;0) and 115 negatively associated with PCa (b_SMR\u0026thinsp;\u0026lt;\u0026thinsp;0) (\u003cb\u003eSupplementary data 1\u003c/b\u003e). A secondary exploration of whole blood \u003cem\u003ecis\u003c/em\u003e-eQTL from GTEx yielded 121 significant genes (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003cb\u003eSupplementary data 1\u003c/b\u003e). Of these, 65 overlapped in both aforementioned \u003cem\u003ecis\u003c/em\u003e-eQTL analyses, with only one gene (\u003cem\u003eMDM4\u003c/em\u003e) producing divergent results (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditionally, the prostate tissue \u003cem\u003ecis\u003c/em\u003e-eQTL analysis from GTEx implicated 74 significant genes (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003cb\u003eSupplementary data 1\u003c/b\u003e), and 16 genes recurred across all three analyses. These included eight with positive roles (\u003cem\u003eRP11-6L6.2, PSORS1C3, CYP21A1P, UHRF1BP1, MARVELD1, C10orf32, WFDC3, DNTTIP1\u003c/em\u003e) and eight with negative effects (\u003cem\u003eRAB7L1, AC079922.3, ALS2CR12, STK19P, L3MBTL3, SF3B2, ZDHHC7, POLI\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). We further probed potential associated drugs within three databases - Drugbank, TTD, ChEMBL - and found 10 targetable genes (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), with \u003cem\u003eSIK2\u003c/em\u003e, \u003cem\u003eCASP8\u003c/em\u003e, and \u003cem\u003eFAAH\u003c/em\u003e appeared in two database and had several targeted therapies.\u003c/p\u003e \u003cp\u003e \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\u003eTarget drugs for 65 genes in eQTL SMR results.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDatabase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDrug relations\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrugbank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSIK2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFostamatinib\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eTherapeutic Target Database (TTD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCASP8①\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAmooranin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSIK2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGRN-300\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZNF217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eISIS 114527\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFAAH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eThiopental\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eASRGL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCalaspargase pegol\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePYGB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePMID26666989-Compound-Figure9middle11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL3MBTL3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUNC1215\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLPAR2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNAEPA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSF3B2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH3B-8800\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eChEMBL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLAMC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOcriplasmin\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFAAH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAcetaminophen, JNJ-42165279, PF-04457845\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCASP8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNivocasan, Emricasan\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 \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cem\u003eCASP8\u003c/em\u003e gene was both identified in TWAS results and target drugs.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe colocalization results for 65 genes indicated that 45 genes likely contained a shared causal variant (PP.H4\u0026thinsp;\u0026gt;\u0026thinsp;0.5), with 26 genes presented robust association with PCa (PP.H4\u0026thinsp;\u0026gt;\u0026thinsp;0.8), while 14 genes were associated with PCa but shared different causal variants (PP.H3\u0026thinsp;\u0026gt;\u0026thinsp;0.5) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Of the 26 genes, 15 genes showed a significant correlation with PCa in TWAS results (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cb\u003eSupplementary data 1\u003c/b\u003e). For instance, \u003cem\u003eCASP8\u003c/em\u003e, encoding a caspase protein, was associated with PCa risk and found higher expressed in the serum of high-grade PCa patients(Liu et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This was in align with our blood SMR analysis result (b_SMR\u0026thinsp;=\u0026thinsp;0.202).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePromoters or enhancers often harbor disease-associated target genes regulated by DNAm(Wu et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In the blood, candidate causal genes for cancers and their possible underlying epigenetic mechanism of gene regulation were in need of investigation. Therefore, \u003cem\u003ecis\u003c/em\u003e-eQTL and \u003cem\u003ecis\u003c/em\u003e-mQTL SMR analyses was done and only the significant results were interpreted as suggestive causal genes. In concrete, 1655 DNAm probes were identified at the FDR-significant level of 0.05, including 862 with positive roles and 793 antagonizing PCa (\u003cb\u003eSupplementary data 1\u003c/b\u003e). Further integration of overlapped 45 genes from \u003cem\u003ecis\u003c/em\u003e-eQTL and \u003cem\u003ecis\u003c/em\u003e-mQTL putative causal results implied 85 DNAm probes of 30 genes contributed to their expression levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Moreover, 15 genes were also replicated in GTEx blood \u003cem\u003ecis\u003c/em\u003e-eQTL analysis including 8 positively associated with PCa: \u003cem\u003eNSUN4, FAAH, ECHDC2, SLC39A1, ZBTB38, PBX2, SIK2, GATAD2A\u003c/em\u003e, and 7 negatively associated with PCa: \u003cem\u003eLAMC1, HAAO, TRIM26, TTC16, HKDC1, TRIM8, PYGB\u003c/em\u003e, suggesting that these 15 genes were likely to be influenced by DNAm, which in turn affected PCa.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eProteomic research had the potential to enhance our understanding of molecular mechanisms and identify potential therapeutic targets(Finan et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Therefore, we conducted SMR analysis to investigate the potential association between \u003cem\u003ecis\u003c/em\u003e-pQTL and PCa. However, no significant results were found under FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and p_HEIDI\u0026thinsp;\u0026gt;\u0026thinsp;0.01. Nevertheless, when excluding the HEIDI test which was used to assess the heterogeneity, the most significant results was KLK3 (PSA, b_SMR\u0026thinsp;=\u0026thinsp;2.884, \u003cb\u003eSupplementary data 1\u003c/b\u003e), which was already used in the clinical for PCa detection or diagnosis.\u003c/p\u003e \u003cp\u003eWe then employed two-sample MR analysis using 2697 sets of metabolite data as exposure. Four urinary metabolites (phenyllactate (PLA): OR\u0026thinsp;=\u0026thinsp;1.165, N-lactoyl phenylalanine: OR\u0026thinsp;=\u0026thinsp;1.185, indolelactate: OR\u0026thinsp;=\u0026thinsp;1.149, adenosine 3',5'-cyclic monophosphate (cAMP): OR\u0026thinsp;=\u0026thinsp;1.290) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) were identified as being associated with PCa. While PCa served as the exposure, no significant association observed.\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\u003eTwo-sample MR results for metabolites in PCa.\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=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003emetabolite(s)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003enSNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ebeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ese\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ephenyllactate (PLA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.560 X 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.540 X 10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN-lactoyl phenylalanine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.887 X 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.270 X 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eindolelactate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.506 X 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.170 X 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eadenosine 3',5'-cyclic monophosphate (cAMP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.312 X 10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.600 X 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e\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* The IVW method was employed and metabolites were as exposure in these results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2 BCa was associated with the expression of GSTM1, BTNL8, BTNL9 and PSCA methylation\u003c/h2\u003e \u003cp\u003eAt a significance level of FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05, we identified three signals within \u003cem\u003ecis\u003c/em\u003e-mQTL analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, \u003cb\u003eSupplementary data 2\u003c/b\u003e). Notably, the significant DNAm site cg13446199 was located within the body of \u003cem\u003ePSCA\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA), manifesting a positive influence on BCa (b_SMR\u0026thinsp;=\u0026thinsp;5.934 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e). \u003cem\u003ePSCA\u003c/em\u003e was first marked as a prostate-specific cell-surface antigen with high expression in urological cancers(Wang et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). However, no association between the \u003cem\u003ePSCA\u003c/em\u003e expression level and BCa was discovered in our study. The other two substantial DNAm sites were cg04035553 and cg27028750, both showing positive effects (b_SMR\u0026thinsp;=\u0026thinsp;8.028 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e and b_SMR\u0026thinsp;=\u0026thinsp;7.739 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e respectively). However, the cg04035553 was located approximately 4 kb upstream from the transcription start site of \u003cem\u003ePSCA\u003c/em\u003e. The cg27028750 was not mapped to any recognized gene or CpG island and located in the intergenic region between \u003cem\u003eCLPTM1L\u003c/em\u003e and \u003cem\u003eSLC6A3\u003c/em\u003e genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eIn terms of \u003cem\u003ecis\u003c/em\u003e-eQTL analysis, three genes (\u003cem\u003eGSTM1\u003c/em\u003e, \u003cem\u003eBTNL8\u003c/em\u003e, and \u003cem\u003eBTNL9\u003c/em\u003e) were identified and were all strongly associated with BCa according to the colocalization results (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). \u003cem\u003eGSTM1\u003c/em\u003e, encoding the anti-carcinogenic enzyme glutathione S-transferase M1, was reported to play a protective role in BCa among both smokers and non-smokers(Bell et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Garc\u0026iacute;a-Closas et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Our study corroborates these findings, denoting \u003cem\u003eGSTM1\u003c/em\u003e as protective against BCa (b_SMR = -9.103 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e). Furthermore, 16 target drugs for \u003cem\u003eGSTM1\u003c/em\u003e were indicated - approved or experimental according to Drugbank, including Busulfan, Cisplatin, Carboplatin, Oxaliplatin et al. The genes \u003cem\u003eBTNL8\u003c/em\u003e and \u003cem\u003eBTNL9\u003c/em\u003e belong to the immunoglobulin superfamily and specifically, \u003cem\u003eBTNL8\u003c/em\u003e was implicated in T cell regulation(Blazquez et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Both genes were found to play negative roles (\u003cem\u003eBTNL8\u003c/em\u003e: b_SMR = -9.312 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e, \u003cem\u003eBTNL9\u003c/em\u003e: b_SMR = -1.654 X 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e) in our study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3 No association was identified between KC and multi-omics analysis\u003c/h2\u003e \u003cp\u003eInitially, we identified 4428 signals in \u003cem\u003ecis\u003c/em\u003e-mQTL and 824 signals in \u003cem\u003ecis\u003c/em\u003e-eQTL (p_SMR\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (\u003cb\u003eSupplementary data 3\u003c/b\u003e). However, following FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05, none of these signals sustained significant levels. Distinct causal associations were absent within integrated \u003cem\u003ecis\u003c/em\u003e-pQTL, whole blood \u003cem\u003ecis\u003c/em\u003e-eQTL, or kidney cortex \u003cem\u003ecis\u003c/em\u003e-eQTL. The use of at least two SNPs as instrumental variables allowed us to observe that 32 types of metabolites \u0026minus;\u0026thinsp;18 from plasma and 14 from urine - reached significance in terms of p-values with the IVW analyses (\u003cb\u003eSupplementary data 3\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe increasing prevalence of cancer necessitates a heightened focus on patient treatment and care. Our study delved into the correlation between multi-omics data and the risk of urologic cancers through MR analysis. This comprehensive approach enables a thorough exploration of the intricate interplay among genetic variations, gene expression, protein levels, and metabolites in the context of urologic cancers. Our findings not only enhance comprehension of the complex molecular landscape in these diseases but also pave the way for future precision medicine and personalized treatment strategies.\u003c/p\u003e \u003cp\u003eBlood-based biomarkers are being explored to diagnose and monitor PCa progression(Kohaar et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In other terms, blood may be utilized as a valuable proxy to characterize genetic effects on gene expression and understand the intricate etiology of PCa. In our study, 15 putative causal genes were detected through genetically epigenomic and transcriptomic regulation analysis, with genes such as \u003cem\u003eNSUN4, FAAH\u003c/em\u003e, and \u003cem\u003eSIK2\u003c/em\u003e which have already been reported positively associated with PCa risk. Consistent with our results, \u003cem\u003eNSUN4\u003c/em\u003e methylation and its expression level, as well as susceptibility loci were all related to PCa risk(Kar et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). FAAH has been reported involving in prostate tumorigenesis(Endsley et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) and SIK2 was highly expressed in aggressive PCa patients\u0026rsquo; blood which may serve as a potential blood marker for PCa diagnosis(Bon et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), Our results further indicated that the DNAm level of \u003cem\u003eFAAH\u003c/em\u003e and \u003cem\u003eSIK2\u003c/em\u003e had an impact on PCa perhaps resulting in an increased risk associated with gene expression. In addition, we identified several target drugs for FAAH and SIK2. These drugs, such as GRN-300 and Acetaminophen have been previously utilized or investigated in ovarian cancer, non-small cell lung cancer, lymphoma, glioma, and breast cancer treatment. Therefore, it is plausible that suppressing these two targets with these drugs and disrupting DNAm could be a potential therapeutic approach for PCa.\u003c/p\u003e \u003cp\u003eConsidering genes whose PP.H4 fell between 0.5 and 0.8 could be regarded as a moderate indication of colocalization, \u003cem\u003eHAAO\u003c/em\u003e and \u003cem\u003eGATAD2A\u003c/em\u003e were identified causally associated with PCa occurrence or progression. The \u003cem\u003eHAAO\u003c/em\u003e gene was frequently found to be hypermethylated in PCa; however, the association with PCa risk remains controversial(Li et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Mahapatra et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In our study, we found \u003cem\u003eHAAO\u003c/em\u003e had a protective effect against PCa. Specially, DNAm site cg09480054 could promote the occurrence of PCa by inhibiting HAAO expression, suggesting an intricate but vital role of epigenetic factors.\u003c/p\u003e \u003cp\u003eApart from genes mentioned above, which exhibited varying associations with PCa, additional genes \u003cem\u003eECHDC2, TRIM26, PBX2, HKDC1\u003c/em\u003e, and \u003cem\u003eTRIM8\u003c/em\u003e, although not previously reported in relation to PCa, had demonstrated significance in other types of cancers(Alholle et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Caratozzolo et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Lin et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Xia et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Taking \u003cem\u003eTRIM8\u003c/em\u003e as an example, TRIM8 was reported to be involved in multiple immune pathways and exert a dual action in various cancers(Caratozzolo, Marzano, Mastropasqua, Sbis\u0026agrave; and Tullo \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Seong et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Based on our results, \u003cem\u003eTRIM8\u003c/em\u003e demonstrated a protective effect against PCa no matter in DNAm level or expression level. The observed association at the expression level was determined to be mediated by diverse genetic variants (PP.H3\u0026thinsp;=\u0026thinsp;0.926). Moreover, its significant relationship was further confirmed by TWAS.\u003c/p\u003e \u003cp\u003eBy incorporating tissue analysis, we were able to identify both overlapping and unique findings compared to blood analysis. This suggests that the potential influence of gene expression in blood and prostate could lead to varying effects on PCa. Among the shared results, there were also several genes that have been previously established as being associated with cancers. In concrete, the expression of \u003cem\u003eMARVELD1\u003c/em\u003e was found to be decreased in approximately half of PCa patients. In other cancer types, it was hypermethylated and underwent epigenetic inactivation by CpG methylation(Wang, Li, Han, Hu, Yue, Yu, Zhang, He, Zheng, Shi, Fu and Wu 2009). A positive relationship between \u003cem\u003eMARVELD1\u003c/em\u003e expression and PCa was found (b_SMR\u0026thinsp;=\u0026thinsp;0.112) in tissue SMR analysis but no significant association was observed in blood DNAm SMR results. One potential explanation could be that the DNAm data utilized derived from blood rather than tissue. Therefore, it is necessary to validate these results using data obtained from tissue in future research. The elevated expression of UHRF1BP1 may function as a tumor suppressor, impeding cell growth. However, conflicting findings from other studies have reported its potential cancer-promoting effects(Dan et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; El Baroudi et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). We found that both blood and tissue SMR results showed that \u003cem\u003eUHRF1BP1\u003c/em\u003e gene was correlated with increased PCa risk and TWAS results also confirmed this association.\u003c/p\u003e \u003cp\u003eTissue gene expression analysis has been demonstrated to clarify distinct biological molecular mechanisms(Yang et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). These observations highlighted the essential nature of tissue analysis alongside blood analysis. Certain genes, such as \u003cem\u003eC10orf32\u003c/em\u003e, were identified in both analyses, while others were exclusive to tissue analysis. For instance, the \u003cem\u003ePPP1R14A\u003c/em\u003e gene, although absent in the blood analysis results, emerged as the most significant tissue-specific signal (b_SMR\u0026thinsp;=\u0026thinsp;0.478) and it was reported as a target of PCa risk loci or microRNAs in previous studies(Emami et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Given the diverse impact of gene expression across different tissues on PCa, conducting integrated multi-omics analysis from multiple relevant tissues to identify risk factor becomes imperative.\u003c/p\u003e \u003cp\u003eGiven the advantages of convenience, cost-effectiveness, and minimal invasiveness, metabolite assessments in blood and urine tests presented indicators of considerable interest. In our study, we prioritized four specific metabolites in urine \u0026ndash; cAMP, PLA, N-lactoyl phenylalanine, and indolelactate - all of which showed positive correlation with PCa. cAMP, extensively studied before, could influence the release of neurotransmitters and hormones, neurodegeneration, and tumor growth besides regulating pro- and anti-inflammatory responses(Bergantin \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Notably, PLA, previously reported as a potential metabolic biomarker for ovarian cancer, oral squamous cell carcinoma, and cervical cancer(Li et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) which could serve as a promising subject for future cancer research. Less was known about N-lactoyl phenylalanine, a blood-borne signaling metabolite induced by exercise stimulation and linked with suppressing feeding and obesity(Li et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). No prior associations between N-lactoyl phenylalanine and cancers have been documented, yet our observations unveiled a significantly positive connection with PCa (OR\u0026thinsp;=\u0026thinsp;1.185). Indolelactate, hinted at reducing inflammation and insulin resistance through aryl hydrocarbon receptor activation, presents contradictory evidence - an association with type 2 diabetes was reported elsewhere(Qi et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Our investigation contradicted a previous serum metabolomic profiling study involving 146 participants where indolelactate was found downregulated in PCa(Khan et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This discrepancy might be due to the blood data deficiency of Indolelactate, different sample types (serum versus urine) or different population across studies. The positive associations exhibited by these four metabolites with PCa underscored their potential utility in cancer detection or screening.\u003c/p\u003e \u003cp\u003eOur investigation into BCa led to the identification of three significant genes and DNAm sites. \u003cem\u003eGSTM1\u003c/em\u003e was recognized for its protective influence on BCa in both smokers and non-smokers(Bell, Taylor, Paulson, Robertson, Mohler and Lucier \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1993\u003c/span\u003e; Garc\u0026iacute;a-Closas, Malats, Silverman, Dosemeci, Kogevinas, Hein, Tard\u0026oacute;n, Serra, Carrato, Garc\u0026iacute;a-Closas, Lloreta, Casta\u0026ntilde;o-Vinyals, Yeager, Welch, Chanock, Chatterjee, Wacholder, Samanic, Tor\u0026agrave;, Fern\u0026aacute;ndez, Real and Rothman 2005), a finding that our study echoed (b_SMR = -9.103 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e). \u003cem\u003eBTNL8\u003c/em\u003e and \u003cem\u003eBTNL9\u003c/em\u003e are members of the immunoglobulin superfamily which involve in T-cell regulation(Blazquez, Benyamine, Pasero and Olive \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Prior studies reported downregulated \u003cem\u003eBTNL8\u003c/em\u003e expression in colon tumors(Blazquez, Benyamine, Pasero and Olive \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and decreased \u003cem\u003eBTNL9\u003c/em\u003e expression in osteosarcoma, colon cancer, lung adenocarcinoma, and breast cancer(Mo et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In alignment with these observations, our results revealed negative correlations between both \u003cem\u003eBTNL8\u003c/em\u003e (b_SMR = -9.312 X 10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e) and \u003cem\u003eBTNL9\u003c/em\u003e (b_SMR = -1.654 X 10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e) gene expressions and BCa risk. \u003cem\u003ePSCA\u003c/em\u003e stood out due to the positive association of the DNAm site cg13446199, but no additional associations were identified between the expression of \u003cem\u003ePSCA\u003c/em\u003e and BCa. However, it was observed that hypomethylation of PSCA could potentially influence prostate carcinogenesis by mediating the effect of DEHP in rats(Xia et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In addition, the expression of \u003cem\u003ePSCA\u003c/em\u003e was up-regulated in numerous cancers, including PCa and BCa, with genetic variants in PSCA flagged as risk factors for BCa. Specifically, rs2294008 and rs2978974 have been implicated in modulating BCa susceptibility through influencing \u003cem\u003ePSCA\u003c/em\u003e gene expression and interaction with regulatory elements(Furukawa et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere were also some limitations in our study. First, we didn\u0026rsquo;t find any significant associations with KC. Although studies have shown an association between KC risk and certain mutations in blood DNA, it was important to note that not all previous blood-based findings in other studies were replicated(Chow et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Additionally, the tissue analysis specifically focused on kidney cortex eQTL and other renal regions were not analyzed. Second, despite we identified some metabolites associated with PCa risk, considering the involvement of kidney in urine formation, future research necessitates a more comprehensive exploration of metabolism-related data to unveil novel findings. Third, no association was discerned with \u003cem\u003ecis\u003c/em\u003e-pQTL. One potential explanation for such outcomes could be our exclusive usage of \u003cem\u003ecis\u003c/em\u003e-pQTL, whereas combinatorial employment of \u003cem\u003ecis\u003c/em\u003e- and \u003cem\u003etrans\u003c/em\u003e-pQTL might have led to diverse results(Xu et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Fourth, the effects of some genes on PCa were contrary to previous reports such as \u003cem\u003eZBTB38(\u003c/em\u003ede Dieuleveult et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ding et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hotta et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), \u003cem\u003eSLC39A1(\u003c/em\u003eWang et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), \u003cem\u003eLAMC1(\u003c/em\u003eNishikawa et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Pasqualini et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), \u003cem\u003ePYGB(\u003c/em\u003eWang et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and \u003cem\u003ePOLI(\u003c/em\u003eMancuso et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sakiyama et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Yang et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Yuan et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The discrepancy might be attributed to the different study objects, their diverse genetic backgrounds and the neglect of gene-gene interaction.\u003c/p\u003e \u003cp\u003eIn conclusion, our study utilized a diverse range of multi-omics data, enabling us to comprehensively investigate the intricate interplay between genetic variations, gene expression, protein levels, and metabolites in the context of urologic cancers. This integrative approach not only enhanced our understanding of the complex molecular landscape underlying these diseases but also opened up new avenues for precision medicine and personalized treatment strategies. Furthermore, the inclusion of both blood and tissue samples in our analysis allowed for a comprehensive assessment of the systemic and local factors contributing to urologic cancers.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: F.X., C.L., G.T.; Data curation: C.X., X.Y., S.W., Y.L., X.C., Z.H., J.M.; Formal analysis: C.X., X.Y. ; Investigation: C.X., X.Y., F.X.; Methodology: C.X., X.Y., Z.H., H.H., F.X.; Software: C.X., X.Y., F.X.; Supervision: G.T., F.X., C.L.; Visualization: C.X.; Writing – original draft: C.X., X.Y., F.X., C.L.; Writing – review \u0026amp; editing: C.X., X.Y., F.X., G.T., C.L. All authors read and approved the finalmanuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by Taishan Scholar Program (Tsqn202103198), Taishan Scholars Construction Engineering, Major Basic Research Project of Shandong Provincial Natural Science Foundation (ZR2019ZD27), Key research and development program of shandong province (2023CXPTO12), Shandong Province Higher Educational Youth Innovation Science and Technology Program (2019KJE013), and Binzhou Medical University Research Start-up Fund (50012305190).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003enot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics, Consent to Participate, and Consent to Publish declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003enot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe source of data used in our study were described in the Materials section and all data analyzed were included in the supplementary files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlholle A, Brini AT, Gharanei S, et al. 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Nucleic Acids Res. 2024;52:D1465\u0026ndash;77. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/nar/gkad751\u003c/span\u003e\u003cspan address=\"10.1093/nar/gkad751\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Urologic cancer, Mendelian randomization, Integrated omics, Colocalization, TWAS","lastPublishedDoi":"10.21203/rs.3.rs-6043857/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6043857/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eUrologic cancers continue to present an ongoing challenge to human health, underscoring the necessity for a systematic exploration of their underlying mechanisms. Hence, we employed a multi-omics Mendelian randomization (MR) approach to infer potential pathogenic genes and unravel the associated mechanisms.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eGenome-wide association study (GWAS) summary statistics on urologic cancers, along with expression quantitative trait loci (QTLs), DNA methylation QTLs, and protein QTLs were employed for summary data-based MR (SMR), colocalization, and transcriptome-wide association study (TWAS) analysis to identify putative causal genes. Additionally, metabolites GWAS summary statistics from both blood and urine were analyzed by two-sample MR method to uncover potential relationships with the risk of urologic cancers.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 15 genes from blood were identified related to prostate cancer (PCa) risk using SMR, colocalization, and TWAS analysis, with C10orf32, MARVELD1, UHRF1BP1, and POLI were being replicated in tissue SMR analysis. Additionally, 15 genes were prioritized as potential causal genes with their methylation regulatory, encompassing both well-established genes (NSUN4, FAAH, and SIK2) as well as novel candidates, such as HAAO, and UHRF1BP1. Furthermore, four metabolites, including phenyllactate, N-lactoyl phenylalanine, indolelactate, and adenosine 3',5'-cyclic monophosphate, were positively associated with PCa risk. For bladder cancer, its risk was associated with the expression variation of GSTM1, BTNL8, and BTNL9 and PSCA methylation.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis integrative approach not only enhances our understanding of the complex molecular landscape underlying urologic cancers but also opens up new avenues for future precision medicine and personalized treatment strategies.\u003c/p\u003e","manuscriptTitle":"Integrated multi-omics analyses reveal causal insights into the molecular landscape of urologic cancers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-26 19:30:03","doi":"10.21203/rs.3.rs-6043857/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"cf9f5326-3b34-4bb8-b7d6-35aa87f7d4cf","owner":[],"postedDate":"February 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-09T08:23:34+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-26 19:30:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6043857","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6043857","identity":"rs-6043857","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}