Integrative Genomic Profiling Identifies MLPH as a Candidate Gene in Prostate Cancer

Integrative Genomic Profiling Identifies MLPH as a Candidate Gene in Prostate Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Integrative Genomic Profiling Identifies MLPH as a Candidate Gene in Prostate Cancer Runyi Wang, Jiayu Wang, Zhiyi Zhao, Xiaopeng Hu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7662167/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: Prostate cancer (PCa) is a highly heterogeneous malignancy with complex genetic underpinnings. While genome-wide association studies (GWAS) have identified numerous genetic loci linked to PCa susceptibility, integrative multi-omics validation is still needed to confirm causality and uncover functional roles of candidate genes in PCa pathogenesis. Objectives: This study aims to systematically identify and prioritize candidate genes associated with PCa risk through integrative multi-omics analyses, and to elucidate their potential biological roles in tumorigenesis. Materials and Methods: We applied cross-tissue and single-tissue transcriptome-wide association studies (TWAS) on the largest available GWAS and the Genotype-Tissue Expression (GTEx) V8 datasets. Subsequent gene-level association tests refined candidate signals. Summary data-based Mendelian randomization (SMR) and Bayesian colocalization were conducted to infer causality. Expression validation was performed at both transcriptional and protein levels. Gene network and pathway enrichment analyses further explored functional contexts. Results: A total of 1,248 genes were identified by single-tissue TWAS, with 35 robust genes confirmed after conditional analysis overlapping with cross-tissue results. Integration with gene-level association test yielded 23 consensus candidate genes. SMR and colocalization prioritized MLPH and GGCX as putative causal genes. MLPH was significantly upregulated in PCa tissues at both the mRNA and protein levels, while GGCX showed no difference. Functional analyses revealed the involvement of MLPH in vesicle-mediated transport and androgen-related signaling pathways, highlighting its biological relevance in PCa pathogenesis. Discussion and Conclusion: Our integrative multi-omics approach establishes MLPH as a biologically and statistically supported gene linked to PCa susceptibility. Its roles in vesicle trafficking and hormone-regulated pathways underscore its potential as a therapeutic target, warranting further mechanistic and translational investigations. Prostate cancer genome-wide association studies (GWAS) MLPH transcriptome-wide association studies (TWAS) colocalization Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 Introduction Prostate cancer (PCa) remains a major global health concern, ranking as the second most commonly diagnosed malignancy and the fifth leading cause of cancer-related death among men worldwide 1 , 2 . In the United States alone, an estimated 299,010 new PCa cases were reported in 2024 3 . Early-stage PCa is often asymptomatic, advanced disease may present with hematuria, dysuria, and other urinary symptoms 4 . A considerable proportion of patients eventually progress to metastatic disease, highlighting the urgent need to elucidate the molecular mechanisms underlying PCa and to develop more effective therapeutic strategies. Genetic predisposition plays a significant role in the pathogenesis of PCa, which is considered one of the most heritable cancers 5 – 7 . To date, genome-wide association studies (GWAS) have identified over 170 risk loci associated with PCa susceptibility 8 , 9 . However, the biological mechanisms by which these loci influence tumor development remain largely unresolved. A substantial number of the identified risk variants reside in noncoding regions, complicating functional interpretation 10 . Furthermore, complex patterns of linkage disequilibrium (LD) often hinder the precise localization of causal variants 11 . Transcriptome-wide association studies (TWAS) have emerged as a powerful approach to prioritize candidate genes by integrating expression quantitative trait loci (eQTL) information with GWAS summary statistics 12 . Compared with traditional GWAS, TWAS improves gene-level resolution and offers insight into the regulatory architecture of complex traits 11 . Particularly, cross-tissue TWAS frameworks such as the unified test for molecular signatures (UTMOST) 13 enhance detection power by leveraging shared eQTL effects across tissues while preserving tissue-specific signals via a group-lasso penalty model 14 . This cross-tissue approach has demonstrated utility in multiple complex diseases, including osteoarthritis, erectile dysfunction, Alzheimer's disease and age-related macular degeneration 15 – 18 . In this study, we integrated GWAS summary statistics for PCa from the GWAS Catalog with eQTL data from the Genotype-Tissue Expression (GTEx) project (version 8) 19 to conduct TWAS. To systematically identify PCa–associated genes, we applied both cross-tissue and single-tissue TWAS, followed by conditional analysis and complementary gene-level association testing. Causal inference between gene expression and disease risk was assessed using Summary data-based Mendelian randomization (SMR) and Bayesian colocalization 20 , 21 . MLPH , as a candidate gene, was validated at the transcriptional and protein levels, and its interaction networks and biological functions were further characterized. 2 Methods 2.1 PCa GWAS data sources GWAS data for PCa were obtained from a large multi-ancestry meta-analysis study 22 . To reduce potential confounding from population stratification, we selected the European-ancestry subset for downstream analyses. This subset, indexed as GCST90274714 in the GWAS Catalog, comprises 122,188 prostate cancer cases and 604,640 controls. 2.2 TWAS analyses in cross-tissue TWAS framework employed in this study is illustrated in Fig. 1 . During the discovery phase, we utilized the UTMOST framework, which leverages multi-tissue eQTL data to assess gene–trait associations at a systemic level 13 . This approach incorporates eQTL effect estimates derived from 49 tissues in the GTEx V8 dataset, enabling the construction of cross-tissue predictive models for individual genes. By integrating across tissues, UTMOST improves both the power to detect gene–phenotype associations and the accuracy of expression imputation, particularly in tissues enriched for trait heritability. To aggregate tissue-specific associations, we implemented the generalized Berk-Jones (GBJ) test 23 , which accounts for correlation structures among tissues using covariance estimates from single-tissue summary statistics. Statistical significance was defined using two thresholds: genes reaching an FDR of < 1 × 10⁻⁵ were considered genome-wide significant after correction for multiple testing, while those with a P -value < 0.01 were considered to reach nominal significance 17 . 2.3 TWAS analyses in single-tissue and conditional analysis We conducted a TWAS for PCa by integrating GWAS statistics with eQTL data from 49 tissues available in the GTEx V8 reference panel, utilizing the functional summary-based imputation (FUSION) pipeline 24 . LD patterns between SNPs and expression prediction models were inferred using European samples from the 1000 Genomes Project 25 . FUSION assessed several modeling approaches for imputing gene expression and selected the optimal model based on cross-validation performance 26 . The resulting gene expression weights were then integrated with PCa GWAS Z-scores to estimate gene–trait associations. Genes were retained for further investigation if they met both of the following thresholds: (1) an FDR < 0.05 in the single-tissue analysis, and (2) Bonferroni-corrected P < 0.05 in at least one individual tissue analysis. To evaluate whether these associations identified in single-tissue TWAS are conditionally independent, we performed conditional and joint analysis (COJO) using FUSION software package 27 . This analysis accounts for LD among variants, enabling a more refined interpretation of the genetic architecture underlying trait variation. Genes that retained statistical significance after conditioning were classified as jointly significant, whereas those that lost significance were considered marginally significant 28 . In line with the FUSION pipeline, we applied a correlation filter of TOP.SNP.COR² ≤ 0.6 to exclude genes whose lead SNPs exhibited high LD with conditioned variants, thereby reducing redundancy and enhancing signal independence. Only genes that were jointly significant and satisfied this LD threshold were retained for downstream analysis. 2.4 MAGMA-based gene association analysis Gene-level association analysis was performed using Multi-marker Analysis of Genomic Annotation (MAGMA) (v1.08), a widely used tool for evaluating gene-based associations from GWAS summary data 29 . MAGMA identifies trait-associated genes by aggregating the effects of multiple SNPs assigned to each gene based on their genomic location. In this study, we used the default parameters to derive gene-level association statistics from SNP-level data 30 , 31 . Genes reaching the Bonferroni-corrected significance threshold ( P < 0.05) were considered statistically significant and were regarded as potential contributors to the trait 29 , 32 . 2.5 Gene-based association analysis using GCTA-fastBAT Gene-based association analysis for prostate cancer was conducted using the fastBAT module implemented in the GCTA software 33 , 34 . This method aggregates SNP-level z-statistics within a gene region into a composite test statistic, while accounting for LD among variants 35 . By modeling the correlation structure of nearby SNPs, fastBAT efficiently estimates the joint significance of multiple variants mapped to a gene. 2.6 SMR analyses and Bayesian colocalization We applied SMR to explore potential pleiotropic associations between gene expression and prostate cancer risk, using integrated summary statistics from PCa GWAS and eQTL datasets. This method enables the prioritization of genes that may be causally linked to disease risk, thereby offering insights into the functional basis of GWAS signals 36 . SMR probes were considered significant if they met an FDR-adjusted P 0.01 indicating no significant heterogeneity 20 . To visualize the effect sizes and confidence intervals of candidate genes identified by SMR, we used the “forestplot” R package (v3.1.6) to generate a forest plot. To further evaluate the likelihood of a shared causal variant underlying both eQTL and GWAS signals, we performed Bayesian colocalization analysis using the coloc package in R (v5.2.3) 37 . This method computes posterior probabilities for five mutually exclusive hypotheses: H0, no association with either trait; H1, association with the first trait only; H2, association with the second trait only; H3, association with both traits but driven by different causal variants; and H4, association with both traits driven by the same causal variant. Evidence supporting colocalization was defined as a posterior probability for hypothesis 4 (PP.H4) > 0.70 38 . 2.7 External validation with TCGA-PRAD data RNA sequencing and clinical data for prostate adenocarcinoma (PRAD) were obtained from the UCSC Xena platform 39 , comprising 554 prostate tissue samples from The Cancer Genome Atlas (TCGA). Of these, 483 were tumor samples. Differential gene expression analysis was conducted using the DESeq2 package in R (v1.46.0) 40 . P -values were corrected for multiple testing using the Benjamini–Hochberg method. Genes with an adjusted P 0.58 were considered differentially expressed 41 . Visualization of results was carried out using “ggplot2” R package (v3.5.2). 2.8 Protein-level validation in clinical prostate tissues To validate protein-level expression of candidate genes, immunohistochemistry (IHC) data were obtained from the Human Protein Atlas (HPA; Antibody ID: HPA014685) 42 , 43 . In this study, we examined staining profiles in PCa and normal prostate tissues to assess differences in protein expression. To quantify MLPH expression, three non-overlapping regions were randomly selected from each IHC image. The MLPH-positive area in each region was quantified using ImageJ through the Fiji platform 44 , and the mean value was used to represent protein expression per specimen. Statistical analysis and visualization were performed using GraphPad Prism 10 software. 2.9 GeneMANIA analysis and functional enrichment analysis We employed the GeneMANIA platform, a network-based tool that integrates diverse genomic and proteomic datasets, including co-expression, genetic interactions, and pathway annotations 45 . To further investigate the functional context of genes prioritized by GeneMANIA, we conducted gene set enrichment analysis using the clusterProfiler R package (v4.14.6) 46 . Enrichment was assessed across multiple curated annotation resources, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome databases, to identify biological processes and pathways significantly enriched among the input gene set 47 – 49 . 3 Results 3.1 TWAS identifies cross-tissue PCa-associated genes In the cross-tissue TWAS analysis performed using the UTMOST framework, 240 genes reached the nominal significance threshold ( P < 0.01), among which 117 genes surpassed the genome-wide significance level ( P < 1 × 10⁻⁵) (Table S1 ). These results provide initial evidence for transcriptome-wide associations with PCa across multiple tissues. 3.2 Single-tissue TWAS and COJO analysis identify independent loci Single-tissue TWAS identified 73 genes that overlapped with cross-tissue TWAS results in at least one GTEx tissue using a Bonferroni-corrected threshold ( P < 0.05) (Table S2 ). To evaluate the independence of these associations, we conducted COJO on the significant genes identified in single-tissue TWAS, identifying 946 that remained jointly significant. Among these, 35 genes overlapped with the 117 genome-wide significant genes from the UTMOST cross-tissue TWAS (Table 1 ), suggesting these represent robust and tissue-consistent associations. These genes were primarily located on chromosomes 2, 4, and 22, indicating the presence of gene-dense regions with potential biological relevance to PCa. Table 1 Candidate genes identified by cross-tissue and single-tissue TWAS after conditional analysis gene_id gene CHR FDR start end ENSG00000249264 EEF1A1P9 4 3.45E-10 106405855 106407237 ENSG00000162882 HAAO 2 6.43E-10 42994229 43019733 ENSG00000223374 AC005104.3 2 8.16E-10 242290755 242292519 ENSG00000003400 CASP10 2 1.92E-09 202047604 202094129 ENSG00000100290 BIK 22 1.92E-09 43506754 43525718 ENSG00000100294 MCAT 22 1.92E-09 43528212 43539400 ENSG00000101751 POLI 18 1.92E-09 51795774 51847636 ENSG00000115486 GGCX 2 1.92E-09 85771846 85788670 ENSG00000115504 EHBP1 2 1.92E-09 62900986 63273622 ENSG00000115648 MLPH 2 1.92E-09 238394071 238463961 ENSG00000115761 NOL10 2 1.92E-09 10710892 10830101 ENSG00000138777 PPA2 4 1.92E-09 106290234 106395238 ENSG00000152256 PDK1 2 1.92E-09 173420101 173489823 ENSG00000152518 ZFP36L2 2 1.92E-09 43449541 43453748 ENSG00000163106 HPGDS 4 1.92E-09 95219686 95264027 ENSG00000168385 SEPT2 2 1.92E-09 242254515 242293442 ENSG00000168769 TET2 4 1.92E-09 106067032 106200973 ENSG00000168883 USP39 2 1.92E-09 85829979 85876403 ENSG00000184058 TBX1 22 1.92E-09 19744226 19771116 ENSG00000236699 ARHGEF38 4 1.92E-09 106473777 106629250 ENSG00000169435 RASSF6 4 2.42E-08 74437267 74486348 ENSG00000091409 ITGA6 2 7.52E-08 173292082 173371181 ENSG00000145391 SETD7 4 0.000000101 140417095 140527853 ENSG00000064012 CASP8 2 0.000000118 202098166 202152434 ENSG00000198612 COPS8 2 0.000000378 237993955 238009109 ENSG00000176635 HORMAD2 22 0.000000429 30476163 30573064 ENSG00000132466 ANKRD17 4 0.000000462 73939093 74124515 ENSG00000231609 AC009501.4 2 0.000000707 63271057 63275775 ENSG00000003402 CFLAR 2 0.00000117 201980827 202041410 ENSG00000115677 HDLBP 2 0.00000145 242166679 242256476 ENSG00000100300 TSPO 22 0.00000169 43547520 43559248 ENSG00000196290 NIF3L1 2 0.0000021 201754050 201768655 ENSG00000248641 HMGA1P2 4 0.00000337 73964539 73964862 ENSG00000057935 MTA3 2 0.00000608 42721709 42984087 ENSG00000100325 ASCC2 22 0.00000673 30184597 30234271 Abbreviations: FDR, false discovery rate; CHR, chromosome 3.3 Gene-level association and burden tests support convergent loci MAGMA gene-level analysis identified 742 genes significantly associated with PCa after Bonferroni correction ( P < 0.05; Table S4). The top ten genes with the most significant associations are annotated in the Manhattan plot (Fig. 2 A). MAGMA-based pathway enrichment analysis revealed significant enrichment of transmembrane transport pathways (Figure S1 A). Tissue-specific enrichment further pointed to the prostate as the most relevant tissue, with additional signals observed in minor salivary gland, vagina, and esophagus (Figure S1 B), supporting an epithelial origin for prostate cancer susceptibility. Additionally, fastBAT gene-based burden testing identified 6,010 genes with significant associations (P < 0.05). Among these, 46 genes overlapped with the results from both cross-tissue and single-tissue TWAS analyses, highlighting their potential relevance to PCa and providing complementary evidence to the TWAS and MAGMA findings (Table S5). 3.4 Integrated analysis prioritizes 23 high-confidence candidate genes To refine our candidate gene list, we intersected the jointly significant genes from single-tissue TWAS (via COJO), cross-tissue TWAS results, MAGMA gene-level associations, and fastBAT burden signals. This integrative approach yielded a consensus set of 23 genes with multilayered statistical support for PCa involvement: MCAT , HORMAD2 , SEPT2 , USP39 , ZFP36L2 , MTA3 , HAAO , ANKRD17 , GGCX , CFLAR , CASP10 , TBX1 , TSPO , TET2 , RASSF6 , MLPH , BIK , HDLBP , EHBP1 , PPA2 , NOL10 , CASP8 , and PDK1 (Fig. 2 B). These genes represent strong candidates for further investigation at the functional and clinical levels. 3.5 SMR and colocalization analyses refine causal inference To further prioritize putative causal genes, we conducted SMR and colocalization analyses. For each candidate gene, we used eQTL data from the GTEx V8 tissues in which the gene was significant in single-tissue TWAS combined with COJO results, together with the PCa GWAS summary statistics. SMR analysis initially identified seven genes with P _SMR < 0.05, indicating significant associations between genetically predicted expression and PCa risk (Fig. 3 A, Fig. 3 B, Table S6). Of these, only three genes— MLPH , CASP8 , and GGCX —also passed the HEIDI heterogeneity test ( p _HEIDI > 0.01), suggesting that their associations are unlikely to arise from LD with neighboring variants and may represent shared causal signals between gene expression and disease risk. To validate these findings, we performed colocalization analysis. Among the 3 genes tested, two— MLPH and GGCX —showed suggestive or strong support for a shared causal variant (PP.H4 > 0.70; Fig. 4 , Figure S2 , Table S7), suggesting that the same causal variant likely underlies both the eQTL and GWAS associations. For MLPH , the PP.H4 reached 0.761 in the testis, indicating strong colocalization of GWAS and eQTL signals in testis tissue. Notably, rs7582964 was identified as the top SNP in the SMR and emerged as the key colocalized site for MLPH in testis tissue, reinforcing the likelihood that this variant serves as a shared causal driver at this locus. For GGCX , strong colocalization signals were observed at rs6743030 in adrenal gland tissue. 3.6 Integrated transcriptional and protein-level validation of candidate genes in TCGA and HPA To investigate transcriptional changes of candidate genes, we analyzed RNA-seq data from PRAD cohort in TCGA. Among the prioritized genes, MLPH showed significant upregulation in PCa tissues, with a log₂FC of 0.86 (adjusted P = 4.92×10⁻¹⁷) (Fig. 5 A, Fig. 5 B). By contrast, GGCX did not exhibit differential expression between tumor and normal tissues (log₂FC < 0.58), further supporting the selection of MLPH as the most plausible candidate gene. To further validate MLPH expression at the protein level, we examined IHC data from the HPA. The results showed increased MLPH protein expression in tumor samples relative to normal controls (Fig. 5 C, Fig. 5 D), consistent with the transcriptomic findings. 3.7 Gene network analyses and pathway enrichment The protein interaction network conducted by GeneMANIA platform (Fig. 6 A) revealed that MLPH physically interacts with several vesicle trafficking–related genes, including RAB27A , RAB27B, MYO5A , and multiple members of the SYTL family. In addition, colocalization relationships were identified between MLPH and cancer-relevant genes such as TFF1 and CAIX , both previously implicated in prostate tumorigenesis. 50 – 52 The most significantly enriched biological processes within this network included vesicle-mediated transport, pigment granule localization, and regulation of exocytosis (Table S8), consistent with the transmembrane transport–related pathways highlighted by MAGMA-based enrichment analysis. Subsequent pathway enrichment analyses using GO, KEGG, and Reactome databases yielded consistent results (Fig. 6 B). GO analysis highlighted significant enrichment in terms such as exocytosis, transport vesicle, and small GTPase binding, the latter aligning with MLPH’s known function as an effector of RAB27 GTPase. These pathways support the role of MLPH in coordinating vesicle docking and release events. In the KEGG database, pathways including the synaptic vesicle cycle, prostate cancer, and notably the estrogen signaling pathway were enriched, suggesting potential hormone-regulated vesicle transport mechanisms in prostate cancer progression. Reactome enrichment predominantly involved melanin biosynthesis and pigment granule transport–related pathways, consistent with the canonical role of MLPH in melanosome trafficking. 4 Discussion In this study, we conducted an integrative multi-omics analysis to systematically identify and validate genes associated with PCa risk. By combining cross-tissue and single-tissue TWAS approaches, we identified over a thousand candidate genes, among which 35 overlapped between the two analytical frameworks after conditional analysis. Additional evidence from gene-level association methods (MAGMA and fastBAT) yielded a consensus set of 23 genes supported by multiple lines of statistical inference. Notably, SMR and Bayesian colocalization analysis prioritized MLPH , CASP8 , and GGCX as putative causal genes. MLPH showed a particularly strong signal in testis tissue ( P _SMR = 8.36 × 10⁻⁸; p _HEIDI = 0.056; PP.H4 = 0.761), supporting a shared causal variant between expression and PCa risk. Differential expression analysis in the TCGA cohort confirmed significant MLPH upregulation in tumor tissues (log₂FC = 0.86; adjusted P = 4.92 × 10⁻¹⁷), further supported by elevated protein levels in immunohistochemistry data from the HPA. Functional network and pathway analyses revealed that MLPH directly interacts with key regulators of vesicle trafficking, including RAB27, members of the SYTL family, and MYO5A, and is involved in biological processes such as exocytosis and hormone-responsive signaling. Together, these findings implicate MLPH as a biologically relevant and potentially targetable gene in the pathogenesis of PCa. TWAS has emerged as a powerful approach for identifying susceptibility genes in complex diseases. A previous PCa TWAS using the FUSION platform identified 217 candidate genes 53 , but lacked further validation of genetic independence, causal inference, and functional assessment at the transcriptomic and proteomic levels. Additionally, the biological context of these genes within regulatory networks and relevant pathways remained underexplored. Based on integrative multi-omics analysis, MLPH , a gene encoding melanophilin, was identified as a potential risk gene for PCa. Prior studies utilizing chromosome conformation capture combined with immunoprecipitation have identified chromatin interactions linking MLPH to genomic loci associated with PCa risk 54 . In a large-scale proteomic resource comprising over 18,000 transcripts and 12,000 proteins, MLPH received a Global Label Score of 3, reflecting consistent tissue-specific expression across multiple datasets 55 . Notably, MLPH ranked among the top prostate-expressed genes or proteins in several datasets 56 , 57 . These results support a possible role for MLPH in prostate tissue biology and its relevance to prostate cancer pathogenesis. MLPH encodes a critical component of the melanosome transport complex and has been linked to tumor progression in several malignancies, including breast, pancreatic and colorectal cancers 58 – 60 . Our GeneMANIA analysis revealed direct physical interactions between MLPH and several key vesicle transport genes such as RAB27A , MYO5A , and members of the SYTL family. Functional enrichment further highlighted roles in vesicle-mediated transport, pigment granule localization, and exocytotic regulation. Given the established involvement of RAB27A/B in exosome secretion, including modulation of prostate-specific antigen and prostatic acid phosphatase via the PI3K pathway, it is plausible that MLPH —acting as a RAB27 effector—participates in secretory mechanisms central to PCa pathogenesis 61 , 62 . Additionally, the co-localization of MLPH with known PCa-related genes such as TFF1 and CAIX reinforces its potential functional relevance. TFF1 , a stable secretory peptide, has been found elevated in PCa patient plasma and associated with adverse clinical features 51 , 63 . CAIX , a hypoxia-inducible enzyme, is overexpressed in acidic tumor environments, including PCa, and is implicated in extracellular acidification and metastasis 52 . Previous TWAS studies suggested that reduced MLPH expression in the prostate may be protective 53 , 64 , our study further confirmed this association in prostate using the FUSION single-tissue model (Z = − 6.92, Bonferroni-adjusted P = 8.33 × 10⁻⁸). However, this signal did not pass COJO or SMR filtering, suggesting possible confounding from LD or non-independence of regulatory variants. Our study identified a significant positive association between MLPH expression and PCa risk in the testis tissue (Z = 4.31; P _SMR = 4.3 × 10⁻⁶; p _HEIDI > 0.01), this apparent discordance may reflect tissue-specific regulatory architectures or distal regulatory effects mediated by endocrine axes 65 – 67 . Notably, testis-derived androgen production plays a central role in PCa biology, and MLPH expression in prostate tissues has been correlated with androgen receptor (AR) mRNA and protein levels 64 , 68 . Furthermore, RAB27B , a known upstream effector of MLPH , is positively correlated with AR expression, and RAB27A is reportedly regulated by androgen deprivation therapy 69 . These findings suggest that MLPH may exert distinct roles depending on tissue context: a locally suppressive effect in prostate epithelium and a systemic, androgen-linked effect via testicular expression. The opposite directions of association across tissues underscore the need for integrative approaches to resolve complex regulatory architectures in hormone-driven cancers. Our study offers several notable advancements over previous TWAS efforts. We utilized UTMOST to incorporate multi-tissue expression models, applied stringent COJO criteria (joint significance and TOP.SNP.COR² ≤ 0.6), and relied on updated GWAS (n = 726,828) and GTEx v8 datasets with 49% more RNA-seq samples than earlier versions 70 . The convergence of genetic, transcriptomic, and proteomic evidence strengthens the case for MLPH as a functionally relevant PCa-associated gene. Nonetheless, limitations remain. Our analyses are restricted to European-ancestry cohorts, limiting generalizability. Comprehensive evaluation of MLPH expression in prostate and other disease-relevant tissues was constrained by the limited tissue coverage and expression variability within the available datasets. Additionally, some tissues in GTEx v8 have limited sample sizes, which may affect detection power. Future studies should incorporate larger, multi-ethnic cohorts, integrate single-cell resolution datasets, and experimentally characterize the vesicular roles of MLPH in PCa progression. 5 Conclusion Our multi-omics approach highlights MLPH as a biologically plausible and statistically supported candidate gene for PCa. Its established roles in vesicle transport, interaction with secretory and AR-associated regulators, and consistent overexpression in PCa tissue suggest potential for MLPH as a therapeutic target worthy of further mechanistic and translational investigation. Abbreviations GWAS genome-wide association studies TWAS transcriptome-wide association studies GTEx Genotype-Tissue Expression UTMOST unified test for molecular signatures FUSION Functional Summary-based Imputation MAGMA Multi-marker Analysis of Genomic Annotation fastBAT fast set-Based Association Test GCTA Genome-wide Complex Trait Analysis COJO conditional and joint analysis TCGA The Cancer Genome Atlas HPA Human Protein Atlas. Declarations Acknowledgments We sincerely thank all participants and research teams whose contributions enabled the generation of the public datasets analyzed in this study. We are especially grateful to the GTEx, TCGA, and HPA consortia for providing access to high-quality transcriptomic and proteomic resources. We also acknowledge the continued efforts of database curators in maintaining and updating these platforms, which were essential for the completion of this work. Author contributions R.W. drafted the manuscript and performed data analyses. J.W. and Z.Z. contributed to data acquisition and assisted with data analysis. X.H. supervised the study design and provided overall guidance. All authors reviewed the manuscript. Funding This research received no external funding Data availability All datasets and computational tools utilized in this study are publicly available. Transcriptomic prediction models were obtained from the UTMOST repository (https://github.com/Joker-Jerome/UTMOST/), and eQTL reference panels were derived from GTEx V8 (https://ftp.ebi.ac.uk/pub/databases/spot/eQTL/imported/GTEx_V8/). GWAS summary statistics for prostate cancer were accessed via the GWAS Catalog (https://www.ebi.ac.uk/gwas/). TWAS analyses were conducted using the FUSION (http://gusevlab.org/projects/fusion/) and UTMOST pipelines. MAGMA gene-level analyses were performed using the MAGMA tool (https://cncr.nl/research/magma/), and SMR analyses were carried out using the SMR software (https://yanglab.westlake.edu.cn/software/smr/#SMR&HEIDIanalysis/). Immunohistochemical data were retrieved from the Human Protein Atlas (https://www.proteinatlas.org/ ), and gene interaction networks were constructed via GeneMANIA (https://genemania.org/). Ethics approval and consent to participate Not applicable . Consent for publication Not applicable. Competing interests The authors declare no competing interests. References Dadaev T, Saunders EJ, Newcombe PJ, et al. Fine-mapping of prostate cancer susceptibility loci in a large meta-analysis identifies candidate causal variants. Nat Commun . 2018;9(1):2256. doi:10.1038/s41467-018-04109-8 Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 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10:25:34","extension":"xml","order_by":23,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":173550,"visible":true,"origin":"","legend":"","description":"","filename":"1b4cac3896c746299daf7fa79727f5521structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7662167/v1/3b778748e8e8d9f9cf4a020c.xml"},{"id":94752068,"identity":"a20bb394-79ec-4736-9441-dbc442453f89","added_by":"auto","created_at":"2025-10-30 10:25:34","extension":"html","order_by":24,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":185184,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7662167/v1/7a47f377cd297995d206af21.html"},{"id":94752047,"identity":"0b1107f3-e1ea-4c5f-bcea-991b6d850a30","added_by":"auto","created_at":"2025-10-30 10:25:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":7625141,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe overall study framework\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbbreviations: GWAS, genome-wide association studies; TWAS, transcriptome-wide association studies; GTEx, Genotype-Tissue Expression; UTMOST, unified test for molecular signatures; FUSION, Functional Summary-based Imputation; MAGMA, Multi-marker Analysis of Genomic Annotation; fastBAT, fast set-Based Association Test; GCTA, Genome-wide Complex Trait Analysis; COJO, conditional and joint analysis; TCGA, The Cancer Genome Atlas; HPA, Human Protein Atlas.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7662167/v1/5b14e6fb422f9a3e71076ca8.png"},{"id":94823208,"identity":"68003ac3-f7b6-476e-907c-748b263e0d82","added_by":"auto","created_at":"2025-10-31 06:46:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4020147,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGene-level association and integrative prioritization of prostate cancer susceptibility genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Manhattan plot of gene-based association results from MAGMA. Each point represents a gene, plotted according to its chromosomal position on the X-axis and −log₁₀(P) value on the Y-axis, derived from the z-score test. The top 10 most significant genes are annotated directly on the plot. A total of 742 genes reached statistical significance following Bonferroni correction (P \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e(B) Venn diagram summarizing the intersection of candidate genes identified by four independent analytical frameworks: MAGMA, fastBAT, UTMOST, and FUSION TWAS. 23 genes were consistently prioritized across all methods.\u003c/p\u003e\n\u003cp\u003eAbbreviations: MAGMA, Multi-marker Analysis of Genomic Annotation; fastBAT, fast set-Based Association Test; UTMOST, unified test for molecular signatures; FUSION, Functional Summary-based Imputation; TWAS, Transcriptome-wide association studies.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7662167/v1/4cb65d6ccce496536fc6e087.png"},{"id":94752043,"identity":"2c780783-852e-42bc-94a3-799b2388e126","added_by":"auto","created_at":"2025-10-30 10:25:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":8125163,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSMR analysis prioritizes putative causal genes for prostate cancer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Forest plot summarizing SMR results for seven genes across multiple tissues. ORs with 95% CIs are shown, along with \u003cem\u003eP\u003c/em\u003e_SMR and \u003cem\u003ep\u003c/em\u003e_HEIDI values for each tissue. \u003cem\u003eMLPH\u003c/em\u003e, \u003cem\u003eGGCX\u003c/em\u003e and \u003cem\u003eCASP8\u003c/em\u003e surpassed the SMR significance threshold (FDR-adjusted \u003cem\u003eP\u003c/em\u003e_SMR \u0026lt; 0.05) and showed no evidence of heterogeneity (\u003cem\u003ep\u003c/em\u003e_HEIDI \u0026gt; 0.01), supporting potential shared causal variants.\u003c/p\u003e\n\u003cp\u003e(B–D) Regional plots illustrating SMR results for \u003cem\u003eMLPH\u003c/em\u003e (B), \u003cem\u003eGGCX\u003c/em\u003e (C), and \u003cem\u003eCASP8\u003c/em\u003e(D). Each dot represents a SNP. Top SNPs used in the SMR analysis are highlighted.\u003c/p\u003e\n\u003cp\u003eAbbreviations: SMR, summary-data-based Mendelian randomization; HEIDI, heterogeneity in dependent instruments; OR, odds ratio; CI, confidence interval; SNP, single nucleotide polymorphism.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7662167/v1/d949de63b2725532bf21e4ad.png"},{"id":94824148,"identity":"d5b667d8-a947-4bf1-9823-321c5b972277","added_by":"auto","created_at":"2025-10-31 06:48:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":11670570,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eColocalization results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) For \u003cem\u003eMLPH\u003c/em\u003ein testis tissue, the lead SNP rs7582964 shows strong association with both gene expression (top) and disease risk (bottom), with a posterior probability of colocalization (PP.H4 = 0.761), indicating a likely shared causal variant.\u003c/p\u003e\n\u003cp\u003e(B) For \u003cem\u003eGGCX\u003c/em\u003ein adrenal gland tissue, the lead SNP rs6743030 also shows colocalized association peaks (PP.H4 = 0.968).\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7662167/v1/d7c47c7a8b00ac70df4b3da1.png"},{"id":94752056,"identity":"ffca3a88-c7d4-4586-8ced-73a1272aff1d","added_by":"auto","created_at":"2025-10-30 10:25:34","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":22601699,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTranscriptomic and protein-level upregulation of MLPH in prostate cancer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Boxplot showing \u003cem\u003eMLPH\u003c/em\u003e mRNA expression levels in PRAD tissues (n = 483) compared with adjacent normal tissues (n = 51) from TCGA cohort. \u003cem\u003eMLPH\u003c/em\u003e was significantly upregulated in tumor tissues (log₂FC = 0.86; FDR = 4.92 × 10⁻¹⁷).\u003c/p\u003e\n\u003cp\u003e(B) Volcano plot of differential gene expression in PRAD, with \u003cem\u003eMLPH\u003c/em\u003e highlighted in red among significantly upregulated genes (adjusted P \u0026lt; 0.05, |log₂FC| \u0026gt; 1).\u003c/p\u003e\n\u003cp\u003e(C) Representative immunohistochemical staining of MLPH protein in prostate tissues from the HPA. \u003cem\u003eMLPH\u003c/em\u003e expression was upregulated in tumors compared to normal tissues.\u003c/p\u003e\n\u003cp\u003e(D) Quantification of MLPH-positive staining area (%) and mean OD across normal and PCa tissues. A significant increase in MLPH-positive area was observed in prostate cancer tissues compared with normal counterparts.\u003c/p\u003e\n\u003cp\u003eAbbreviations: TCGA, The Cancer Genome Atlas; PRAD, prostate adenocarcinoma; HPA, Human Protein Atlas; PCa, prostate cancer; OD, optical density.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7662167/v1/52afddad3a23552d6188c8e5.png"},{"id":94752045,"identity":"c1f83208-2847-482f-8025-17b7a8f16986","added_by":"auto","created_at":"2025-10-30 10:25:34","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":15508828,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional enrichment and GeneMANIA gene network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Functional enrichment analysis of 21 genes identified from the \u003cem\u003eMLPH\u003c/em\u003e interaction network using GO, KEGG, and Reactome databases.\u003c/p\u003e\n\u003cp\u003e(B) Gene interaction network generated using GeneMANIA.\u003c/p\u003e\n\u003cp\u003eAbbreviations: GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-7662167/v1/3063212b0874fd7a0ba9cb35.png"},{"id":108976376,"identity":"067ee85e-1f37-4b91-86ec-66d639721cd3","added_by":"auto","created_at":"2026-05-11 11:09:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":68368892,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7662167/v1/bf299bbf-1ba3-4b23-a78e-9b134b60499c.pdf"},{"id":94752042,"identity":"e7fd4646-c4c8-48da-aaa6-f88d4ef91dfb","added_by":"auto","created_at":"2025-10-30 10:25:34","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":241483,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7662167/v1/664506b73e181f0f3b7d1393.xlsx"},{"id":94823199,"identity":"08c39997-d198-45e5-bc12-8ef3c88f70b4","added_by":"auto","created_at":"2025-10-31 06:46:42","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":476250,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure.docx","url":"https://assets-eu.researchsquare.com/files/rs-7662167/v1/8849ceb401f2864585a9bb24.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrative Genomic Profiling Identifies MLPH as a Candidate Gene in Prostate Cancer","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eProstate cancer (PCa) remains a major global health concern, ranking as the second most commonly diagnosed malignancy and the fifth leading cause of cancer-related death among men worldwide\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. In the United States alone, an estimated 299,010 new PCa cases were reported in 2024\u003csup\u003e3\u003c/sup\u003e. Early-stage PCa is often asymptomatic, advanced disease may present with hematuria, dysuria, and other urinary symptoms\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. A considerable proportion of patients eventually progress to metastatic disease, highlighting the urgent need to elucidate the molecular mechanisms underlying PCa and to develop more effective therapeutic strategies.\u003c/p\u003e\u003cp\u003eGenetic predisposition plays a significant role in the pathogenesis of PCa, which is considered one of the most heritable cancers\u003csup\u003e\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. To date, genome-wide association studies (GWAS) have identified over 170 risk loci associated with PCa susceptibility\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. However, the biological mechanisms by which these loci influence tumor development remain largely unresolved. A substantial number of the identified risk variants reside in noncoding regions, complicating functional interpretation\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Furthermore, complex patterns of linkage disequilibrium (LD) often hinder the precise localization of causal variants\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTranscriptome-wide association studies (TWAS) have emerged as a powerful approach to prioritize candidate genes by integrating expression quantitative trait loci (eQTL) information with GWAS summary statistics\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Compared with traditional GWAS, TWAS improves gene-level resolution and offers insight into the regulatory architecture of complex traits\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Particularly, cross-tissue TWAS frameworks such as the unified test for molecular signatures (UTMOST)\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e enhance detection power by leveraging shared eQTL effects across tissues while preserving tissue-specific signals via a group-lasso penalty model\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. This cross-tissue approach has demonstrated utility in multiple complex diseases, including osteoarthritis, erectile dysfunction, Alzheimer's disease and age-related macular degeneration\u003csup\u003e\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn this study, we integrated GWAS summary statistics for PCa from the GWAS Catalog with eQTL data from the Genotype-Tissue Expression (GTEx) project (version 8)\u003csup\u003e19\u003c/sup\u003e to conduct TWAS. To systematically identify PCa\u0026ndash;associated genes, we applied both cross-tissue and single-tissue TWAS, followed by conditional analysis and complementary gene-level association testing. Causal inference between gene expression and disease risk was assessed using Summary data-based Mendelian randomization (SMR) and Bayesian colocalization\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eMLPH\u003c/em\u003e, as a candidate gene, was validated at the transcriptional and protein levels, and its interaction networks and biological functions were further characterized.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 PCa GWAS data sources\u003c/h2\u003e\u003cp\u003eGWAS data for PCa were obtained from a large multi-ancestry meta-analysis study\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. To reduce potential confounding from population stratification, we selected the European-ancestry subset for downstream analyses. This subset, indexed as GCST90274714 in the GWAS Catalog, comprises 122,188 prostate cancer cases and 604,640 controls.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 TWAS analyses in cross-tissue\u003c/h2\u003e\u003cp\u003eTWAS framework employed in this study is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. During the discovery phase, we utilized the UTMOST framework, which leverages multi-tissue eQTL data to assess gene\u0026ndash;trait associations at a systemic level\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. This approach incorporates eQTL effect estimates derived from 49 tissues in the GTEx V8 dataset, enabling the construction of cross-tissue predictive models for individual genes. By integrating across tissues, UTMOST improves both the power to detect gene\u0026ndash;phenotype associations and the accuracy of expression imputation, particularly in tissues enriched for trait heritability. To aggregate tissue-specific associations, we implemented the generalized Berk-Jones (GBJ) test\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, which accounts for correlation structures among tissues using covariance estimates from single-tissue summary statistics. Statistical significance was defined using two thresholds: genes reaching an FDR of \u0026lt;\u0026thinsp;1 \u0026times; 10⁻⁵ were considered genome-wide significant after correction for multiple testing, while those with a \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01 were considered to reach nominal significance\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 TWAS analyses in single-tissue and conditional analysis\u003c/h2\u003e\u003cp\u003eWe conducted a TWAS for PCa by integrating GWAS statistics with eQTL data from 49 tissues available in the GTEx V8 reference panel, utilizing the functional summary-based imputation (FUSION) pipeline\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. LD patterns between SNPs and expression prediction models were inferred using European samples from the 1000 Genomes Project\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. FUSION assessed several modeling approaches for imputing gene expression and selected the optimal model based on cross-validation performance\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. The resulting gene expression weights were then integrated with PCa GWAS Z-scores to estimate gene\u0026ndash;trait associations. Genes were retained for further investigation if they met both of the following thresholds: (1) an FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in the single-tissue analysis, and (2) Bonferroni-corrected \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in at least one individual tissue analysis. To evaluate whether these associations identified in single-tissue TWAS are conditionally independent, we performed conditional and joint analysis (COJO) using FUSION software package\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. This analysis accounts for LD among variants, enabling a more refined interpretation of the genetic architecture underlying trait variation. Genes that retained statistical significance after conditioning were classified as jointly significant, whereas those that lost significance were considered marginally significant\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. In line with the FUSION pipeline, we applied a correlation filter of TOP.SNP.COR\u0026sup2; \u0026le; 0.6 to exclude genes whose lead SNPs exhibited high LD with conditioned variants, thereby reducing redundancy and enhancing signal independence. Only genes that were jointly significant and satisfied this LD threshold were retained for downstream analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 MAGMA-based gene association analysis\u003c/h2\u003e\u003cp\u003eGene-level association analysis was performed using Multi-marker Analysis of Genomic Annotation (MAGMA) (v1.08), a widely used tool for evaluating gene-based associations from GWAS summary data\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. MAGMA identifies trait-associated genes by aggregating the effects of multiple SNPs assigned to each gene based on their genomic location. In this study, we used the default parameters to derive gene-level association statistics from SNP-level data\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Genes reaching the Bonferroni-corrected significance threshold (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were considered statistically significant and were regarded as potential contributors to the trait\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Gene-based association analysis using GCTA-fastBAT\u003c/h2\u003e\u003cp\u003eGene-based association analysis for prostate cancer was conducted using the fastBAT module implemented in the GCTA software\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. This method aggregates SNP-level z-statistics within a gene region into a composite test statistic, while accounting for LD among variants\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. By modeling the correlation structure of nearby SNPs, fastBAT efficiently estimates the joint significance of multiple variants mapped to a gene.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 SMR analyses and Bayesian colocalization\u003c/h2\u003e\u003cp\u003eWe applied SMR to explore potential pleiotropic associations between gene expression and prostate cancer risk, using integrated summary statistics from PCa GWAS and eQTL datasets. This method enables the prioritization of genes that may be causally linked to disease risk, thereby offering insights into the functional basis of GWAS signals\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. SMR probes were considered significant if they met an FDR-adjusted \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, while evidence of heterogeneity was assessed using the heterogeneity in dependent instruments (HEIDI) test, with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.01 indicating no significant heterogeneity\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. To visualize the effect sizes and confidence intervals of candidate genes identified by SMR, we used the \u0026ldquo;forestplot\u0026rdquo; R package (v3.1.6) to generate a forest plot. To further evaluate the likelihood of a shared causal variant underlying both eQTL and GWAS signals, we performed Bayesian colocalization analysis using the coloc package in R (v5.2.3)\u003csup\u003e37\u003c/sup\u003e. This method computes posterior probabilities for five mutually exclusive hypotheses: H0, no association with either trait; H1, association with the first trait only; H2, association with the second trait only; H3, association with both traits but driven by different causal variants; and H4, association with both traits driven by the same causal variant. Evidence supporting colocalization was defined as a posterior probability for hypothesis 4 (PP.H4)\u0026thinsp;\u0026gt;\u0026thinsp;0.70\u003csup\u003e38\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.7 External validation with TCGA-PRAD data\u003c/h2\u003e\u003cp\u003eRNA sequencing and clinical data for prostate adenocarcinoma (PRAD) were obtained from the UCSC Xena platform\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e, comprising 554 prostate tissue samples from The Cancer Genome Atlas (TCGA). Of these, 483 were tumor samples. Differential gene expression analysis was conducted using the DESeq2 package in R (v1.46.0)\u003csup\u003e40\u003c/sup\u003e. \u003cem\u003eP\u003c/em\u003e-values were corrected for multiple testing using the Benjamini\u0026ndash;Hochberg method. Genes with an adjusted \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and an absolute log₂ fold change (|log₂FC|)\u0026thinsp;\u0026gt;\u0026thinsp;0.58 were considered differentially expressed\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Visualization of results was carried out using \u0026ldquo;ggplot2\u0026rdquo; R package (v3.5.2).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.8 Protein-level validation in clinical prostate tissues\u003c/h2\u003e\u003cp\u003eTo validate protein-level expression of candidate genes, immunohistochemistry (IHC) data were obtained from the Human Protein Atlas (HPA; Antibody ID: HPA014685)\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. In this study, we examined staining profiles in PCa and normal prostate tissues to assess differences in protein expression. To quantify MLPH expression, three non-overlapping regions were randomly selected from each IHC image. The MLPH-positive area in each region was quantified using ImageJ through the Fiji platform\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, and the mean value was used to represent protein expression per specimen. Statistical analysis and visualization were performed using GraphPad Prism 10 software.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.9 GeneMANIA analysis and functional enrichment analysis\u003c/h2\u003e\u003cp\u003eWe employed the GeneMANIA platform, a network-based tool that integrates diverse genomic and proteomic datasets, including co-expression, genetic interactions, and pathway annotations\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. To further investigate the functional context of genes prioritized by GeneMANIA, we conducted gene set enrichment analysis using the clusterProfiler R package (v4.14.6)\u003csup\u003e46\u003c/sup\u003e. Enrichment was assessed across multiple curated annotation resources, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome databases, to identify biological processes and pathways significantly enriched among the input gene set\u003csup\u003e\u003cspan additionalcitationids=\"CR48\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.1 TWAS identifies cross-tissue PCa-associated genes\u003c/h2\u003e\u003cp\u003eIn the cross-tissue TWAS analysis performed using the UTMOST framework, 240 genes reached the nominal significance threshold (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), among which 117 genes surpassed the genome-wide significance level (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1 \u0026times; 10⁻⁵) (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). These results provide initial evidence for transcriptome-wide associations with PCa across multiple tissues.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Single-tissue TWAS and COJO analysis identify independent loci\u003c/h2\u003e\u003cp\u003eSingle-tissue TWAS identified 73 genes that overlapped with cross-tissue TWAS results in at least one GTEx tissue using a Bonferroni-corrected threshold (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). To evaluate the independence of these associations, we conducted COJO on the significant genes identified in single-tissue TWAS, identifying 946 that remained jointly significant. Among these, 35 genes overlapped with the 117 genome-wide significant genes from the UTMOST cross-tissue TWAS (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), suggesting these represent robust and tissue-consistent associations. These genes were primarily located on chromosomes 2, 4, and 22, indicating the presence of gene-dense regions with potential biological relevance to PCa.\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\u003eCandidate genes identified by cross-tissue and single-tissue TWAS after conditional analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003egene_id\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\u003eCHR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFDR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003estart\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eend\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eENSG00000249264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.92E-09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e202047604\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e202094129\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eENSG00000100290\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBIK\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.92E-09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e43506754\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e43525718\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eENSG00000100294\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMCAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.92E-09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e43528212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e43539400\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eENSG00000101751\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePOLI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.92E-09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e51795774\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e51847636\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eENSG00000115486\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGGCX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.92E-09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e85771846\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e85788670\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eENSG00000115504\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEHBP1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.92E-09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e62900986\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e63273622\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eENSG00000115648\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMLPH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.92E-09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e238394071\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e238463961\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eENSG00000115761\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNOL10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.92E-09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10710892\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10830101\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eENSG00000138777\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePPA2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.92E-09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e106290234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e106395238\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eENSG00000152256\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePDK1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.92E-09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e173420101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e173489823\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eENSG00000152518\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eZFP36L2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.92E-09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e43449541\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e43453748\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eENSG00000163106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHPGDS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.92E-09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e95219686\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e95264027\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eENSG00000168385\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSEPT2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.92E-09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e242254515\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e242293442\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eENSG00000168769\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTET2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.92E-09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e106067032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e106200973\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eENSG00000168883\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eUSP39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.92E-09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e85829979\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e85876403\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eENSG00000184058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTBX1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.92E-09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e19744226\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e19771116\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eENSG00000236699\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eARHGEF38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.92E-09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e106473777\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e106629250\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eENSG00000169435\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRASSF6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.42E-08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e74437267\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e74486348\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eENSG00000091409\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eITGA6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.52E-08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e173292082\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e173371181\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eENSG00000145391\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSETD7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000000101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e140417095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e140527853\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eENSG00000064012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCASP8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000000118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e202098166\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e202152434\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eENSG00000198612\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOPS8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000000378\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e237993955\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e238009109\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eENSG00000176635\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHORMAD2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000000429\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e30476163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e30573064\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eENSG00000132466\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eANKRD17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000000462\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e73939093\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e74124515\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eENSG00000231609\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAC009501.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000000707\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e63271057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e63275775\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eENSG00000003402\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCFLAR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.00000117\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e201980827\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e202041410\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eENSG00000115677\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHDLBP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.00000145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e242166679\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e242256476\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eENSG00000100300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTSPO\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.00000169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e43547520\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e43559248\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eENSG00000196290\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNIF3L1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0000021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e201754050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e201768655\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eENSG00000248641\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHMGA1P2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.00000337\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e73964539\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e73964862\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eENSG00000057935\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMTA3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.00000608\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e42721709\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e42984087\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eENSG00000100325\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eASCC2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.00000673\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e30184597\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e30234271\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviations: FDR, false discovery rate; CHR, chromosome\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Gene-level association and burden tests support convergent loci\u003c/h2\u003e\u003cp\u003eMAGMA gene-level analysis identified 742 genes significantly associated with PCa after Bonferroni correction (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Table S4). The top ten genes with the most significant associations are annotated in the Manhattan plot (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). MAGMA-based pathway enrichment analysis revealed significant enrichment of transmembrane transport pathways (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA). Tissue-specific enrichment further pointed to the prostate as the most relevant tissue, with additional signals observed in minor salivary gland, vagina, and esophagus (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eB), supporting an epithelial origin for prostate cancer susceptibility. Additionally, fastBAT gene-based burden testing identified 6,010 genes with significant associations (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Among these, 46 genes overlapped with the results from both cross-tissue and single-tissue TWAS analyses, highlighting their potential relevance to PCa and providing complementary evidence to the TWAS and MAGMA findings (Table S5).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Integrated analysis prioritizes 23 high-confidence candidate genes\u003c/h2\u003e\u003cp\u003eTo refine our candidate gene list, we intersected the jointly significant genes from single-tissue TWAS (via COJO), cross-tissue TWAS results, MAGMA gene-level associations, and fastBAT burden signals. This integrative approach yielded a consensus set of 23 genes with multilayered statistical support for PCa involvement: \u003cem\u003eMCAT\u003c/em\u003e, \u003cem\u003eHORMAD2\u003c/em\u003e, \u003cem\u003eSEPT2\u003c/em\u003e, \u003cem\u003eUSP39\u003c/em\u003e, \u003cem\u003eZFP36L2\u003c/em\u003e, \u003cem\u003eMTA3\u003c/em\u003e, \u003cem\u003eHAAO\u003c/em\u003e, \u003cem\u003eANKRD17\u003c/em\u003e, \u003cem\u003eGGCX\u003c/em\u003e, \u003cem\u003eCFLAR\u003c/em\u003e, \u003cem\u003eCASP10\u003c/em\u003e, \u003cem\u003eTBX1\u003c/em\u003e, \u003cem\u003eTSPO\u003c/em\u003e, \u003cem\u003eTET2\u003c/em\u003e, \u003cem\u003eRASSF6\u003c/em\u003e, \u003cem\u003eMLPH\u003c/em\u003e, \u003cem\u003eBIK\u003c/em\u003e, \u003cem\u003eHDLBP\u003c/em\u003e, \u003cem\u003eEHBP1\u003c/em\u003e, \u003cem\u003ePPA2\u003c/em\u003e, \u003cem\u003eNOL10\u003c/em\u003e, \u003cem\u003eCASP8\u003c/em\u003e, and \u003cem\u003ePDK1\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). These genes represent strong candidates for further investigation at the functional and clinical levels.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.5 SMR and colocalization analyses refine causal inference\u003c/h2\u003e\u003cp\u003eTo further prioritize putative causal genes, we conducted SMR and colocalization analyses. For each candidate gene, we used eQTL data from the GTEx V8 tissues in which the gene was significant in single-tissue TWAS combined with COJO results, together with the PCa GWAS summary statistics. SMR analysis initially identified seven genes with \u003cem\u003eP\u003c/em\u003e_SMR\u0026thinsp;\u0026lt;\u0026thinsp;0.05, indicating significant associations between genetically predicted expression and PCa risk (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, Table S6). Of these, only three genes\u0026mdash;\u003cem\u003eMLPH\u003c/em\u003e, \u003cem\u003eCASP8\u003c/em\u003e, and \u003cem\u003eGGCX\u003c/em\u003e\u0026mdash;also passed the HEIDI heterogeneity test (\u003cem\u003ep\u003c/em\u003e_HEIDI\u0026thinsp;\u0026gt;\u0026thinsp;0.01), suggesting that their associations are unlikely to arise from LD with neighboring variants and may represent shared causal signals between gene expression and disease risk.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo validate these findings, we performed colocalization analysis. Among the 3 genes tested, two\u0026mdash;\u003cem\u003eMLPH\u003c/em\u003e and \u003cem\u003eGGCX\u003c/em\u003e\u0026mdash;showed suggestive or strong support for a shared causal variant (PP.H4\u0026thinsp;\u0026gt;\u0026thinsp;0.70; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e, Table S7), suggesting that the same causal variant likely underlies both the eQTL and GWAS associations. For \u003cem\u003eMLPH\u003c/em\u003e, the PP.H4 reached 0.761 in the testis, indicating strong colocalization of GWAS and eQTL signals in testis tissue. Notably, rs7582964 was identified as the top SNP in the SMR and emerged as the key colocalized site for MLPH in testis tissue, reinforcing the likelihood that this variant serves as a shared causal driver at this locus. For \u003cem\u003eGGCX\u003c/em\u003e, strong colocalization signals were observed at rs6743030 in adrenal gland tissue.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Integrated transcriptional and protein-level validation of candidate genes in TCGA and HPA\u003c/h2\u003e\u003cp\u003eTo investigate transcriptional changes of candidate genes, we analyzed RNA-seq data from PRAD cohort in TCGA. Among the prioritized genes, \u003cem\u003eMLPH\u003c/em\u003e showed significant upregulation in PCa tissues, with a log₂FC of 0.86 (adjusted \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.92\u0026times;10⁻\u0026sup1;⁷) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). By contrast, \u003cem\u003eGGCX\u003c/em\u003e did not exhibit differential expression between tumor and normal tissues (log₂FC\u0026thinsp;\u0026lt;\u0026thinsp;0.58), further supporting the selection of \u003cem\u003eMLPH\u003c/em\u003e as the most plausible candidate gene.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo further validate \u003cem\u003eMLPH\u003c/em\u003e expression at the protein level, we examined IHC data from the HPA. The results showed increased \u003cem\u003eMLPH\u003c/em\u003e protein expression in tumor samples relative to normal controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD), consistent with the transcriptomic findings.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.7 Gene network analyses and pathway enrichment\u003c/h2\u003e\u003cp\u003eThe protein interaction network conducted by GeneMANIA platform (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA) revealed that \u003cem\u003eMLPH\u003c/em\u003e physically interacts with several vesicle trafficking\u0026ndash;related genes, including \u003cem\u003eRAB27A\u003c/em\u003e, \u003cem\u003eRAB27B, MYO5A\u003c/em\u003e, and multiple members of the \u003cem\u003eSYTL\u003c/em\u003e family. In addition, colocalization relationships were identified between \u003cem\u003eMLPH\u003c/em\u003e and cancer-relevant genes such as \u003cem\u003eTFF1\u003c/em\u003e and \u003cem\u003eCAIX\u003c/em\u003e, both previously implicated in prostate tumorigenesis.\u003csup\u003e\u003cspan additionalcitationids=\"CR51\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e The most significantly enriched biological processes within this network included vesicle-mediated transport, pigment granule localization, and regulation of exocytosis (Table S8), consistent with the transmembrane transport\u0026ndash;related pathways highlighted by MAGMA-based enrichment analysis. Subsequent pathway enrichment analyses using GO, KEGG, and Reactome databases yielded consistent results (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). GO analysis highlighted significant enrichment in terms such as exocytosis, transport vesicle, and small GTPase binding, the latter aligning with \u003cem\u003eMLPH\u0026rsquo;s\u003c/em\u003e known function as an effector of \u003cem\u003eRAB27\u003c/em\u003e GTPase. These pathways support the role of \u003cem\u003eMLPH\u003c/em\u003e in coordinating vesicle docking and release events. In the KEGG database, pathways including the synaptic vesicle cycle, prostate cancer, and notably the estrogen signaling pathway were enriched, suggesting potential hormone-regulated vesicle transport mechanisms in prostate cancer progression. Reactome enrichment predominantly involved melanin biosynthesis and pigment granule transport\u0026ndash;related pathways, consistent with the canonical role of \u003cem\u003eMLPH\u003c/em\u003e in melanosome trafficking.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eIn this study, we conducted an integrative multi-omics analysis to systematically identify and validate genes associated with PCa risk. By combining cross-tissue and single-tissue TWAS approaches, we identified over a thousand candidate genes, among which 35 overlapped between the two analytical frameworks after conditional analysis. Additional evidence from gene-level association methods (MAGMA and fastBAT) yielded a consensus set of 23 genes supported by multiple lines of statistical inference. Notably, SMR and Bayesian colocalization analysis prioritized \u003cem\u003eMLPH\u003c/em\u003e, \u003cem\u003eCASP8\u003c/em\u003e, and \u003cem\u003eGGCX\u003c/em\u003e as putative causal genes. \u003cem\u003eMLPH\u003c/em\u003e showed a particularly strong signal in testis tissue (\u003cem\u003eP\u003c/em\u003e_SMR\u0026thinsp;=\u0026thinsp;8.36 \u0026times; 10⁻⁸; \u003cem\u003ep\u003c/em\u003e_HEIDI\u0026thinsp;=\u0026thinsp;0.056; PP.H4\u0026thinsp;=\u0026thinsp;0.761), supporting a shared causal variant between expression and PCa risk. Differential expression analysis in the TCGA cohort confirmed significant \u003cem\u003eMLPH\u003c/em\u003e upregulation in tumor tissues (log₂FC\u0026thinsp;=\u0026thinsp;0.86; adjusted \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.92 \u0026times; 10⁻\u0026sup1;⁷), further supported by elevated protein levels in immunohistochemistry data from the HPA. Functional network and pathway analyses revealed that MLPH directly interacts with key regulators of vesicle trafficking, including RAB27, members of the SYTL family, and MYO5A, and is involved in biological processes such as exocytosis and hormone-responsive signaling. Together, these findings implicate \u003cem\u003eMLPH\u003c/em\u003e as a biologically relevant and potentially targetable gene in the pathogenesis of PCa.\u003c/p\u003e\u003cp\u003eTWAS has emerged as a powerful approach for identifying susceptibility genes in complex diseases. A previous PCa TWAS using the FUSION platform identified 217 candidate genes\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e, but lacked further validation of genetic independence, causal inference, and functional assessment at the transcriptomic and proteomic levels. Additionally, the biological context of these genes within regulatory networks and relevant pathways remained underexplored.\u003c/p\u003e\u003cp\u003eBased on integrative multi-omics analysis, \u003cem\u003eMLPH\u003c/em\u003e, a gene encoding melanophilin, was identified as a potential risk gene for PCa. Prior studies utilizing chromosome conformation capture combined with immunoprecipitation have identified chromatin interactions linking \u003cem\u003eMLPH\u003c/em\u003e to genomic loci associated with PCa risk\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. In a large-scale proteomic resource comprising over 18,000 transcripts and 12,000 proteins, \u003cem\u003eMLPH\u003c/em\u003e received a Global Label Score of 3, reflecting consistent tissue-specific expression across multiple datasets\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. Notably, \u003cem\u003eMLPH\u003c/em\u003e ranked among the top prostate-expressed genes or proteins in several datasets\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e,\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. These results support a possible role for \u003cem\u003eMLPH\u003c/em\u003e in prostate tissue biology and its relevance to prostate cancer pathogenesis.\u003c/p\u003e\u003cp\u003e\u003cem\u003eMLPH\u003c/em\u003e encodes a critical component of the melanosome transport complex and has been linked to tumor progression in several malignancies, including breast, pancreatic and colorectal cancers\u003csup\u003e\u003cspan additionalcitationids=\"CR59\" citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Our GeneMANIA analysis revealed direct physical interactions between \u003cem\u003eMLPH\u003c/em\u003e and several key vesicle transport genes such as \u003cem\u003eRAB27A\u003c/em\u003e, \u003cem\u003eMYO5A\u003c/em\u003e, and members of the \u003cem\u003eSYTL\u003c/em\u003e family. Functional enrichment further highlighted roles in vesicle-mediated transport, pigment granule localization, and exocytotic regulation. Given the established involvement of \u003cem\u003eRAB27A/B\u003c/em\u003e in exosome secretion, including modulation of prostate-specific antigen and prostatic acid phosphatase via the \u003cem\u003ePI3K\u003c/em\u003e pathway, it is plausible that \u003cem\u003eMLPH\u003c/em\u003e\u0026mdash;acting as a \u003cem\u003eRAB27\u003c/em\u003e effector\u0026mdash;participates in secretory mechanisms central to PCa pathogenesis\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e,\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. Additionally, the co-localization of \u003cem\u003eMLPH\u003c/em\u003e with known PCa-related genes such as \u003cem\u003eTFF1\u003c/em\u003e and \u003cem\u003eCAIX\u003c/em\u003e reinforces its potential functional relevance. \u003cem\u003eTFF1\u003c/em\u003e, a stable secretory peptide, has been found elevated in PCa patient plasma and associated with adverse clinical features\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e,\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. \u003cem\u003eCAIX\u003c/em\u003e, a hypoxia-inducible enzyme, is overexpressed in acidic tumor environments, including PCa, and is implicated in extracellular acidification and metastasis\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003ePrevious TWAS studies suggested that reduced \u003cem\u003eMLPH\u003c/em\u003e expression in the prostate may be protective\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e, our study further confirmed this association in prostate using the FUSION single-tissue model (Z = \u0026minus;\u0026thinsp;6.92, Bonferroni-adjusted \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8.33 \u0026times; 10⁻⁸). However, this signal did not pass COJO or SMR filtering, suggesting possible confounding from LD or non-independence of regulatory variants. Our study identified a significant positive association between \u003cem\u003eMLPH\u003c/em\u003e expression and PCa risk in the testis tissue (Z\u0026thinsp;=\u0026thinsp;4.31; \u003cem\u003eP\u003c/em\u003e_SMR\u0026thinsp;=\u0026thinsp;4.3 \u0026times; 10⁻⁶; \u003cem\u003ep\u003c/em\u003e_HEIDI\u0026thinsp;\u0026gt;\u0026thinsp;0.01), this apparent discordance may reflect tissue-specific regulatory architectures or distal regulatory effects mediated by endocrine axes\u003csup\u003e\u003cspan additionalcitationids=\"CR66\" citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. Notably, testis-derived androgen production plays a central role in PCa biology, and \u003cem\u003eMLPH\u003c/em\u003e expression in prostate tissues has been correlated with androgen receptor (AR) mRNA and protein levels\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e,\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. Furthermore, \u003cem\u003eRAB27B\u003c/em\u003e, a known upstream effector of \u003cem\u003eMLPH\u003c/em\u003e, is positively correlated with AR expression, and \u003cem\u003eRAB27A\u003c/em\u003e is reportedly regulated by androgen deprivation therapy\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e. These findings suggest that \u003cem\u003eMLPH\u003c/em\u003e may exert distinct roles depending on tissue context: a locally suppressive effect in prostate epithelium and a systemic, androgen-linked effect via testicular expression. The opposite directions of association across tissues underscore the need for integrative approaches to resolve complex regulatory architectures in hormone-driven cancers.\u003c/p\u003e\u003cp\u003eOur study offers several notable advancements over previous TWAS efforts. We utilized UTMOST to incorporate multi-tissue expression models, applied stringent COJO criteria (joint significance and TOP.SNP.COR\u0026sup2; \u0026le; 0.6), and relied on updated GWAS (n\u0026thinsp;=\u0026thinsp;726,828) and GTEx v8 datasets with 49% more RNA-seq samples than earlier versions\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. The convergence of genetic, transcriptomic, and proteomic evidence strengthens the case for \u003cem\u003eMLPH\u003c/em\u003e as a functionally relevant PCa-associated gene.\u003c/p\u003e\u003cp\u003eNonetheless, limitations remain. Our analyses are restricted to European-ancestry cohorts, limiting generalizability. Comprehensive evaluation of \u003cem\u003eMLPH\u003c/em\u003e expression in prostate and other disease-relevant tissues was constrained by the limited tissue coverage and expression variability within the available datasets. Additionally, some tissues in GTEx v8 have limited sample sizes, which may affect detection power. Future studies should incorporate larger, multi-ethnic cohorts, integrate single-cell resolution datasets, and experimentally characterize the vesicular roles of \u003cem\u003eMLPH\u003c/em\u003e in PCa progression.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eOur multi-omics approach highlights \u003cem\u003eMLPH\u003c/em\u003e as a biologically plausible and statistically supported candidate gene for PCa. Its established roles in vesicle transport, interaction with secretory and AR-associated regulators, and consistent overexpression in PCa tissue suggest potential for \u003cem\u003eMLPH\u003c/em\u003e as a therapeutic target worthy of further mechanistic and translational investigation.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGWAS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003egenome-wide association studies\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTWAS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003etranscriptome-wide association studies\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGTEx\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGenotype-Tissue Expression\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eUTMOST\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eunified test for molecular signatures\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eFUSION\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eFunctional Summary-based Imputation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMAGMA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eMulti-marker Analysis of Genomic Annotation\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003efastBAT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003efast set-Based Association Test\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eGCTA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eGenome-wide Complex Trait Analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCOJO\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003econditional and joint analysis\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTCGA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eThe Cancer Genome Atlas\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHPA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHuman Protein Atlas.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely thank all participants and research teams whose contributions enabled the generation of the public datasets analyzed in this study. We are especially grateful to the GTEx, TCGA, and HPA consortia for providing access to high-quality transcriptomic and proteomic resources. We also acknowledge the continued efforts of database curators in maintaining and updating these platforms, which were essential for the completion of this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eR.W. drafted the manuscript and performed data analyses. J.W. and Z.Z. contributed to data acquisition and assisted with data analysis. X.H. supervised the study design and provided overall guidance. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no external funding\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll datasets and computational tools utilized in this study are publicly available. Transcriptomic prediction models were obtained from the UTMOST repository (https://github.com/Joker-Jerome/UTMOST/), and eQTL reference panels were derived from GTEx V8 (https://ftp.ebi.ac.uk/pub/databases/spot/eQTL/imported/GTEx_V8/). GWAS summary statistics for prostate cancer were accessed via the GWAS Catalog (https://www.ebi.ac.uk/gwas/). TWAS analyses were conducted using the FUSION (http://gusevlab.org/projects/fusion/) and UTMOST pipelines. MAGMA gene-level analyses were performed using the MAGMA tool (https://cncr.nl/research/magma/), and SMR analyses were carried out using the SMR software (https://yanglab.westlake.edu.cn/software/smr/#SMR\u0026amp;HEIDIanalysis/). Immunohistochemical data were retrieved from the Human Protein Atlas (https://www.proteinatlas.org/ ), and gene interaction networks were constructed via GeneMANIA (https://genemania.org/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDadaev T, Saunders EJ, Newcombe PJ, et al. 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The GTEx Consortium atlas of genetic regulatory effects across human tissues. \u003cem\u003eScience\u003c/em\u003e. 2020;369(6509):1318-1330. doi:10.1126/science.aaz1776\u003c/li\u003e\n\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":"Prostate cancer, genome-wide association studies (GWAS), MLPH, transcriptome-wide association studies (TWAS), colocalization","lastPublishedDoi":"10.21203/rs.3.rs-7662167/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7662167/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eProstate cancer (PCa) is a highly heterogeneous malignancy with complex genetic underpinnings. While genome-wide association studies (GWAS) have identified numerous genetic loci linked to PCa susceptibility, integrative multi-omics validation is still needed to confirm causality and uncover functional roles of candidate genes in PCa pathogenesis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjectives:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study aims to systematically identify and prioritize candidate genes associated with PCa risk through integrative multi-omics analyses, and to elucidate their potential biological roles in tumorigenesis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials and Methods:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe applied cross-tissue and single-tissue transcriptome-wide association studies (TWAS) on the largest available GWAS and the Genotype-Tissue Expression (GTEx) V8 datasets. Subsequent gene-level association tests refined candidate signals. Summary data-based Mendelian randomization (SMR) and Bayesian colocalization were conducted to infer causality. Expression validation was performed at both transcriptional and protein levels. Gene network and pathway enrichment analyses further explored functional contexts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 1,248 genes were identified by single-tissue TWAS, with 35 robust genes confirmed after conditional analysis overlapping with cross-tissue results. Integration with gene-level association test yielded 23 consensus candidate genes. SMR and colocalization prioritized \u003cem\u003eMLPH\u003c/em\u003e and \u003cem\u003eGGCX\u003c/em\u003eas putative causal genes. MLPH was significantly upregulated in PCa tissues at both the mRNA and protein levels, while GGCX showed no difference. Functional analyses revealed the involvement of \u003cem\u003eMLPH\u003c/em\u003e in vesicle-mediated transport and androgen-related signaling pathways, highlighting its biological relevance in PCa pathogenesis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscussion and Conclusion:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur integrative multi-omics approach establishes \u003cem\u003eMLPH\u003c/em\u003e as a biologically and statistically supported gene linked to PCa susceptibility. Its roles in vesicle trafficking and hormone-regulated pathways underscore its potential as a therapeutic target, warranting further mechanistic and translational investigations.\u003c/p\u003e","manuscriptTitle":"Integrative Genomic Profiling Identifies MLPH as a Candidate Gene in Prostate Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-30 10:25:29","doi":"10.21203/rs.3.rs-7662167/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":"08aa9aa5-97ec-4f14-8597-0628ea3176c3","owner":[],"postedDate":"October 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-16T14:56:56+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-30 10:25:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7662167","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7662167","identity":"rs-7662167","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}