Comprehensive multi-omics analysis reveals the molecular mechanism of prostate cancer recurrence

Comprehensive multi-omics analysis reveals the molecular mechanism of prostate cancer recurrence | 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 Comprehensive multi-omics analysis reveals the molecular mechanism of prostate cancer recurrence lin li, Yawei Zhao, Liujiang Ma, Lei Tang, Jiaxin Liu, qianyue Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4765793/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 Accurately detecting prostate cancer recurrence currently poses a challenge for clinicians. In addition, biochemical recurrence (BCR) is a crucial risk factor for clinical recurrence and metastasis. The understanding of genes involved in BCR and their mechanisms is limited. Therefore, this study aims to comprehensively explore the genes associated with BCR and their biological mechanisms in prostate cancer using bioinformatics techniques. Methods Data from 473 non-recurrence (n = 412) and recurrence (n = 61) samples, were obtained from the TCGA public database. The key genes between groups were identified using the Limma package. Mendelian Randomization (MR) was employed to screen for key genes, describing their eQTL-positive outcomes in causality. Relationships between key genes and immune infiltration, immune cells, drug sensitivity, and signaling pathways were analyzed. Further, the enrichment of transcriptome gene sets, prediction of transcription factors, and specific situations in single cells were evaluated. Results In all, 486 DEGs were found, comprising 380 upregulated and 106 downregulated genes. MR identified DENND4B, KCNK6, MPHOSPH6, SPNS1, SYTL3, and ZNF419 as pivotal genes. Multi-omics analysis suggested these genes as predictive and diagnostic markers for BCR. Conclusion This study identified prostate cancer recurrence-related DEGs and their functions using bioinformatics and MR analysis, offering significant clinical implications for accurate prediction and assessment of prostate cancer recurrence. It also provided effective targets for managing recurrent prostate cancer. Prostate Cancer biochemical recurrence Mendelian Randomization Bioinformatics Prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Introduction Prostate cancer is a significant global health concern, ranking as one of the most prevalent malignancies affecting men worldwide. In 2022, it accounted for approximately 1.5 million new cases, representing approximately 7.3% of all new cancer cases worldwide [ 1 ] . Prostate cancer also stood as the second most common type of cancer worldwide and a significant cause of cancer-related deaths among men. Approximately 379,000 deaths were attributed to prostrate cancer, constituting approximately 4.1% of all cancer-related mortality worldwide. Biochemical recurrence (BCR) of prostate cancer, which is defined as two consecutive increases in serum prostate-specific antigen (PSA) levels to at least 0.2 ng/mL after radical prostatectomy (RP), is a crucial indicator of tumor recurrence [ 2 ] . Despite advancements in the detection methods relying on PSA levels, Gleason grading, and clinical features, accurately predicting BCR remains a challenge. Approximately 30% of all patients diagnosed with BCR progress without undergoing further treatment, with a median survival duration of 5–8 years [ 3 ]−[ 6 ] . A lack of precise and sensitive predictive factors impedes clinicians from accurately assessing BCR development, underscoring the urgency to identify more accurate biomarkers and treatment targets [ 7 ] . Moreover, the integration of Mendelian Randomization (MR), a technique using single nucleotide polymorphisms (SNPs) as instrumental variables (IVs) for determining causal relationships between exposure changes and medical outcomes, with bioinformatics offers a comprehensive understanding of how genomic variations influence cancer development and treatment response [ 8 – 12 ] . This combined approach enhances correlation analysis and provides a deeper insight into the role of key genes in cancer progression. Bioinformatics has emerged as a pivotal tool in medical research, facilitating biomarker screening, protein interaction network establishment and pathway enrichment [ 13 , 14 ] . Leveraging bioinformatics enables rapid analysis of large datasets encompassing genomics, transcriptomics, and drug sensitivity profiling of numerous patients [ 15 , 16 ] . This approach has significantly advanced precision medicine in cancer by identifying potential biomarkers and treatment targets for experimental validation [ 17 , 18 ] . In summary, the accurate exploration of sensitive BCR biomarkers and effective treatment targets is imperative to improve BCR detection accuracy and avoid overtreatment. Transcriptome and MR, offers a promising avenue to address this challenge by elucidating the intricate molecular mechanisms underlying prostate cancer recurrence and facilitating the discovery of novel therapeutic interventions. Materials and methods 1.1. Data download: The primary genetic data for this study was sourced from TCGA, a comprehensive cancer database accessible at https://portal.gdc.cancer.gov/. It contains various data categories, including SNPs, copy number variation, lncRNA and miRNA expression, gene expression, and DNA methylation. In particular, data from 412 prostate cancer non-recurrence cases and 61 recurrence cases were used from the processed PRAD original expression data. For single-cell analysis, data for eight samples were downloaded from GSE193337 in the NCBI GEO public database. Exposure data were sourced from the eQTLGen consortium (https://www.egtlgen.org/), focused on studying blood gene expression and genetic architecture to understand complex traits. This large-scale project, currently in its second phase, included a genome-wide analysis to extract insights from blood samples. Outcome data were obtained from the FinnGene database, a genetic research resource focused on European populations. FinnGene's vast dataset encompasses samples from various regions, facilitating research on genetic diseases and mutations in European populations. Particularly relevant to this study, the prostate cancer dataset (finngen_R10_C3_PROSTATE_EXALLC) comprised 15199 cases and 131266 controls. 1.2. Differential expression analysis: The Limma package in R is a powerful tool for conducting differential expression analysis, particularly for identifying significantly differentially expressed genes between different groups. In this study, gene expression profiles from recurrence group and non-recurrence group samples were compared using the Limma package to examine molecular processes in disease sample data. The criteria for identifying differentially expressed genes were set at adj.P.Val<0.01. Heat maps and Volcano plots were created to display outcomes. 1.3. Functional analysis: In order to further analyze the network pathways involved in differential genes, the Metascape database (www.metascape.org) was used for functional annotation. In particular, Gene Ontology (GO) pathway analysis and KEGG analysis were conducted on specific genes. Statistically significant pathways were determined with criteria set at Min overlap≥3 and p≤0.01. 1.4. MR analysis: The outcome IDs obtained from the FinnGene database were cross-referenced with GWAS summary data from https://gwas.mrcieu.ac.uk/ to extract relevant causal relationships in the expression Quantitative Trait Locus (eQTL). To select potential instrumental variables (IVs), a significance threshold of P < 1e-8 was applied to SNPs within each gene locus. Linkage disequilibrium (LD) between SNPs was calculated, and SNPs with an LD threshold of R2 < 0.001 (using a clumping window size of 10,000kb) were retained. Subsequently, the retained SNPs underwent analysis following the inverse variance weighted (IVW), MR Egger, weighted median, and weighted mode methods. These methods assess causal relationships between genetic variants and prostate cancer risk, each providing unique insights into causality. Then, statistical methods were used to evaluate the reliability of causality. The Wald ratio was selected when there was only one available instrumental variable corresponding to the genes in the exposure data. 1.5. Sensitivity analysis: To evaluate the impact of specific genetic variants on prostate cancer risk, we conducted an MR leave-one-out sensitivity analysis. This method systematically excludes individual SNPs, recalculates the pooled effect size of the remaining SNPs, and evaluates the SNP's unique contribution and robustness to the overall results. We assessed the robustness of our analysis by determining the effect of removing a single SNP on the overall results. This was done by comparing estimates following the removal of each SNP with the overall estimates including all SNPs. 1.6. Immune cell infiltration analysis: The CIBERSORT method is widely used for assessing immune cell types within a microenvironment. Based on support vector regression, it conducts deconvolution analysis on immune cell subtype expression matrices, using 547 biomarkers for distinguishing 22 human immune cell phenotypes. These phenotypes include T cells, B cells, plasma cells, and various myeloid cell subsets. In this study, the CIBERSORT algorithm was applied to analyze patient data, estimating the relative proportions of these immune infiltrating cells and conducting correlation analysis between gene expression and immune cell content. 1.7. Drug sensitivity analysis: Using data from the GDSC Cancer Drug Sensitivity Genomics Database (https://www.cancerrxgene.org/), we employed the R package “pRRophetic” to predict the sensitivity of each tumor sample to chemotherapy. Through regression analysis, we estimated the IC50 value for each specific chemotherapeutic drug treatment. To validate our predictions, we used the GDSC training set and conducted 10-fold cross-validation to assess regression and prediction accuracy. Default parameter values were employed, including the use of “combat” to eliminate batch effects and averaging of duplicate gene expression data. 1.8. GSEA analysis: Patients were categorized into high and low expression groups based on their gene expression levels. Gene Set Enrichment Analysis (GSEA) was then employed to explore differences in signaling pathways between these two groups. Annotated gene sets downloaded from the MsigDB database were used as the background gene set for subtype pathways. Differential expression analysis of pathways was conducted between subtypes, focusing on significantly enriched gene sets (adjusted p-value < 0.05) determined based on the consistency score. GSEA analysis, a valuable tool in research combining disease classification with biological significance, facilitated the identification of pathways associated with different gene expression profiles. 1.9. Gene Set Variation Analysis: Gene Set Variation Analysis (GSVA) is a non-parametric method for assessing enrichment in gene sets across transcriptomes. It evaluates pathway-level changes by scoring gene sets of interest, allowing for the determination of biological functions within samples. In this study, gene sets from the Molecular Signatures Database (v7.0 version) were downloaded, and the GSVA algorithm was applied to comprehensively score each gene set. This facilitated the evaluation of potential changes in biological functions across different samples. 1.10. Regulatory network analysis of important genes: This study used the R package "RcisTarget" to predict transcription factors, with calculations based on motifs. The total amount of motifs in the database determined each motif's normalized enrichment score (NES). Based on gene sequence and motif similarity, more annotation files were conjectured. The area under the curve (AUC) for each motif-motif set pair was calculated to estimate the overexpression of a motif on a gene set. The gene set's recovery curve was calculated against motif ordering to achieve this. Finally, the AUC distribution of all motifs in the gene set was used to calculate the NES of each motif. 1.11. Single cell analysis: The expression profile was initially read using the Seurat package. Low-mass cell were filtered out based on criteria such as nFeature_RNA > 500 and percent.mt < 5. Subsequently, standardization, normalization and principal component analysis (PCA), were sequentially performed on the data. By looking at the Elbow Plot, the ideal number of primary components was obtained. Via t-distributed stochastic neighbor embedding (t-SNE) analysis, the spatial link between clusters was determined. Furthermore, using the celldex software, cluster annotation was performed with an emphasis on cells believed to be important for tumor growth. 1.12. Statistical analysis: The R programming language, version 4.2, was used for all statistical analyses, with a significance threshold of p < 0.05. Results 2.1. Identification of Differential Genes Using the TCGA public database, we retrieved the PRAD data set, which includes data from 473 samples 412 cases in the non-recurrence group and 61 in the recurrence group. Subsequently, we analyzed the differential expression levels between the two patient groups using the Limma package. The differential gene screening conditions were as follows: adj.P.Val < 0.01. Finally, 486 differential genes were screened out, including 380 up-regulated genes and 106 down-regulated genes (Fig.1 A-B). We then performed further pathway analysis on the identified differential genes. The genes were mainly concentrated in pathways associated with the regulation of the cell cycle process and the mitotic cell cycle process, according to the GOKEGG findings (Fig.1C). 2.2. Identification of Key Genes through MR Analysis To further identify the key genes affecting prostate cancer based on the differential genes, an outcome ID was obtained by determining the summary statistics of 146465 prostate cancer-related samples (Cases: 15199; Controls: 131266): finngen_R10_C3_PROSTATE_EXALLC. In all, 260 pairs of causal links between genes and outcomes were extracted by sequentially using extract_instruments and extract_outcome_data. Six pairs of genes were found to be causally related to positive eQTL results after additional screening via MR analysis. (Fig.2A-F, IVW pval < 0.05). The corresponding genes were as follows: DENND4B, KCNK6, MPHOSPH6, SPNS1, SYTL3, and ZNF419. The gene KCNK6 (0.8659;0.5058−0.9305; p = 0.0001) was associated with a low risk of prostate cancer, whereas the genes DENND4B (1.0917;1.0163−1.1727; p = 0.0162), MPHOSPH6 (1.0647;1.0172–1.1144; p = 0.0071), SPNS1 (1.0752;1.0058–1.1495; p = 0.0333), SYTL3 (1.1091;1.0007–1.2293; p = 0.0484), ZNF419 (1.0742;1.0111–1.1412; p = 0.0205) were associated with a high risk of prostate cancer. To ascertain the dependability of the causal linkages of these six genes, sensitivity analysis was further performed. The findings demonstrated the robustness of the six pairs of causal links we chose because the effect of the exclusion of any one SNP on the total error bar was not readily apparent (Fig.3A-F). 2.3. Exploration of Molecular Mechanisms through Immune Infiltration Analysis The immune system, extracellular matrix, different growth factors, inflammatory factors, and unique physical and chemical properties make up the majority of the microenvironment. These components have a substantial impact on clinical treatment sensitivity and illness diagnosis. This study further explored the potential molecular mechanisms through which key genes influence prostate cancer progression by analyzing the relationship between key genes and immune infiltration in the prostate cancer data set. Further, the percentage of immune cells in each patient was displayed in this study, along with the relationship between immune cells in various forms(Fig.4A-B). In addition, the research results showed a significant difference in the eosinophil and M2 macrophage levels between the two groups (Fig.4C). 2.4. Exploration of the Relationship of Key Genes with Immune Cells This investigation on the connection between key genes and immune cells revealed that there is a strong correlation between the key genes in cancer progression and immune cells (Fig.4D-I). Among them, DENND4B has a significant positive correlation with naive B cells, and a significant negative correlation with resting Mast cells; KCNK6 has a significant positive correlation with follicular helper T cells, and has a significant negative correlation with M2 macrophages; MPHOSPH6 has a significant negative correlation with CD8 + T cells. There is a significant positive correlation and a significant negative correlation with activated NK cells; SPNS1 has a significant negative correlation with neutrophils; SYTL3 has a significant positive correlation with neutrophils and a significant negative correlation with M2 macrophages; ZNF419 has a significant negative correlation with regulatory T cells (Tregs). Significant positive correlation, significant negative correlation with Neutrophils, etc. The study used the TISIDB database to determine the link between these six key genes and various immunological components, such as chemokines, cell receptors, and immune regulatory factors(Fig.5A-E). According to our analyses, these key genes have a significant impact on the immune milieu and are strongly correlated with the degree of immune cell infiltration. 2.5. Analysis of Drug Sensitivity The treatment effect of early-stage prostate cancer combined with surgery and chemotherapy is clear. Our study is based on the drug sensitivity data included in the GDSC database and uses the R package "pRRophetic" to predict the chemotherapy sensitivity of each tumor sample and explore DENND4B, KCNK6, MPHOSPH6, SPNS1, SYTL3, and ZNF419 and their sensitivity toward common chemotherapy drugs. The research results showed that KCNK6 expression was significantly associated with the sensitivity of the tumors toward ABT.263, ABT.888, and AG.014699 (Fig.6A), MPHOSPH6 expression was significantly related to the sensitivity toward ABT.888 (Fig.6B), and SPNS1 expression was related to the sensitivity toward AP. The sensitivities of tumors toward 24534, ABT.888, and AG.014699 were significantly correlated (Fig.6C), SYTL3 expression was significantly correlated with the sensitivities to ABT.263, AP.24534, CCT007093, and ABT.888 (Fig.6D), and ZNF419 expression was significantly correlated with the sensitivities to ABT.263, AMG.706, AP.24534, CCT007093, ABT.888, and AG.014699 (Fig.6E). DENND4B expression was significantly associated with sensitivities toward ABT.263, AMG.706, ABT.888, and AG.014699 (Fig.6F). Simultaneously, we explored the sensitivity of DENND4B, KCNK6, MPHOSPH6, SPNS1, SYTL3, and ZNF419 to Bicalutamide and Docetaxel. The research results indicated that KCNK6 expression was significantly associated with the tumor sensitivity to Bicalutamide, MPHOSPH6 expression was significantly associated with the tumor sensitivity to Bicalutamide. SYTL3 expression was significantly associated with the sensitivity toward Bicalutamide and Docetaxel, and DENND4B expression was significantly associated with the tumor sensitivity toward Bicalutamide and Docetaxel (Fig.7A-F). 2.6. Pathway Enrichment Analysis of Key Genes We examined the distinct signaling pathways associated with the six pivotal genes and investigated the plausible molecular routes through which the pivotal genes influence the course of events. GSEA results showed that DENND4B - enriched pathways included the Chemokine signaling pathway, mRNA surveillance pathway, and other pathways (Fig.8A); KCNK6-enriched pathways included mRNA surveillance pathway, PPAR signaling pathway, and other pathways (Fig.8B); MPHOSPH6-enriched pathways included JAK-STAT signaling pathway and NOD - like receptor signaling pathway (Fig.8C); pathways enriched by SPNS1 included mRNA surveillance pathway, Polycomb repressive complex, and other pathways (Fig.8D); SYTL3-enriched pathways included cGMP-PKG signaling pathway, MAPK signaling pathway, and other pathways (Fig.8E); the pathways enriched by ZNF419 included the cAMP signaling pathway, chemokine signaling pathway, and other pathways (Fig.8F). 2.7. Pathway Enrichment Analysis via GSVA GSVA results show that DEND4B can be enriched with signaling channels such as WNT BETA CATENIN SIGNALING and TGF BETA SIGNALING, among others (Fig.9A); high expression of KCNK6 can enrich the REACTIVE OXYGEN PATHWAY, IL6 JAK Stat3 signaling (Fig.9B), P53 PATHWAY, and other signaling pathways (Fig.9C); high expression of SPNS1 can enrich signaling pathways such as WNT BETA CATENIN SIGNALING and IL6 JAK STAT3 SIGNALING (Fig.9D); high expression of SYTL3 can enrich signaling pathways such as NOTCH SIGNALING and IL6 JAK STAT3 SIGNALING (Fig.9E); and high expression of ZNF419 can enrich signaling pathways such as WNT BETA CATENIN SIGNALING and UNFOLDED PROTEIN RESPONSE (Fig.9F). 2.8. Enrichment Analysis of Transcription Factors Regulating Key Genes This investigation discovered that the six key genes used as the gene set for analysis are regulated through numerous transcription factors and other common pathways. Therefore, making use of cumulative recovery curves, enrichment analysis of these transcription factors was performed. The most highly ranked motif (NES: 7.61) according to Motif-TF annotation and selection study of significant genes was cisbp__M5114. All of the enriched motifs and associated transcription factors for key genes are shown in this study(Fig.10A-B). 2.9. Analysis of Disease-Related Regulatory Genes and Correlation with Key Genes We obtained disease-related regulatory genes through the GeneCards database ( https://www.genecards.org/ ). The expression levels of 20 genes with the highest Relevance Score were determined, and the results showed that the levels of AR, BRCA1, CHEK2, PCA3, PCAT1, and PCAT7 were different between the two patient groups (Fig.10C). Furthermore, we ran correlation analyses on disease-regulated genes and key genes. Significant correlations were found between the expression levels of disease-regulated genes and the expression levels of key genes. Among them, DENND4B and BRCA1 were significantly positively correlated (r= 0.493), and KCNK6 and PCAT7 were significantly negatively correlated ( r = −0.346) (Fig.10D). 2.10. Single Cell Analysis of GSE193337 Dataset We obtained the GSE193337 single cell data, which includes eight samples in total. The Seurat software was used for single cell analysis, and the tSNE technique was used to cluster cells. Finally, 12 subpopulations were obtained through TSNE analysis (Fig.11A). These were annotated to six cell categories: T_cell, Epithelial_cells, Monocyte, B_cell, Endothelial_cells, and Smooth_muscle_cells (Fig.11B). In addition, we analyzed the expression levels of key genes in T_cell, Epithelial_cells, Monocyte, B_cell, Endothelial_cells, and Smooth_muscle_cells in the single cell data (Fig.11C-D). Moreover, the immunological and metabolic pathways were quantified using AUCell, and the relationship between key genes and immune and metabolic pathways were researched and demonstrated (Fig.11E).Next, we visualized the gene co-expression of prostate cancer-related regulatory genes (AR, BRCA2, HOXB13) and the six key genes in the single-cell level (Fig 12-14A-F). Discussion One of the primary concerns for prostate cancer patients after undergoing curative surgery is the possibility and timing of BCR. Therefore, accurately predicting the timing of BCR becomes extremely crucial [ 19 ] . The chances of survival and quality of life of patients with prostrate cancer could be increased if it were feasible to forecast the onset and timing of BCR with sufficient accuracy [ 20 ] . Through the use of comprehensive bioinformatics analysis, we were able to identify 486 genes, 380 of which were upregulated and 106 of which were downregulated, that were linked to prostate cancer recurrence. By combining MR analysis, we further screened six key genes, namely DENND4B, KCNK6, MPHOSPH6, SPNS1, SYTL3, and ZNF419. These differentially expressed genes were subjected to GO and KEGG pathway analyses. The findings indicated that these genes are primarily enriched in pathways related to the regulation of cell cycle and mitotic cell cycle. Regulation of the cell cycle is crucial for cell proliferation, growth, and repair, and is also closely associated with the occurrence, development, and metastasis of tumors [ 21 , 22 ] . In addition, the regulation of cell cycle is closely associated with the synergistic action of chemotherapy drugs (both inhibitory and promoting effects), which may enhance the sensitivity of tumor cells to chemotherapy drugs [ 23 – 25 ] . We next used the R package "pRRophetic" to uncover significant associations between key genes and particular medications according to the drug sensitivity data from the GDSC database. For example, SPNS1 and ZNF419 showed significant sensitivity to Bicalutamide, Docetaxel, ABT.888, AG.014699, and others. Bicalutamide and Docetaxel, as commonly used drugs for prostate cancer, have significant correlations with SPNS1 and ZNF419, indirectly suggesting SPNS1 and ZNF419 as new directions in the treatment of prostate cancer. An essential component in the development of cancer is the immunological microenvironment. To shed light on the possible roles of these genes in the tumor immune environment, we also examined the relationships between key genes and immune cells in a prostate cancer immune microenvironment. The immune microenvironment, which is a core component of tumor biology, includes immune cells, extracellular matrix, growth factors, and inflammatory factors. The relationship between the immune microenvironment and tumors is increasingly being emphasized [ 26 – 30 ] . Through the analysis of immune cell distribution in prostate cancer samples and their relationship with the expression of key genes, we discovered important connections between particular immune cell types and genes including DENND4B, KCNK6, and MPHOSPH6. For instance, DENND4B was positively correlated with immature B cells and negatively correlated with resting mast cells, with recent reports increasingly discussing the relationship between resting mast cells and tumors [ 31 – 34 ] . This suggests that these genes can become key therapeutic targets in prostate cancer treatment. Subsequently, we explored the functions of these genes in multiple signaling pathways. These genes are involved in several critical biological processes, including the PPAR signaling pathway, the JAKSTAT signaling pathway, the mRNA surveillance pathway, the chemokine signaling pathway, and others, according GSEA and GSVA. For example, the enrichment of DENND4B in the Chemokine signaling pathway and mRNA surveillance pathway may indicate its dual role in cell communication and gene expression regulation. Previous studies have identified the Chemokine signaling pathway to play a key role in tumor initiation and progression, potentially leading to invasion and migration [ 35 – 39 ] . In addition, in the GSVA enrichment process, key genes were also found to be enriched in pathways such as WNT BETA CATENIN SIGNALING, NOTCH SIGNALING, P53 PATHWAY, TGF BETA SIGNALING, IL6 JAK STAT3 SIGNALING, and all of which have been extensively reported in the development of various tumors [ 40 – 44 ] , further validating the crucial role that these six key genes possibly play in prostate cancer progression. We also used the GeneCards database to identify regulatory genes, such as AR, PCAT17, and BRCA1, closely associated with prostate cancer development, and found significant expression correlations with the key genes. This correlation analysis not only validated the relevance of our choice of key genes but also provided a basis for further validation of potential therapeutic targets. Using the Seurat package for single-cell analysis and applying the t-SNE algorithm for cell clustering, we identified 12 cell subgroups and annotated six major cell categories. Within these cell categories, we observed a high expression of SYTL3 in T cells, whereas KCNK6 and SPNS1 showed higher expression in monocytes. This discovery is especially significant because monocytes and T lymphocytes are essential for the development and progression of malignancies [ 45 – 48 ] . Lastly, we performed a correlation study between these six key genes and the most influential three genes — Androgen Receptor (AR), BRCA2, and HOXB13 — as determined by Relevance score. Of particular significance was the role of AR, which has been extensively studied and found to be intimately linked to the development of prostate cancer in both initial stages and progression [ 49 – 51 ] . Our analysis data indicates significant correlations between these six key genes and AR. In particular, DENND4B, KCNK6, SPNS1, and SYTL3 showed significant correlations with AR. In conclusion, the biological functions of these six key genes in prostate cancer merit further investigation. However, our analysis is based on public big data and lacks more experimental data to validate our findings. This is a limitation of our study. Conclusions In this study, through multiple bioinformatics analyses, six key genes — DENND4B, KCNK6, MPHOSPH6, SPNS1, SYTL3, and ZNF419 — were identified, which may become potential key genes for managing the recurrence of prostate cancer. These findings provide new directions for clinical physicians with regard to the management of prostate cancer recurrence and offer a scientific basis for further exploration of therapeutic targets. Abbreviations BCR Biochemical Recurrence MR Mendelian Randomization RP Radical Prostatectomy PSA Prostate-Specific Antigen SNPs Single Nucleotide Polymorphisms IVs Instrumental Variables GO Gene Ontology eQTL Expression Quantitative Trait Locus LD Linkage Disequilibrium IVW Inverse Variance Weighted GSEA Gene Set Enrichment Analysis GSVA Gene Set Variation Analysis NES Normalized Enrichment Score AUC Area Under Curve PCA Principal Component Analysis t-SNE t-distributed Stochastic Neighbor Embedding Declarations Authors' contributions : LL, QYL and YWZ designed the study and carried out the data analyses. LL and LJM draft the manuscript. JXL and LT revised and polished the manuscript. QYL reviewed the manuscrip. All authorsread and approved the final manuscript. Acknowledgements : Not applicable. Funding : This research was supported by the General Project of the Corps Science and Technology Program (Project No. 2021AB036). Availability of data and materials: This study's expression spectrum data are sourced from the TCGA(https://portal.gdc.cancer.gov/) and GEO databases. The exposure data for Mendelian analysis come from the eQTLGen Consortium(https://www.egtlgen.org/) database, and the outcome data are from the FinnGen Consortium R10 release(index (finngen.fi)) database. All the above data are publicly available. Corresponding authors can be contacted for more information. Ethics approval and consent to participate : The data analyzed during the study come from the relevant studies where written informed consent was received prior to participation. 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Transcription-independent functions of p53 in DNA repair pathway selection. BIOESSAYS. 2022; 45 (1): e2200122. doi: 10.1002/bies.202200122 Cai, J, Qiao, Y, Chen, L, et al. Regulation of the Notch signaling pathway by natural products for cancer therapy. J NUTR BIOCHEM. 2023; 123 109483. doi: 10.1016/j.jnutbio.2023.109483 Fan, W, Cao, W, Shi, J, et al. Contributions of bone marrow monocytes/macrophages in myeloproliferative neoplasms with JAK2V617F mutation. ANN HEMATOL. 2023; 102 (7): 1745-1759. doi: 10.1007/s00277-023-05284-5 Juusola, M, Kuuliala, K, Kuuliala, A, et al. Pancreatic cancer is associated with aberrant monocyte function and successive differentiation into macrophages with inferior anti-tumour characteristics. PANCREATOLOGY. 2021; 21 (2): 397-405. doi: 10.1016/j.pan.2020.12.025 Brown, LE, Zhang, D, Cui, W. Flow Cytometric Analysis of Monocytes and Granulocytes May Be Useful in the Distinction of Myeloid Neoplasms from Reactive Conditions: A Single Institution Experience and Literature Review. ANN CLIN LAB SCI. 2020; 50 ANN CLIN LAB SCI. PMID: 32581021 Lopes-Coelho, F, Silva, F, Gouveia-Fernandes, S, et al. Monocytes as Endothelial Progenitor Cells (EPCs), Another Brick in the Wall to Disentangle Tumor Angiogenesis. Cells. 2020; 9 Cells. doi: 10.3390/cells9010107 Ricke, EA, Williams, K, Lee, YF, et al. Androgen hormone action in prostatic carcinogenesis: stromal androgen receptors mediate prostate cancer progression, malignant transformation and metastasis. CARCINOGENESIS. 2012; 33 (7): 1391-8. doi: 10.1093/carcin/bgs153 Trivunic-Dajko, S, Bogdanovic, J, Vojinov, S, et al. Stereological analysis of androgen receptors in prostate cancer and benign prostatic hyperplasia Med Pregl. 2018; 71 (3-4): 89-95. doi: 10.2298/mpns1804089t Sehgal, PD, Bauman, TM, Nicholson, TM, et al. Tissue-specific quantification and localization of androgen and estrogen receptors in prostate cancer. HUM PATHOL. 2019; 89 99-108. doi: 10.1016/j.humpath.2019.04.009 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4765793","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":333278461,"identity":"8cda5388-58d3-4a70-a2a3-bd55c8eae938","order_by":0,"name":"lin li","email":"","orcid":"","institution":"Shihezi University","correspondingAuthor":false,"prefix":"","firstName":"lin","middleName":"","lastName":"li","suffix":""},{"id":333278466,"identity":"1fd04476-c6db-4eed-8107-f58e71b32d95","order_by":1,"name":"Yawei Zhao","email":"","orcid":"","institution":"Hospital of Xinjiang Production and Construction Corps, Urumqi, People’s Republic of China.","correspondingAuthor":false,"prefix":"","firstName":"Yawei","middleName":"","lastName":"Zhao","suffix":""},{"id":333278468,"identity":"391cd945-44ca-4a3b-ab69-3d0cbe0fbbd2","order_by":2,"name":"Liujiang Ma","email":"","orcid":"","institution":"Hospital of Xinjiang Production and Construction Corps, Urumqi, People’s Republic of China.","correspondingAuthor":false,"prefix":"","firstName":"Liujiang","middleName":"","lastName":"Ma","suffix":""},{"id":333278471,"identity":"88d4b72c-1ff8-4ba1-9aa7-c1a1ea4bcba1","order_by":3,"name":"Lei Tang","email":"","orcid":"","institution":"Hospital of Xinjiang Production and Construction Corps, Urumqi, People’s Republic of China.","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Tang","suffix":""},{"id":333278473,"identity":"a512a9a9-7f75-430e-bde1-77022291c374","order_by":4,"name":"Jiaxin Liu","email":"","orcid":"","institution":"Shihezi University","correspondingAuthor":false,"prefix":"","firstName":"Jiaxin","middleName":"","lastName":"Liu","suffix":""},{"id":333278476,"identity":"0118832b-0c69-412d-b2a1-604418014683","order_by":5,"name":"qianyue Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8ElEQVRIiWNgGAWjYBACPmYGhgNgFjPz8Z8fKiTk5AlpYYNrYWdLkJY4Y2Fs2EBIC5zFz2MgwdtWkQg1AY8WduaNB37uOJzYz8xjYCA5TyKBsYH54aMbeB3GVnCw98zhxJnNbAUJhdsk8tgZ2IyNc/Bq4TE4wNt2OHHDYeYNByS3SRQzNvCwSRPScvAvUMv+wwyGDbxzJBIbDhCh5TDYFmYWYwbeBqK0sBUclm1LN55xmC2NWeKYhLFhMwG/8PMf3vzxbZu1bH//4WOMH2rq5OTZmx8+xqcFCAyAuBmJz4xfOUxLHWFlo2AUjIJRMHIBAHrHR8yGiXQ4AAAAAElFTkSuQmCC","orcid":"","institution":"Hospital of Xinjiang Production and Construction Corps, Urumqi, People’s Republic of China.","correspondingAuthor":true,"prefix":"","firstName":"qianyue","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-07-19 02:57:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4765793/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4765793/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63023474,"identity":"d164fd24-b424-4599-a4cf-e40809fc061a","added_by":"auto","created_at":"2024-08-22 07:59:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3517151,"visible":true,"origin":"","legend":"\u003cp\u003eDifference and enrichment analysis. (A) Volcano plot of differentially expressed genes, with pink indicating up-regulation of differential expression and blue indicating down-regulation of differential expression. (B) Heat map of differential genes, yellow indicates high expression and green indicates low expression. (C) GO-KEGG enrichment analysis using Metascape database.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-4765793/v1/da1369d3da59ff715ac6cae3.png"},{"id":63024234,"identity":"561de11b-0642-47e2-a220-45e8a3fc49d5","added_by":"auto","created_at":"2024-08-22 08:07:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2382423,"visible":true,"origin":"","legend":"\u003cp\u003eMendelian randomization analysis. (A-F) Scatter plots of MR analysis of key genes. Different colors represent different statistical methods, and the slope of the line represents the causal effect of each method respectively.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-4765793/v1/cf4c9e5c6b0224c3b00d7e55.png"},{"id":63024232,"identity":"4512dc9a-bfaa-4e9b-8775-6d01bc64a602","added_by":"auto","created_at":"2024-08-22 08:07:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2947461,"visible":true,"origin":"","legend":"\u003cp\u003eLeave-out test. (A-F) Forest plot of leave-out test of SNPs corresponding to key genes.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-4765793/v1/e4c53134029bb8c544319983.png"},{"id":63024233,"identity":"edb01863-fcd3-4313-9ed4-80cc410480e0","added_by":"auto","created_at":"2024-08-22 08:07:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2533159,"visible":true,"origin":"","legend":"\u003cp\u003eImmune infiltration analysis. (A) Relative percentages of 21 immune cell subsets. (B) Pearson correlation between 21 types of immune cells, blue indicates negative correlation and red indicates positive correlation. (C) Differences in immune cell content between control and tumor samples. (D-I) Correlation between key genes and immune cell content.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-4765793/v1/12bf36c139ccff5330ad4776.png"},{"id":63023420,"identity":"eb648e42-0cbd-4adf-9387-92130a325865","added_by":"auto","created_at":"2024-08-22 07:59:19","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1024789,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between key genes and immune factors. (A-E) Correlation of key genes with chemokine, Immunoinhibitor, Immunostimulator, MHC and receptor.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-4765793/v1/ac7f7581fcc1ced3314f4945.png"},{"id":63024798,"identity":"59cc1839-5af0-4697-96e6-4d0e46e4fd5e","added_by":"auto","created_at":"2024-08-22 08:15:20","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3149605,"visible":true,"origin":"","legend":"\u003cp\u003eDrug sensitivity correlation. (A-F) Sensitivity analysis of key genes and chemotherapy drugs.\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-4765793/v1/5214f0ae4146dc69bdf3b012.png"},{"id":63023439,"identity":"d6714940-c8cf-4402-a38b-b513857dc6bc","added_by":"auto","created_at":"2024-08-22 07:59:20","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":2494875,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation of sensitivity to commonly used drugs in prostate cancer. (A-F) Analysis of Key Genes in Relation to the Drug Sensitivity of Bicalutamide and Docetaxel.\u003c/p\u003e","description":"","filename":"Fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-4765793/v1/5bcfdc07fd98a9236e5506e0.png"},{"id":63023489,"identity":"a914dd60-1a79-4670-8222-a02c471addbd","added_by":"auto","created_at":"2024-08-22 07:59:22","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":2331530,"visible":true,"origin":"","legend":"\u003cp\u003eGSEA analysis of key genes. (A-F) Key genes involved in the KEGG signaling pathway, as well as pathway regulation and involved genes.\u003c/p\u003e","description":"","filename":"Fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-4765793/v1/bd068ba9e94c5f1a30f1be38.png"},{"id":63023454,"identity":"63c82ce2-f0d3-4305-8121-ed1aa1b402a4","added_by":"auto","created_at":"2024-08-22 07:59:20","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1773869,"visible":true,"origin":"","legend":"\u003cp\u003eGSVA analysis of key genes. (A-F) GSVA analysis of key genes, blue indicates the signaling pathways involved in high expression of genes, green indicates the signaling pathways involved in low expression genes, and the background gene set is hallmark.\u003c/p\u003e","description":"","filename":"Fig9.png","url":"https://assets-eu.researchsquare.com/files/rs-4765793/v1/295f7e8da43b2d9af2f38bc7.png"},{"id":63024236,"identity":"b7fdb75e-df02-447a-b4e3-690351a44c29","added_by":"auto","created_at":"2024-08-22 08:07:19","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":1571571,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation between key gene-related transcriptional regulation and prostate cancer regulatory genes. (A) Transcriptional regulatory network of key genes, with pink representing key genes and green representing transcription factors. (B) All enriched motifs and corresponding transcription factors of key genes are displayed. (C) Expression differences of tumor regulatory genes, blue indicates control patients, pink indicates tumor patients. (D) Pearson correlation analysis of key genes and disease genes. Blue indicates negative correlation and red indicates positive correlation.\u003c/p\u003e","description":"","filename":"Fig10.png","url":"https://assets-eu.researchsquare.com/files/rs-4765793/v1/91ede38a90517721da1c7bdd.png"},{"id":63023431,"identity":"3875f27a-5c8e-4c41-a75e-0ad20f09a587","added_by":"auto","created_at":"2024-08-22 07:59:19","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":1930073,"visible":true,"origin":"","legend":"\u003cp\u003eAnnotation of cells. (A) We divided cells into 12 clusters by the tsne algorithm based on the significant components available in PCA. (B) Cell annotation of 12 clusters. 12 clusters are annotated into 6 cell types, namely T_cells, Epithelial_cells, Monocyte, B_cell, Endothelial_cells, and Smooth_muscle_cells. (C-D) Expression profile of key genes in single cells. (E) Correlation between key genes and immunometabolic pathways.\u003c/p\u003e","description":"","filename":"Fig11.png","url":"https://assets-eu.researchsquare.com/files/rs-4765793/v1/ed20397222149c23c311456e.png"},{"id":63023428,"identity":"539dc82b-a72d-4223-b753-f436f3530e2e","added_by":"auto","created_at":"2024-08-22 07:59:19","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":848989,"visible":true,"origin":"","legend":"\u003cp\u003eCo-expression analysis of key genes in single cells. (A-F) Gene co-expression of disease genes and key genes in single-cell data, and co-expressed gene correlations.\u003c/p\u003e","description":"","filename":"Fig12.png","url":"https://assets-eu.researchsquare.com/files/rs-4765793/v1/423d82c162e5087d21c1a827.png"},{"id":63023443,"identity":"55b49acd-c2e1-4137-90f5-153628f984f0","added_by":"auto","created_at":"2024-08-22 07:59:20","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":1810137,"visible":true,"origin":"","legend":"\u003cp\u003eCo-expression analysis of key genes in single cells. (A-F) Gene co-expression of disease genes and key genes in single-cell data, and co-expressed gene correlations.\u003c/p\u003e","description":"","filename":"Fig13.png","url":"https://assets-eu.researchsquare.com/files/rs-4765793/v1/702ac448aca28e450b5dd447.png"},{"id":63023450,"identity":"71997dc6-cbc7-45fa-b9bb-c7f3c37a2758","added_by":"auto","created_at":"2024-08-22 07:59:20","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":1754384,"visible":true,"origin":"","legend":"\u003cp\u003eCo-expression analysis of key genes in single cells. (A-F) Gene co-expression of disease genes and key genes in single-cell data, and co-expressed gene correlations.\u003c/p\u003e","description":"","filename":"Fig14.png","url":"https://assets-eu.researchsquare.com/files/rs-4765793/v1/cc8a880e493a2e8c9433a0b1.png"},{"id":67060113,"identity":"42b97cd0-f005-4f69-87a5-a720e390317e","added_by":"auto","created_at":"2024-10-20 19:16:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":24906187,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4765793/v1/cfae0be7-f5ac-4397-8df9-6bad360be114.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comprehensive multi-omics analysis reveals the molecular mechanism of prostate cancer recurrence","fulltext":[{"header":"Introduction","content":"\u003cp\u003eProstate cancer is a significant global health concern, ranking as one of the most prevalent malignancies affecting men worldwide. In 2022, it accounted for approximately 1.5\u0026nbsp;million new cases, representing approximately 7.3% of all new cancer cases worldwide\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Prostate cancer also stood as the second most common type of cancer worldwide and a significant cause of cancer-related deaths among men. Approximately 379,000 deaths were attributed to prostrate cancer, constituting approximately 4.1% of all cancer-related mortality worldwide. Biochemical recurrence (BCR) of prostate cancer, which is defined as two consecutive increases in serum prostate-specific antigen (PSA) levels to at least 0.2 ng/mL after radical prostatectomy (RP), is a crucial indicator of tumor recurrence\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite advancements in the detection methods relying on PSA levels, Gleason grading, and clinical features, accurately predicting BCR remains a challenge. Approximately 30% of all patients diagnosed with BCR progress without undergoing further treatment, with a median survival duration of 5\u0026ndash;8 years\u003csup\u003e[\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u0026minus;[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. A lack of precise and sensitive predictive factors impedes clinicians from accurately assessing BCR development, underscoring the urgency to identify more accurate biomarkers and treatment targets\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMoreover, the integration of Mendelian Randomization (MR), a technique using single nucleotide polymorphisms (SNPs) as instrumental variables (IVs) for determining causal relationships between exposure changes and medical outcomes, with bioinformatics offers a comprehensive understanding of how genomic variations influence cancer development and treatment response\u003csup\u003e[\u003cspan additionalcitationids=\"CR9 CR10 CR11\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. This combined approach enhances correlation analysis and provides a deeper insight into the role of key genes in cancer progression.\u003c/p\u003e \u003cp\u003eBioinformatics has emerged as a pivotal tool in medical research, facilitating biomarker screening, protein interaction network establishment and pathway enrichment\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Leveraging bioinformatics enables rapid analysis of large datasets encompassing genomics, transcriptomics, and drug sensitivity profiling of numerous patients\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. This approach has significantly advanced precision medicine in cancer by identifying potential biomarkers and treatment targets for experimental validation\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e .\u003c/p\u003e \u003cp\u003eIn summary, the accurate exploration of sensitive BCR biomarkers and effective treatment targets is imperative to improve BCR detection accuracy and avoid overtreatment. Transcriptome and MR, offers a promising avenue to address this challenge by elucidating the intricate molecular mechanisms underlying prostate cancer recurrence and facilitating the discovery of novel therapeutic interventions.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003e1.1. Data download:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe primary genetic data for this study was sourced from TCGA, a comprehensive cancer database accessible at https://portal.gdc.cancer.gov/. It contains various data categories, including SNPs, copy number variation, lncRNA and miRNA expression, gene expression, and DNA methylation. In particular, data from 412 prostate cancer non-recurrence cases and 61 recurrence cases were used from the processed PRAD original expression data. For single-cell analysis, data for eight samples were downloaded from GSE193337 in the NCBI GEO public database.\u003c/p\u003e\n\n\u003cp\u003eExposure data were sourced from the eQTLGen consortium (https://www.egtlgen.org/), focused on studying blood gene expression and genetic architecture to understand complex traits. This large-scale project, currently in its second phase, included a genome-wide analysis to extract insights from blood samples.\u003c/p\u003e\n\n\u003cp\u003eOutcome data were obtained from the FinnGene database, a genetic research resource focused on European populations. FinnGene\u0026apos;s vast dataset encompasses samples from various regions, facilitating research on genetic diseases and mutations in European populations. Particularly relevant to this study, the prostate cancer dataset (finngen_R10_C3_PROSTATE_EXALLC) comprised 15199 cases and 131266 controls.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e1.2. Differential expression analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Limma package in R is a powerful tool for conducting differential expression analysis, particularly for identifying significantly differentially expressed genes between different groups. In this study, gene expression profiles from recurrence group and non-recurrence group samples were compared using the Limma package to examine molecular processes in disease sample data. The criteria for identifying differentially expressed genes were set at adj.P.Val\u0026lt;0.01. Heat maps and Volcano plots were created to display outcomes.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e1.3. Functional analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn order to further analyze the network pathways involved in differential genes, the Metascape database (www.metascape.org) was used for functional annotation. In particular, Gene Ontology (GO) pathway analysis and KEGG analysis were conducted on specific genes. Statistically significant pathways were determined with criteria set at Min overlap\u0026ge;3 and p\u0026le;0.01.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e1.4. MR analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe outcome IDs obtained from the FinnGene database were cross-referenced with GWAS summary data from https://gwas.mrcieu.ac.uk/ to extract relevant causal relationships in the expression Quantitative Trait Locus (eQTL). To select potential instrumental variables (IVs), a significance threshold of P \u0026lt; 1e-8 was applied to SNPs within each gene locus. Linkage disequilibrium (LD) between SNPs was calculated, and SNPs with an LD threshold of R2 \u0026lt; 0.001 (using a clumping window size of 10,000kb) were retained.\u003c/p\u003e\n\n\u003cp\u003eSubsequently, the retained SNPs underwent analysis following the inverse variance weighted (IVW), MR Egger, weighted median, and weighted mode methods. These methods assess causal relationships between genetic variants and prostate cancer risk, each providing unique insights into causality. Then, statistical methods were used to evaluate the reliability of causality. The Wald ratio was selected when there was only one available instrumental variable corresponding to the genes in the exposure data.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e1.5. Sensitivity analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate the impact of specific genetic variants on prostate cancer risk, we conducted an MR leave-one-out sensitivity analysis. This method systematically excludes individual SNPs, recalculates the pooled effect size of the remaining SNPs, and evaluates the SNP\u0026apos;s unique contribution and robustness to the overall results. We assessed the robustness of our analysis by determining the effect of removing a single SNP on the overall results. This was done by comparing estimates following the removal of each SNP with the overall estimates including all SNPs.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e1.6. Immune cell infiltration analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe CIBERSORT method is widely used for assessing immune cell types within a microenvironment. Based on support vector regression, it conducts deconvolution analysis on immune cell subtype expression matrices, using 547 biomarkers for distinguishing 22 human immune cell phenotypes. These phenotypes include T cells, B cells, plasma cells, and various myeloid cell subsets. In this study, the CIBERSORT algorithm was applied to analyze patient data, estimating the relative proportions of these immune infiltrating cells and conducting correlation analysis between gene expression and immune cell content.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e1.7. Drug sensitivity analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing data from the GDSC Cancer Drug Sensitivity Genomics Database (https://www.cancerrxgene.org/), we employed the R package \u0026ldquo;pRRophetic\u0026rdquo; to predict the sensitivity of each tumor sample to chemotherapy. Through regression analysis, we estimated the IC50 value for each specific chemotherapeutic drug treatment. To validate our predictions, we used the GDSC training set and conducted 10-fold cross-validation to assess regression and prediction accuracy. Default parameter values were employed, including the use of \u0026ldquo;combat\u0026rdquo; to eliminate batch effects and averaging of duplicate gene expression data.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e1.8. GSEA analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients were categorized into high and low expression groups based on their gene expression levels. Gene Set Enrichment Analysis (GSEA) was then employed to explore differences in signaling pathways between these two groups. Annotated gene sets downloaded from the MsigDB database were used as the background gene set for subtype pathways. Differential expression analysis of pathways was conducted between subtypes, focusing on significantly enriched gene sets (adjusted p-value \u0026lt; 0.05) determined based on the consistency score. GSEA analysis, a valuable tool in research combining disease classification with biological significance, facilitated the identification of pathways associated with different gene expression profiles.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e1.9. Gene Set Variation Analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGene Set Variation Analysis (GSVA) is a non-parametric method for assessing enrichment in gene sets across transcriptomes. It evaluates pathway-level changes by scoring gene sets of interest, allowing for the determination of biological functions within samples. In this study, gene sets from the Molecular Signatures Database (v7.0 version) were downloaded, and the GSVA algorithm was applied to comprehensively score each gene set. This facilitated the evaluation of potential changes in biological functions across different samples.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e1.10. Regulatory network analysis of important genes:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used the R package \u0026quot;RcisTarget\u0026quot; to predict transcription factors, with calculations based on motifs. The total amount of motifs in the database determined each motif\u0026apos;s normalized enrichment score (NES). Based on gene sequence and motif similarity, more annotation files were conjectured. The area under the curve (AUC) for each motif-motif set pair was calculated to estimate the overexpression of a motif on a gene set. The gene set\u0026apos;s recovery curve was calculated against motif ordering to achieve this. Finally, the AUC distribution of all motifs in the gene set was used to calculate the NES of each motif.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e1.11. Single cell analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe expression profile was initially read using the Seurat package. Low-mass cell were filtered out based on criteria such as nFeature_RNA \u0026gt; 500 and percent.mt \u0026lt; 5. Subsequently, standardization, normalization and principal component analysis (PCA), were sequentially performed on the data. By looking at the Elbow Plot, the ideal number of primary components was obtained. Via t-distributed stochastic neighbor embedding (t-SNE) analysis, the spatial link between clusters was determined. Furthermore, using the celldex software, cluster annotation was performed with an emphasis on cells believed to be important for tumor growth.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e1.12. Statistical analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe R programming language, version 4.2, was used for all statistical analyses, with a significance threshold of p \u0026lt; 0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e2.1. Identification of Differential Genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing the TCGA public database, we retrieved the PRAD data set, which includes data from 473 samples 412 cases in the non-recurrence group and 61 in the recurrence group. Subsequently, we analyzed the differential expression levels between the two patient groups using the Limma package. The differential gene screening conditions were as follows: adj.P.Val \u0026lt; 0.01. Finally, 486 differential genes were screened out, including 380 up-regulated genes and 106 down-regulated genes (Fig.1 A-B). We then performed further pathway analysis on the identified differential genes. The genes were mainly concentrated in pathways associated with the regulation of the cell cycle process and the mitotic cell cycle process, according to the GOKEGG findings (Fig.1C).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2. Identification of Key Genes through MR Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further identify the key genes affecting prostate cancer based on the differential genes, an outcome ID was obtained by determining the summary statistics of 146465 prostate cancer-related samples (Cases: 15199; Controls: 131266): finngen_R10_C3_PROSTATE_EXALLC. In all, 260 pairs of causal links between genes and outcomes were extracted by sequentially using extract_instruments and extract_outcome_data. Six pairs of genes were found to be causally related to positive eQTL results after additional screening via MR analysis. (Fig.2A-F, IVW pval \u0026lt; 0.05). The corresponding genes were as follows: DENND4B, KCNK6, MPHOSPH6, SPNS1, SYTL3, and ZNF419. The gene KCNK6 (0.8659;0.5058\u0026minus;0.9305; p = 0.0001) was associated with a low risk of prostate cancer, whereas the genes DENND4B (1.0917;1.0163\u0026minus;1.1727; p = 0.0162), MPHOSPH6 (1.0647;1.0172\u0026ndash;1.1144; p = 0.0071), SPNS1 (1.0752;1.0058\u0026ndash;1.1495; p = 0.0333), SYTL3 (1.1091;1.0007\u0026ndash;1.2293; p = 0.0484), ZNF419 (1.0742;1.0111\u0026ndash;1.1412; p = 0.0205) were associated with a high risk of prostate cancer. To ascertain the dependability of the causal linkages of these six genes, sensitivity analysis was further performed. The findings demonstrated the robustness of the six pairs of causal links we chose because the effect of the exclusion of any one SNP on the total error bar was not readily apparent (Fig.3A-F).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3. Exploration of Molecular Mechanisms through Immune Infiltration Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe immune system, extracellular matrix, different growth factors, inflammatory factors, and unique physical and chemical properties make up the majority of the microenvironment. These components have a substantial impact on clinical treatment sensitivity and illness diagnosis. This study further explored the potential molecular mechanisms through which key genes influence prostate cancer progression by analyzing the relationship between key genes and immune infiltration in the prostate cancer data set. Further, the percentage of immune cells in each patient was displayed in this study, along with the relationship between immune cells in various forms(Fig.4A-B). In addition, the research results showed a significant difference in the eosinophil and M2 macrophage levels between the two groups (Fig.4C).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4. Exploration of the Relationship of Key Genes with Immune Cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis investigation on the connection between key genes and immune cells revealed that there is a strong correlation between the key genes in\u0026nbsp;cancer progression\u0026nbsp;and immune cells (Fig.4D-I). Among them, DENND4B has a significant positive correlation with naive B cells, and a significant negative correlation with resting Mast cells; KCNK6 has a significant positive correlation with follicular helper T cells, and has a significant negative correlation with M2 macrophages; MPHOSPH6 has a significant negative correlation with CD8\u003csup\u003e+\u003c/sup\u003e T cells. There is a significant positive correlation and a significant negative correlation with activated NK cells; SPNS1 has a significant negative correlation with neutrophils; SYTL3 has a significant positive correlation with neutrophils and a significant negative correlation with M2 macrophages; ZNF419 has a significant negative correlation with regulatory T cells (Tregs). Significant positive correlation, significant negative correlation with Neutrophils, etc. The study used the TISIDB database to determine the link between these six key genes and various immunological components, such as chemokines, cell receptors, and immune regulatory factors(Fig.5A-E). According to our analyses, these key genes have a significant impact on the immune milieu and are strongly correlated with the degree of immune cell infiltration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5. Analysis of Drug Sensitivity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe treatment effect of early-stage prostate cancer combined with surgery and chemotherapy is clear. Our study is based on the drug sensitivity data included in the GDSC database and uses the R package \u0026quot;pRRophetic\u0026quot; to predict the chemotherapy sensitivity of each tumor sample and explore DENND4B, KCNK6, MPHOSPH6, SPNS1, SYTL3, and ZNF419 and their sensitivity toward common chemotherapy drugs. The research results showed that KCNK6 expression was significantly associated with the sensitivity of the tumors toward ABT.263, ABT.888, and AG.014699 (Fig.6A), MPHOSPH6 expression was significantly related to the sensitivity toward ABT.888 (Fig.6B), and SPNS1 expression was related to the sensitivity toward AP. The sensitivities of tumors toward 24534, ABT.888, and AG.014699 were significantly correlated (Fig.6C), SYTL3 expression was significantly correlated with the sensitivities to ABT.263, AP.24534, CCT007093, and ABT.888 (Fig.6D), and ZNF419 expression was significantly correlated with the sensitivities to ABT.263, AMG.706, AP.24534, CCT007093, ABT.888, and AG.014699 (Fig.6E). DENND4B expression was significantly associated with sensitivities toward ABT.263, AMG.706, ABT.888, and AG.014699 (Fig.6F). Simultaneously, we explored the sensitivity of DENND4B, KCNK6, MPHOSPH6, SPNS1, SYTL3, and ZNF419 to Bicalutamide and Docetaxel. The research results indicated that KCNK6 expression was significantly associated with the tumor sensitivity to Bicalutamide, MPHOSPH6 expression was significantly associated with the tumor sensitivity to Bicalutamide. SYTL3 expression was significantly associated with the sensitivity toward Bicalutamide and Docetaxel, and DENND4B expression was significantly associated with the tumor sensitivity toward Bicalutamide and Docetaxel (Fig.7A-F).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6. Pathway Enrichment Analysis of Key Genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe examined the distinct signaling pathways associated with the six pivotal genes and investigated the plausible molecular routes through which the pivotal genes influence the course of events. GSEA results showed that DENND4B - enriched pathways included the Chemokine signaling pathway, mRNA surveillance pathway, and other pathways (Fig.8A); KCNK6-enriched pathways included mRNA surveillance pathway, PPAR signaling pathway, and other pathways (Fig.8B); MPHOSPH6-enriched pathways included JAK-STAT signaling pathway and NOD - like receptor signaling pathway (Fig.8C); pathways enriched by SPNS1 included mRNA surveillance pathway, Polycomb repressive complex, and other pathways (Fig.8D); SYTL3-enriched pathways included cGMP-PKG signaling pathway, MAPK signaling pathway, and other pathways (Fig.8E); the pathways enriched by ZNF419 included the cAMP signaling pathway, chemokine signaling pathway, and other pathways (Fig.8F).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7. Pathway Enrichment Analysis via GSVA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGSVA results show that DEND4B can be enriched with signaling channels such as WNT BETA CATENIN SIGNALING and TGF BETA SIGNALING, among others (Fig.9A); high expression of KCNK6 can enrich the REACTIVE OXYGEN PATHWAY, IL6 JAK Stat3 signaling (Fig.9B), P53 PATHWAY, and other signaling pathways (Fig.9C); high expression of SPNS1 can enrich signaling pathways such as WNT BETA CATENIN SIGNALING and IL6 JAK STAT3 SIGNALING (Fig.9D); high expression of SYTL3 can enrich signaling pathways such as NOTCH SIGNALING and IL6 JAK STAT3 SIGNALING (Fig.9E); and high expression of ZNF419 can enrich signaling pathways such as WNT BETA CATENIN SIGNALING and UNFOLDED PROTEIN RESPONSE (Fig.9F).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.8. Enrichment Analysis of Transcription Factors Regulating Key Genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis investigation discovered that the six key genes used as the gene set for analysis are regulated through numerous transcription factors and other common pathways. Therefore, making use of cumulative recovery curves, enrichment analysis of these transcription factors was performed. The most highly ranked motif (NES: 7.61) according to Motif-TF annotation and selection study of significant genes was cisbp__M5114. All of the enriched motifs and associated transcription factors for key genes are shown in this study(Fig.10A-B).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.9. Analysis of Disease-Related Regulatory Genes and Correlation with Key Genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe obtained disease-related regulatory genes through the GeneCards database (\u003ca href=\"https://www.genecards.org/\"\u003ehttps://www.genecards.org/\u003c/a\u003e). The expression levels of 20 genes with the highest Relevance Score were determined, and the results showed that the levels of AR, BRCA1, CHEK2, PCA3, PCAT1, and PCAT7 were different between the two patient groups (Fig.10C). Furthermore, we ran correlation analyses on disease-regulated genes and key genes. Significant correlations were found between the expression levels of disease-regulated genes and the expression levels of key genes. Among them, DENND4B and BRCA1 were significantly positively correlated (r= 0.493), and KCNK6 and PCAT7 were significantly negatively correlated ( r = \u0026minus;0.346) (Fig.10D).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.10. Single Cell Analysis of GSE193337 Dataset\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe obtained the GSE193337 single cell data, which includes eight samples in total. The Seurat software was used for single cell analysis, and the tSNE technique was used to cluster cells. Finally, 12 subpopulations were obtained through TSNE analysis (Fig.11A). These were annotated to six cell categories: T_cell, Epithelial_cells, Monocyte, B_cell, Endothelial_cells, and Smooth_muscle_cells (Fig.11B). In addition, we analyzed the expression levels of key genes in T_cell, Epithelial_cells, Monocyte, B_cell, Endothelial_cells, and Smooth_muscle_cells in the single cell data (Fig.11C-D). Moreover, the immunological and metabolic pathways were quantified using AUCell, and the relationship between key genes and immune and metabolic pathways were researched and demonstrated (Fig.11E).Next, we visualized the gene co-expression of prostate cancer-related regulatory genes (AR, BRCA2, HOXB13) and the six key genes in the single-cell level (Fig 12-14A-F).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOne of the primary concerns for prostate cancer patients after undergoing curative surgery is the possibility and timing of BCR. Therefore, accurately predicting the timing of BCR becomes extremely crucial\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. The chances of survival and quality of life of patients with prostrate cancer could be increased if it were feasible to forecast the onset and timing of BCR with sufficient accuracy\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Through the use of comprehensive bioinformatics analysis, we were able to identify 486 genes, 380 of which were upregulated and 106 of which were downregulated, that were linked to prostate cancer recurrence. By combining MR analysis, we further screened six key genes, namely DENND4B, KCNK6, MPHOSPH6, SPNS1, SYTL3, and ZNF419.\u003c/p\u003e \u003cp\u003eThese differentially expressed genes were subjected to GO and KEGG pathway analyses. The findings indicated that these genes are primarily enriched in pathways related to the regulation of cell cycle and mitotic cell cycle. Regulation of the cell cycle is crucial for cell proliferation, growth, and repair, and is also closely associated with the occurrence, development, and metastasis of tumors\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. In addition, the regulation of cell cycle is closely associated with the synergistic action of chemotherapy drugs (both inhibitory and promoting effects), which may enhance the sensitivity of tumor cells to chemotherapy drugs\u003csup\u003e[\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e .\u003c/p\u003e \u003cp\u003eWe next used the R package \"pRRophetic\" to uncover significant associations between key genes and particular medications according to the drug sensitivity data from the GDSC database. For example, SPNS1 and ZNF419 showed significant sensitivity to Bicalutamide, Docetaxel, ABT.888, AG.014699, and others. Bicalutamide and Docetaxel, as commonly used drugs for prostate cancer, have significant correlations with SPNS1 and ZNF419, indirectly suggesting SPNS1 and ZNF419 as new directions in the treatment of prostate cancer.\u003c/p\u003e \u003cp\u003eAn essential component in the development of cancer is the immunological microenvironment. To shed light on the possible roles of these genes in the tumor immune environment, we also examined the relationships between key genes and immune cells in a prostate cancer immune microenvironment. The immune microenvironment, which is a core component of tumor biology, includes immune cells, extracellular matrix, growth factors, and inflammatory factors. The relationship between the immune microenvironment and tumors is increasingly being emphasized\u003csup\u003e[\u003cspan additionalcitationids=\"CR27 CR28 CR29\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Through the analysis of immune cell distribution in prostate cancer samples and their relationship with the expression of key genes, we discovered important connections between particular immune cell types and genes including DENND4B, KCNK6, and MPHOSPH6. For instance, DENND4B was positively correlated with immature B cells and negatively correlated with resting mast cells, with recent reports increasingly discussing the relationship between resting mast cells and tumors\u003csup\u003e[\u003cspan additionalcitationids=\"CR32 CR33\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. This suggests that these genes can become key therapeutic targets in prostate cancer treatment. Subsequently, we explored the functions of these genes in multiple signaling pathways. These genes are involved in several critical biological processes, including the PPAR signaling pathway, the JAKSTAT signaling pathway, the mRNA surveillance pathway, the chemokine signaling pathway, and others, according GSEA and GSVA. For example, the enrichment of DENND4B in the Chemokine signaling pathway and mRNA surveillance pathway may indicate its dual role in cell communication and gene expression regulation. Previous studies have identified the Chemokine signaling pathway to play a key role in tumor initiation and progression, potentially leading to invasion and migration\u003csup\u003e[\u003cspan additionalcitationids=\"CR36 CR37 CR38\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. In addition, in the GSVA enrichment process, key genes were also found to be enriched in pathways such as WNT BETA CATENIN SIGNALING, NOTCH SIGNALING, P53 PATHWAY, TGF BETA SIGNALING, IL6 JAK STAT3 SIGNALING, and all of which have been extensively reported in the development of various tumors\u003csup\u003e[\u003cspan additionalcitationids=\"CR41 CR42 CR43\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e, further validating the crucial role that these six key genes possibly play in prostate cancer progression.\u003c/p\u003e \u003cp\u003eWe also used the GeneCards database to identify regulatory genes, such as AR, PCAT17, and BRCA1, closely associated with prostate cancer development, and found significant expression correlations with the key genes. This correlation analysis not only validated the relevance of our choice of key genes but also provided a basis for further validation of potential therapeutic targets. Using the Seurat package for single-cell analysis and applying the t-SNE algorithm for cell clustering, we identified 12 cell subgroups and annotated six major cell categories. Within these cell categories, we observed a high expression of SYTL3 in T cells, whereas KCNK6 and SPNS1 showed higher expression in monocytes. This discovery is especially significant because monocytes and T lymphocytes are essential for the development and progression of malignancies\u003csup\u003e[\u003cspan additionalcitationids=\"CR46 CR47\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e .\u003c/p\u003e \u003cp\u003eLastly, we performed a correlation study between these six key genes and the most influential three genes \u0026mdash; Androgen Receptor (AR), BRCA2, and HOXB13 \u0026mdash; as determined by Relevance score. Of particular significance was the role of AR, which has been extensively studied and found to be intimately linked to the development of prostate cancer in both initial stages and progression\u003csup\u003e[\u003cspan additionalcitationids=\"CR50\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003e. Our analysis data indicates significant correlations between these six key genes and AR. In particular, DENND4B, KCNK6, SPNS1, and SYTL3 showed significant correlations with AR. In conclusion, the biological functions of these six key genes in prostate cancer merit further investigation. However, our analysis is based on public big data and lacks more experimental data to validate our findings. This is a limitation of our study.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study, through multiple bioinformatics analyses, six key genes \u0026mdash; DENND4B, KCNK6, MPHOSPH6, SPNS1, SYTL3, and ZNF419 \u0026mdash; were identified, which may become potential key genes for managing the recurrence of prostate cancer. These findings provide new directions for clinical physicians with regard to the management of prostate cancer recurrence and offer a scientific basis for further exploration of therapeutic targets.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eBCR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eBiochemical Recurrence\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eMendelian Randomization\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eRadical Prostatectomy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003ePSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eProstate-Specific Antigen\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eSNPs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eSingle Nucleotide Polymorphisms\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eIVs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eInstrumental Variables\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eGO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eGene Ontology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eeQTL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eExpression Quantitative Trait Locus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eLD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eLinkage Disequilibrium\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eInverse Variance Weighted\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eGSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eGene Set Enrichment Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eGSVA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eGene Set Variation Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eNES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eNormalized Enrichment Score\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003eArea Under Curve\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003ePCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003ePrincipal Component Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003et-SNE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\" valign=\"top\"\u003e\n \u003cp\u003et-distributed Stochastic Neighbor Embedding\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e: LL, QYL and YWZ designed the study and carried out the data analyses. LL and LJM draft the manuscript. JXL and LT revised and polished the manuscript. QYL reviewed the manuscrip. All authorsread and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e: Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: This research was supported by the General Project of the Corps Science and Technology Program (Project No. 2021AB036).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eThis study\u0026apos;s expression spectrum data are sourced from the TCGA(https://portal.gdc.cancer.gov/) and GEO databases. The exposure data for Mendelian analysis come from the eQTLGen Consortium(https://www.egtlgen.org/) database, and the outcome data are from the FinnGen Consortium R10 release(index (finngen.fi)) database. All the above data are publicly available. Corresponding authors can be contacted for more information.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e: The data analyzed during the study come from the relevant studies where written informed consent was received prior to participation. This study was approved by Approved by Clinical Medical Research Ethics Committee of the Second Affiliated Hospital of Shihezi University School of Medicine\u0026nbsp;\u0026middot;\u0026nbsp;Xinjiang Production and Construction Corps Hospital.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e: Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e: The authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBray, F, Laversanne, M, Sung, H, et al. 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Pancreatic cancer is associated with aberrant monocyte function and successive differentiation into macrophages with inferior anti-tumour characteristics. PANCREATOLOGY. 2021; 21 (2): 397-405. doi: 10.1016/j.pan.2020.12.025\u003c/li\u003e\n \u003cli\u003eBrown, LE, Zhang, D, Cui, W. Flow Cytometric Analysis of Monocytes and Granulocytes May Be Useful in the Distinction of Myeloid Neoplasms from Reactive Conditions: A Single Institution Experience and Literature Review. ANN CLIN LAB SCI. 2020; 50 ANN CLIN LAB SCI. PMID: 32581021\u003c/li\u003e\n \u003cli\u003eLopes-Coelho, F, Silva, F, Gouveia-Fernandes, S, et al. Monocytes as Endothelial Progenitor Cells (EPCs), Another Brick in the Wall to Disentangle Tumor Angiogenesis. Cells. 2020; 9 Cells. doi: 10.3390/cells9010107\u003c/li\u003e\n \u003cli\u003eRicke, EA, Williams, K, Lee, YF, et al. 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HUM PATHOL. 2019; 89 99-108. doi: 10.1016/j.humpath.2019.04.009\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, biochemical recurrence, Mendelian Randomization, Bioinformatics, Prognosis","lastPublishedDoi":"10.21203/rs.3.rs-4765793/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4765793/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAccurately detecting prostate cancer recurrence currently poses a challenge for clinicians. In addition, biochemical recurrence (BCR) is a crucial risk factor for clinical recurrence and metastasis. The understanding of genes involved in BCR and their mechanisms is limited. Therefore, this study aims to comprehensively explore the genes associated with BCR and their biological mechanisms in prostate cancer using bioinformatics techniques.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData from 473 non-recurrence (n\u0026thinsp;=\u0026thinsp;412) and recurrence (n\u0026thinsp;=\u0026thinsp;61) samples, were obtained from the TCGA public database. The key genes between groups were identified using the Limma package. Mendelian Randomization (MR) was employed to screen for key genes, describing their eQTL-positive outcomes in causality. Relationships between key genes and immune infiltration, immune cells, drug sensitivity, and signaling pathways were analyzed. Further, the enrichment of transcriptome gene sets, prediction of transcription factors, and specific situations in single cells were evaluated.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn all, 486 DEGs were found, comprising 380 upregulated and 106 downregulated genes. MR identified DENND4B, KCNK6, MPHOSPH6, SPNS1, SYTL3, and ZNF419 as pivotal genes. Multi-omics analysis suggested these genes as predictive and diagnostic markers for BCR.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study identified prostate cancer recurrence-related DEGs and their functions using bioinformatics and MR analysis, offering significant clinical implications for accurate prediction and assessment of prostate cancer recurrence. It also provided effective targets for managing recurrent prostate cancer.\u003c/p\u003e","manuscriptTitle":"Comprehensive multi-omics analysis reveals the molecular mechanism of prostate cancer recurrence","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-22 07:59:13","doi":"10.21203/rs.3.rs-4765793/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":"a991ba73-2117-4074-9c05-d6745afb448e","owner":[],"postedDate":"August 22nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-25T05:23:39+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-22 07:59:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4765793","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4765793","identity":"rs-4765793","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}