Gene selection driven by DNA methylation in relation to lymph node metastasis in prostate cancer and prognosis analysis

Gene selection driven by DNA methylation in relation to lymph node metastasis in prostate cancer and prognosis analysis | 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 Gene selection driven by DNA methylation in relation to lymph node metastasis in prostate cancer and prognosis analysis Ji Sun, Xing Wang, Tie-Jun Liang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4022664/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 Purpose Lymph node metastasis is an independent prognostic factor for prostate cancer (PCa), and this study aims to explore the intrinsic molecular mechanisms of PCa lymph node metastasis based on epigenetic modifications using bioinformatics. Methods Gene expression data set TCGA PRAD was downloaded from the UCSC Xena database for differential analysis. Differential genes between patients with and without lymph node metastasis were identified and functionally annotated. DNA methylation data from the GSE220910 dataset were used to identify differential methylation sites (DMPs) using the "ChAMP" R package. The correlation between differential gene expression values and methylation probe beta values was calculated and tested for significance. Finally, a prognosis analysis was conducted for the selected genes regulated by DNA methylation. Results We identified 1380 significantly differentially expressed genes (DEGs), including 906 upregulated and 474 downregulated genes. GO analysis revealed that upregulated genes in patients with lymph node metastasis were mainly involved in processes such as cell division and mitosis, while downregulated genes participated in the cellular response to copper and zinc ions. Subsequently, we further selected 81009 differential methylation sites (DMPs), ultimately retaining 263 DEGs associated with 382 DMPs. Correlation analysis showed that LTA, DOK3, TNFRSF25, and CHRM1 had Pearson correlation coefficients of -0.4092, -0.4111, -0.4054, and − 0.4598, respectively (P < 0.05), with their corresponding methylation probes. Survival analysis indicated that high expression of LTA, DOK3, and TNFRSF25 genes was associated with a shortened progression-free interval (PFI) in PCa patients, while CHRM1 showed the opposite trend (P < 0.05). Conclusion Lymph node metastasis in PCa patients is associated with active cell division and suppression of the response to metal ions. We also discovered that LTA, DOK3, TNFRSF25, and CHRM1 are regulated by DNA methylation, and their abnormal expression significantly impacts patient prognosis. Lymph node metastasis prostate cancer DNA methylation prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Prostate cancer (PCa) ranks as the second most common cancer and the fifth leading cause of cancer-related deaths in men globally[ 1 ]. Lymph node metastasis is one of the major pathways for the spread of this disease[ 2 ]. Studies have reported that over 15% of PCa patients exhibit lymph node metastasis during radical prostatectomy[ 3 ]. Lymph node metastasis refers to the process where cancer cells migrate to lymph nodes through the lymphatic system, and its occurrence is closely associated with factors such as the pathological grade, clinical stage, and serum PSA levels of patients[ 4 ]. These patients have a higher risk of recurrence and a poorer prognosis after initial treatment[ 5 ]. Therefore, predicting lymph node metastasis holds significant clinical importance. Recent research on cancer etiology has identified a crucial role played by epigenetics, including tumor-specific changes in epigenetic factors such as DNA methylation (DNAm). DNAm has been widely utilized as a molecular marker for the diagnosis, prognosis, and prediction of treatment response in tumors[ 6 , 7 ]. Aberrant DNA methylation is a key feature observed in the early development, progression, and metastasis of tumors[ 8 ]. Studies have identified methylation changes as a critical driving factor in the transformation and progression of PCa, leading to a transition towards a more invasive phenotype in metastatic PCa[ 9 ]. The clinical course of PCa is heterogeneous, making it challenging to determine the most appropriate treatment or monitoring strategy for individual patients[ 10 ]. Therefore, identifying patient populations prone to metastasis and screening for specific prognostic biomarkers can aid in identifying high-risk patients for early intervention and reducing the risk of metastasis. This study aims to use bioinformatics to screen for PCa lymph node metastasis-related genes based on DNA methylation and validate their significance, with the hope of identifying potential molecular markers for PCa lymph node metastasis. Materials and methods Data Collection and Preprocessing A search was conducted in the GEO database ( https://www.ncbi.nlm.nih.gov/geo/ ) using the keywords "prostate cancer" and "lymph node metastasis." The GSE220910 dataset was selected and downloaded, comprising DNA methylation data from 60 primary tumor samples and 40 lymph node metastasis samples. Additionally, data from the TCGA Prostate adenocarcinoma (PRAD) dataset were collected from the UCSC Xena database. This dataset included gene expression data from 498 tumor tissues, DNA methylation data, and clinical information for 52 healthy tissues. Based on the clinical information, specifically the "number_of_lymphnodes_positive_by_he" record, the 498 patients were categorized into 81 cases with lymph node metastasis, 326 cases without lymph node metastasis, and 91 cases with unknown status. To ensure consistency in methylation probes, the probe information provided by the sequencing platforms of the GSE220910 dataset (GPL21145, Infinium MethylationEPIC) and TCGA PRAD (Illumina HumanMethylation450 BeadChip) was used. The intersection of these platforms resulted in the retention of 259,531 probes for downstream analysis. Additionally, the SU2C dataset, which includes gene expression and clinical follow-up information for prostate cancer patients with lymph node metastasis, was downloaded from cBioPortal ( https://www.cbioportal.org/ )[ 11 ]. Differential Gene Selection and Functional Annotation Differential gene selection was performed using R software on patients with and without lymph node metastasis in the TCGA PRAD dataset. The mean expression values of each gene in the two groups were calculated, and the fold change was determined using the Wilcoxon rank-sum test for differential comparison. False discovery rate (FDR) correction was applied to adjust the p-values, and genes with FDR 1 were considered significantly differentially expressed genes (DEGs). To annotate and analyze the biological functions of DEGs, the DAVID tool ( https://david.ncifcrf.gov/ ) was utilized for gene ontology (GO) analysis. Enrichment results with p-values less than 0.05 were retained for further analysis. Quantification Scoring with GSVA Four gene sets, namely "GOBP_CELL_DIVISION," "GOBP_MITOTIC_CELL_CYCLE," "GOBP_RESPONSE_TO_COPPER_ION," and "GOBP_RESPONSE_TO_ZINC_ION," were downloaded from the MSigDB ( https://www.gsea-msigdb.org/gsea/msigdb/ ) database. These gene sets are specific to the human species and contain 636, 937, 42, and 49 relevant genes, respectively[ 12 , 13 ]. Utilizing gene expression data from TCGA PRAD dataset for patients with and without lymph node metastasis, the R package "GSVA" (version: 1.42.0) and its "gsva" algorithm were employed to quantitatively score the samples. Differential comparisons were conducted using the Wilcoxon rank-sum test. Selection and Correlation Analysis of DMPs The mean beta values of methylation probes for patients with and without lymph node metastasis were calculated in both TCGA PRAD and GSE220910 datasets. The fold change (FC) was computed, and differential comparison was performed using the "ChAMP" R package (Version: 2.28.0) to filter DMPs. DMPs with FDR < 0.01 and consistent methylation direction (either increased or decreased) were retained. The genes covered by DMPs in both datasets were intersected with the differentially expressed genes identified in TCGA PRAD. Pearson correlation coefficients (PCC) between gene expression values and methylation probe beta values were calculated, and correlation tests (cor.test) were conducted. Correlation relationships with PCC < -0.3 and a test p-value < 0.01 were retained. Prognostic Analysis Prognostic follow-up data for prostate cancer (PCa) patients were collected from the TCGA PRAD dataset. Based on the expression values of LTA (cg16243606), TNFRSF25 (cg00834988), DOK3 (cg20138861), and CHRM1 (cg23890832), patients were stratified into high and low expression groups. The Kaplan-Meier curve was employed to assess the progression-free interval (PFI) of patients, utilizing the "survival" R package (Version: 3.4-0). Hazard ratios (HR) with a [95% CI] and log-rank test p-values were recorded from the survival model. Results Increased activity in cell division-induced inhibition in response to metal ions From the TCGA PRAD dataset, we identified 1380 DEGs, including 906 upregulated and 474 downregulated genes (see Table 1 , Fig. 1 ). GO analysis results indicated that upregulated genes primarily participate in biological processes such as "GO:0051301cell division," "GO:0000281mitotic cytokinesis," and "GO:0000278mitotic cell cycle." On the other hand, downregulated genes are mainly involved in processes like "GO:0010273detoxification of copper ion," "GO:0071280cellular response to copper ion," and "GO:0071294cellular response to zinc ion" (see Table 2, Fig. 2 ). Based on the functional annotation results, we collected four relevant gene sets. Utilizing the "GSVA" algorithm from the R package, we quantified scores and conducted differential comparisons. The results indicate that lymph node metastasis patients exhibit significantly higher GSVA scores for cell division and the mitotic cell cycle compared to non-metastatic patients, with p-values of 0.0003 and 0.0015, respectively. The GSVA scores for cellular response to copper ion and zinc ion in lymph node metastasis patients are significantly lower than those in patients without lymph node metastasis, with p-values of 1.2e-08 and 4.8e-05, respectively (see Fig. 3 ). These findings suggest that patients with lymph node metastasis display heightened activity in cell division while exhibiting suppressed responses to metal ions. Filtering DNA Methylation-Regulated DEGs in Patients with Lymph Node Metastasis Subsequently, we utilized the "ChAMP" R package to analyze the DNA methylation data detected in the TCGA PRAD dataset. We identified 18,498 DMPs, including 1,636 with increased methylation levels and 37,993 with decreased methylation levels, covering a total of 8,155 genes. Based on the DNA methylation data from the GSE220910 dataset, we filtered 81,009 DMPs, comprising 43,016 with increased methylation levels and 37,993 with decreased methylation levels, covering a total of 15,185 genes. Combining the previously identified 1,380 DEGs from TCGA PRAD, we ultimately obtained 263 DEGs related to 382 DMPs (refer to Table 3 and Fig. 4 ). To validate whether the aforementioned DEGs are regulated by DNA methylation, we separately calculated the Pearson correlation coefficients (PCC) between the 263 DEGs and their corresponding 259,531 methylation probe beta values. Correlation tests were conducted, resulting in 13 relationships with PCC < -0.3 and test p-values < 0.01. Among these, LTA (cg16243606), DOK3 (cg20138861), TNFRSF25 (cg00834988), and CHRM1 (cg23890832) exhibited PCC coefficients of -0.4092, -0.4111, -0.4054, and − 0.4598, respectively, with p-values < 0.01 (Table 1 and Fig. 5 ). Table 1 13 DEGs Regulated by DNA Methylation DMP DEGs PCC cor.test pValue cg16243606 LTA -0.4092 1.57E-21 cg11261261 GFI1 -0.3844 5.55E-19 cg00842595 ARHGAP15 -0.3390 7.36E-15 cg14730097 LAT -0.3020 5.78E-12 cg02741305 COL11A2 -0.3106 1.35E-12 cg20138861 DOK3 -0.4111 9.95E-22 cg00834988 TNFRSF25 -0.4054 4.01E-21 cg23890832 CHRM1 -0.4598 2.05E-27 cg27587826 LENG8 -0.3022 5.68E-12 cg14519392 ENGASE -0.3378 9.28E-15 cg12969193 IL21R -0.3013 6.53E-12 cg17429587 BMPER -0.3338 1.99E-14 cg09641955 KIF2C -0.3429 3.49E-15 Aberrant expression of LTA, DOK3, TNFRSF25, and CHRM1 is associated with the prognosis of PCa lymph node metastasis We observed a significant upregulation of LTA, DOK3, and TNFRSF25 genes in patients with lymph node metastasis, while CHRM1 showed a significant downregulation, with fold change (FC) values of 1.3316, 1.1148, 1.1095, and 0.8813, respectively (refer to Table 3). The p-values were 2.47e-07, 5.42e-07, 0.0003, and 7.46e-08, respectively (see Fig. 6 ). Corresponding probe methylation levels can be seen in Figure S1. To further validate the potential of the above genes in predicting clinical outcomes, we integrated patient data from 498 cases with recorded gene expression and prognosis information. Prognostic analyses were conducted for LTA, TNFRSF25, DOK3, and CHRM1. Patients were stratified into high and low expression groups based on the expression levels of LTA (threshold: 0.1787). The constructed survival model yielded a Hazard Ratio (HR) of 2.1620 [95% CI: 1.3930–3.3570] with a test p-value of 0.0004 (see Fig. 7 A). Additionally, focusing on lymph node metastasis patients, 81 cases were categorized into high and low expression groups using a threshold of 11.2949. The resulting survival model had an HR of 2.4840 [95% CI: 0.8405-7.3400] with a test p-value of 0.0890 (see Fig. 7 B). The same approach was applied to TNFRSF25, DOK3, and CHRM1 genes (see Fig. 7 C-D, Fig. 8 ). Kaplan-Meier survival analysis revealed that, regardless of lymph node metastasis occurrence, upregulation of LTA, TNFRSF25, and DOK3 expression was associated with a shortened Progression-Free Interval (PFI) in PCa patients, while CHRM1 exhibited the opposite effect. Additionally, we supplemented the prognosis analysis for the probes corresponding to these four genes (see Figure S2, Figure S3). Discussion As of today, metastatic prostate cancer (PCa) remains an incurable condition, and lymph node metastasis indicates a poor prognosis for patients. Therefore, elucidating the molecular mechanisms of lymph node metastasis is crucial for understanding disease progression and developing new therapeutic targets. In this study, we observed heightened tumor cell proliferation in patients with lymph node metastasis, coupled with a suppression of response to copper and zinc ions. This finding is consistent with previous reports that uncontrolled cell proliferation and dysregulation of cell cycle processes are characteristic features of cancer [ 14 ]. The relationship between cellular response to metal ions and cancer is closely intertwined. For instance, copper serves as a therapeutic agent in cancer treatment with low adverse reactions [ 15 ]. Zinc deficiency can lead to severe diseases in the brain, pancreas, liver, kidneys, and reproductive organs, and zinc loss is indicative of PCa development [ 16 ]. Furthermore, the dysregulation of key genes in this process also promotes the lymphatic spread of PCa [ 17 ]. Our study proposes four DNA methylation-based biomarkers: LTA (Lymphotoxin Alpha), TNFRSF25 (TNF Receptor Superfamily Member 25), DOK3 (Downstream Of Tyrosine Kinase 3), and CHRM1 (Cholinergic Receptor Muscarinic 1). In patients with lymph node metastasis of prostate cancer (PCa), low methylation of LTA, TNFRSF25, and DOK3 leads to high gene expression, while high methylation of CHRM1 results in low gene expression (refer to Supplementary Table 1 and Supplementary Table 3 for details). It is well-known that tumor cells exhibiting overall DNA hypomethylation may contribute to genetic instability and promote the occurrence and development of cancer, particularly in metastatic PCa [ 18 ]. Furthermore, we observed that, regardless of lymph node metastasis occurrence, elevated expression of LTA, TNFRSF25, and DOK3 is associated with an unfavorable prognosis for PCa patients, while the opposite holds true for CHRM1. LTA and TNFRSF25 belong to the tumor necrosis factor (TNF) family, and TNF has been found to be involved in the occurrence and development of various cancers [ 19 ]. Existing data suggest that elevated TNF is a risk factor for cancer [ 19 ]. Evidence indicates an association between LTA polymorphism and cancer risk in elderly Japanese men [ 20 ]. Research findings predict that LTA appears to promote the growth and spread of prostate cancer (PCa) by increasing vascular endothelial growth factor in PCa cell lines, providing ample nutrients and oxygen supply to the tumor [ 21 ]. Patients with high Lymphotoxin (LT) levels are more likely to develop resistance to prostate cancer, making them potential major beneficiaries of anti-LT treatment [ 22 , 23 ]. The protein encoded by the TNFRSF25 gene is a member of the TNF receptor superfamily, with this receptor being preferentially expressed in tissues rich in lymphocytes. Studies indicate differential expression of TNFRSF25 between non-progressing and progressing PCa groups, significantly correlating with non-progressive survival, making it a potential target for PCa treatment [ 24 ]. Aberrant methylation of TNFRSF25 is a significant feature in PCa, playing a crucial role in cell apoptosis escape during tumor development [ 25 ]. Both LT and TNF bind to receptors in the TNF receptor superfamily, inducing cell apoptosis and necrosis with efficacy similar to TNF [ 26 ]. It is noteworthy that our functional enrichment analysis results also indicate that upregulated LTA and TNFRSF25 genes in patients with lymph node metastasis are involved in apoptosis processes. Therefore, we speculate that abnormal DNA methylation of LTA and TNFRSF25 induces apoptosis escape in PCa cells, thereby promoting PCa progression. However, this statement currently lacks experimental evidence and requires further data for validation of its effectiveness. Recent studies have reported on the role of DOK3 in tumor progression. Jin et al.'s research confirmed the upregulation of DOK3 in PCa cell lines and tissues, and its overexpression promoted PCa progression. Elevated levels of DOK3 were associated with higher pathological stages and poorer prognosis. Similar results were observed in samples from PCa patients, marking the first report on the role of DOK3 in promoting PCa development [ 27 ]. CHRM1 is primarily distributed in the central and peripheral nervous systems. Studies have reported that CHRM1 is overexpressed in PCa tissues compared to control groups. Overexpression of CHRM1 positively regulates PCa cell migration and invasion [ 28 , 29 ]. This contradicts our conclusion that "CHRM1 expression is reduced in PCa lymph node metastasis compared to primary tumors." However, gene expression changes during tumor development and metastasis vary, depending on factors such as the tumor microenvironment, cell signaling, and transcriptional regulation during the metastatic process. In this study, it was discovered that abnormal methylation of the LTA, TNFRSF25, DOK3, and CHRM1 genes affects their expression and functions, thereby participating in the regulation of PCa metastasis. The detection of these biomarkers can provide clinicians with more comprehensive and accurate diagnostic and prognostic information, assisting them in formulating better treatment plans and predicting disease progression. Additionally, these genes may also serve as potential targets for the development of new diagnostic and therapeutic approaches. References SUNG H, FERLAY J, SIEGEL RL et al (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries [J]. Cancer J Clin 71(3):209–249 HIJAZI S, MELLER B, LEITSMANN C et al (2016) See the unseen: Mesorectal lymph node metastases in prostate cancer [J]. 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Asian J Androl 20(6):608–614 Tables Tables 2 and 3 are not available with this version. Supplementary Information Supplementary Information is not available with this version Additional Declarations The authors declare no competing interests. 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-4022664","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":276878590,"identity":"d6e1390e-c384-4305-bf3a-6a90c96bc364","order_by":0,"name":"Ji Sun","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ji","middleName":"","lastName":"Sun","suffix":""},{"id":276878591,"identity":"f8f0da8f-0eac-4be6-a56d-364086a69b0e","order_by":1,"name":"Xing Wang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xing","middleName":"","lastName":"Wang","suffix":""},{"id":276878592,"identity":"e8e1ffe3-7714-4600-bf6d-a5abfab93361","order_by":2,"name":"Tie-Jun Liang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIiWNgGAWjYDACZiB+wMAmB6QYDzAwJBCpJSGBzRjEIlILA1gLQ2ID0VoMjvMe/JD4gy+9XyL/wIEPFWkM/O3d+PVJNvMlSwAdljtzRjLDwRlnchgkzpzdgFcLPzOPGcgvuRtuJDMc5m2rYDCQyMWvhQ2qJd2AaC0wWxKgWnIIa5Fs5jGWSEhjM5zZ89gA6Jc0HoJ+MTh/xvDDB5tj8vzsiQ8ffKhIluNv78WvBQqOwVk8xCgHgRpiFY6CUTAKRsFIBACY/kJ4EGnx9QAAAABJRU5ErkJggg==","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Tie-Jun","middleName":"","lastName":"Liang","suffix":""}],"badges":[],"createdAt":"2024-03-07 03:02:05","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-4022664/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4022664/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53118234,"identity":"ee2ad5ec-a77b-456b-95ee-8ecb02d77605","added_by":"auto","created_at":"2024-03-20 19:55:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":850473,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential Gene Expression in Patients with and without Lymph Node Metastasis in TCGA PRAD Dataset. 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RESPONSE_TO_ZINC_ION.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4022664/v1/fa4072d88dad7a048903b3cf.png"},{"id":53117037,"identity":"4a4bb7b8-c5ad-4125-a6ff-295df0e05108","added_by":"auto","created_at":"2024-03-20 19:47:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":799204,"visible":true,"origin":"","legend":"\u003cp\u003eSelection of DMPs. A-B Heatmap displays of DMPs in patients with and without lymph node metastasis. C Intersection resulting in 263 DEGs related to DMPs.\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-4022664/v1/5ef29fdd8618389cbbc8a54b.png"},{"id":53117036,"identity":"71737090-f882-4ae4-8452-8f0d80f2beba","added_by":"auto","created_at":"2024-03-20 19:47:04","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":310741,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation Analysis between Gene Expression and Methylation Probe Beta Values. A. LTA and cg16243606. B. DOK3 and cg20138861. C. TNFRSF25 and cg00834988. D. CHRM1 and cg23890832.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-4022664/v1/0c5bb6e42d24c86a7ed70f31.png"},{"id":53117033,"identity":"5df6945e-177f-4a85-b160-8a2adbfc4b1f","added_by":"auto","created_at":"2024-03-20 19:47:04","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":165522,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential Gene Expression between Patients with and without Lymph Node Metastasis in the TCGA PRAD Dataset. A. LTA. B. DOK3. C. TNFRSF25. D. CHRM1.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-4022664/v1/916ca92159da0af163b3c607.png"},{"id":53118235,"identity":"aac1f9ae-1913-4213-9f9a-545cda0f71af","added_by":"auto","created_at":"2024-03-20 19:55:04","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":156733,"visible":true,"origin":"","legend":"\u003cp\u003eImpact of LTA and TNFRSF25 Gene Expression on the Prognosis of PCa Patients. A and B depict Progression-Free Interval (PFI) survival Kaplan-Meier curves for LTA. C and D show PFI survival Kaplan-Meier curves for TNFRSF25.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-4022664/v1/92b787a9624f55fb642b6488.png"},{"id":53117038,"identity":"dffe8c9c-f0f2-4477-9da0-b2908c456bea","added_by":"auto","created_at":"2024-03-20 19:47:04","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":156917,"visible":true,"origin":"","legend":"\u003cp\u003eImpact of DOK3 and CHRM1 Gene Expression on the Prognosis of PCa Patients. A and B depict Progression-Free Interval (PFI) survival Kaplan-Meier curves for DOK3. C and D show PFI survival Kaplan-Meier curves for CHRM1.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-4022664/v1/94c9757793453a98b1855ad4.png"},{"id":53118714,"identity":"43e73880-8cc4-4698-bff7-6523b330bc79","added_by":"auto","created_at":"2024-03-20 20:03:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3551465,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4022664/v1/7fbc565d-6f8b-41a3-b71c-932e216456e0.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eGene selection driven by DNA methylation in relation to lymph node metastasis in prostate cancer and prognosis analysis\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eProstate cancer (PCa) ranks as the second most common cancer and the fifth leading cause of cancer-related deaths in men globally[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Lymph node metastasis is one of the major pathways for the spread of this disease[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Studies have reported that over 15% of PCa patients exhibit lymph node metastasis during radical prostatectomy[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Lymph node metastasis refers to the process where cancer cells migrate to lymph nodes through the lymphatic system, and its occurrence is closely associated with factors such as the pathological grade, clinical stage, and serum PSA levels of patients[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. These patients have a higher risk of recurrence and a poorer prognosis after initial treatment[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Therefore, predicting lymph node metastasis holds significant clinical importance.\u003c/p\u003e \u003cp\u003eRecent research on cancer etiology has identified a crucial role played by epigenetics, including tumor-specific changes in epigenetic factors such as DNA methylation (DNAm). DNAm has been widely utilized as a molecular marker for the diagnosis, prognosis, and prediction of treatment response in tumors[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Aberrant DNA methylation is a key feature observed in the early development, progression, and metastasis of tumors[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Studies have identified methylation changes as a critical driving factor in the transformation and progression of PCa, leading to a transition towards a more invasive phenotype in metastatic PCa[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The clinical course of PCa is heterogeneous, making it challenging to determine the most appropriate treatment or monitoring strategy for individual patients[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Therefore, identifying patient populations prone to metastasis and screening for specific prognostic biomarkers can aid in identifying high-risk patients for early intervention and reducing the risk of metastasis. This study aims to use bioinformatics to screen for PCa lymph node metastasis-related genes based on DNA methylation and validate their significance, with the hope of identifying potential molecular markers for PCa lymph node metastasis.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Collection and Preprocessing\u003c/h2\u003e \u003cp\u003eA search was conducted in the GEO database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) using the keywords \"prostate cancer\" and \"lymph node metastasis.\" The GSE220910 dataset was selected and downloaded, comprising DNA methylation data from 60 primary tumor samples and 40 lymph node metastasis samples. Additionally, data from the TCGA Prostate adenocarcinoma (PRAD) dataset were collected from the UCSC Xena database. This dataset included gene expression data from 498 tumor tissues, DNA methylation data, and clinical information for 52 healthy tissues. Based on the clinical information, specifically the \"number_of_lymphnodes_positive_by_he\" record, the 498 patients were categorized into 81 cases with lymph node metastasis, 326 cases without lymph node metastasis, and 91 cases with unknown status.\u003c/p\u003e \u003cp\u003eTo ensure consistency in methylation probes, the probe information provided by the sequencing platforms of the GSE220910 dataset (GPL21145, Infinium MethylationEPIC) and TCGA PRAD (Illumina HumanMethylation450 BeadChip) was used. The intersection of these platforms resulted in the retention of 259,531 probes for downstream analysis. Additionally, the SU2C dataset, which includes gene expression and clinical follow-up information for prostate cancer patients with lymph node metastasis, was downloaded from cBioPortal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cbioportal.org/\u003c/span\u003e\u003cspan address=\"https://www.cbioportal.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDifferential Gene Selection and Functional Annotation\u003c/h2\u003e \u003cp\u003eDifferential gene selection was performed using R software on patients with and without lymph node metastasis in the TCGA PRAD dataset. The mean expression values of each gene in the two groups were calculated, and the fold change was determined using the Wilcoxon rank-sum test for differential comparison. False discovery rate (FDR) correction was applied to adjust the p-values, and genes with FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and |log2FC| \u0026gt; 1 were considered significantly differentially expressed genes (DEGs).\u003c/p\u003e \u003cp\u003eTo annotate and analyze the biological functions of DEGs, the DAVID tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david.ncifcrf.gov/\u003c/span\u003e\u003cspan address=\"https://david.ncifcrf.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was utilized for gene ontology (GO) analysis. Enrichment results with p-values less than 0.05 were retained for further analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eQuantification Scoring with GSVA\u003c/h2\u003e \u003cp\u003eFour gene sets, namely \"GOBP_CELL_DIVISION,\" \"GOBP_MITOTIC_CELL_CYCLE,\" \"GOBP_RESPONSE_TO_COPPER_ION,\" and \"GOBP_RESPONSE_TO_ZINC_ION,\" were downloaded from the MSigDB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/gsea/msigdb/\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/gsea/msigdb/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database. These gene sets are specific to the human species and contain 636, 937, 42, and 49 relevant genes, respectively[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Utilizing gene expression data from TCGA PRAD dataset for patients with and without lymph node metastasis, the R package \"GSVA\" (version: 1.42.0) and its \"gsva\" algorithm were employed to quantitatively score the samples. Differential comparisons were conducted using the Wilcoxon rank-sum test.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSelection and Correlation Analysis of DMPs\u003c/h2\u003e \u003cp\u003eThe mean beta values of methylation probes for patients with and without lymph node metastasis were calculated in both TCGA PRAD and GSE220910 datasets. The fold change (FC) was computed, and differential comparison was performed using the \"ChAMP\" R package (Version: 2.28.0) to filter DMPs. DMPs with FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and consistent methylation direction (either increased or decreased) were retained. The genes covered by DMPs in both datasets were intersected with the differentially expressed genes identified in TCGA PRAD. Pearson correlation coefficients (PCC) between gene expression values and methylation probe beta values were calculated, and correlation tests (cor.test) were conducted. Correlation relationships with PCC \u0026lt; -0.3 and a test p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01 were retained.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003ePrognostic Analysis\u003c/h2\u003e \u003cp\u003ePrognostic follow-up data for prostate cancer (PCa) patients were collected from the TCGA PRAD dataset. Based on the expression values of LTA (cg16243606), TNFRSF25 (cg00834988), DOK3 (cg20138861), and CHRM1 (cg23890832), patients were stratified into high and low expression groups. The Kaplan-Meier curve was employed to assess the progression-free interval (PFI) of patients, utilizing the \"survival\" R package (Version: 3.4-0). Hazard ratios (HR) with a [95% CI] and log-rank test p-values were recorded from the survival model.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eIncreased activity in cell division-induced inhibition in response to metal ions\u003c/h2\u003e \u003cp\u003eFrom the TCGA PRAD dataset, we identified 1380 DEGs, including 906 upregulated and 474 downregulated genes (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). GO analysis results indicated that upregulated genes primarily participate in biological processes such as \"GO:0051301cell division,\" \"GO:0000281mitotic cytokinesis,\" and \"GO:0000278mitotic cell cycle.\" On the other hand, downregulated genes are mainly involved in processes like \"GO:0010273detoxification of copper ion,\" \"GO:0071280cellular response to copper ion,\" and \"GO:0071294cellular response to zinc ion\" (see Table\u0026nbsp;2, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBased on the functional annotation results, we collected four relevant gene sets. Utilizing the \"GSVA\" algorithm from the R package, we quantified scores and conducted differential comparisons. The results indicate that lymph node metastasis patients exhibit significantly higher GSVA scores for cell division and the mitotic cell cycle compared to non-metastatic patients, with p-values of 0.0003 and 0.0015, respectively. The GSVA scores for cellular response to copper ion and zinc ion in lymph node metastasis patients are significantly lower than those in patients without lymph node metastasis, with p-values of 1.2e-08 and 4.8e-05, respectively (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These findings suggest that patients with lymph node metastasis display heightened activity in cell division while exhibiting suppressed responses to metal ions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eFiltering DNA Methylation-Regulated DEGs in Patients with Lymph Node Metastasis\u003c/h2\u003e \u003cp\u003eSubsequently, we utilized the \"ChAMP\" R package to analyze the DNA methylation data detected in the TCGA PRAD dataset. We identified 18,498 DMPs, including 1,636 with increased methylation levels and 37,993 with decreased methylation levels, covering a total of 8,155 genes. Based on the DNA methylation data from the GSE220910 dataset, we filtered 81,009 DMPs, comprising 43,016 with increased methylation levels and 37,993 with decreased methylation levels, covering a total of 15,185 genes. Combining the previously identified 1,380 DEGs from TCGA PRAD, we ultimately obtained 263 DEGs related to 382 DMPs (refer to Table\u0026nbsp;3 and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo validate whether the aforementioned DEGs are regulated by DNA methylation, we separately calculated the Pearson correlation coefficients (PCC) between the 263 DEGs and their corresponding 259,531 methylation probe beta values. Correlation tests were conducted, resulting in 13 relationships with PCC \u0026lt; -0.3 and test p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.01. Among these, LTA (cg16243606), DOK3 (cg20138861), TNFRSF25 (cg00834988), and CHRM1 (cg23890832) exhibited PCC coefficients of -0.4092, -0.4111, -0.4054, and \u0026minus;\u0026thinsp;0.4598, respectively, with p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.01 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e13 DEGs Regulated by DNA Methylation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDMP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDEGs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePCC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ecor.test pValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg16243606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.4092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.57E-21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg11261261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGFI1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.3844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.55E-19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg00842595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eARHGAP15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.3390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.36E-15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg14730097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.3020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.78E-12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg02741305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCOL11A2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.3106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.35E-12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg20138861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDOK3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.4111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.95E-22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg00834988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTNFRSF25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.4054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.01E-21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg23890832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHRM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.4598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.05E-27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg27587826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLENG8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.3022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.68E-12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg14519392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eENGASE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.3378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.28E-15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg12969193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIL21R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.3013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.53E-12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg17429587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBMPER\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.3338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.99E-14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecg09641955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKIF2C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.3429\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.49E-15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eAberrant expression of LTA, DOK3, TNFRSF25, and CHRM1 is associated with the prognosis of PCa lymph node metastasis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe observed a significant upregulation of LTA, DOK3, and TNFRSF25 genes in patients with lymph node metastasis, while CHRM1 showed a significant downregulation, with fold change (FC) values of 1.3316, 1.1148, 1.1095, and 0.8813, respectively (refer to Table\u0026nbsp;3). The p-values were 2.47e-07, 5.42e-07, 0.0003, and 7.46e-08, respectively (see Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Corresponding probe methylation levels can be seen in Figure S1.\u003c/p\u003e \u003cp\u003eTo further validate the potential of the above genes in predicting clinical outcomes, we integrated patient data from 498 cases with recorded gene expression and prognosis information. Prognostic analyses were conducted for LTA, TNFRSF25, DOK3, and CHRM1. Patients were stratified into high and low expression groups based on the expression levels of LTA (threshold: 0.1787). The constructed survival model yielded a Hazard Ratio (HR) of 2.1620 [95% CI: 1.3930\u0026ndash;3.3570] with a test p-value of 0.0004 (see Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Additionally, focusing on lymph node metastasis patients, 81 cases were categorized into high and low expression groups using a threshold of 11.2949. The resulting survival model had an HR of 2.4840 [95% CI: 0.8405-7.3400] with a test p-value of 0.0890 (see Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). The same approach was applied to TNFRSF25, DOK3, and CHRM1 genes (see Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC-D, Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Kaplan-Meier survival analysis revealed that, regardless of lymph node metastasis occurrence, upregulation of LTA, TNFRSF25, and DOK3 expression was associated with a shortened Progression-Free Interval (PFI) in PCa patients, while CHRM1 exhibited the opposite effect. Additionally, we supplemented the prognosis analysis for the probes corresponding to these four genes (see Figure S2, Figure S3).\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eAs of today, metastatic prostate cancer (PCa) remains an incurable condition, and lymph node metastasis indicates a poor prognosis for patients. Therefore, elucidating the molecular mechanisms of lymph node metastasis is crucial for understanding disease progression and developing new therapeutic targets. In this study, we observed heightened tumor cell proliferation in patients with lymph node metastasis, coupled with a suppression of response to copper and zinc ions. This finding is consistent with previous reports that uncontrolled cell proliferation and dysregulation of cell cycle processes are characteristic features of cancer [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The relationship between cellular response to metal ions and cancer is closely intertwined. For instance, copper serves as a therapeutic agent in cancer treatment with low adverse reactions [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Zinc deficiency can lead to severe diseases in the brain, pancreas, liver, kidneys, and reproductive organs, and zinc loss is indicative of PCa development [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Furthermore, the dysregulation of key genes in this process also promotes the lymphatic spread of PCa [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur study proposes four DNA methylation-based biomarkers: LTA (Lymphotoxin Alpha), TNFRSF25 (TNF Receptor Superfamily Member 25), DOK3 (Downstream Of Tyrosine Kinase 3), and CHRM1 (Cholinergic Receptor Muscarinic 1). In patients with lymph node metastasis of prostate cancer (PCa), low methylation of LTA, TNFRSF25, and DOK3 leads to high gene expression, while high methylation of CHRM1 results in low gene expression (refer to Supplementary Table\u0026nbsp;1 and Supplementary Table\u0026nbsp;3 for details). It is well-known that tumor cells exhibiting overall DNA hypomethylation may contribute to genetic instability and promote the occurrence and development of cancer, particularly in metastatic PCa [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Furthermore, we observed that, regardless of lymph node metastasis occurrence, elevated expression of LTA, TNFRSF25, and DOK3 is associated with an unfavorable prognosis for PCa patients, while the opposite holds true for CHRM1.\u003c/p\u003e \u003cp\u003eLTA and TNFRSF25 belong to the tumor necrosis factor (TNF) family, and TNF has been found to be involved in the occurrence and development of various cancers [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Existing data suggest that elevated TNF is a risk factor for cancer [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Evidence indicates an association between LTA polymorphism and cancer risk in elderly Japanese men [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Research findings predict that LTA appears to promote the growth and spread of prostate cancer (PCa) by increasing vascular endothelial growth factor in PCa cell lines, providing ample nutrients and oxygen supply to the tumor [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Patients with high Lymphotoxin (LT) levels are more likely to develop resistance to prostate cancer, making them potential major beneficiaries of anti-LT treatment [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The protein encoded by the TNFRSF25 gene is a member of the TNF receptor superfamily, with this receptor being preferentially expressed in tissues rich in lymphocytes. Studies indicate differential expression of TNFRSF25 between non-progressing and progressing PCa groups, significantly correlating with non-progressive survival, making it a potential target for PCa treatment [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Aberrant methylation of TNFRSF25 is a significant feature in PCa, playing a crucial role in cell apoptosis escape during tumor development [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Both LT and TNF bind to receptors in the TNF receptor superfamily, inducing cell apoptosis and necrosis with efficacy similar to TNF [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. It is noteworthy that our functional enrichment analysis results also indicate that upregulated LTA and TNFRSF25 genes in patients with lymph node metastasis are involved in apoptosis processes. Therefore, we speculate that abnormal DNA methylation of LTA and TNFRSF25 induces apoptosis escape in PCa cells, thereby promoting PCa progression. However, this statement currently lacks experimental evidence and requires further data for validation of its effectiveness.\u003c/p\u003e \u003cp\u003eRecent studies have reported on the role of DOK3 in tumor progression. Jin et al.'s research confirmed the upregulation of DOK3 in PCa cell lines and tissues, and its overexpression promoted PCa progression. Elevated levels of DOK3 were associated with higher pathological stages and poorer prognosis. Similar results were observed in samples from PCa patients, marking the first report on the role of DOK3 in promoting PCa development [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. CHRM1 is primarily distributed in the central and peripheral nervous systems. Studies have reported that CHRM1 is overexpressed in PCa tissues compared to control groups. Overexpression of CHRM1 positively regulates PCa cell migration and invasion [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This contradicts our conclusion that \"CHRM1 expression is reduced in PCa lymph node metastasis compared to primary tumors.\" However, gene expression changes during tumor development and metastasis vary, depending on factors such as the tumor microenvironment, cell signaling, and transcriptional regulation during the metastatic process.\u003c/p\u003e \u003cp\u003eIn this study, it was discovered that abnormal methylation of the LTA, TNFRSF25, DOK3, and CHRM1 genes affects their expression and functions, thereby participating in the regulation of PCa metastasis. The detection of these biomarkers can provide clinicians with more comprehensive and accurate diagnostic and prognostic information, assisting them in formulating better treatment plans and predicting disease progression. Additionally, these genes may also serve as potential targets for the development of new diagnostic and therapeutic approaches.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSUNG H, FERLAY J, SIEGEL RL et al (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries [J]. Cancer J Clin 71(3):209\u0026ndash;249\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHIJAZI S, MELLER B, LEITSMANN C et al (2016) See the unseen: Mesorectal lymph node metastases in prostate cancer [J]. Prostate 76(8):776\u0026ndash;780\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eABDOLLAH F, SUARDI N (2013) Extended pelvic lymph node dissection in prostate cancer: a 20-year audit in a single center [J]. Ann Oncol 24(6):1459\u0026ndash;1466\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHINEV A I ANAKIEVSKID, KOLEV N H et al (2014) Validation of nomograms predicting lymph node involvement in patients with prostate cancer undergoing extended pelvic lymph node dissection [J]. Urol Int 92(3):300\u0026ndash;305\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTOUIJER K A, MAZZOLA C R, SJOBERG D D et al (2014) Long-term outcomes of patients with lymph node metastasis treated with radical prostatectomy without adjuvant androgen-deprivation therapy [J]. Eur Urol 65(1):20\u0026ndash;25\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKRISTIANSEN G. Markers of clinical utility in the differential diagnosis and prognosis of prostate cancer [J]. Mod Pathol, (2018) 31(S1): S143\u0026ndash;S155\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLI P, LIU S (2022) Liquid biopsies based on DNA methylation as biomarkers for the detection and prognosis of lung cancer [J]. Clin Epigenetics 14(1):118\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eARYEE MJ, LIU W (2013) DNA methylation alterations exhibit intraindividual stability and interindividual heterogeneity in prostate cancer metastases [J]. Sci Transl Med 5(169):169ra10\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBELTRAN H, PRANDI D, MOSQUERA JM et al (2016) Divergent clonal evolution of castration-resistant neuroendocrine prostate cancer [J]. Nat Med 22(3):298\u0026ndash;305\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEGGENER S E, SCARDINO P T, WALSH P C et al (2011) Predicting 15-year prostate cancer specific mortality after radical prostatectomy [J]. J Urol 185(3):869\u0026ndash;875\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eABIDA W, CYRTA J, HELLER G et al (2019) Genomic correlates of clinical outcome in advanced prostate cancer [J]. Proc Natl Acad Sci U S A 116(23):11428\u0026ndash;11436\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSUBRAMANIAN A, TAMAYO P, MOOTHA V K et al (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles [J]. Proc Natl Acad Sci U S A 102(43):15545\u0026ndash;15550\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLIBERZON A, SUBRAMANIAN A, PINCHBACK R et al (2011) Molecular signatures database (MSigDB) 3.0 [J]. Bioinformatics 27(12):1739\u0026ndash;1740\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHANAHAN D, WEINBERG RA (2011) Hallmarks of cancer: the next generation [J]. Cell 144(5):646\u0026ndash;674\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDA SILVA D A, DE LUCA A, SQUITTI R et al (2022) Copper in tumors and the use of copper-based compounds in cancer treatment [J]. J Inorg Biochem 226:111634\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTO P K, DO M H, CHO JH et al (2020) Growth Modulatory Role of Zinc in Prostate Cancer and Application to Cancer Therapeutics [J]. Int J Mol Sci, 21(8)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXU N, CHEN S H, LIN T T et al (2020) Development and validation of hub genes for lymph node metastasis in patients with prostate cancer [J]. J Cell Mol Med 24(8):4402\u0026ndash;4414\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZHAO S G, CHEN W S, LI H et al (2020) The DNA methylation landscape of advanced prostate cancer [J]. Nat Genet 52(8):778\u0026ndash;789\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLEBREC H, PONCE R, PRESTON B D et al (2015) Tumor necrosis factor, tumor necrosis factor inhibition, and cancer risk [J]. Curr Med Res Opin 31(3):557\u0026ndash;574\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTAKEI K, IKEDA S, ARAI T et al (2008) Lymphotoxin-alpha polymorphisms and presence of cancer in 1,536 consecutive autopsy cases [J]. BMC Cancer 8(1):235\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFERRER F A, MILLER L J, ANDRAWIS R I et al (1998) Angiogenesis and prostate cancer: in vivo and in vitro expression of angiogenesis factors by prostate cancer cells [J]. Urology 51(1):161\u0026ndash;167\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJACOBS E J, HSING A W, BAIN E B et al (2008) Polymorphisms in angiogenesis-related genes and prostate cancer [J]. Cancer Epidemiol Biomarkers Prev 17(4):972\u0026ndash;977\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAMMIRANTE M, LUO J L, GRIVENNIKOV S et al (2010) B-cell-derived lymphotoxin promotes castration-resistant prostate cancer [J]. Nature 464(7286):302\u0026ndash;305\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFENG D, ZHANG F, LI D et al (2022) Developing an immune-related gene prognostic index associated with progression and providing new insights into the tumor immune microenvironment of prostate cancer [J]. Immunology 166(2):197\u0026ndash;209\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFUKUSHIGE S, MIYAUCHI T, OKUBO T et al (2017) Abstract 4354: Methylation-mediated silenced PYCARD plays a key role in human prostate cancer [J]. Cancer Res 77(13 Supplement):4354\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eETEMADI N, HOLIEN J K, CHAU D et al (2013) Lymphotoxin α induces apoptosis, necroptosis and inflammatory signals with the same potency as tumour necrosis factor [J]. Febs j 280(21):5283\u0026ndash;5297\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJIN K, QIU S, CHEN B et al (2023) DOK3 promotes proliferation and inhibits apoptosis of prostate cancer via the NF-κB signaling pathway [J]. Chin Med J (Engl) 136(4):423\u0026ndash;432\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWANG Q, CHEN J, ZHANG M et al (2022) Autophagy Induced by Muscarinic Acetylcholine Receptor 1 Mediates Migration and Invasion Targeting Atg5 via AMPK/mTOR Pathway in Prostate Cancer [J]. Journal of oncology, 2022: 6523195\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYIN Q Q, XU L H, ZHANG M et al (2018) Muscarinic acetylcholine receptor M1 mediates prostate cancer cell migration and invasion through hedgehog signaling [J]. Asian J Androl 20(6):608\u0026ndash;614\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 2 and 3 are not available with this version.\u003c/p\u003e"},{"header":"Supplementary Information","content":"\u003cp\u003eSupplementary Information is not available with this version\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Xinchang People’s Hospital of Zhejiang Province","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":"Lymph node metastasis, prostate cancer, DNA methylation, prognosis","lastPublishedDoi":"10.21203/rs.3.rs-4022664/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4022664/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003ePurpose\u003c/b\u003e\u003c/p\u003e \u003cp\u003eLymph node metastasis is an independent prognostic factor for prostate cancer (PCa), and this study aims to explore the intrinsic molecular mechanisms of PCa lymph node metastasis based on epigenetic modifications using bioinformatics.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eGene expression data set TCGA PRAD was downloaded from the UCSC Xena database for differential analysis. Differential genes between patients with and without lymph node metastasis were identified and functionally annotated. DNA methylation data from the GSE220910 dataset were used to identify differential methylation sites (DMPs) using the \"ChAMP\" R package. The correlation between differential gene expression values and methylation probe beta values was calculated and tested for significance. Finally, a prognosis analysis was conducted for the selected genes regulated by DNA methylation.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe identified 1380 significantly differentially expressed genes (DEGs), including 906 upregulated and 474 downregulated genes. GO analysis revealed that upregulated genes in patients with lymph node metastasis were mainly involved in processes such as cell division and mitosis, while downregulated genes participated in the cellular response to copper and zinc ions. Subsequently, we further selected 81009 differential methylation sites (DMPs), ultimately retaining 263 DEGs associated with 382 DMPs. Correlation analysis showed that LTA, DOK3, TNFRSF25, and CHRM1 had Pearson correlation coefficients of -0.4092, -0.4111, -0.4054, and \u0026minus;\u0026thinsp;0.4598, respectively (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with their corresponding methylation probes. Survival analysis indicated that high expression of LTA, DOK3, and TNFRSF25 genes was associated with a shortened progression-free interval (PFI) in PCa patients, while CHRM1 showed the opposite trend (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eLymph node metastasis in PCa patients is associated with active cell division and suppression of the response to metal ions. We also discovered that LTA, DOK3, TNFRSF25, and CHRM1 are regulated by DNA methylation, and their abnormal expression significantly impacts patient prognosis.\u003c/p\u003e","manuscriptTitle":"Gene selection driven by DNA methylation in relation to lymph node metastasis in prostate cancer and prognosis analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-20 19:46:59","doi":"10.21203/rs.3.rs-4022664/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":"aba090ad-e82e-4629-9e01-e38affe81694","owner":[],"postedDate":"March 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-20T19:46:59+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-20 19:46:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4022664","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4022664","identity":"rs-4022664","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}