Comprehensive analysis of m7G-related Genes METTL1 and WDR4 for predicting prognosis and oncogenic functions in prostate cancer

Comprehensive analysis of m7G-related Genes METTL1 and WDR4 for predicting prognosis and oncogenic functions in prostate cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Comprehensive analysis of m7G-related Genes METTL1 and WDR4 for predicting prognosis and oncogenic functions in prostate cancer Degeng Kong, Juanyi Shi, Cong Lai, Jintao Hu, Yelisudan Mulati, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5724815/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background Prostate cancer is a prominent global health concern, posing a substantial threat to men's well-being and longevity. N-7methylguanosine (m7G) modification orchestrated by a complex involving METTL1 and WDR4 , has garnered attention as a post-transcriptional modification with implications in numerous tumor types. Nevertheless, there is a paucity of research addressing potential pivotal roles of METTL1 and WDR4 in driving prostate cancer progression. Methods We obtained mRNA expression data for METTL1 and WDR4 from the TCGA and GSEA databases in prostate cancer patients, analyzing their impact on survival and tumor immune microenvironment. GO and KEGG analyses were performed on associated genes. Univariate and multivariate Cox analyses identified METTL1 and WDR4 as independent prognostic factors, leading to a two-gene predictive model that evaluated tumor mutation burden, immune infiltration, and immune function changes. Importantly, we substantiated the impact of METTL1 and WDR4 on prostate cancer development in vitro . Results In prostate cancer, high METTL1 and WDR4 expression correlated with reduced overall survival and increased plasmacytoid dendritic cells, with decreased adaptive immune cells. Functional enrichment analysis indicated their influence on ribosome-related functions. Our model revealed critical mutation sites and immune infiltration alterations. In vitro, METTL1 or WDR4 knockdown inhibited prostate cancer cell proliferation, migration, and invasion. Conclusion Our study unveils the oncogenic roles of both METTL1 and WDR4 in prostate cancer development. Additionally, the prognostic model founded on METTL1 and WDR4 exhibits enhanced predictive precision for OS, thereby serving as a valuable clinical tool for prostate cancer. Prostate cancer N-7methylguanosine m7G METTL1 WDR4 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Prostate cancer is the most prevalent malignancy within the urinary system, affecting millions of men globally. In 2022, it ranked as the second most common cancer and the fifth leading cause of cancer-related mortality among men worldwide [ 1 ]. While patients with localized prostate cancer experience prolonged survival, those with advanced forms face significant challenges even with multimodal combination therapies [ 2 ]. Roughly 15% of prostate cancer patients are classified as high-risk at diagnosis [ 3 ]. Antiandrogen therapy constitutes the standard treatment for these high-risk patients, yet a substantial proportion subsequently transitions to castration-resistant prostate cancer (CRPC) and neuroendocrine prostate cancer (NEPC) later in life [ 4 ]. Despite the use of androgen receptor signaling inhibitors, such as enzalutamide and abiraterone, in advanced prostate cancer management [ 5 , 6 ]. some patients develop resistance to enzalutamide over time, necessitating the exploration of alternative approaches for the treatment of advanced prostate cancer [ 7 , 8 ]. In recent years, epitranscriptomics has witnessed a surge in interest concerning post-transcriptional modifications within eukaryotes [ 9 ]. Over 170 distinct modifications have been identified in various biomolecules, including mRNA, tRNA, rRNA, and miRNA, among others [ 10 , 11 ]. Among these, N7-methylguanosine (m7G) modification is particularly prevalent, primarily occurring at the 5' caps and internal positions of eukaryotic mRNAs, as well as within rRNAs and tRNAs across all species. In yeast, m7G modifications have been linked to the emergence of temperature-sensitive phenotypes [ 12 ]. In humans, this modification primarily proceeds through mediation by the methyltransferase-like 1 ( METTL1 ) / WD repeat domain 4 ( WDR4 ) complex. METTL1 assumes a catalytic role, while WDR4 functions as a cofactor, facilitating modification stabilization [ 13 ]. m7G modification has implications in a range of diseases, with particular relevance to the development of several cancers [ 14 , 15 , 16 , 17 , 18 ]. The roles of METTL1 and WDR4 in prostate cancer, as well as their underlying molecular mechanisms, remain to be fully elucidated [ 19 ]. In this investigation, we conducted a comprehensive analysis of METTL1 and WDR4 expression disparities in prostate cancer. Additionally, we performed gene enrichment analysis to construct and validate a two-gene model. Furthermore, we carried out mutational and immunological assessments across distinct risk groups. Ultimately, we carried out loss-of-function experiments to elucidate the roles of METTL1 and WDR4 in prostate cancer. 2. Methods 2.1 Public Data Collection and Processing We used the R package to retrieve RNA sequencing data and clinical information from the Cancer Genome Atlas (TCGA, https://www.cancer.gov/ccg/research/genome-sequencing/tcga ) and the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo ) databases. Specifically, we downloaded all available prostate cancer-related datasets from TCGA. Datasets without survival or clinical follow-up data were excluded to ensure the reliability of the analysis. We then analyzed the mRNA expression levels of METTL1 and WDR4 in different subgroups of patients. Violin plots were generated to compare the differences in the expression levels of METTL1 and WDR4 between prostate cancer and paracancerous tissues. We assessed the diagnostic efficacy of METTL1 and WDR4 using receiver operating characteristic (ROC) curves. Additionally, we examined the relationship between the expression of METTL1 and WDR4 and biochemical recurrence in prostate cancer patients through survival curve analysis. 2.2 Relationship between METTL1 and WDR4 and immune cell infiltration The Single Sample Genome Enrichment Analysis (ssGSEA) algorithm was utilized to evaluate the influence of individual genes on immune cell infiltration levels. Violin plots were employed to illustrate the relationship between key immune cells and gene expression levels. For the high-risk and low-risk groups identified by the prediction model, the CIBERSORT algorithm was used to assess differences in immune cells and immune function between these groups. This study leveraged the ssGSEA method to identify a set of 24 immune-related genes, including pDC, NK CD56bright cells, Treg, Cytotoxic cells, CD8 T cells, NK CD56dim cells, aDC, DC, Th2 cells, T cells, B cells, iDC, Macrophages, Th17 cells, Th1 cells, NK cells, TFH, Mast cells, Tgd, Neutrophils, Tem, T helper cells, Eosinophils, and Tcm scores, in each patient within the prostate cancer cohort. 2.3 Functional analysis Genes with high and low expression levels co-expressed with METTL1 and WDR4 were screened, and their intersections were displayed using Venn diagrams. The intersecting genes were then analyzed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to assess their impact on prostate cancer cell function and signaling pathways. GO includes three ontologies: molecular function (MF), cellular component (CC), and biological process (BP). KEGG pathway enrichment analysis was performed to functionally annotate the genes and elucidate their relevant functions and signaling pathways. 2.4 Construction and Validation of Prognostic Model Based on METTL1 and WDR4 Using data from TCGA, we incorporated clinical characteristics—including METTL1 and WDR4 expression levels, age, Gleason score, T-stage, N-stage, M-stage, and other relevant factors—into univariate and multivariate COX regression analyses. Independent prognostic factors for prostate cancer (PCa) patients were identified with a significance threshold of P < 0.05. These factors were used to construct a risk score model, and nomograms were developed to predict biochemical recurrence at 1, 3, and 5 years. Calibration plots were generated to assess the predictive accuracy of these nomograms, and ROC curves were plotted to determine the AUC, reflecting the predictive efficacy. The results were validated using the GSE70770 dataset. 2.5 Correlation of Risk Scores with Immune Cell Infiltration CIBERSORT ( https://cibersortx.stanford.edu/ ) is an analytical tool developed by the Alizadeh and Newman laboratories. Based on the principle of linear support vector regression, it estimates the abundance of various cell types in a mixed cell population using gene expression data. In this study, the CIBERSORT method was utilized to determine the abundance of 22 immune cell types per sample in prostate cancer, including naïve B cells, memory B cells, plasma cells, CD8 + T cells, naïve CD4 + T cells, resting memory CD4 + T cells, activated memory CD4 + T cells, follicular helper T cells, regulatory T cells (Tregs), gamma delta T cells, resting NK cells, activated NK cells, monocytes, macrophages (M0, M1, M2), resting dendritic cells, activated dendritic cells, resting mast cells, activated mast cells, eosinophils, and neutrophils. The abundance of these immune cells, particularly resting mast cells, activated mast cells, eosinophils, and neutrophils, was then analyzed using the "BiocManager," "limma," "reshape2," and "ggpubr" packages. A box plot was generated to compare the differences in the abundance of these 22 immune cells between high-risk and low-risk osteosarcoma groups, clarifying the impact of risk scores on immune cell infiltration. 2.6 Cell Lines and Culture Human prostate cancer cells were sourced from the American Type Culture Collection (ATCC, Manassas, VA, USA). PC-3 cells were nurtured in RPMI 1640 medium (Gibco, New York, USA), while DU145 cells were cultivated in DMEM (Gibco, NY, USA). Both culture media were enhanced with 10% fetal bovine serum (Invitrogen, Carlsbad, USA) and the appropriate concentrations of penicillin and streptomycin. The cell cultures were sustained at 37°C in an environment with 5% carbon dioxide. 2.7 RNAi Transfection, RNA Extraction and qRT-PCR For siRNA experiments, Lipofectamine iMAX (Thermo Scientific, USA) served as the transfection reagent. Details of the siRNA and Primers sequences can be found in the Supplementary file(Table.s1,s2). We conducted total RNA extraction and reverse transcription using the RNA-Quick Purification Kit and Reverse Transcription cDNA Premix (YEASEN, Shanghai, China). Real-time quantitative PCR experiments were executed with Hieff UNICON® qPCR SYBR Green Master Mix (YEASEN, Shanghai, China) as the fluorescent dye, performed on a Quanstudio DX instrument (ABI, USA). For qRT-PCR analysis, primer sequences were designed and validated for the target genes. The PCR reaction was performed in a 20 µL reaction mixture, including 10 µL of SYBR Green Master Mix, 1 µL of each forward and reverse primer (10 µM), and 2 µL of cDNA template. The qPCR cycling conditions were as follows: an initial denaturation at 95°C for 10 minutes, followed by 40 cycles of 95°C for 15 seconds and 60°C for 1 minute. The relative expression levels of the target genes were calculated using the ΔΔCt method, where the Ct value of the target gene was normalized to the Ct value of GAPDH, and fold changes were calculated relative to the control group. 2.8 Cell Counting Kit-8 (CCK-8) Prostate cancer cells were dissociated from petri dishes, resuspended, and quantified. Following this, 2000 cells were introduced into individual wells of a 96-well cell culture plate. On the subsequent day, we introduced 10 µl of the Cell Counting Kit-8 (CCK-8) stock solution (APExBIO, USA) into each well, and incubated the plate for 2 hours. The absorbance at 450 nm was then determined using the Tecan Spark 10M (Tecan, Austria) spectrophotometer. The resulting data was plotted after 5 consecutive days of treatment. 2.9 Colony Formation The prostate cancer cells, having undergone treatment, were introduced into 6-well plates at a density of 1000 cells per well, and cultivation was continued for around 2 weeks. Upon completion of this incubation period, the culture medium was decanted, and the cells were thoroughly washed with PBS. The cells were then immobilized through a 15-minute exposure to a 4% paraformaldehyde solution (Servicebio, Wuhan, China) and subsequently stained with 0.1% crystal violet for an additional 15 minutes. After washing and drying, the colonies were photographed and analyzed using AID vSpot Spectrum (AID, Germany). 2.10 Transwell Assay Prostate cancer cells, subjected to the previously specified treatment, were resuspended in serum-void medium and added to the upper chambers (8 µm pore size, Falcon, USA) at a concentration of 50,000 cells per chamber. The lower chamber was filled with medium supplemented with 10% FBS, and the plates were positioned in an appropriate incubation setting for the allotted time. Subsequent to this, cells within the lower chambers were fixed using a 4% paraformaldehyde solution (Servicebio, Wuhan, China) for 30 minutes. After fixation, they were stained with a 0.1% crystal violet solution for 15 minutes. Cells present on the upper side of the chambers were gently swabbed off using a cotton swab. The lower chambers were then observed and photographed under a Leica DM2000 microscope (Leica Camera AG, Wetzlar, Germany), and cell counts were obtained using Image J software. For invasion experiments, Matrigel (BD Science, USA) was pre-added to the upper chambers and allowed to solidify for 30 minutes in the incubator. 2.11 Western blot The gel was placed into the electrophoresis tank with enough buffer to avoid leakage. After gently removing the comb, protein samples and markers were loaded into the wells. Electrophoresis was performed at 80V, then increased to 120V once the marker entered the separating gel. The process was stopped when the loading buffer was 1 cm from the bottom. For western blot transfer, the transfer buffer was prepared (800ml distilled water, 3.03g Tris base, 14.4g glycine, 200ml methanol), and the PVDF membrane was activated with methanol for 30s. The gel and membrane were assembled in the transfer apparatus, with buffer added, and transfer was done at 250mA for 2.5 hours in an ice bath. After transfer, the membrane was blocked with TBST buffer containing 5g non-fat dry milk for 1 hour at room temperature, then washed with TBST and incubated with primary antibody at 4°C overnight. After washing, the membrane was incubated with secondary antibody for 1 hour at room temperature, washed three times, and then treated with chemiluminescent substrate. Bands were visualized and analyzed using a chemiluminescence imaging system. 2.12 Dot blot Cells were collected by centrifugation after routine digestion, and total RNA was extracted. RNA concentration was measured and adjusted to 500-1000ng/µl by dilution. The RNA solution was heated at 95°C for 3 minutes and then rapidly transferred to ice to linearize the RNA. A nylon membrane of appropriate size was prepared, and 2µl of RNA solution at different concentrations was spotted on the membrane, preferably at the center, and allowed to air dry. A duplicate membrane was prepared for the internal reference control. Once the membranes dried, they were crosslinked in a UV crosslinker at 1.2×10^6 Joules for 1 minute, repeated twice. The membranes were washed with TBST for 5 minutes with gentle shaking. The internal reference membrane was stained with methylene blue for 20 minutes and air-dried, while the membrane for exposure was blocked with 5% non-fat milk solution for 1 hour. After blocking, the membrane was incubated with m7G antibody at 4°C overnight. The membranes were washed three times with TBST (10 minutes each). The secondary antibody was incubated at room temperature for 1 hour, followed by washing with TBST three times (10 minutes each). The membrane was developed, and the internal reference membrane was photographed and analyzed. 2.13 Statistical Analysis Public database analyses were executed using the R package software. RNA expression levels between tumors and paracancerous tissues were compared via paired t-tests. To evaluate overall survival in distinct groups, Kaplan–Meier analysis was employed. Furthermore, both univariate and multivariate Cox regression analyses were conducted to pinpoint independent prognostic factors. For the cellular experiments, GraphPad Prism 8 was utilized for statistical analyses. In the context of this article, statistical significance was stipulated as a p-value less than 0.05 (* p < 0.05; ** p < 0.01; *** p < 0.001; ns = no significance). 3. Results 3.1 Expression levels of METTL1 and WDR4 in prostate cancer patients and their association with OS To explore the roles of METTL1 and WDR4 in prostate cancer, we initially conducted an analysis of their expression differences between tumor and normal tissues using the TCGA database. Remarkably, both METTL1 and WDR4 displayed elevated mRNA expression levels in prostate cancer tissues compared to normal prostate tissues ( Fig. 1 A, B ) . Furthermore, within paired samples of prostate cancer and adjacent paracancerous tissues, both METTL1 and WDR4 displayed elevated expression in tumor tissues ( Fig. 1 C, D ) . Upon constructing diagnostic ROC curves ( Fig. 1 E, F ) , both genes possessed significant diagnostic potential for prostate cancer (AUC > 0.7). Survival analysis revealed that higher expression of METTL1 and WDR4 was associated with an unfavorable impact on patient survival ( Fig. 1 G, H ) . 3.2 Association of METTL1 and WDR4 with immune cells in prostate cancer To further elucidate the roles of METTL1 and WDR4 in prostate cancer progression, we conducted analyses of their associations with immune cell infiltration levels. As depicted in ( Fig. 2 A ) , METTL1 expression positively correlated with immunosuppressive immune cells included plasmacytoid-like dendritic cells (pDCs) and regulatory T cells (Tregs), which are commonly found in various tumor tissues and are typically associated with tumor progression and a poor prognosis [ 20 ]. Conversely, several immune cells displaying a negative correlation with METTL1 expression included central memory T cells (Tcm), eosinophils, T helper cells, effector memory T cells (Tem), neutrophils, Tγδ cells (Tgd), mast cells, follicular helper T cells (Tfh), NK cells, indicating a potential regulatory role of METTL1 in immune evasion in prostate cancer. WDR4 expression positively correlated with Th2 cells, while showing negative correlations with mast cells, Tgd cells, NK cells, eosinophils, Th1 cells, and neutrophils ( Fig. 2 B ). which may be linked to immune evasion in tumors. In addition, violin plots further detailed the significant correlations of specific immune cell infiltrations with METTL1 and WDR4 expression ( Fig. 2 C-J ). To further elucidate the mechanism by which METTL1 and WDR4 expressions influence immune cell infiltration in prostate cancer, we first compared cytokine profiles between normal prostate tissues and prostate cancer samples. Subsequent correlation analysis revealed that both METTL1 and WDR4 expression levels significantly associate with key cytokines specifically dysregulated in PC tissues (Fig. s1 ) . The results indicated that both METTL1 and WDR4 may regulate the expression of several key cytokines, thereby altering the immune microenvironment in prostate cancer. These findings suggest that the modulation of cytokine expression by METTL1 and WDR4 could play a pivotal role in shaping the immune landscape of prostate cancer. 3.3 Enrichment Analysis of Genes Associated with METTL1 and WDR4 We initially identified 2,027 genes positively correlated with METTL1 expression and 832 genes positively correlated with WDR4 expression. Similarly, genes negatively correlated with the expression of these two genes were also analyzed. After cross-analyzing these gene sets, we identified 650 positively correlated genes and 158 negatively correlated genes for subsequent GO and KEGG pathway analyses ( Fig. 3 A, B ) . GO analysis revealed that the positively correlated genes were primarily associated with structural components of ribosomes, negative regulation of the mitotic cell cycle, and cellular respiratory processes ( Fig. 3 C ) . Conversely, the negatively correlated genes were predominantly linked to biological functions such as actin binding and membrane regions ( Fig. 3 D ) . KEGG analysis indicated that the positively correlated genes significantly impacted pathways related to ribosomes, RNA transport, and the spliceosome ( Fig. 3 E ) . In contrast, the negatively correlated genes influenced pathways including the cGMP-PKG signaling pathway, focal adhesion, PI3K-Akt signaling pathway, calcium signaling pathway, and Rap1 signaling pathway ( Fig. 3 F ) . In addition, signaling pathways related to prostate cancer progression, such as the Pentose Phosphate Pathway and the NF-κB pathway, were identified based on GSEA enrichment analysis. ( Fig. 3 G-L ) 3.4 Establishment and validation of a two-gene model for METTL1 and WDR4 To ascertain the prognostic impact of METTL1 and WDR4 in prostate cancer patients, univariate and multivariate Cox regression analysis were implemented. The results revealed that both METTL1 and WDR4 expression levels can serve as autonomous prognostic indicators for predicting the OS of prostate cancer patients. ( Table 1 ) . Subsequently, a two-gene model incorporating patient T-stage, Gleason score, and the expression levels of these two genes, was developed to estimate the likelihood of biochemical recurrence at 1, 3, and 5 years ( Fig. 4 A, B ) . The model exhibited reasonable predictive performance, as evidenced by good diagnostic accuracy in the validation set with AUCs of 0.743, 0.738, and 0.751 for 1, 3, and 5-year overall survival (OS), respectively ( Fig. 4 C ) . Moreover, it aligned well with the standard prediction curve, underscoring the utility of this two-gene model in forecasting biochemical recurrence in prostate cancer patients ( Fig. 4 D ) . In addition, we included the GSE70770 dataset as an external validation to test the accuracy of our biochemical relapse prediction model. The model's predicted AUC values for 1-, 3-, and 5-year recurrence rates for these patients were all above 0.7 ( Fig. 4 E, F ) . Table 1 Univariate and multivariate Cox regression analyses of METTL1 and WDR4 . Characteristics Univariate analysis Multivariate analysis Hazard ratio (95% CI) P value Hazard ratio (95% CI) P value T stage ≤T2 Reference ≥T3 3.785 (2.140–6.693) < 0.001 2.694 (1.326–5.473) 0.006 N stage N0 Reference N1 1.946 (1.202–3.150) 0.007 0.851 (0.508–1.424) 0.538 M stage M0 Reference M1 3.566 (0.494–25.753) 0.208 0.000 (0.000-Inf) 0.995 Age ≤ 60 Reference >60 1.302 (0.863–1.963) 0.208 1.253 (0.795–1.973) 0.331 Gleason score ≤ 7 Reference ≥ 8 4.675 (2.957–7.391) < 0.001 3.352 (1.945–5.776) < 0.001 WDR4 Low Reference High 1.531 (1.011–2.318) 0.044 1.674 (1.061–2.642) 0.027 METTL1 Low Reference High 1.698 (1.114–2.586) 0.014 1.356 (1.054–2.159) 0.031 3.5 Mutation Burden in Distinct Risk Groups To further elucidate the specific mechanisms through which METTL1 and WDR4 function in prostate cancer, we then separately examined differences in mutation patterns between high and low-risk groups. The grouping of high and low-risk patients was based on the two-gene model we developed. After scoring all patients, those with higher scores were classified into the high-risk group, while those with lower scores were classified into the low-risk group. The findings revealed a similar set of genes with heightened mutation frequencies in both groups, including SPOP, TTN, TP53, KMT2D, and FOXA1 ( Fig. 5 A, B ) . However, the frequency of mutations in these genes varied among cases across different risk groups. Notably, mutations in the SPOP gene are prevalent in prostate cancer and have been extensively linked to disease progression and an unfavorable prognosis in multiple studies [ 21 , 22 , 23 ]. In the high-risk group, the incidence of SPOP mutations is several times higher than in the low-risk group, corm firming the robustness of our two-gene model. We analyzed the survival analysis of patients by combining high and low mutation burdens with high- and low-risk groups obtained from our model. The results indicated that low-risk patients with a low mutation burden had the most favorable OS ( Fig. 5 C ) . 3.6 Assessment of Immune Microenvironment in Varied Risk Groups Immune infiltration analysis revealed a significant decrease in plasma cell in patients of high-risk group. Conversely, the infiltrations of Tregs and M2 macrophages were markedly elevated compared to the low-risk group. Numerous studies have previously substantiated the significant association between the infiltration of these two immune cell types and prostate cancer progression, as well as adverse prognosis [ 24 , 25 , 26 , 27 ]. Additionally, we noted a heightened infiltration of resting mast cells among patients in the low-risk group ( Fig. 6 A, B ) . 3.7 In Vitro Validation of Biological Functions of METTL1 and WDR4 in Prostate Cancer Cells Studies We designed and synthesized siRNAs targeting METTL1 and WDR4 , which were subsequently verified for their robust knockdown efficiency through RT-qPCR ( Fig. 7 A-D ). A series of in vitro experiments, encompassing CCK-8 assays, colony formation, and transwell assays were conducted, utilizing two distinct prostate cancer cell lines, PC3 and DU145. The results consistently exhibited a marked decrease in the proliferation ( Fig. 7 E-H ) , colony-forming ( Fig. 7 I, J, K ) , as well as the migratory and invasive capabilities ( Fig. 8 A-F ) of prostate cancer cells following the knockdown of METTL1 and WDR4 , respectively. This compelling evidence underscores the pivotal roles played by both METTL1 and WDR4 in prostate cancer progression, thus justifying our development of the novel predictive model based on these two genes. To gain a more direct understanding of the impact of the two m7G-related genes on m7G modification in prostate cancer, we conducted dot blot experiments. The results revealed that the knockdown of both genes significantly altered the global m7G modification levels in prostate cancer cells ( Fig. 9 A ) . These findings suggest that the regulation of prostate cancer proliferation and metastasis by METTL1 and WDR4 may be dependent on epigenetic modifications. Furthermore, in the previously mentioned pathway enrichment analysis, we identified pathways associated with the cell cycle, cell adhesion, and epithelial-mesenchymal transition (EMT) that were significantly enriched. To validate these findings, we performed cell cycle assays and measured the protein levels of EMT markers. The results demonstrated that knockdowns of METTL1 and WDR4 induced significant cell cycle arrest (G1 phase accumulation) and suppressed EMT markers in PC3 and DU145 cells. However, DU145 cells exhibited attenuated cell cycle arrest, potentially due to their elevated baseline p21 levels and PTEN-null genetic background, which may bypass METTL1/WDR4-mediated cell cycle regulation. These experiments further corroborated the enrichment of cell cycle and EMT-related pathways ( Fig. 9 B, C ) . 4. Discussion Prostate cancer incidence is a major global health concern for men. Although prostate cancer is not among the most aggressive malignancies [ 1 ], its potential progression to castration-resistant prostate cancer (CRPC) presents a significant challenge, underscoring the need for an in-depth study of its underlying mechanisms. Post-transcriptional modification is a crucial regulatory mechanism of human gene expression, with m7G modification being one of its key components. This modification is facilitated by the METTL1 - WDR4 complex and has been associated with several cancer types [ 12 ]. However, its involvement in prostate cancer remains relatively unexplored. Our study found that METTL1 and WDR4 are highly expressed in prostate cancer tissues and are linked to poor prognosis. We also analyzed their relationship with immune cell infiltration and assessed the cellular functions and signaling pathways potentially affected. A prognostic model was constructed based on the results of multifactorial regression analysis, and its association with patient prognosis was evaluated in conjunction with tumor mutation burden. We classified patients into high- and low-risk groups according to the model and analyzed the differences in the immune environment between the two groups. Finally, we conducted a series of functional experiments in prostate cancer cells to support our conclusions. Previous studies have shown that tumor-infiltrating plasmacytoid dendritic cells (pDCs) often exhibit dysfunction, promote Treg cell differentiation, and facilitate tumor growth [ 28 , 29 ]. Although activated pDCs can stimulate anti-tumor T cell responses, their endogenous effects seem biased towards tumor promotion [ 30 , 31 ]. We observed increased infiltration of pDCs with elevated METTL1 and WDR4 expression, consistent with prevailing views. This suggests that METTL1 and WDR4 play a critical role in regulating the immunosuppressive phenomena within the tumor microenvironment and contribute to prostate cancer progression. Moreover, after performing enrichment analysis on genes positively associated with METTL1 and WDR4 , we found that these genes are closely linked to ribosome-related cellular functions. This finding is significant because m7G modifications on tRNAs affect the translation of pro-oncogenic proteins closely associated with ribosomes [ 32 ]. This implies that in prostate cancer, METTL1 and WDR4 may influence tumor progression by regulating the translation efficiency of these proteins. Intriguingly, the cGMP-PKG pathway was significantly negatively correlated with these two genes [ 33 ]. Activation of this pathway has been shown to inhibit prostate cancer proliferation, suggesting that m7G modifications may enrich genes in this pathway, thus significantly affecting prostate cancer proliferation and metastasis. We also confirmed this phenomenon through in vitro functional experiments. Currently, there is a lack of precise and effective methods for predicting the survival time of prostate cancer patients. However, the prognostic model we developed, which combines METTL1 and WDR4 gene expression levels with the patient's puncture score, provides a better prediction of overall survival (OS), potentially guiding clinical decision-making. 5. Conclusion Our results identified the m7G modification-related genes, METTL1 and WDR4 , which are significantly overexpressed in prostate cancer and closely associated with its malignant progression. This may occur through the regulation of translation efficiency of pro-oncogenic proteins, a mechanism verified by our in vitro functional experiments. The prediction model established based on these findings has potential utility in guiding clinical decision-making. Abbreviations m7G N-7methylguanosine METTL1 Methyltransferase-like 1 WDR4 WD repeat domain 4 TCGA The Cancer Genome Atlas GSEA Sample Genome Enrichment Analysis GO Gene Ontology KEGG Kyoto Encyclopedia of Genes and Genomes CRPC Castration-resistant prostate cancer NEPC Neuroendocrine prostate cancer pDCs Plasmacytoid-like dendritic cells Tregs Regulatory T cells Tcm Central memory T cells CCK-8 Cell Counting Kit-8 OS Overall survival Th1 T helper 1 cells Declarations Ethics approval and consent to participate Not applicable Consent for publication Consent to publication was obtained by all the participants CrediT Authorship contribution statement Degeng Kong: Experimentation, Data analysis, Figure creation, Writing – original draft. Juanyi Shi: Experimental design, Writing – review & editing. Cong Lai: Experimental design, Figure creation, Writing – original draft. Jintao Hu、Yelisudan Mulati、Jiawen Luo、 Junjie Wang and Yunfei Xiao: Supervision. Cheng Liu: Writing- review & editing. Kewei Xu: Writing- review & editing. All authors contributed to the revision, have read and approved the final submitted manuscript. Conflict of interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding sources This study was supported by the Key-Area Research and Development Program of Guangdong Province (2023B1111030006), National Natural Science Foundation of China (82372766 and 82072841), Natural Science Foundation of Guangdong Province (2021A1515010199). Author Contribution Degeng Kong: Experimentation, Data analysis, Figure creation, Writing – original draft. Juanyi Shi: Experimental design, Writing – review & editing. Cong Lai: Experimental design, Figure creation, Writing – original draft. Jintao Hu、Yelisudan Mulati、Jiawen Luo、 Junjie Wang and Yunfei Xiao: Supervision. Cheng Liu: Writing- review & editing. Kewei Xu: Writing- review & editing. 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N7-Methylguanosine tRNA modification enhances oncogenic mRNA translation and promotes intrahepatic cholangiocarcinoma progression. Mol Cell. 2021;81:3339–e33558. Ying X, Liu B, Yuan Z, Huang Y, Chen C, Jiang X, et al. METTL1 -m7 G-EGFR/EFEMP1 axis promotes the bladder cancer development. Clin Transl Med. 2021;11:e675. Reizis B. Plasmacytoid Dendritic Cells: Development, Regulation, and Function. Immunity. 2019;50:37–50. Shi Q, Jin X, Zhang P, Li Q, Lv Z, Ding Y, et al. SPOP mutations promote p62/SQSTM1-dependent autophagy and Nrf2 activation in prostate cancer. Cell Death Differ. 2022;29:1228–39. Shi L, Yan Y, He Y, Yan B, Pan Y, Orme JJ, et al. Mutated SPOP E3 Ligase Promotes 17βHSD4 Protein Degradation to Drive Androgenesis and Prostate Cancer Progression. Cancer Res. 2021;81:3593–606. Dai X, Gan W, Li X, Wang S, Zhang W, Huang L, et al. Prostate cancer-associated SPOP mutations confer resistance to BET inhibitors through stabilization of BRD4. Nat Med. 2017;23:1063–71. Chen S, Lu K, Hou Y, You Z, Shu C, Wei X, et al. YY1 complex in M2 macrophage promotes prostate cancer progression by upregulating IL-6. J Immunother Cancer. 2023;11:e006020. Wu N, Wang Y, Wang K, Zhong B, Liao Y, Liang J, et al. Cathepsin K regulates the tumor growth and metastasis by IL-17/CTSK/EMT axis and mediates M2 macrophage polarization in castration-resistant prostate cancer. Cell Death Dis. 2022;13:813. Xie T, Fu D-J, Li Z-M, Lv D-J, Song X-L, Yu Y-Z, et al. CircSMARCC1 facilitates tumor progression by disrupting the crosstalk between prostate cancer cells and tumor-associated macrophages via miR-1322/CCL20/CCR6 signaling. Mol Cancer. 2022;21:173. Ju M, Fan J, Zou Y, Yu M, Jiang L, Wei Q, et al. Computational Recognition of a Regulatory T-cell-specific Signature With Potential Implications in Prognosis, Immunotherapy, and Therapeutic Resistance of Prostate Cancer. Front Immunol. 2022;13:807840. Conrad C, Gregorio J, Wang Y-H, Ito T, Meller S, Hanabuchi S, et al. Plasmacytoid dendritic cells promote immunosuppression in ovarian cancer via ICOS costimulation of Foxp3(+) T-regulatory cells. Cancer Res. 2012;72:5240–9. Labidi-Galy SI, Sisirak V, Meeus P, Gobert M, Treilleux I, Bajard A, et al. Quantitative and functional alterations of plasmacytoid dendritic cells contribute to immune tolerance in ovarian cancer. Cancer Res. 2011;71:5423–34. Le Mercier I, Poujol D, Sanlaville A, Sisirak V, Gobert M, Durand I, et al. Tumor promotion by intratumoral plasmacytoid dendritic cells is reversed by TLR7 ligand treatment. Cancer Res. 2013;73:4629–40. Reizis B. Plasmacytoid Dendritic Cells: Development, Regulation, and Function. Immunity. 2019;50:37–50. Orellana EA, Liu Q, Yankova E, Pirouz M, De Braekeleer E, Zhang W, et al. METTL1 -mediated m7G modification of Arg-TCT tRNA drives oncogenic transformation. Mol Cell. 2021;81:3323–e333814. Li W, Yin X, Yan Y, Liu C, Li G. STEAP4 knockdown inhibits the proliferation of prostate cancer cells by activating the cGMP-PKG pathway under lipopolysaccharide-induced inflammatory microenvironment. Int Immunopharmacol. 2021;101 Pt B:108311. Wang G, Zhao D, Spring DJ, DePinho RA. Genetics and biology of prostate cancer. Genes Dev. 2018;32:1105–40. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.pdf Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 06 May, 2025 Editor assigned by journal 24 Apr, 2025 Reviews received at journal 16 Apr, 2025 Reviewers agreed at journal 10 Apr, 2025 Reviewers invited by journal 10 Apr, 2025 Submission checks completed at journal 09 Apr, 2025 First submitted to journal 03 Apr, 2025 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5724815","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":441341549,"identity":"b782ec9a-ce26-45e1-b977-7b4f1f5829b5","order_by":0,"name":"Degeng Kong","email":"","orcid":"","institution":"Sun Yat-sen Memorial Hospital, Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Degeng","middleName":"","lastName":"Kong","suffix":""},{"id":441341556,"identity":"28c46da3-2306-4895-a5d7-5c6bb8d3c748","order_by":1,"name":"Juanyi Shi","email":"","orcid":"","institution":"Sun Yat-sen Memorial Hospital, Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Juanyi","middleName":"","lastName":"Shi","suffix":""},{"id":441341561,"identity":"c55b5b0e-8ded-48c3-a265-6436eee1c24e","order_by":2,"name":"Cong Lai","email":"","orcid":"","institution":"Sun Yat-sen Memorial Hospital, Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Cong","middleName":"","lastName":"Lai","suffix":""},{"id":441341568,"identity":"42264d2b-71f4-42f9-9822-a2d118fe359f","order_by":3,"name":"Jintao Hu","email":"","orcid":"","institution":"Sun Yat-sen Memorial Hospital, Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Jintao","middleName":"","lastName":"Hu","suffix":""},{"id":441341570,"identity":"c08d844f-49ba-477a-a0fd-46f9ee5a6dba","order_by":4,"name":"Yelisudan Mulati","email":"","orcid":"","institution":"Sun Yat-sen Memorial Hospital, Sun Yat-sen 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University","correspondingAuthor":false,"prefix":"","firstName":"Yunfei","middleName":"","lastName":"Xiao","suffix":""},{"id":441341579,"identity":"cb0fc861-96d9-4f9d-b051-71954894354a","order_by":8,"name":"Cheng Liu","email":"","orcid":"","institution":"Sun Yat-sen Memorial Hospital, Sun Yat-sen University","correspondingAuthor":false,"prefix":"","firstName":"Cheng","middleName":"","lastName":"Liu","suffix":""},{"id":441341581,"identity":"87284b78-1d29-4d8b-9c1b-ecec3cbb097e","order_by":9,"name":"Kewei Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIiWNgGAWjYFACNhAhIcfAwNh4ACYmQYwWY6CWBpK0MCQ2AAnitBjcSEt8zFNhkb62/TDQlj+H7Q0OMB+8zcNgl4dLi+SMtMPGPGckcredSWw4wNh2OHHDAbZkax6G5GJcWvgl0tukc9uAWg6AtDQcTjA4wGMmzcNwAOxUrD6RSG//nftPIt3s/EOYw/i/4dXCL5F2jDm3QSLB7AbQFga2w4wbDvCw4dUi2fMsWfrPMQnDbTeAtiS2pSfOPMxmbDnHIBmnFoPjaYYfZ9TUyZudT3/44MMfa3u+480Pb7ypsMOpBRUkMDQzMDCDjSJKPRjUEa90FIyCUTAKRgwAAFPxW2bjbftuAAAAAElFTkSuQmCC","orcid":"","institution":"Sun Yat-sen Memorial Hospital, Sun Yat-sen University","correspondingAuthor":true,"prefix":"","firstName":"Kewei","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2024-12-28 07:08:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5724815/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5724815/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80578124,"identity":"5041742d-56d8-4685-a401-419c9e5e124e","added_by":"auto","created_at":"2025-04-14 23:00:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":384908,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of \u003cem\u003eMETTL1\u003c/em\u003e and \u003cem\u003eWDR4\u003c/em\u003e expression, diagnostic efficacy and OS. \u003cem\u003eMETTL1\u003c/em\u003e mRNA expression levels in prostate cancer, unpaired \u003cstrong\u003e(A) \u003c/strong\u003eand paired \u003cstrong\u003e(C), \u003c/strong\u003ediagnostic efficacy \u003cstrong\u003e(E)\u003c/strong\u003e and survival curves \u003cstrong\u003e(G)\u003c/strong\u003e.\u003cstrong\u003e \u003c/strong\u003e\u003cem\u003eWDR4\u003c/em\u003e mRNA expression levels in prostate cancer, unpaired \u003cstrong\u003e(B) \u003c/strong\u003eand paired \u003cstrong\u003e(D)\u003c/strong\u003e, diagnostic efficacy \u003cstrong\u003e(F)\u003c/strong\u003e and survival curves \u003cstrong\u003e(H).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-5724815/v1/37527dbb475503275a26287c.png"},{"id":80578123,"identity":"05294fc9-baec-4af0-86e4-6a3f6a963bc9","added_by":"auto","created_at":"2025-04-14 23:00:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":608598,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation of \u003cem\u003eMETTL1\u003c/em\u003e\u003cstrong\u003e \u003c/strong\u003eand \u003cem\u003eWDR4\u003c/em\u003e with immune cells in prostate cancer. Relationship between the expression levels of \u003cem\u003eMETTL1\u003c/em\u003e \u003cstrong\u003e(A)\u003c/strong\u003e and \u003cem\u003eWDR4\u003c/em\u003e \u003cstrong\u003e(B)\u003c/strong\u003e and the degree of immune cell infiltration in prostate cancer. Relationship between immune cells with more significant differences and \u003cem\u003eMETTL1\u003c/em\u003e expression levels \u003cstrong\u003e(C-F)\u003c/strong\u003e, Relationship between immune cells with more significant differences and \u003cem\u003eWDR4\u003c/em\u003eexpression levels \u003cstrong\u003e(G-J)\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-5724815/v1/d6d852587fb7416d551e53aa.png"},{"id":80578120,"identity":"65158eaf-7649-4e77-a6cf-2c7176def760","added_by":"auto","created_at":"2025-04-14 23:00:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1125027,"visible":true,"origin":"","legend":"\u003cp\u003eGenomic Analysis and Enrichment of \u003cem\u003eMETTL1\u003c/em\u003e and \u003cem\u003eWDR4\u003c/em\u003e Associated Genes. \u003cstrong\u003e(A) \u003c/strong\u003eIdentification of genes positively linked to \u003cem\u003eMETTL1\u003c/em\u003eand \u003cem\u003eWDR4\u003c/em\u003e.\u003cstrong\u003e (B)\u003c/strong\u003e Identification of genes negatively linked to \u003cem\u003eMETTL1\u003c/em\u003eand \u003cem\u003eWDR4\u003c/em\u003e. \u0026nbsp;\u003cstrong\u003e(C) \u003c/strong\u003eGene Ontology (GO) analysis of positively correlated gene sets. \u003cstrong\u003e(D)\u003c/strong\u003e Gene Ontology (GO) analysis of negatively correlated gene sets.\u003cstrong\u003e (E) \u003c/strong\u003eKyoto Encyclopedia of Genes and Genomes (KEGG) analysis of positively associated gene sets (\u003cstrong\u003eF)\u003c/strong\u003e Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of negatively correlated gene sets. \u003cstrong\u003e(G-L) \u003c/strong\u003eGSEA prostate cancer-related pathway enrichment analysis.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-5724815/v1/4ec72dd2f3b902556fa1c81b.png"},{"id":80580806,"identity":"5e8c87c5-ded8-49bc-854e-da1b63f4018f","added_by":"auto","created_at":"2025-04-14 23:16:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":841656,"visible":true,"origin":"","legend":"\u003cp\u003eThe novel two-gene prediction model for prostate cancer. (\u003cstrong\u003eA)\u003c/strong\u003e Nomogram graph for the predictive model. \u003cstrong\u003e(B)\u003c/strong\u003eK-M curves for the OS of patients in high and low risk groups according to the modeling (\u003cstrong\u003eC)\u003c/strong\u003e Diagnostic efficacy plot of model at 1, 3, 5-years.\u003cstrong\u003e (D) \u003c/strong\u003eValidation of model fit to true prognosis. \u003cstrong\u003e(E, F)\u003c/strong\u003e AUC plots and validation curves for external validation sets\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-5724815/v1/d855881a97fe04dda8e1eeb2.png"},{"id":80579569,"identity":"385b2c4d-2377-4555-a504-2f2b5222ab8d","added_by":"auto","created_at":"2025-04-14 23:08:46","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":597292,"visible":true,"origin":"","legend":"\u003cp\u003eMutation analysis in high and low risk groups. Waterfall plot showing the names and frequencies of mutations in the \u003cstrong\u003e(A) \u003c/strong\u003ehigh and \u003cstrong\u003e(B) \u003c/strong\u003elow risk groups. \u003cstrong\u003e(C) \u003c/strong\u003eSurvival analysis of High and Low risk score combined mutation burden.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-5724815/v1/857db4cfa3c799bb62003337.png"},{"id":80578129,"identity":"4279a711-75cd-4d1e-b323-32f299b1c22e","added_by":"auto","created_at":"2025-04-14 23:00:46","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":328172,"visible":true,"origin":"","legend":"\u003cp\u003eThe immune landscape for the high(cluster2) and low(cluster1)-risk groups. \u003cstrong\u003e(A)\u003c/strong\u003eHeat map of immune cell infiltration in different risk groups. \u003cstrong\u003e(B) \u003c/strong\u003eBoxplot of 22 immune cells in high and low risk groups.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-5724815/v1/474120c6cbd74f51cb885b01.png"},{"id":80578122,"identity":"3cca54bd-6f69-41b2-82b0-0b1d95acc658","added_by":"auto","created_at":"2025-04-14 23:00:46","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":979500,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eIn vitro\u003c/em\u003e assays confirmed that \u003cem\u003eMETTL1\u003c/em\u003e and \u003cem\u003eWDR4\u003c/em\u003e promote prostate cancer cells proliferation. \u003cstrong\u003e(A-D)\u003c/strong\u003eValidation of target mRNA expression levels after small interfering RNA treatment was conducted. \u003cstrong\u003e(E-H)\u003c/strong\u003e Cell proliferation ability was detected using a CCK8 assay. \u003cstrong\u003e(I-K) \u003c/strong\u003eRepresentative images and statistical quantification were performed for clone formation experiments. Data are presented as mean ± SEM from three independent biological replicates. Statistical significance was determined by one-way ANOVA.\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-5724815/v1/25fce059cec276e1d2dd7e7c.png"},{"id":80578121,"identity":"100c8f34-755c-4947-9106-e3ed8e7c363b","added_by":"auto","created_at":"2025-04-14 23:00:46","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1735685,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eIn vitro\u003c/em\u003e assays confirmed that \u003cem\u003eMETTL1\u003c/em\u003e and \u003cem\u003eWDR4\u003c/em\u003e promote prostate cancer cells migration and invasion. \u003cstrong\u003e(A-C)\u003c/strong\u003eRepresentative images and quantitative data of migration experiments of PC-3 and DU145 after transfection with control and target siRNAs.\u003cstrong\u003e (D-F)\u003c/strong\u003eRepresentative images and quantitative data of invasion experiments of PC-3 and DU145 after transfection with control and target siRNAs. Data are presented as mean ± SEM from three independent biological replicates. Statistical significance was determined by one-way ANOVA.\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-5724815/v1/dd3a3de9efdf44f63981def3.png"},{"id":80580808,"identity":"3afb68ee-f17f-4a28-a771-82640a38a076","added_by":"auto","created_at":"2025-04-14 23:16:46","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":749783,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMETTL1 and WDR4 influence m7G modification, EMT markers, and cell cycle distribution in prostate cancer cells.\u003c/strong\u003e (A) Dot blot showing global m7G modification changes in PC-3 and DU145 cells after METTL1 and WDR4 knockdown. (B) EMT marker protein expression in PC-3 and DU145 cells following knockdown. (C) Cell cycle distribution changes in PC-3 and DU145 cells after METTL1 and WDR4 knockdown. Data are presented as mean ± SEM from three independent biological replicates.\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-5724815/v1/bde3967acf56e9557fb5c208.png"},{"id":80582526,"identity":"7cf1ca09-e759-4827-9e94-9674e849c5ca","added_by":"auto","created_at":"2025-04-14 23:32:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9038108,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5724815/v1/9917aa94-7ffb-452d-a850-7a157207ca21.pdf"},{"id":80578119,"identity":"89fd7dd2-ca51-4df4-b9f7-90ee47b1b8b9","added_by":"auto","created_at":"2025-04-14 23:00:46","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":376604,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5724815/v1/64ea01c093508f3cb4a26199.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comprehensive analysis of m7G-related Genes METTL1 and WDR4 for predicting prognosis and oncogenic functions in prostate cancer","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eProstate cancer is the most prevalent malignancy within the urinary system, affecting millions of men globally. In 2022, it ranked as the second most common cancer and the fifth leading cause of cancer-related mortality among men worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. While patients with localized prostate cancer experience prolonged survival, those with advanced forms face significant challenges even with multimodal combination therapies [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Roughly 15% of prostate cancer patients are classified as high-risk at diagnosis [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Antiandrogen therapy constitutes the standard treatment for these high-risk patients, yet a substantial proportion subsequently transitions to castration-resistant prostate cancer (CRPC) and neuroendocrine prostate cancer (NEPC) later in life [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Despite the use of androgen receptor signaling inhibitors, such as enzalutamide and abiraterone, in advanced prostate cancer management [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. some patients develop resistance to enzalutamide over time, necessitating the exploration of alternative approaches for the treatment of advanced prostate cancer [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn recent years, epitranscriptomics has witnessed a surge in interest concerning post-transcriptional modifications within eukaryotes [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Over 170 distinct modifications have been identified in various biomolecules, including mRNA, tRNA, rRNA, and miRNA, among others [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Among these, N7-methylguanosine (m7G) modification is particularly prevalent, primarily occurring at the 5' caps and internal positions of eukaryotic mRNAs, as well as within rRNAs and tRNAs across all species. In yeast, m7G modifications have been linked to the emergence of temperature-sensitive phenotypes [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In humans, this modification primarily proceeds through mediation by the methyltransferase-like 1 (\u003cem\u003eMETTL1\u003c/em\u003e) / WD repeat domain 4 (\u003cem\u003eWDR4\u003c/em\u003e) complex. \u003cem\u003eMETTL1\u003c/em\u003e assumes a catalytic role, while \u003cem\u003eWDR4\u003c/em\u003e functions as a cofactor, facilitating modification stabilization [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. m7G modification has implications in a range of diseases, with particular relevance to the development of several cancers [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The roles of \u003cem\u003eMETTL1\u003c/em\u003e and \u003cem\u003eWDR4\u003c/em\u003e in prostate cancer, as well as their underlying molecular mechanisms, remain to be fully elucidated [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this investigation, we conducted a comprehensive analysis of \u003cem\u003eMETTL1\u003c/em\u003e and \u003cem\u003eWDR4\u003c/em\u003e expression disparities in prostate cancer. Additionally, we performed gene enrichment analysis to construct and validate a two-gene model. Furthermore, we carried out mutational and immunological assessments across distinct risk groups. Ultimately, we carried out loss-of-function experiments to elucidate the roles of \u003cem\u003eMETTL1\u003c/em\u003e and \u003cem\u003eWDR4\u003c/em\u003e in prostate cancer.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Public Data Collection and Processing\u003c/h2\u003e \u003cp\u003eWe used the R package to retrieve RNA sequencing data and clinical information from the Cancer Genome Atlas (TCGA, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cancer.gov/ccg/research/genome-sequencing/tcga\u003c/span\u003e\u003cspan address=\"https://www.cancer.gov/ccg/research/genome-sequencing/tcga\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the Gene Expression Omnibus (GEO, \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) databases. Specifically, we downloaded all available prostate cancer-related datasets from TCGA. Datasets without survival or clinical follow-up data were excluded to ensure the reliability of the analysis. We then analyzed the mRNA expression levels of \u003cem\u003eMETTL1\u003c/em\u003e and \u003cem\u003eWDR4\u003c/em\u003e in different subgroups of patients. Violin plots were generated to compare the differences in the expression levels of \u003cem\u003eMETTL1\u003c/em\u003e and \u003cem\u003eWDR4\u003c/em\u003e between prostate cancer and paracancerous tissues. We assessed the diagnostic efficacy of \u003cem\u003eMETTL1\u003c/em\u003e and \u003cem\u003eWDR4\u003c/em\u003e using receiver operating characteristic (ROC) curves. Additionally, we examined the relationship between the expression of \u003cem\u003eMETTL1\u003c/em\u003e and \u003cem\u003eWDR4\u003c/em\u003e and biochemical recurrence in prostate cancer patients through survival curve analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Relationship between \u003cem\u003eMETTL1\u003c/em\u003e and \u003cem\u003eWDR4\u003c/em\u003e and immune cell infiltration\u003c/h2\u003e \u003cp\u003eThe Single Sample Genome Enrichment Analysis (ssGSEA) algorithm was utilized to evaluate the influence of individual genes on immune cell infiltration levels. Violin plots were employed to illustrate the relationship between key immune cells and gene expression levels. For the high-risk and low-risk groups identified by the prediction model, the CIBERSORT algorithm was used to assess differences in immune cells and immune function between these groups. This study leveraged the ssGSEA method to identify a set of 24 immune-related genes, including pDC, NK CD56bright cells, Treg, Cytotoxic cells, CD8 T cells, NK CD56dim cells, aDC, DC, Th2 cells, T cells, B cells, iDC, Macrophages, Th17 cells, Th1 cells, NK cells, TFH, Mast cells, Tgd, Neutrophils, Tem, T helper cells, Eosinophils, and Tcm scores, in each patient within the prostate cancer cohort.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Functional analysis\u003c/h2\u003e \u003cp\u003eGenes with high and low expression levels co-expressed with \u003cem\u003eMETTL1\u003c/em\u003e and \u003cem\u003eWDR4\u003c/em\u003e were screened, and their intersections were displayed using Venn diagrams. The intersecting genes were then analyzed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to assess their impact on prostate cancer cell function and signaling pathways. GO includes three ontologies: molecular function (MF), cellular component (CC), and biological process (BP). KEGG pathway enrichment analysis was performed to functionally annotate the genes and elucidate their relevant functions and signaling pathways.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Construction and Validation of Prognostic Model Based on \u003cem\u003eMETTL1\u003c/em\u003e and \u003cem\u003eWDR4\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eUsing data from TCGA, we incorporated clinical characteristics\u0026mdash;including \u003cem\u003eMETTL1\u003c/em\u003e and \u003cem\u003eWDR4\u003c/em\u003e expression levels, age, Gleason score, T-stage, N-stage, M-stage, and other relevant factors\u0026mdash;into univariate and multivariate COX regression analyses. Independent prognostic factors for prostate cancer (PCa) patients were identified with a significance threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. These factors were used to construct a risk score model, and nomograms were developed to predict biochemical recurrence at 1, 3, and 5 years. Calibration plots were generated to assess the predictive accuracy of these nomograms, and ROC curves were plotted to determine the AUC, reflecting the predictive efficacy. The results were validated using the GSE70770 dataset.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Correlation of Risk Scores with Immune Cell Infiltration\u003c/h2\u003e \u003cp\u003eCIBERSORT (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cibersortx.stanford.edu/\u003c/span\u003e\u003cspan address=\"https://cibersortx.stanford.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is an analytical tool developed by the Alizadeh and Newman laboratories. Based on the principle of linear support vector regression, it estimates the abundance of various cell types in a mixed cell population using gene expression data. In this study, the CIBERSORT method was utilized to determine the abundance of 22 immune cell types per sample in prostate cancer, including na\u0026iuml;ve B cells, memory B cells, plasma cells, CD8\u0026thinsp;+\u0026thinsp;T cells, na\u0026iuml;ve CD4\u0026thinsp;+\u0026thinsp;T cells, resting memory CD4\u0026thinsp;+\u0026thinsp;T cells, activated memory CD4\u0026thinsp;+\u0026thinsp;T cells, follicular helper T cells, regulatory T cells (Tregs), gamma delta T cells, resting NK cells, activated NK cells, monocytes, macrophages (M0, M1, M2), resting dendritic cells, activated dendritic cells, resting mast cells, activated mast cells, eosinophils, and neutrophils. The abundance of these immune cells, particularly resting mast cells, activated mast cells, eosinophils, and neutrophils, was then analyzed using the \"BiocManager,\" \"limma,\" \"reshape2,\" and \"ggpubr\" packages. A box plot was generated to compare the differences in the abundance of these 22 immune cells between high-risk and low-risk osteosarcoma groups, clarifying the impact of risk scores on immune cell infiltration.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Cell Lines and Culture\u003c/h2\u003e \u003cp\u003eHuman prostate cancer cells were sourced from the American Type Culture Collection (ATCC, Manassas, VA, USA). PC-3 cells were nurtured in RPMI 1640 medium (Gibco, New York, USA), while DU145 cells were cultivated in DMEM (Gibco, NY, USA). Both culture media were enhanced with 10% fetal bovine serum (Invitrogen, Carlsbad, USA) and the appropriate concentrations of penicillin and streptomycin. The cell cultures were sustained at 37\u0026deg;C in an environment with 5% carbon dioxide.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 RNAi Transfection, RNA Extraction and qRT-PCR\u003c/h2\u003e \u003cp\u003eFor siRNA experiments, Lipofectamine iMAX (Thermo Scientific, USA) served as the transfection reagent. Details of the siRNA and Primers sequences can be found in the Supplementary file(Table.s1,s2). We conducted total RNA extraction and reverse transcription using the RNA-Quick Purification Kit and Reverse Transcription cDNA Premix (YEASEN, Shanghai, China). Real-time quantitative PCR experiments were executed with Hieff UNICON\u0026reg; qPCR SYBR Green Master Mix (YEASEN, Shanghai, China) as the fluorescent dye, performed on a Quanstudio DX instrument (ABI, USA).\u003c/p\u003e \u003cp\u003eFor qRT-PCR analysis, primer sequences were designed and validated for the target genes. The PCR reaction was performed in a 20 \u0026micro;L reaction mixture, including 10 \u0026micro;L of SYBR Green Master Mix, 1 \u0026micro;L of each forward and reverse primer (10 \u0026micro;M), and 2 \u0026micro;L of cDNA template. The qPCR cycling conditions were as follows: an initial denaturation at 95\u0026deg;C for 10 minutes, followed by 40 cycles of 95\u0026deg;C for 15 seconds and 60\u0026deg;C for 1 minute. The relative expression levels of the target genes were calculated using the ΔΔCt method, where the Ct value of the target gene was normalized to the Ct value of GAPDH, and fold changes were calculated relative to the control group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Cell Counting Kit-8 (CCK-8)\u003c/h2\u003e \u003cp\u003eProstate cancer cells were dissociated from petri dishes, resuspended, and quantified. Following this, 2000 cells were introduced into individual wells of a 96-well cell culture plate. On the subsequent day, we introduced 10 \u0026micro;l of the Cell Counting Kit-8 (CCK-8) stock solution (APExBIO, USA) into each well, and incubated the plate for 2 hours. The absorbance at 450 nm was then determined using the Tecan Spark 10M (Tecan, Austria) spectrophotometer. The resulting data was plotted after 5 consecutive days of treatment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Colony Formation\u003c/h2\u003e \u003cp\u003eThe prostate cancer cells, having undergone treatment, were introduced into 6-well plates at a density of 1000 cells per well, and cultivation was continued for around 2 weeks. Upon completion of this incubation period, the culture medium was decanted, and the cells were thoroughly washed with PBS. The cells were then immobilized through a 15-minute exposure to a 4% paraformaldehyde solution (Servicebio, Wuhan, China) and subsequently stained with 0.1% crystal violet for an additional 15 minutes. After washing and drying, the colonies were photographed and analyzed using AID vSpot Spectrum (AID, Germany).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Transwell Assay\u003c/h2\u003e \u003cp\u003eProstate cancer cells, subjected to the previously specified treatment, were resuspended in serum-void medium and added to the upper chambers (8 \u0026micro;m pore size, Falcon, USA) at a concentration of 50,000 cells per chamber. The lower chamber was filled with medium supplemented with 10% FBS, and the plates were positioned in an appropriate incubation setting for the allotted time. Subsequent to this, cells within the lower chambers were fixed using a 4% paraformaldehyde solution (Servicebio, Wuhan, China) for 30 minutes. After fixation, they were stained with a 0.1% crystal violet solution for 15 minutes. Cells present on the upper side of the chambers were gently swabbed off using a cotton swab. The lower chambers were then observed and photographed under a Leica DM2000 microscope (Leica Camera AG, Wetzlar, Germany), and cell counts were obtained using Image J software. For invasion experiments, Matrigel (BD Science, USA) was pre-added to the upper chambers and allowed to solidify for 30 minutes in the incubator.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11 Western blot\u003c/h2\u003e \u003cp\u003eThe gel was placed into the electrophoresis tank with enough buffer to avoid leakage. After gently removing the comb, protein samples and markers were loaded into the wells. Electrophoresis was performed at 80V, then increased to 120V once the marker entered the separating gel. The process was stopped when the loading buffer was 1 cm from the bottom. For western blot transfer, the transfer buffer was prepared (800ml distilled water, 3.03g Tris base, 14.4g glycine, 200ml methanol), and the PVDF membrane was activated with methanol for 30s. The gel and membrane were assembled in the transfer apparatus, with buffer added, and transfer was done at 250mA for 2.5 hours in an ice bath. After transfer, the membrane was blocked with TBST buffer containing 5g non-fat dry milk for 1 hour at room temperature, then washed with TBST and incubated with primary antibody at 4\u0026deg;C overnight. After washing, the membrane was incubated with secondary antibody for 1 hour at room temperature, washed three times, and then treated with chemiluminescent substrate. Bands were visualized and analyzed using a chemiluminescence imaging system.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.12 Dot blot\u003c/h2\u003e \u003cp\u003eCells were collected by centrifugation after routine digestion, and total RNA was extracted. RNA concentration was measured and adjusted to 500-1000ng/\u0026micro;l by dilution. The RNA solution was heated at 95\u0026deg;C for 3 minutes and then rapidly transferred to ice to linearize the RNA. A nylon membrane of appropriate size was prepared, and 2\u0026micro;l of RNA solution at different concentrations was spotted on the membrane, preferably at the center, and allowed to air dry. A duplicate membrane was prepared for the internal reference control. Once the membranes dried, they were crosslinked in a UV crosslinker at 1.2\u0026times;10^6 Joules for 1 minute, repeated twice. The membranes were washed with TBST for 5 minutes with gentle shaking. The internal reference membrane was stained with methylene blue for 20 minutes and air-dried, while the membrane for exposure was blocked with 5% non-fat milk solution for 1 hour. After blocking, the membrane was incubated with m7G antibody at 4\u0026deg;C overnight. The membranes were washed three times with TBST (10 minutes each). The secondary antibody was incubated at room temperature for 1 hour, followed by washing with TBST three times (10 minutes each). The membrane was developed, and the internal reference membrane was photographed and analyzed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.13 Statistical Analysis\u003c/h2\u003e \u003cp\u003ePublic database analyses were executed using the R package software. RNA expression levels between tumors and paracancerous tissues were compared via paired t-tests. To evaluate overall survival in distinct groups, Kaplan\u0026ndash;Meier analysis was employed. Furthermore, both univariate and multivariate Cox regression analyses were conducted to pinpoint independent prognostic factors. For the cellular experiments, GraphPad Prism 8 was utilized for statistical analyses. In the context of this article, statistical significance was stipulated as a p-value less than 0.05 (* p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; ns\u0026thinsp;=\u0026thinsp;no significance).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Expression levels of \u003cem\u003eMETTL1\u003c/em\u003e and \u003cem\u003eWDR4\u003c/em\u003e in prostate cancer patients and their association with OS\u003c/h2\u003e \u003cp\u003eTo explore the roles of \u003cem\u003eMETTL1\u003c/em\u003e and \u003cem\u003eWDR4\u003c/em\u003e in prostate cancer, we initially conducted an analysis of their expression differences between tumor and normal tissues using the TCGA database. Remarkably, both \u003cem\u003eMETTL1\u003c/em\u003e and \u003cem\u003eWDR4\u003c/em\u003e displayed elevated mRNA expression levels in prostate cancer tissues compared to normal prostate tissues \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA, B\u003cb\u003e)\u003c/b\u003e. Furthermore, within paired samples of prostate cancer and adjacent paracancerous tissues, both \u003cem\u003eMETTL1\u003c/em\u003e and \u003cem\u003eWDR4\u003c/em\u003e displayed elevated expression in tumor tissues \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC, D\u003cb\u003e)\u003c/b\u003e. Upon constructing diagnostic ROC curves \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE, F\u003cb\u003e)\u003c/b\u003e, both genes possessed significant diagnostic potential for prostate cancer (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.7). Survival analysis revealed that higher expression of \u003cem\u003eMETTL1\u003c/em\u003e and \u003cem\u003eWDR4\u003c/em\u003e was associated with an unfavorable impact on patient survival \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG, H\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Association of \u003cem\u003eMETTL1\u003c/em\u003e and \u003cem\u003eWDR4\u003c/em\u003e with immune cells in prostate cancer\u003c/h2\u003e \u003cp\u003eTo further elucidate the roles of \u003cem\u003eMETTL1\u003c/em\u003e and \u003cem\u003eWDR4\u003c/em\u003e in prostate cancer progression, we conducted analyses of their associations with immune cell infiltration levels. As depicted in\u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e, \u003cem\u003eMETTL1\u003c/em\u003e expression positively correlated with immunosuppressive immune cells included plasmacytoid-like dendritic cells (pDCs) and regulatory T cells (Tregs), which are commonly found in various tumor tissues and are typically associated with tumor progression and a poor prognosis [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Conversely, several immune cells displaying a negative correlation with \u003cem\u003eMETTL1\u003c/em\u003e expression included central memory T cells (Tcm), eosinophils, T helper cells, effector memory T cells (Tem), neutrophils, Tγδ cells (Tgd), mast cells, follicular helper T cells (Tfh), NK cells, indicating a potential regulatory role of \u003cem\u003eMETTL1\u003c/em\u003e in immune evasion in prostate cancer. \u003cem\u003eWDR4\u003c/em\u003e expression positively correlated with Th2 cells, while showing negative correlations with mast cells, Tgd cells, NK cells, eosinophils, Th1 cells, and neutrophils \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB\u003cb\u003e).\u003c/b\u003e which may be linked to immune evasion in tumors. In addition, violin plots further detailed the significant correlations of specific immune cell infiltrations with \u003cem\u003eMETTL1\u003c/em\u003e and \u003cem\u003eWDR4\u003c/em\u003e expression \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC-J\u003cb\u003e).\u003c/b\u003e To further elucidate the mechanism by which METTL1 and WDR4 expressions influence immune cell infiltration in prostate cancer, we first compared cytokine profiles between normal prostate tissues and prostate cancer samples. Subsequent correlation analysis revealed that both METTL1 and WDR4 expression levels significantly associate with key cytokines specifically dysregulated in PC tissues \u003cb\u003e(Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003es1\u003c/span\u003e)\u003c/b\u003e. The results indicated that both METTL1 and WDR4 may regulate the expression of several key cytokines, thereby altering the immune microenvironment in prostate cancer. These findings suggest that the modulation of cytokine expression by METTL1 and WDR4 could play a pivotal role in shaping the immune landscape of prostate cancer.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e3.3 Enrichment Analysis of Genes Associated with\u003c/b\u003e \u003cb\u003eMETTL1\u003c/b\u003e \u003cb\u003eand\u003c/b\u003e \u003cb\u003eWDR4\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eWe initially identified 2,027 genes positively correlated with \u003cem\u003eMETTL1\u003c/em\u003e expression and 832 genes positively correlated with \u003cem\u003eWDR4\u003c/em\u003e expression. Similarly, genes negatively correlated with the expression of these two genes were also analyzed. After cross-analyzing these gene sets, we identified 650 positively correlated genes and 158 negatively correlated genes for subsequent GO and KEGG pathway analyses \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, B\u003cb\u003e)\u003c/b\u003e. GO analysis revealed that the positively correlated genes were primarily associated with structural components of ribosomes, negative regulation of the mitotic cell cycle, and cellular respiratory processes \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. Conversely, the negatively correlated genes were predominantly linked to biological functions such as actin binding and membrane regions \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e. KEGG analysis indicated that the positively correlated genes significantly impacted pathways related to ribosomes, RNA transport, and the spliceosome \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE\u003cb\u003e)\u003c/b\u003e. In contrast, the negatively correlated genes influenced pathways including the cGMP-PKG signaling pathway, focal adhesion, PI3K-Akt signaling pathway, calcium signaling pathway, and Rap1 signaling pathway \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF\u003cb\u003e)\u003c/b\u003e. In addition, signaling pathways related to prostate cancer progression, such as the Pentose Phosphate Pathway and the NF-κB pathway, were identified based on GSEA enrichment analysis. \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG-L\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Establishment and validation of a two-gene model for \u003cem\u003eMETTL1\u003c/em\u003e and \u003cem\u003eWDR4\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eTo ascertain the prognostic impact of \u003cem\u003eMETTL1\u003c/em\u003e and \u003cem\u003eWDR4\u003c/em\u003e in prostate cancer patients, univariate and multivariate Cox regression analysis were implemented. The results revealed that both \u003cem\u003eMETTL1\u003c/em\u003e and \u003cem\u003eWDR4\u003c/em\u003e expression levels can serve as autonomous prognostic indicators for predicting the OS of prostate cancer patients. \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Subsequently, a two-gene model incorporating patient T-stage, Gleason score, and the expression levels of these two genes, was developed to estimate the likelihood of biochemical recurrence at 1, 3, and 5 years \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B\u003cb\u003e)\u003c/b\u003e. The model exhibited reasonable predictive performance, as evidenced by good diagnostic accuracy in the validation set with AUCs of 0.743, 0.738, and 0.751 for 1, 3, and 5-year overall survival (OS), respectively \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. Moreover, it aligned well with the standard prediction curve, underscoring the utility of this two-gene model in forecasting biochemical recurrence in prostate cancer patients \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e. In addition, we included the GSE70770 dataset as an external validation to test the accuracy of our biochemical relapse prediction model. The model's predicted AUC values for 1-, 3-, and 5-year recurrence rates for these patients were all above 0.7\u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE, F\u003cb\u003e)\u003c/b\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\u003eUnivariate and multivariate Cox regression analyses of \u003cem\u003eMETTL1\u003c/em\u003e and \u003cem\u003eWDR4\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;T2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;T3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.785 (2.140\u0026ndash;6.693)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.694 (1.326\u0026ndash;5.473)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.946 (1.202\u0026ndash;3.150)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.851 (0.508\u0026ndash;1.424)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.538\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM stage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.566 (0.494\u0026ndash;25.753)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000 (0.000-Inf)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.302 (0.863\u0026ndash;1.963)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.253 (0.795\u0026ndash;1.973)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.331\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGleason score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.675 (2.957\u0026ndash;7.391)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.352 (1.945\u0026ndash;5.776)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eWDR4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.531 (1.011\u0026ndash;2.318)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.044\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.674 (1.061\u0026ndash;2.642)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.027\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMETTL1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.698 (1.114\u0026ndash;2.586)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.356 (1.054\u0026ndash;2.159)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.031\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Mutation Burden in Distinct Risk Groups\u003c/h2\u003e \u003cp\u003eTo further elucidate the specific mechanisms through which METTL1 and WDR4 function in prostate cancer, we then separately examined differences in mutation patterns between high and low-risk groups. The grouping of high and low-risk patients was based on the two-gene model we developed. After scoring all patients, those with higher scores were classified into the high-risk group, while those with lower scores were classified into the low-risk group. The findings revealed a similar set of genes with heightened mutation frequencies in both groups, including \u003cem\u003eSPOP, TTN, TP53, KMT2D, and FOXA1\u003c/em\u003e \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, B\u003cb\u003e)\u003c/b\u003e. However, the frequency of mutations in these genes varied among cases across different risk groups. Notably, mutations in the \u003cem\u003eSPOP\u003c/em\u003e gene are prevalent in prostate cancer and have been extensively linked to disease progression and an unfavorable prognosis in multiple studies [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In the high-risk group, the incidence of \u003cem\u003eSPOP\u003c/em\u003e mutations is several times higher than in the low-risk group, corm firming the robustness of our two-gene model. We analyzed the survival analysis of patients by combining high and low mutation burdens with high- and low-risk groups obtained from our model. The results indicated that low-risk patients with a low mutation burden had the most favorable OS \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Assessment of Immune Microenvironment in Varied Risk Groups\u003c/h2\u003e \u003cp\u003eImmune infiltration analysis revealed a significant decrease in plasma cell in patients of high-risk group. Conversely, the infiltrations of Tregs and M2 macrophages were markedly elevated compared to the low-risk group. Numerous studies have previously substantiated the significant association between the infiltration of these two immune cell types and prostate cancer progression, as well as adverse prognosis [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Additionally, we noted a heightened infiltration of resting mast cells among patients in the low-risk group \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, B\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.7 In Vitro Validation of Biological Functions of \u003cem\u003eMETTL1\u003c/em\u003e and \u003cem\u003eWDR4\u003c/em\u003e in Prostate Cancer Cells Studies\u003c/h2\u003e \u003cp\u003eWe designed and synthesized siRNAs targeting \u003cem\u003eMETTL1\u003c/em\u003e and \u003cem\u003eWDR4\u003c/em\u003e, which were subsequently verified for their robust knockdown efficiency through RT-qPCR \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA-D\u003cb\u003e).\u003c/b\u003e A series of \u003cem\u003ein vitro\u003c/em\u003e experiments, encompassing CCK-8 assays, colony formation, and transwell assays were conducted, utilizing two distinct prostate cancer cell lines, PC3 and DU145. The results consistently exhibited a marked decrease in the proliferation \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE-H\u003cb\u003e)\u003c/b\u003e, colony-forming \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eI, J, K\u003cb\u003e)\u003c/b\u003e, as well as the migratory and invasive capabilities \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA-F\u003cb\u003e)\u003c/b\u003e of prostate cancer cells following the knockdown of \u003cem\u003eMETTL1\u003c/em\u003e and \u003cem\u003eWDR4\u003c/em\u003e, respectively. This compelling evidence underscores the pivotal roles played by both \u003cem\u003eMETTL1\u003c/em\u003e and \u003cem\u003eWDR4\u003c/em\u003e in prostate cancer progression, thus justifying our development of the novel predictive model based on these two genes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo gain a more direct understanding of the impact of the two m7G-related genes on m7G modification in prostate cancer, we conducted dot blot experiments. The results revealed that the knockdown of both genes significantly altered the global m7G modification levels in prostate cancer cells \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. These findings suggest that the regulation of prostate cancer proliferation and metastasis by METTL1 and WDR4 may be dependent on epigenetic modifications. Furthermore, in the previously mentioned pathway enrichment analysis, we identified pathways associated with the cell cycle, cell adhesion, and epithelial-mesenchymal transition (EMT) that were significantly enriched. To validate these findings, we performed cell cycle assays and measured the protein levels of EMT markers. The results demonstrated that knockdowns of METTL1 and WDR4 induced significant cell cycle arrest (G1 phase accumulation) and suppressed EMT markers in PC3 and DU145 cells. However, DU145 cells exhibited attenuated cell cycle arrest, potentially due to their elevated baseline p21 levels and PTEN-null genetic background, which may bypass METTL1/WDR4-mediated cell cycle regulation. These experiments further corroborated the enrichment of cell cycle and EMT-related pathways \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB, C\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eProstate cancer incidence is a major global health concern for men. Although prostate cancer is not among the most aggressive malignancies [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], its potential progression to castration-resistant prostate cancer (CRPC) presents a significant challenge, underscoring the need for an in-depth study of its underlying mechanisms. Post-transcriptional modification is a crucial regulatory mechanism of human gene expression, with m7G modification being one of its key components. This modification is facilitated by the \u003cem\u003eMETTL1\u003c/em\u003e-\u003cem\u003eWDR4\u003c/em\u003e complex and has been associated with several cancer types [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, its involvement in prostate cancer remains relatively unexplored. Our study found that \u003cem\u003eMETTL1\u003c/em\u003e and \u003cem\u003eWDR4\u003c/em\u003e are highly expressed in prostate cancer tissues and are linked to poor prognosis. We also analyzed their relationship with immune cell infiltration and assessed the cellular functions and signaling pathways potentially affected. A prognostic model was constructed based on the results of multifactorial regression analysis, and its association with patient prognosis was evaluated in conjunction with tumor mutation burden. We classified patients into high- and low-risk groups according to the model and analyzed the differences in the immune environment between the two groups. Finally, we conducted a series of functional experiments in prostate cancer cells to support our conclusions.\u003c/p\u003e \u003cp\u003ePrevious studies have shown that tumor-infiltrating plasmacytoid dendritic cells (pDCs) often exhibit dysfunction, promote Treg cell differentiation, and facilitate tumor growth [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Although activated pDCs can stimulate anti-tumor T cell responses, their endogenous effects seem biased towards tumor promotion [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. We observed increased infiltration of pDCs with elevated \u003cem\u003eMETTL1\u003c/em\u003e and \u003cem\u003eWDR4\u003c/em\u003e expression, consistent with prevailing views. This suggests that \u003cem\u003eMETTL1\u003c/em\u003e and \u003cem\u003eWDR4\u003c/em\u003e play a critical role in regulating the immunosuppressive phenomena within the tumor microenvironment and contribute to prostate cancer progression.\u003c/p\u003e \u003cp\u003eMoreover, after performing enrichment analysis on genes positively associated with \u003cem\u003eMETTL1\u003c/em\u003e and \u003cem\u003eWDR4\u003c/em\u003e, we found that these genes are closely linked to ribosome-related cellular functions. This finding is significant because m7G modifications on tRNAs affect the translation of pro-oncogenic proteins closely associated with ribosomes [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. This implies that in prostate cancer, \u003cem\u003eMETTL1\u003c/em\u003e and \u003cem\u003eWDR4\u003c/em\u003e may influence tumor progression by regulating the translation efficiency of these proteins. Intriguingly, the cGMP-PKG pathway was significantly negatively correlated with these two genes [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Activation of this pathway has been shown to inhibit prostate cancer proliferation, suggesting that m7G modifications may enrich genes in this pathway, thus significantly affecting prostate cancer proliferation and metastasis. We also confirmed this phenomenon through in vitro functional experiments.\u003c/p\u003e \u003cp\u003eCurrently, there is a lack of precise and effective methods for predicting the survival time of prostate cancer patients. However, the prognostic model we developed, which combines \u003cem\u003eMETTL1\u003c/em\u003e and \u003cem\u003eWDR4\u003c/em\u003e gene expression levels with the patient's puncture score, provides a better prediction of overall survival (OS), potentially guiding clinical decision-making.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eOur results identified the m7G modification-related genes, \u003cem\u003eMETTL1\u003c/em\u003e and \u003cem\u003eWDR4\u003c/em\u003e, which are significantly overexpressed in prostate cancer and closely associated with its malignant progression. This may occur through the regulation of translation efficiency of pro-oncogenic proteins, a mechanism verified by our in vitro functional experiments. The prediction model established based on these findings has potential utility in guiding clinical decision-making.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 272px;\"\u003e\n \u003cp\u003em7G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 272px;\"\u003e\n \u003cp\u003eN-7methylguanosine\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 272px;\"\u003e\n \u003cp\u003eMETTL1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 272px;\"\u003e\n \u003cp\u003eMethyltransferase-like 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 272px;\"\u003e\n \u003cp\u003eWDR4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 272px;\"\u003e\n \u003cp\u003eWD repeat domain 4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 272px;\"\u003e\n \u003cp\u003eTCGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 272px;\"\u003e\n \u003cp\u003eThe Cancer Genome Atlas\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 272px;\"\u003e\n \u003cp\u003eGSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 272px;\"\u003e\n \u003cp\u003eSample Genome Enrichment Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 272px;\"\u003e\n \u003cp\u003eGO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 272px;\"\u003e\n \u003cp\u003eGene Ontology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 272px;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 272px;\"\u003e\n \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 272px;\"\u003e\n \u003cp\u003eCRPC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 272px;\"\u003e\n \u003cp\u003eCastration-resistant prostate cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 272px;\"\u003e\n \u003cp\u003eNEPC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 272px;\"\u003e\n \u003cp\u003eNeuroendocrine prostate cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 272px;\"\u003e\n \u003cp\u003epDCs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 272px;\"\u003e\n \u003cp\u003ePlasmacytoid-like dendritic cells\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 272px;\"\u003e\n \u003cp\u003eTregs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 272px;\"\u003e\n \u003cp\u003eRegulatory T cells\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 272px;\"\u003e\n \u003cp\u003eTcm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 272px;\"\u003e\n \u003cp\u003eCentral memory T cells\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 272px;\"\u003e\n \u003cp\u003eCCK-8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 272px;\"\u003e\n \u003cp\u003eCell Counting Kit-8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 272px;\"\u003e\n \u003cp\u003eOS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 272px;\"\u003e\n \u003cp\u003eOverall survival\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 272px;\"\u003e\n \u003cp\u003eTh1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 272px;\"\u003e\n \u003cp\u003eT helper 1 cells\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eConsent to publication was obtained by all the participants\u003c/p\u003e\n\u003ch2\u003eCrediT Authorship contribution statement\u003c/h2\u003e\n\u003cp\u003eDegeng Kong: Experimentation, Data analysis, Figure creation, Writing \u0026ndash; original draft. Juanyi Shi: Experimental design, Writing \u0026ndash; review \u0026amp; editing. Cong Lai: Experimental design, Figure creation, Writing \u0026ndash; original draft. Jintao Hu、Yelisudan Mulati、Jiawen Luo、 Junjie Wang and Yunfei Xiao: Supervision. Cheng Liu: Writing- review \u0026amp; editing. Kewei Xu: Writing- review \u0026amp; editing. All authors contributed to the revision, have read and approved the final submitted manuscript.\u003c/p\u003e\n\u003ch2\u003eConflict of interest\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003ch2\u003eFunding sources\u003c/h2\u003e\n\u003cp\u003eThis study was supported by the Key-Area Research and Development Program of Guangdong Province (2023B1111030006), National Natural Science Foundation of China (82372766 and 82072841), Natural Science Foundation of Guangdong Province (2021A1515010199).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eDegeng Kong: Experimentation, Data analysis, Figure creation, Writing \u0026ndash; original draft. Juanyi Shi: Experimental design, Writing \u0026ndash; review \u0026amp; editing. Cong Lai: Experimental design, Figure creation, Writing \u0026ndash; original draft. Jintao Hu、Yelisudan Mulati、Jiawen Luo、 Junjie Wang and Yunfei Xiao: Supervision. Cheng Liu: Writing- review \u0026amp; editing. Kewei Xu: Writing- review \u0026amp; editing. All authors contributed to the revision, have read and approved the final submitted manuscript.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eData is provided within the manuscript or supplementary information files\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJokhadze N, Das A, Dizon DS. Global cancer statistics: A healthy population relies on population health. CA Cancer J Clin. 2024;74:224\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang G, Zhao D, Spring DJ, DePinho RA. Genetics and biology of prostate cancer. 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Genes Dev. 2018;32:1105\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Prostate cancer, N-7methylguanosine, m7G, METTL1, WDR4","lastPublishedDoi":"10.21203/rs.3.rs-5724815/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5724815/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eProstate cancer is a prominent global health concern, posing a substantial threat to men's well-being and longevity. N-7methylguanosine (m7G) modification orchestrated by a complex involving \u003cem\u003eMETTL1\u003c/em\u003e and \u003cem\u003eWDR4\u003c/em\u003e, has garnered attention as a post-transcriptional modification with implications in numerous tumor types. Nevertheless, there is a paucity of research addressing potential pivotal roles of \u003cem\u003eMETTL1\u003c/em\u003e and \u003cem\u003eWDR4\u003c/em\u003e in driving prostate cancer progression.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe obtained mRNA expression data for \u003cem\u003eMETTL1\u003c/em\u003e and \u003cem\u003eWDR4\u003c/em\u003e from the TCGA and GSEA databases in prostate cancer patients, analyzing their impact on survival and tumor immune microenvironment. GO and KEGG analyses were performed on associated genes. Univariate and multivariate Cox analyses identified \u003cem\u003eMETTL1\u003c/em\u003e and \u003cem\u003eWDR4\u003c/em\u003e as independent prognostic factors, leading to a two-gene predictive model that evaluated tumor mutation burden, immune infiltration, and immune function changes. Importantly, we substantiated the impact of \u003cem\u003eMETTL1\u003c/em\u003e and \u003cem\u003eWDR4\u003c/em\u003e on prostate cancer development \u003cem\u003ein vitro\u003c/em\u003e.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn prostate cancer, high \u003cem\u003eMETTL1\u003c/em\u003e and \u003cem\u003eWDR4\u003c/em\u003e expression correlated with reduced overall survival and increased plasmacytoid dendritic cells, with decreased adaptive immune cells. Functional enrichment analysis indicated their influence on ribosome-related functions. Our model revealed critical mutation sites and immune infiltration alterations. In vitro, \u003cem\u003eMETTL1\u003c/em\u003e or \u003cem\u003eWDR4\u003c/em\u003e knockdown inhibited prostate cancer cell proliferation, migration, and invasion.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur study unveils the oncogenic roles of both \u003cem\u003eMETTL1\u003c/em\u003e and \u003cem\u003eWDR4\u003c/em\u003e in prostate cancer development. Additionally, the prognostic model founded on \u003cem\u003eMETTL1\u003c/em\u003e and \u003cem\u003eWDR4\u003c/em\u003e exhibits enhanced predictive precision for OS, thereby serving as a valuable clinical tool for prostate cancer.\u003c/p\u003e","manuscriptTitle":"Comprehensive analysis of m7G-related Genes METTL1 and WDR4 for predicting prognosis and oncogenic functions in prostate cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-14 23:00:40","doi":"10.21203/rs.3.rs-5724815/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-06T10:46:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-24T18:41:02+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-16T22:44:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"309623004812027040716668408122505456629","date":"2025-04-10T19:56:58+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-10T13:41:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-09T14:37:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2025-04-03T09:02:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bfd1618f-5050-4de0-b492-bed0e576a806","owner":[],"postedDate":"April 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-05-28T11:08:38+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-14 23:00:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5724815","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5724815","identity":"rs-5724815","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}