Integrated Multi-Omics Analysis Reveals Folate Metabolism Related Genes as Prognostic Markers and Therapeutic Targets in Clear Cell Renal Cell Carcinoma

Integrated Multi-Omics Analysis Reveals Folate Metabolism Related Genes as Prognostic Markers and Therapeutic Targets in Clear Cell Renal Cell Carcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Integrated Multi-Omics Analysis Reveals Folate Metabolism Related Genes as Prognostic Markers and Therapeutic Targets in Clear Cell Renal Cell Carcinoma Jinkang Lin, Sheng Li, Yunxin Zhou, Zecao Han, Weilin Chen, Huanhui Zheng, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9129596/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Clear cell renal cell carcinoma (ccRCC) is an aggressive tumor with high metastatic potential and therapeutic resistance, yet the role of folate metabolism in its pathogenesis and immune evasion remains unclear. This study aims to develop and validate a folate metabolism related gene (FMRG) scoring system to stratify patients by prognostic risk and immune phenotypes, and to explore the functional role of key FMRGs in ccRCC progression. Methods Using transcriptomic and clinical data from The Cancer Genome Atlas (TCGA), we developed a folate metabolism related gene (FMRG) scoring system via integrative machine learning and validated it in an external cohort. We analyzed associations of the FMRG score with clinicopathological features, biological pathways, immune infiltration, therapeutic responsiveness, and drug sensitivity. Single-cell RNA sequencing and spatial transcriptomics mapped candidate gene expression, and in vitro experiments validated the functional role of NGF. Results A ten gene prognostic model based on the FMRG score stratified ccRCC patients into groups with distinct clinical outcomes, immune profiles, and therapeutic responses. NGF was upregulated in ccRCC, with heterogeneous spatial expression. Functional assays showed that NGF enhances proliferation, migration, and invasion, and contributes to an immunosuppressive tumor microenvironment. Conclusions This folate metabolism-based scoring framework facilitates prognostic stratification, tumor microenvironment characterization, and prediction of immunotherapy response in ccRCC. NGF is identified as a functional mediator of tumor progression and immune modulation, offering potential therapeutic targets and insights into metabolic-immune crosstalk. Clear cell renal cell carcinoma Folate metabolism Multi-omics Prognostic signature Tumor microenvironment Immunotherapy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 INTRODUCTION Renal cell carcinoma (RCC) is a common malignancy originating from the renal epithelium, accounting for approximately 434,419 new cases and 155,702 deaths worldwide in 2022, with a pronounced male predominance[ 1 – 3 ]. Clear cell renal cell carcinoma (ccRCC), the most frequent histological subtype and comprising roughly 75% of RCC cases, exhibits a poorer prognosis than other subtypes[ 4 , 5 ]. Localized ccRCC is typically amenable to surgical resection and is associated with a relatively favorable prognosis. However, once the disease progresses to a metastatic stage, it frequently demonstrates marked resistance to conventional radiotherapy and cytotoxic chemotherapy, thereby posing substantial therapeutic challenges[ 6 ]. Notably, approximately 20%-30% of patients present with distant metastases at the time of initial diagnosis. Furthermore, despite undergoing curative-intent surgery, nearly 30% of patients with initially localized ccRCC subsequently experience recurrence and ultimately develop metastatic disease[ 4 ]. Although advances in immune checkpoint inhibitors and molecularly targeted therapies have expanded the therapeutic landscape and improved clinical outcomes compared with traditional treatment modalities, considerable interindividual variability in treatment response persists. Durable remission remains difficult to achieve in a substantial proportion of patients[ 7 ]. Therefore, the identification of reliable prognostic biomarkers and the exploration of novel therapeutic targets continue to represent critical priorities in efforts to enhance long-term survival outcomes for patients with ccRCC. Metabolic reprogramming constitutes a hallmark of ccRCC, driven predominantly by inactivation of the von Hippel-Lindau (VHL) tumor suppressor gene and consequent accumulation of hypoxia-inducible factors (HIFs)[ 8 – 10 ]. This pathogenic cascade promotes extensive remodeling of cellular metabolism, including aberrant accumulation of glycogen and lipid droplets, enhanced glycolytic flux, and dysregulation of glutamine and fatty acid metabolism[ 11 – 17 ]. These metabolic alterations not only fuel tumor growth and survival under hypoxic and nutrient-deprived conditions but also profoundly influence the tumor microenvironment (TME) by modulating immune cell infiltration, facilitating angiogenesis, and promoting therapeutic resistance[ 11 – 17 ]. Consequently, targeting metabolic vulnerabilities has emerged as a promising strategy for ccRCC treatment. Folate metabolism has been implicated in ccRCC pathogenesis due to its fundamental role in one-carbon transfer reactions essential for DNA synthesis, repair, and methylation[ 18 – 22 ]. This has led to the hypothesis that folate status may influence cancer risk by modulating genomic stability[ 20 ]. However, epidemiological and molecular evidence remains inconsistent. While some studies associate specific folate metabolism genes with advanced tumor stage and poor survival in ccRCC[ 18 ], others find no clear link between dietary folate intake and ccRCC risk[ 23 ]. Genetic polymorphisms in key enzymes like MTHFR add further complexity to this relationship[ 24 ]. Crucially, the systematic impact of folate metabolism on the TME and the ensuing clinical outcomes in ccRCC remains largely unexplored. For this reason, we performed an integrative investigation of folate metabolism in ccRCC. We constructed and validated a folate metabolism related gene (FMRG) signature capable of predicting patient prognosis, TME characteristics, and immunotherapeutic responsiveness. Our findings underscore the contribution of folate metabolism to interpatient heterogeneity and identify NGF as a key effector within this regulatory network. Functional experiments further demonstrated that knockdown NGF attenuates the malignant phenotypes of ccRCC cells. Given its high expression in renal tissue and its central roles in metabolic regulation, NGF represents a promising therapeutic target. Collectively, this study presents a novel integrative framework for risk stratification and highlights potential avenues for personalized therapeutic strategies in ccRCC. METHODS Data Collection Clear cell renal cell carcinoma (ccRCC) samples with complete RNA sequencing (RNA-seq) data, clinical annotations, and survival information were obtained from The Cancer Genome Atlas (TCGA) database ( https://portal.gdc.cancer.gov/repository , n = 532). An independent validation cohort, E-MTAB-1980 (n = 106), was retrieved from the ArrayExpress database ( https://www.ebi.ac.uk/biostudies/arrayexpress ), selected on the basis of comparable gene expression profiles and available survival data. Patients lacking survival information or exhibiting low quality sequencing data were excluded. Adjacent normal tissue samples were used only for differential expression analyses and were omitted from survival analyses. Additionally, single-cell RNA-seq datasets from three ccRCC patients (GSE304466) and spatial transcriptomic data from one ccRCC patient (GSM5924030 within GSE175540) were downloaded from the Gene Expression Omnibus (GEO) database ( http://www.ncbi.nlm.nih.gov/geo ). A curated set of 410 folate metabolism related genes (FMRGs) was compiled from the GeneCards database ( https://www.genecards.org/ ). Detailed gene information is provided in Table S1 . Identification of Folate Metabolism Related Differentially Expressed Genes Differential expression analysis between tumor tissues and matched adjacent normal tissues was conducted using the TCGA-KIRC dataset. The "limma" R package was employed to identify differentially expressed genes (DEGs) with statistical significance defined as p < 0.05 and |log2FC| ≥ 1[ 25 ]. Resulting DEGs were visualized using a volcano plot generated with the "ggrepel" package. Folate metabolism related DEGs were subsequently identified by intersecting the 2288 DEGs with a curated list of 410 FMRGs. Functional and Pathway Enrichment Analysis To characterize the expression patterns of the 61 folate metabolism related DEGs, a heatmap was generated using the "pheatmap" R package. Functional annotation was performed through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Enrichment results were visualized with the "ggplot2" R package to display enriched biological processes and pathways. Protein-Protein Interaction Network Analysis A protein-protein interaction (PPI) network was constructed to investigate the functional relationships among the top 20 folate metabolism related DEGs. The network was generated using the STRING database ( https://string-db.org/ ), with a confidence score threshold set at 0.4 to include both experimentally validated and predicted interactions. Hub genes within the network were identified and ranked using the "CytoHubba" plugin in Cytoscape based on the Maximal Clique Centrality (MCC) algorithm. Construction and Validation of an FMRG Risk Signature To develop a prognostic signature for ccRCC, we first identified 20 candidate genes from the intersection of FMRGs and DEGs in the TCGA-KIRC cohort. Univariate Cox regression analysis identified 11 genes associated with overall survival (p < 0.05). To refine the model and mitigate overfitting, least absolute shrinkage and selection operator (LASSO) Cox regression with 10‑fold cross‑validation was applied[ 26 , 27 ], selecting 10 genes with the strongest prognostic value. Multivariate Cox regression was subsequently performed to confirm the independent prognostic contribution of each gene. The final FMRGs risk score was calculated for each patient as a weighted linear combination of the expression levels of the 10 selected genes, using the regression coefficients derived from the LASSO model: In the TCGA-KIRC training cohort (n = 532), patients were dichotomized into high risk and low risk groups based on the median FMRG score. Kaplan-Meier survival analysis and log‑rank tests were used to compare overall survival between the two groups. The predictive accuracy of the signature was evaluated using time‑dependent receiver operating characteristic (ROC) curve analysis. The prognostic performance of the FMRG score was further validated in an independent external cohort (E‑MTAB‑1980) using the same risk-stratification procedure and analytical methods. Development and Validation of a Prognostic Nomogram To evaluate the independent prognostic value of the FMRG score in ccRCC, univariable and multivariate Cox regression analyses were performed, incorporating the FMRG score and key clinical characteristics. Variables identified as independent prognostic factors in the multivariate analysis were subsequently integrated into a nomogram to predict 1‑, 3‑, and 5‑year overall survival (OS). The predictive accuracy of the nomogram and the FMRG score was assessed and compared with that of individual clinical pathological factors using time‑dependent ROC analysis and area under the curve (AUC) values. Association of Risk Stratification with Clinicopathological Characteristics The associations between the defined risk groups and key clinicopathological variables, including age, gender, grade, and TNM stage, were evaluated. The distribution of these clinical characteristics across risk strata was compared using appropriate statistical tests: Chi-square tests for categorical variables and Wilcoxon rank-sum tests for continuous variables. To assess the prognostic relevance of risk grouping within specific clinical subsets, Kaplan-Meier survival analysis was performed across strata of each clinicopathological variable, with survival curves compared using the log-rank test. All analyses were conducted in R software utilizing the "survival" and "survminer" packages. Exploration of the Immune Landscape Across Risk Groups To characterize the tumor immune microenvironment (TIME) in ccRCC, we compared immune features between the high- and low-risk groups in the TCGA-KIRC cohort. The CIBERSORT algorithm was employed to estimate the relative proportions of 22 immune cell types[ 28 ]. Tumor purity, stromal score, immune score, and ESTIMATE score were inferred using the ESTIMATE algorithm[ 29 ]. Immune cell enrichment was further evaluated via single-sample gene set enrichment analysis (ssGSEA) implemented in the "GSVA" package[ 30 ]. Additionally, we examined the relationship between the ten model genes and key immune checkpoint molecules. Spearman correlation analysis was used to assess the association between the FMRG score and immune infiltration levels. Intergroup differences in immune cell abundance were evaluated using the Wilcoxon rank-sum test. Drug Sensitivity Analysis To compare the therapeutic response to conventional chemotherapeutic agents between the high- and low-risk ccRCC groups, we profiled the half-maximal inhibitory concentration (IC 50 ) of commonly used drugs. The IC 50 values were predicted in silico using the "pRRophetic" R package. Identification of Core Prognostic Genes in ccRCC To identify core genes with prognostic significance from the FMRGs model, we evaluated the association between each gene's expression levels and patient survival outcomes. Kaplan-Meier survival analysis was conducted for all 10 model genes using the TCGA-KIRC cohort. For each gene, patients were stratified into high- and low-expression groups based on the median expression value as the cutoff. Differences in overall survival between expression groups were assessed using log-rank tests, with a p value < 0.05 considered statistically significant. This comprehensive survival profiling enabled the identification of genes most strongly associated with clinical outcomes in ccRCC. Processing of Single-cell Sequencing Data We analyzed single-cell RNA-seq data utilizing the R packages "Seurat" and "SingleR"[ 31 , 32 ]. To maintain high quality cellular data, we focused on genes that were expressed in at least three individual cells. Additionally, we excluded cells with gene counts below 200 or above 10,000, and those where more than 20% of the genes were mitochondrial or ribosomal. To correct for batch effects between cancer and adjacent normal samples, we used the "harmony" R package[ 33 ]. To cluster the integrated data, we utilized the "FindNeighbors" and "FindClusters" functions, visualizing the resulting cell groups with UMAP techniques. To pinpoint genes uniquely expressed in each cluster, we performed Wilcoxon tests between pairs of clusters, leveraging the "FindAllMarkers" and "FindMarkers" functions from the "scran" R package. The expression patterns of specific genes were illustrated using the "featureplot" function[ 34 ]. Cell type annotations were derived from the original literature as well as data from the tumor single-cell transcriptome database TISCH ( http://tisch.comp-genomics.org/ ). Quantitative comparisons between cell subgroups were based on the gene expression units (FPKM). The high NGF expression group and low NGF expression group were distinguished based on the median of the overall data. Cell-Cell Communication Analysis Intercellular communication was modeled using the single-cell gene expression matrix with the "CellChat" R package. A normalized Seurat object was used to construct a CellChat object, referencing "CellChatDB.human" for ligand-receptor pairs. Communication probabilities were computed via "computeCommunProb" to assess interaction strength and number between cell types. Enriched ligand-receptor pairs and associated signaling genes were extracted using "extractEnrichedLR" to highlight key pathway interactions. Cell types with fewer than 10 cells were excluded, and interactions with p < 0.05 were deemed significant. Processing of Spatial Transcriptome Sequencing Data The data is analyzed in R using "Seurat"[ 31 ]. UMI counts undergo normalization and scaling, with the most variable features identified through the "SCTransform" function. For unsupervised clustering analysis, dimensionality reduction is performed using "RunPCA". The "FindNeighbors" and "FindClusters" functions are applied with default settings, focusing on the 30 most significant principal components. The "SpatialFeaturePlot" function is employed to visualize subgroups and gene expressions. The Institute of Molecular Biosciences at the University of Queensland has developed an integrated analysis tool called the stlearn package ( https://github.com/BiomedicalMachineLearning/stLearn ). This package utilizes gene expression data, tissue morphology, and spatial location information to initially classify cell types and then reconstruct the distribution of these cell types within tissues. Cell Culture and Reagents The human clear cell renal cell carcinoma (ccRCC) cell lines OS-RC-2 and A-498 were obtained from the American Type Culture Collection (ATCC; Manassas, USA). Both cell lines were authenticated by short tandem repeat (STR) profiling and confirmed to be free of mycoplasma contamination before experimentation. Cells were cultured at 37 ℃ in a humidified incubator containing 5% CO 2 . OS-RC-2 cells were maintained in RPMI-1640 medium (Gibco, Grand Island, USA), while A-498 cells were cultured in Minimum Essential Medium (MEM; Gibco, Grand Island, USA). Both media were supplemented with 10% fetal bovine serum (FBS; Gibco, Grand Island, USA). Transient Transfection of ccRCC Cells Small interfering RNAs (siRNAs) targeting human NGF and a non-targeting negative control (si-NC) were designed and synthesized by GENERAL BIOL (Anhui, China). Two independent siRNA sequences were used to ensure target specificity, their sequences are as follows: si-NGF-1: 5′- CCACAGACAUCAAGGGCAAdTdT-3′ (sense), 5′-UUGCCCUUGAUGUCUGUGGdTdT-3′ (antisense) si-NGF-2: 5′-GACCACCGCCACAGACAUCdTdT-3′ (sense), 5′-GAUGUCUGUGGCGGUGGUCdTdT-3′ (antisense). For transfection, ccRCC cells were seeded into appropriate culture plates and grown to 60–70% confluence. siRNA oligonucleotides were mixed with Lipofectamine 2000 (Lipo2K) Transfection Reagent (APExBIO, Houston, USA) transfection reagent at an optimized ratio (1.6 µg siRNA: 5 µL Lipo2K) in reduced serum medium (Opti-MEM; Gibco, Grand Island, USA) and incubated for 15–20 minutes at room temperature. The resulting complexes were added dropwise to the cells. The culture medium was replaced 6–8 hours after transfection. Knockdown efficiency was evaluated by qRT-PCR analysis 24–48 hours post transfection. Cell Viability Assay Cell proliferation was evaluated using the Cell Counting Kit-8 (CCK-8; APExBIO, Houston, USA). OS-RC-2 and A-498 cells were seeded into 96-well plates at a density of 2 × 10 3 cells per well in 100 µL of complete medium. After a 24 hours incubation to allow cell attachment, the medium was replaced with 100 µL of fresh medium containing 10 µL of CCK-8 reagent per well. The plates were then incubated at 37 ℃ for 2 hours. Absorbance at 450 nm was measured daily using a Spark® microplate reader (TECAN, Männedorf, Switzerland) over 5 consecutive days to generate cell growth curves. Colony Formation Assay The clonogenic capacity of ccRCC cells was evaluated using a colony formation assay. Cells were transfected with either a non-targeting negative control (si-NC) or NGF-targeting siRNA, and 48 hours later they were trypsinized and seeded into 6-well plates at a density of 1000 cells per well. The cells were cultured in complete medium for 10 days to allow colony formation, with the medium replaced every 3–4 days. Colonies were then fixed with 4% paraformaldehyde (Servicebio, Hubei, China) for 20 minutes and stained with 0.1% crystal violet (Servicebio, Hubei, China) for 30 minutes. After gentle washing and air drying, colonies containing at least 50 cells were manually counted under an inverted microscope. The colony formation rate was calculated, and results are presented as the mean ± standard deviation (SD) from three independent experiments. Wound Healing Assay Cell migration was assessed using a scratch wound healing assay. Cells were seeded in 6-well plates and transfected with either NGF-targeting siRNA or a non-targeting negative control for 24 hours. When the monolayers reached approximately 95% confluence, a uniform scratch was generated using a sterile 200 µL pipette tip. Detached cells were removed by washing twice with phosphate-buffered saline (PBS; Servicebio, Hubei, China), after which the cells were incubated in complete medium. Wound closure was monitored, and images from three randomly selected fields per well were captured at 0 and 48 hours using an inverted phase-contrast microscope (Olympus, Tokyo, Japan). The relative wound width or closure area was quantified using ImageJ software. Cell Invasion and Migration Assay Cell invasion and migration assays were conducted using 24-well transwell inserts with 8-µm pore membranes. Cells were trypsinized, resuspended in serum-free medium at a density of 5 × 10 5 cells/mL, and 200 µL of the cell suspension was added to the upper chamber. The lower chamber was loaded with 600 µL of medium supplemented with 20% FBS as a chemoattractant. After 36 h of incubation at 37 ℃ under 5% CO 2 , non-invading cells on the upper surface of the membrane were gently removed with a cotton swab. Invaded cells on the lower surface were fixed with 4% formaldehyde, stained with 0.5% crystal violet, and imaged using an inverted light microscope (Olympus, Tokyo, Japan). Quantification of invasive cells was performed with ImageJ software. RNA Extraction and Quantitative Real-Time PCR (qRT-PCR) Total RNA was extracted from cells using TRIzol reagent (YEASEN, Shanghai, China) according to the manufacturer’s instructions. RNA concentration and purity were assessed spectrophotometrically. Genomic DNA was removed, and 2 µg of total RNA was reverse-transcribed into complementary DNA (cDNA) using the FastKing RT Kit with gDNase (TIANGEN, Beijing, China). qRT-PCR was performed in triplicate using 2× Universal Blue SYBR Green qPCR Master Mix (Servicebio, Hubei, China) on a StepOnePlus™ Real-Time PCR System (Thermo Fisher Scientific, Waltham, USA). The cycling conditions were: 95 ℃ for 30 seconds, followed by 40 cycles of 95 ℃ for 15 seconds and 60 ℃ for 30 seconds. Gene-specific primers were designed and synthesized by Sengon Biotech (Shanghai, China). The primer sequences were as follows: NGF forward, 5′-TGAAGCTGCAGACACTCAGG-3′; reverse, 5′-AGAATTCGCCCCTGTGGAAG-3′; β-actin forward, 5′-CACCATTGGCAATGAGCGGTTC-3′; reverse, 5′-AGGTCTTTGCGGATGTCCACGT-3′. Relative mRNA expression was calculated using the 2 −ΔΔCt method and normalized to the housekeeping gene β-actin. Statistical Analysis All experiments were independently performed at least three times. Data are presented as the mean ± standard deviation (SD). Between-group differences were analyzed using unpaired two-tailed Student’s t tests for normally distributed data or Mann-Whitney U tests for non-normal distributions. For comparisons involving more than two groups, one-way ANOVA or the Kruskal-Wallis test was applied, as appropriate. Correlations were evaluated using Pearson’s correlation analysis for normally distributed variables and Spearman’s rank correlation for non-normal variables. Survival curves were generated using the Kaplan-Meier method and compared using the log-rank test. Model predictive performance was assessed by receiver operating characteristic (ROC) curve analysis, and the area under the curve (AUC) was calculated. Statistical analyses were conducted using SPSS (version 26.0), R (version 4.5.2), and GraphPad Prism (version 10.1.2). A two-sided p value < 0.05 was considered statistically significant. RESULTS Identification and Functional Enrichment of Folate Metabolism Related Differentially Expressed Genes Differential gene expression analysis was performed between 532 tumor samples from TCGA-KIRC and 72 adjacent non-tumor tissues, identifying a total of 2,288 DEGs, including 1,057 upregulated and 1,231 downregulated genes (Fig. 1A and Tables S2). By intersecting these DEGs with 410 known folate metabolism related genes, we identified 61 candidate genes implicated in folate metabolism for subsequent analysis (Fig. 1B and Tables S3). To elucidate the functional relevance of these 61 candidate genes, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted. KEGG pathway analysis revealed significant enrichment in processes such as one-carbon pool by folate, antifolate resistance, folate transport and metabolism, and glycine, serine, and threonine metabolism (Fig. 1C, D). GO analysis further delineated their functional roles. In biological process (BP), the genes were primarily associated with response to nutrient and vitamin, tetrahydrofolate metabolic process, folic acid-containing compound metabolic process, serine family amino acid metabolic process, and pteridine-containing compound metabolic process (Fig. 1E, F). For cellular component (CC), significant terms included the external side of plasma membrane, cytoplasmic vesicle lumen, vesicle lumen, and brush border membrane (Fig. 1E, F). In molecular function (MF), the most enriched terms were vitamin binding, carboxylic acid binding, organic acid binding, pyridoxal phosphate binding, and vitamin B6 binding (Fig. 1E, F). Protein-Protein Interaction (PPI) Network Analysis A protein-protein interaction (PPI) network was generated from the top 20 folate metabolism related DEGs using the STRING database (Fig. 2A). Subsequent topological analysis identified ten pivotal hub genes within this network, namely ALB, ERBB2, TGFB1, PDGFRA, CDH1, CDKN2A, VIM, FASLG, NGF, and SHMT1, suggesting their central roles in the underlying molecular machinery (Fig. 2B-D). Construction and Validation of a Folate Metabolism Related Prognostic Signature in ccRCC To develop a prognostic model for clear cell renal cell carcinoma (ccRCC), we initially performed univariable Cox regression analysis, which identified 11 candidate genes associated with patient survival (p < 0.05). Subsequent LASSO Cox regression analysis was employed to refine the model and prevent overfitting, leading to the selection of 10 pivotal genes with the optimal penalty parameter λ. Expression analysis of these genes using the TCGA-KIRC dataset revealed distinct patterns: CDKN2A, VIM, FASLG, NGF, and SHMT2 were upregulated in tumor tissues, whereas ERBB2, PDGFRA, SHMT1, ABCB1, and TIMP3 were downregulated (Fig. 2D). A comprehensive risk-score model, termed FMRG score, was constructed based on the expression levels of these genes weighted by their respective regression coefficients as follows: Using the median FMRG score as a cutoff, patients were stratified into low-risk (n = 266) and high-risk (n = 266) groups (Tables S4). This stratification effectively distinguished patients with distinct clinical outcomes: higher FMRG scores were associated with shorter overall survival and increased mortality (Fig. 3A, C). Kaplan-Meier analysis confirmed that patients in the low-risk group had better overall survival than those in the high-risk group (log-rank p < 0.001; Fig. 3E). The time-dependent receiver operating characteristic (ROC) analysis demonstrated predictive accuracy of the FMRG score, with area under the curve (AUC) values of 0.752, 0.698, and 0.725 for 1-, 3-, and 5-year survival, respectively (Fig. 3G). The prognostic performance of the FMRG score was further validated in an independent external cohort (E-MTAB-1980). Consistent with the training set, high-risk patients exhibited worse overall survival (log-rank p = 0.001; Fig. 3F), and the model maintained substantial predictive power across multiple timepoints (Fig. 3H). Based on the strong prognostic association of the FMRG score, we integrated it with key clinical parameters to construct a predictive nomogram for estimating 1-, 3-, and 5-year overall survival probabilities (Fig. 4A). Receiver operating characteristic (ROC) analysis and calibration curves showed excellent concordance between nomogram-predicted outcomes and actual observed survival (Fig. 4B, C), supporting the clinical utility of this integrated tool. Association of the FMRG-based Signature with Clinical Characteristics and Prognosis in ccRCC Univariable and multivariate Cox regression analyses identified the FMRG score, along with Grade and T, N, and M stage, as independent prognostic factors for ccRCC (Fig. 4D, E). Specifically, the FMRG score was associated with a hazard ratio (HR) of 1.20 (95% CI: 1.13-1.28, p < 0.001). To further validate the predictive accuracy of the FMRG score, Kaplan-Meier analyses were performed across subgroups stratified by various clinical features. The FMRG score was elevated in patients with more advanced disease (Fig. 4F-I), showing a positive correlation with disease progression across all stage components, though no significant difference was observed between Grade 1 and 2. Stratified survival analysis confirmed that patients with a high FMRG score had worse overall survival (OS), particularly in advanced TNM stages (Fig. 4J-R). This prognostic value of the FMRG score was consistently observed in both young and elderly patient subgroups (Fig. 4J, K), underscoring its broad clinical applicability. The FMRG score Reshapes the Immune Landscape and Predicts Drug Sensitivity in ccRCC The composition of the tumor microenvironment (TME) is a key determinant of immunotherapy response. To investigate this relationship in ccRCC, we evaluated immune profiles according to FMRG score stratification. Analysis of 22 immune cell subsets revealed elevated infiltration of CD8+ T cells, follicular helper T cells, regulatory T cells (Tregs), and M0 macrophages in the high FMRG score group, whereas resting CD4+ memory T cells, resting NK cells, M1 macrophages, resting dendritic cells, and resting mast cells were substantially decreased (Fig. 5E). Furthermore, the high FMRG score group exhibited elevated stromal, immune, and ESTIMATE scores, along with reduced tumor purity (Fig. 5A-D). We also assessed the correlation between the ten model genes and immune cell abundance, finding that most immune cells showed significant associations with all genes (Fig. 5F). To explore the clinical relevance of the FMRG signature, we compared the sensitivity of high- and low-risk groups to conventional ccRCC chemotherapeutic agents. Patients with high FMRG scores demonstrated lower IC 50 values for Sorafenib, Sunitinib, Temsirolimus, and Nilotinib (Fig. 5G-J), suggesting a potential role of the FMRG score in guiding personalized therapy. Collectively, these results indicate that the FMRG score is closely linked to both the immune contexture and drug susceptibility in ccRCC. NGF Serves as a Key Prognostic Factor and is Associated with an Immunosuppressive Microenvironment in ccRCC Survival analysis of the 10 FMRG model genes revealed distinct prognostic associations: low expression of TIMP3, SHMT1, ERBB2, and ABCB1 correlated with poor overall survival (OS), whereas high expression of SHMT2, PDGFRA, NGF, FASLG, CDKN2A, and VIM was associated with reduced OS (Fig. 6A-J). Given its prognostic significance, we investigated the relationship between NGF expression and the tumor immune microenvironment. Analysis of immune cell infiltration demonstrated that high NGF expression was characterized by a altered immune landscape. Specifically, it was associated with elevated infiltration of B cells naive and NK cells resting, concurrent with reduced infiltration of macrophages M2, Dendritic cells activated, and neutrophils (Fig. 6K). This profile suggests that VIM is linked to a distinct, potentially immunosuppressive, immune contexture in ccRCC. NGF Expression Signature in ccRCC via Integrated Single-cell and Spatial Transcriptomics The established prognostic significance of NGF in ccRCC through bulk transcriptomics necessitates a deeper investigation into its cell-type-specific expression and spatial distribution, dimensions obscured by tissue-level averaging. To this end, we integrated single-cell RNA sequencing (scRNA-seq) and spatial transcriptomic (ST) data, enabling the precise mapping of NGF expression across cellular subpopulations and its direct visualization within the native tissue architecture. Analysis of the scRNA-seq dataset (GSE304466) identified 22 distinct cell clusters, which were annotated into 7 major cell types, including B cell, endothelial cells, epithelial cells, macrophage, monocyte, T cells, tissue stem cells (Fig. 7A, B). Interrogation of NGF expression revealed its predominant enrichment in specific populations, including tissue stem cells and endothelial cells (Fig. 7C, D). CellChat analysis was employed to investigate global intercellular communication patterns in ccRCC based on single-cell gene expression profiles. The results revealed distinct differences in communication intensity and interaction strength among cell types. Specifically, B cells and monocytes exhibited significantly higher outgoing signal strength and number of interactions, whereas epithelial cells and macrophages showed pronounced reductions in these measures (Fig. 7E, F). Spatial transcriptomic analysis further contextualized this expression. NGF was highly expressed within the core tumor region, as demarcated by the tumor marker CA9, but was notably absent in the adjacent normal tissue defined by UMOD expression (Fig. 7G-H). This pattern underscores a tumor-specific upregulation of NGF and its potential role in the core tumor niche. NGF Promotes the Malignant Phenotype of ccRCC Cells In Vitro To functionally validate the oncogenic role of NGF in ccRCC, we performed loss-of-function assays in two representative cell lines (OS-RC-2 and A-498). Cells were transfected with NGF-targeting siRNA (si-NGF-1 and si-NGF-2) or a non-targeting control (si-NC), and efficient knockdown was confirmed by qRT-PCR (Fig. 8A). Functional analysis revealed that NGF silencing suppressed the malignant phenotype of ccRCC cells. Cell viability assessed by CCK-8 assay was reduced in si-NGF groups (Fig. 8B). The long-term clonogenic potential was also impaired, as evidenced by a significant decrease in both the number and size of colonies formed in the colony formation assay upon NGF knockdown (Fig. 8C). Furthermore, wound healing demonstrated that NGF depletion attenuated the migratory capacities of both cell lines (Fig. 8D). In addition, Transwell invasion and migration assays showed that NGF knockdown reduced the number of cells through Matrigel-coated membranes (Fig. 8E, F). Collectively, these in vitro results demonstrate that NGF is a critical regulator of ccRCC cell proliferation, clonogenicity, migration, and invasion, substantiating its role in driving tumor progression. DISCUSSION The high recurrence and metastasis rates of clear cell renal cell carcinoma (ccRCC) remain a major therapeutic hurdle. Although immune checkpoint inhibitors combined with targeted agents constitute first-line therapy for advanced disease, their clinical benefit is frequently constrained by intrinsic and acquired resistance, highlighting the urgent need to identify novel mechanisms underlying disease progression and therapeutic failure. Metabolic reprogramming is a defining feature of clear cell renal cell carcinoma (ccRCC), primarily driven by inactivation of the von Hippel-Lindau (VHL) gene and subsequent accumulation of hypoxia-inducible factors (HIFs) [ 8 – 10 ]. This process induces profound metabolic alterations, including glycogen and lipid accumulation, enhanced glycolysis, and disrupted glutamine and fatty acid metabolism[ 11 – 17 ]. Beyond sustaining tumor growth under hypoxic and nutrient-limited conditions, these changes reshape the tumor microenvironment (TME) by affecting immune infiltration, angiogenesis, and therapeutic resistance. Consequently, targeting metabolic dependencies represents a promising therapeutic strategy in ccRCC.Folate metabolism, essential for one-carbon transfer reactions involved in DNA synthesis, repair, and methylation, has been implicated in ccRCC pathogenesis[ 18 – 22 ]. Although altered folate related genes have been associated with advanced stage and poor prognosis, epidemiological data on dietary folate intake and ccRCC risk remain inconsistent, further complicated by polymorphisms in enzymes such as MTHFR[ 23 , 24 ]. Notably, the comprehensive role of folate metabolism in modulating the TME and clinical outcomes in ccRCC has yet to be systematically defined. To this end, we retrieved 410 folate metabolism related genes (FMRGs) from the GeneCards database and intersected this set with differentially expressed genes (DEGs) identified in the TCGA-KIRC cohort. Functional enrichment analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, were subsequently conducted on the overlapping gene set. KEGG pathway analysis revealed significant enrichment in key biological processes, including the one-carbon pool by folate, antifolate resistance, folate transport and metabolism, and glycine, serine, and threonine metabolism. GO analysis further elucidated the functional annotations of these genes. Within the biological process (BP) domain, the genes were associated with nutrient and vitamin responses, tetrahydrofolate metabolism, folic acid-containing compound metabolism, serine family amino acid metabolism, and pteridine-containing compound metabolism. In the cellular component (CC) category, enriched terms included the external side of the plasma membrane, cytoplasmic vesicle lumen, vesicle lumen, and brush border membrane. For molecular function (MF), the most highly enriched terms comprised vitamin binding, carboxylic acid binding, organic acid binding, pyridoxal phosphate binding, and vitamin B6 binding. A protein-protein interaction (PPI) network was subsequently constructed using the STRING database, focusing on the top 20 differentially expressed FMRGs. To investigate the potential clinical relevance of FMRGs in ccRCC, we developed a prognostic model via LASSO Cox regression analysis. Using this model, an individualized FMRG score was calculated for each patient, and ccRCC cases were stratified into high- and low-score groups based on the median score. Kaplan-Meier survival analysis demonstrated that patients in the high-score group exhibited worse overall survival (OS) compared to those in the low-score group, a finding consistently validated in both the TCGA-KIRC cohort and the independent external validation set, E-MTAB-1980. Given the prognostic performance of the FMRG score, we integrated it with key clinicopathological variables to construct a predictive nomogram for estimating 1-, 3-, and 5-year overall survival probabilities. Receiver operating characteristic (ROC) curves and calibration plots revealed excellent concordance between nomogram-predicted and observed survival outcomes, supporting the clinical applicability of this integrative tool. Univariate and multivariate Cox regression analyses further confirmed that the FMRG score served as an independent prognostic risk factor in the TCGA-KIRC cohort, with hazard ratios (HR) greater than 1 and p-values below 0.05. To elucidate the mechanisms underlying the association between elevated FMRG score and poor prognosis, we next examined the correlation between FMRG score and clinicopathological characteristics. The results demonstrated a positive association between FMRG score and disease progression across all tumor stage components; however, no statistically significant difference was observed between Grade 1 and Grade 2. Stratified survival analysis further confirmed that patients with a high FMRG score exhibited worse OS, particularly in advanced TNM stages. Of note, this prognostic value was consistently observed across both younger and elderly patient subgroups, underscoring the broad clinical applicability of the FMRG score. This multi-gene score serves as a independent prognostic indicator and a multi-faceted predictor of tumor microenvironment (TME) composition and therapy response in ccRCC. The high-risk group was associated with an immunosuppressive, pro-fibrotic TME, characterized by elevated infiltration of regulatory T cells (Tregs) and M0 macrophages, higher stromal scores, and lower tumor purity. These findings suggest that dysregulated folate metabolism may contribute to establishing an immune-cold microenvironment, potentially explaining the limited efficacy of immunotherapy in a subset of patients[ 35 ]. This aligns with the established concept that metabolic reprogramming is a key facilitator of immune evasion in cancer[ 36 – 38 ]. Drug sensitivity analysis revealed that patients with high FMRG scores demonstrated lower IC 50 values for Sorafenib, Sunitinib, Temsirolimus, and Nilotinib, suggesting a potential role of the FMRG score in guiding personalized therapy. Our integrated multi-omics approach confirmed the clinical utility of the FMRG score and identified NGF as a pivotal effector within this metabolic network. A key contribution of this study is the precise dissection of NGF expression at cellular and spatial resolution. While bulk analyses linked high NGF to poor prognosis, our single-cell RNA sequencing data delineated its specific cellular origins, revealing predominant enrichment in distinct subsets of tissue stem cells and endothelial cells. This cell-type specificity implies that NGF's function extends beyond its canonical role as a neurotrophic factor, suggesting compartment-specific roles in both tumor stemness and vascular microenvironment regulation. Spatial transcriptomics provided the critical topological context for these observations. We demonstrated that NGF expression is not diffuse but is highly concentrated within the CA9-positive tumor core, while being absent from adjacent UMOD-positive normal tissue. This spatial restriction to the tumor core unveils a previously underappreciated architectural role, positing that NGF-high cells might constitute or modulate a central niche vital for maintaining tumor integrity, stemness, or a specialized microenvironment conducive to tumor growth.The functional relevance of this spatially defined role was corroborated by our in vitro assays. NGF knockdown significantly impaired ccRCC cell proliferation, clonogenicity, migration, and invasion, providing causal evidence that NGF is a functional driver of aggressive behaviors likely originating from the tumor core. These findings collectively suggest that NGF functions as an active executor within a critical tumor core niche, where it propels malignant phenotypes. Furthermore, the FMRG score itself demonstrated significant clinical translational potential. This signature not only served as an independent prognostic factor beyond traditional TNM stage and grade but also maintained robust stratification capability across various clinical subgroups. Notably, although patients in the high-risk group exhibited worse prognosis, they showed lower IC 50 values for conventional ccRCC targeted therapies including Sorafenib and Sunitinib, suggesting that these patients might benefit from more aggressive initial treatment strategies. Additionally, the immune landscape characterized by increased regulatory T cell infiltration and decreased M1 macrophages in the high-risk group raises intriguing questions regarding their potential response to immune checkpoint inhibitors that warrant further investigation. Together, these insights redefine NGF from a passive correlative biomarker to an active executor within a critical tumor core niche, where it drives malignant phenotypes and likely contributes to organizing a permissive tumor microenvironment. This integrated view positions NGF as a compelling therapeutic target, as its inhibition could simultaneously disrupt cell-intrinsic oncogenic pathways and the integrity of the tumor core ecosystem. Beyond NGF, the prognostic model comprised nine additional folate metabolism related genes, including ERBB2, PDGFRA, CDKN2A, VIM, FASLG, SHMT1, SHMT2, ABCB1, and TIMP3. ERBB2 expression was significantly downregulated in ccRCC tumor tissues compared to normal controls in our cohort, consistent with previous reports demonstrating reduced ERBB2 mRNA and protein levels in ccRCC[ 39 ]. This downregulation may appear counterintuitive given ERBB2's well-established oncogenic role in breast and gastric cancers; however, in ccRCC, lower ERBB2 expression has been associated with aggressive clinicopathological features including advanced T stage, M stage, and higher histological grade[ 39 ]. Mechanistically, ERBB2 expression in ccRCC correlates positively with immune cell infiltration, immune checkpoint molecules (TIGIT, LAG3), and PTEN expression, suggesting its downregulation may contribute to immune dysregulation and tumor progression[ 39 ]. Our univariable Cox analysis identifying ERBB2 as a protective factor (HR < 1) aligns with these observations, supporting its context-dependent tumor-suppressive role in ccRCC. PDGFRA, encoding platelet-derived growth factor receptor alpha, was upregulated in our high-risk group and associated with poor prognosis. Intra-tumoral molecular heterogeneity of PDGFRA expression has been documented in ccRCC, with variable expression levels between primary tumors and metastatic lesions potentially influencing differential responses to tyrosine kinase inhibitors including sorafenib and sunitinib[ 40 ]. This heterogeneity may partially explain the diversity of clinical outcomes observed in ccRCC patients receiving targeted therapies. CDKN2A, a critical cell cycle regulator, demonstrated significant upregulation in tumor tissues and high-risk patients in our study, correlating with reduced overall survival. This finding corroborates comprehensive TCGA analyses demonstrating that CDKN2A alterations, including DNA hypermethylation and somatic changes, associate with decreased survival across all major RCC histologic subtype[ 41 ]. The consistent association of CDKN2A dysregulation with poor outcomes underscores its fundamental role in RCC progression independent of histologic subtype. VIM, a canonical mesenchymal marker, was elevated in our high-risk group and associated with adverse prognosis. Recent spatial proteomic analyses using 33-marker immunofluorescence imaging have demonstrated that ccRCC cells losing epithelial markers show increased expression of mesenchymal markers including vimentin, indicating epithelial-to-mesenchymal transition (EMT)[ 42 ]. FASLG, a member of the tumor necrosis factor superfamily, mediates apoptosis through FAS receptor binding and is essential for immune system regulation including activation-induced cell death of T cells. Our observation of FASLG upregulation in high-risk patients suggests its potential role in tumor immune evasion through induction of T lymphocyte apoptosis, a mechanism implicated in the progression of several cancers. SHMT1 and SHMT2, encoding cytoplasmic and mitochondrial serine hydroxymethyltransferase isoenzymes respectively, exhibited opposing expression patterns in our ccRCC cohort. SHMT1 was downregulated in tumor tissues and low expression correlated with poor survival, whereas SHMT2 was upregulated and high expression associated with reduced overall survival. These findings align with comprehensive analyses of SHMT family members in RCC, demonstrating significant SHMT1 downregulation and SHMT2 upregulation in ccRCC patients, with SHMT1 high expression correlating with longer survival periods[ 43 ]. ABCB1, a key ATP-binding cassette transporter, was downregulated in ccRCC tumor tissues compared to normal kidney cortex in our study. Previous investigations have confirmed significantly lower ABCB1 mRNA and protein expression in clear cell RCC compared to adjacent normal cortex tissue[ 44 ]. This downregulation may influence both the elimination of xenobiotics and the intrinsic chemotherapy resistance of ccRCC, potentially contributing to inter-individual differences in drug disposition and treatment outcomes. TIMP3, a key regulator of extracellular matrix remodeling, demonstrated downregulation in ccRCC tissues and was associated with poor prognosis in our model. TIMP3 exerts antitumor effects through both matrix metalloproteinase (MMP)-dependent and MMP-independent pathways[ 45 ]. Due to promoter methylation and miRNA binding, TIMP3 expression is frequently decreased in various cancers, leading to increased cancer cell migration and invasion[ 45 ]. The downregulation observed in our high-risk patients aligns with this tumor-suppressive role and supports TIMP3 as a potential biomarker in ccRCC. The convergence of these nine genes within a single prognostic signature reflects the multifaceted nature of ccRCC pathogenesis, encompassing growth factor signaling, cell cycle regulation, EMT and cytoskeletal remodeling, immune modulation, metabolic reprogramming, drug transport, and extracellular matrix homeostasis. Their coordinated dysregulation underscores the complex interplay between folate metabolism and fundamental oncogenic pathways in ccRCC. However, several limitations of this study should be acknowledged. First, the FMRG score was constructed based on retrospective data and requires validation in large-scale prospective cohorts prior to clinical application. Second, the specific molecular mechanisms through which NGF functions in tissue stem cells and endothelial cells, as well as its regulatory relationship with folate metabolism pathways, warrant further in-depth investigation. Finally, the drug sensitivity predictions derived from the FMRG score are primarily based on in vitro data and require confirmation in in vivo models and clinical trials. CONCLUSIONS In summary, this study establishes the folate metabolism related gene (FMRG) score as a novel, integrative biomarker for prognostic stratification and therapy response prediction in ccRCC. More importantly, by leveraging single-cell and spatial transcriptomic technologies, we have defined a spatially resolved role for its core component, NGF. We demonstrate that NGF is a functionally critical driver specifically enriched within the architecturally central tumor core niche. Functional validation through in vitro loss-of-function assays confirmed that NGF knockdown suppresses key malignant phenotypes, including proliferation, clonogenicity, migration, and invasion in ccRCC cells. This work underscores the power of spatial biology in elucidating the precise ecological context of oncogenic drivers. Targeting NGF presents a promising strategy to disrupt this core tumor niche and its associated immunosuppressive microenvironment, potentially overcoming a key mechanism of treatment resistance in ccRCC. ABBREVIATIONS ATCC American Type Culture Collection AUC Area under the curve BP Biological process CC Cellular component CCK-8 Cell Counting Kit-8 ccRCC Clear cell renal cell carcinoma cDNA Complementary DNA DEGs Differentially expressed genes FBS Fetal bovine serum FMRG Folate metabolism related gene FMRGs Folate metabolism related genes GEO Gene Expression Omnibus GO Gene Ontology HIFs Hypoxia-inducible factors HR Hazard ratio IC50 Half-maximal inhibitory concentration KEGG Kyoto Encyclopedia of Genes and Genomes LASSO Least absolute shrinkage and selection operator Lipo2K Lipofectamine 2000 MCC Maximal Clique Centrality MEM Minimum Essential Medium MF Molecular function OS Overall survival PBS Phosphate-buffered saline PPI Protein-protein interaction RCC Renal cell carcinoma RNA-seq RNA sequencing ROC Receiver operating characteristic scRNA-seq Single-cell RNA sequencing SD Standard deviation siRNAs Small interfering RNAs ssGSEA Single-sample gene set enrichment analysis ST Spatial transcriptomic STR Short tandem repeat TCGA The Cancer Genome Atlas TIME Tumor immune microenvironment TME Tumor microenvironment Tregs Regulatory T cells VHL Von Hippel-Lindau VIM Vimentin Declarations Ethics Approval and Consent to Participate Not applicable. Consent for Publication All authors read and approved the final manuscript. Availability of Data and Materials The datasets analyzed during this study are publicly available. The transcriptomic and clinical data for ccRCC were sourced from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/). The independent validation dataset E-MTAB-1980 was obtained from the ArrayExpress database (https://www.ebi.ac.uk/biostudies/arrayexpress). The single-cell RNA-seq (GSE304466) and spatial transcriptomic (GSM5924030) data were acquired from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/). The curated list of folate metabolism related genes was compiled from the GeneCards database (https://www.genecards.org/). Competing Interests The authors declare that they have no competing interests. Funding This work was supported by grants from the National Natural Science Foundation of China (No. 82203365) and the Natural Science Foundation of Jiangxi Province (No. S2023ZRMSL0904). Authors' Contributions Jinkang Lin, Sheng Li, and Yunxin Zhou conceived and designed the study, performed the majority of the experiments and data analysis, and wrote the original draft. Zecao Han, Weilin Chen, Huanhui Zheng, Shun Liu, Jiabiao Dai, Wei Cheng, and Chen Fu performed parts of the molecular biology experiments. Haibo Xi, Wen Deng, and Jin Zeng contributed to the study design and critically reviewed and revised the manuscript. All authors read and approved the final manuscript. Acknowledgements We thank the contributors of The Cancer Genome Atlas (TCGA), the ArrayExpress database, the Gene Expression Omnibus (GEO), and the GeneCards database for making their valuable data and resources publicly available. 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Supplementary Files TablesS1.FolatemetabolismrelatedgenesFMRGs.xlsx Tables S1. Folate metabolism related genes (FMRGs). TablesS2.Folatemetabolismrelateddifferentiallyexpressedgenes.xlsx Tables S2. Folate metabolism related differentially expressed genes. TablesS3.TheintersectionbetweendifferentiallyexpressedgenesDEGsandfolatemetabolismrelatedgenesFMRGs.xlsx Tables S3. The intersection between differentially expressed genes (DEGs) and folate metabolism related genes (FMRGs). TablesS4.StratificationofccRCCpatientsintolowandhighriskgroupsbasedonthemedianFMRGscore.xlsx Tables S4. Stratification of ccRCC patients into low- and high-risk groups based on the median FMRG score. 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First Affiliated Hospital, Jiangxi Medical College, Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Chen","middleName":"","lastName":"Fu","suffix":""},{"id":612706225,"identity":"3d96c3df-ab22-4e6f-a936-9c878127897b","order_by":10,"name":"Haibo Xi","email":"","orcid":"","institution":"Department of Urology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Haibo","middleName":"","lastName":"Xi","suffix":""},{"id":612706226,"identity":"6d2066eb-868c-4d2e-84e9-08dde93407b2","order_by":11,"name":"Wen Deng","email":"","orcid":"","institution":"Department of Urology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Wen","middleName":"","lastName":"Deng","suffix":""},{"id":612706228,"identity":"857e2ca2-ca5a-4e4c-937b-859674954904","order_by":12,"name":"Jin Zeng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIie3RMUsDMRTA8XcE4hLtGumQr/DCwVER+ll6HDgVEVw6CJ4ceIt+AKH4GZw6v+OBLsWugh10KQ4dlINOHTSt0CnHjYL5DxmS9yOEAIRCfzWNAEYI4vXod6MVsaVMSU3bkk0zFdP+dQtiylv+6J3NISoA6fB+bpBE9aqgf+oj0c3zSU/jAvYEDMhOFvaBZHasIDv3EaGHCWpkdwtROuEBkkq6CijNPUSa5ZYARzlVY0c6q0aitIrftkRAdZVvbpGNRKthAo7YQgLDI9s7lvHRGDMvMeU0rvWawXRmdQ0XbA6eiveX5ajvJe453Z9vuNwNCLegf96NfH02nodCodC/7xsQLFRQ9529FQAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Urology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University","correspondingAuthor":true,"prefix":"","firstName":"Jin","middleName":"","lastName":"Zeng","suffix":""}],"badges":[],"createdAt":"2026-03-15 15:08:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9129596/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9129596/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105574512,"identity":"6337b524-3db7-4381-8500-095fd4eafeee","added_by":"auto","created_at":"2026-03-27 13:35:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":45398168,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScreening and functional enrichment of folate metabolism related DEGs.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Volcano plot of differentially expressed genes (DEGs) in the TCGA-KIRC cohort.\u003c/p\u003e\n\u003cp\u003e(B) Venn diagram depicting the intersection between DEGs and folate metabolism related genes.\u003c/p\u003e\n\u003cp\u003e(C, D) KEGG pathway enrichment analysis of the overlapping folate metabolism related DEGs, shown as bar plot (C) and dot plot (D).\u003c/p\u003e\n\u003cp\u003e(E, F) Gene Ontology (GO) enrichment analysis of the overlapping folate metabolism related DEGs, shown as bar plot (E) and dot plot (F).\u003c/p\u003e","description":"","filename":"Figure01.png","url":"https://assets-eu.researchsquare.com/files/rs-9129596/v1/013e5126dba22e9dfadbf58e.png"},{"id":105574904,"identity":"3b417c5e-1d45-4276-af7c-697ec854c724","added_by":"auto","created_at":"2026-03-27 13:36:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":32272820,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction of the PPI network and FMRG score prognostic model.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) PPI network of folate metabolism related genes (STRING).\u003c/p\u003e\n\u003cp\u003e(B) LASSO coefficient profiles of candidate genes.\u003c/p\u003e\n\u003cp\u003e(C) Partial likelihood deviance for lambda selection (LASSO-Cox).\u003c/p\u003e\n\u003cp\u003e(D) Expression of key prognostic genes in normal versus tumor tissues (TCGA-KIRC).\u003c/p\u003e\n\u003cp\u003eStatistical significance is denoted as *p \u0026lt; 0.05, **p \u0026lt; 0.01, and ***p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure02.png","url":"https://assets-eu.researchsquare.com/files/rs-9129596/v1/0db49e867052756f9bc3f191.png"},{"id":105574472,"identity":"47ffeaf3-69a9-447e-8aaf-1433b26a3ea3","added_by":"auto","created_at":"2026-03-27 13:35:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":36229674,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExternal validation of the prognostic model based on FMRGs.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A-D) Risk score distribution, survival status, and scatterplot of patients in the TCGA-KIRC (A, C) and E-MTAB-1980 (B, D) datasets.\u003c/p\u003e\n\u003cp\u003e(E, F) Kaplan-Meier analysis of overall survival (OS) in high- versus low-risk groups from TCGA-KIRC (E) and E-MTAB-1980 (F).\u003c/p\u003e\n\u003cp\u003e(G, H) Time-dependent receiver operating characteristic (ROC) curves assessing the 1-, 3-, and 5-year predictive accuracy of the FMRG signature in TCGA-KIRC (G) and E-MTAB-1980 (H).\u003c/p\u003e","description":"","filename":"Figure03.png","url":"https://assets-eu.researchsquare.com/files/rs-9129596/v1/89297b03167ae5de974357ce.png"},{"id":105574280,"identity":"2e275330-861a-468a-abc7-fc32558b9e22","added_by":"auto","created_at":"2026-03-27 13:34:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":47120739,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClinical correlation of the FMRG-based prognostic signature.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Nomogram integrating the FMRG score with clinical parameters (Age, Gender, Grade, and TNM stage) for predicting 1-, 3-, and 5-year (OS).\u003c/p\u003e\n\u003cp\u003e(B) Calibration curves for 1-, 3-, and 5-year OS prediction.\u003c/p\u003e\n\u003cp\u003e(C) Receiver operating characteristic (ROC) curves evaluating the predictive accuracy of the nomogram for 1-, 3-, and 5-year OS.\u003c/p\u003e\n\u003cp\u003e(D, E) Univariate and multivariate Cox regression analyses of the risk score.\u003c/p\u003e\n\u003cp\u003e(F-I) Risk score distribution across Grade, and TNM stage.\u003c/p\u003e\n\u003cp\u003e(J-R) Stratified survival analysis by clinical parameters (Age, Gender, Grade, and TNM stage).\u003c/p\u003e","description":"","filename":"Figure04.png","url":"https://assets-eu.researchsquare.com/files/rs-9129596/v1/cd1e1a3eb4a762b5ef0603be.png"},{"id":105574660,"identity":"9ba80b34-846b-4075-8f40-f3930723a067","added_by":"auto","created_at":"2026-03-27 13:35:41","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":45949575,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe FMRG score reshapes the immune landscape and predicts drug sensitivity in ccRCC.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A-D) Comparison of (A) stromal score, (B) immune score, (C) ESTIMATE score, and (D) tumor purity between the high- and low-FMRG score groups. Statistical significance was determined by the Wilcoxon rank-sum test.\u003c/p\u003e\n\u003cp\u003e(E) Differences in the abundance of 22 immune cell types between the high- and low-FMRG score groups. The Wilcoxon rank-sum test was used for comparison.\u003c/p\u003e\n\u003cp\u003e(F) Correlation heatmap depicting the associations between the 10 FMRG model genes and immune infiltration levels. Spearman's correlation coefficients are shown.\u003c/p\u003e\n\u003cp\u003e(G-J) Analysis of the half-maximal inhibitory concentration (IC₅₀) for (G) Sorafenib, (H) Sunitinib, (I) Temsirolimus, and (J) Nilotinib between the high- and low-FMRG score groups.\u003c/p\u003e\n\u003cp\u003eStatistical significance is denoted as *p \u0026lt; 0.05, **p \u0026lt; 0.01, and ***p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure05.png","url":"https://assets-eu.researchsquare.com/files/rs-9129596/v1/b51ff79b2a52047d33190a75.png"},{"id":105574324,"identity":"df0a21d1-1dd1-487a-b3d2-cca270580d4f","added_by":"auto","created_at":"2026-03-27 13:34:11","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":34753806,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEvaluation of a 10 gene FMRG prognostic signature and the role of NGF in ccRCC.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A-J) Kaplan-Meier OS analysis for individual genes.\u003c/p\u003e\n\u003cp\u003e(K) Immune cell abundance across NGF expression groups.\u003c/p\u003e\n\u003cp\u003eStatistical significance is denoted as *p \u0026lt; 0.05, **p \u0026lt; 0.01, and ***p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure06.png","url":"https://assets-eu.researchsquare.com/files/rs-9129596/v1/bbbe62779426c0df11adee4e.png"},{"id":105574380,"identity":"253fb979-90d4-457c-94cd-766b48ab9804","added_by":"auto","created_at":"2026-03-27 13:34:44","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":26084771,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-cell and spatial transcriptomic profiling reveals cell type-specific expression and tumor core enrichment of NGF in ccRCC.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) UMAP visualization of 22 cell clusters identified from integrated scRNA‑seq data (GSE304466).\u003c/p\u003e\n\u003cp\u003e(B) UMAP plot annotated with 7 major cell types based on canonical marker expression.\u003c/p\u003e\n\u003cp\u003e(C, D) CellChat analysis illustrating the intercellular communication networks among the identified cell types, displayed as the total interaction weight (C) and the number of inferred interactions (D).\u003c/p\u003e\n\u003cp\u003e(E, F) Feature plot showing NGF expression levels across cell types, highlighting enrichment in tissue stem cells and endothelial cells.\u003c/p\u003e\n\u003cp\u003e(G) Spatial feature plot mapping NGF expression across the tissue section.\u003c/p\u003e\n\u003cp\u003e(H) Spatial feature plot of the tumor marker CA9, confirming the location of the tumor core region.\u003c/p\u003e\n\u003cp\u003e(I) Spatial feature plot of the normal tubule marker UMOD, delineating adjacent non‑neoplastic tissue.\u003c/p\u003e\n\u003cp\u003eNGF expression (G) colocalizes with the CA9+ tumor core (H) and is absent from the UMOD+ normal area (I), underscoring its tumor‑specific spatial pattern.\u003c/p\u003e","description":"","filename":"Figure07.png","url":"https://assets-eu.researchsquare.com/files/rs-9129596/v1/ed40276538522eccdd71c76f.png"},{"id":105574547,"identity":"8bbe1f00-c085-452b-9b53-fb6f357291f8","added_by":"auto","created_at":"2026-03-27 13:35:17","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":18219369,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNGF knockdown attenuates the malignant phenotype of ccRCC cells in vitro.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) qRT-PCR analysis confirming the efficient knockdown of NGF expression in OS-RC-2 and A-498 cells transfected with VIM-targeting siRNA (si-NGF-1 and si-NGF-2), compared to a non-targeting control siRNA (si-NC).\u003c/p\u003e\n\u003cp\u003e(B) Colony formation assay demonstrating the long-term clonogenic survival of cells following NGF knockdown. Representative images of crystal violet-stained colonies (left) and quantitative analysis (right) are shown.\u003c/p\u003e\n\u003cp\u003e(C) Assessment of cell proliferation after NGF knockdown. Cell viability measured by CCK-8 assay over 5 days.\u003c/p\u003e\n\u003cp\u003e(D) Wound healing assay showing representative images of scratch wounds at 0 and 48 hours post-scratching.\u003c/p\u003e\n\u003cp\u003e(E, F) Transwell migration assay showing cells that migrated through uncoated membranes. Transwell invasion assay showing cells that invaded through Matrigel-coated membranes. Quantitative analyses of migrated and invaded cells are presented.\u003c/p\u003e\n\u003cp\u003eData are presented as the mean ± standard deviation from three independent experiments. Statistical significance is denoted as *p \u0026lt; 0.05, **p \u0026lt; 0.01, and ***p \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"Figure08.png","url":"https://assets-eu.researchsquare.com/files/rs-9129596/v1/6d41db3d8b84fa162b0397e3.png"},{"id":105571994,"identity":"231206be-ff08-4950-9fa5-3e3b38bc1d36","added_by":"auto","created_at":"2026-03-27 13:25:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1210703,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9129596/v1/c283bd74-48d8-4fd4-8c34-0bb534bd4ede.pdf"},{"id":105574657,"identity":"c985a061-d08f-4683-8e22-274cc88caf65","added_by":"auto","created_at":"2026-03-27 13:35:40","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14792,"visible":true,"origin":"","legend":"\u003cp\u003eTables S1. Folate metabolism related genes (FMRGs).\u003c/p\u003e","description":"","filename":"TablesS1.FolatemetabolismrelatedgenesFMRGs.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9129596/v1/ffd41a4e38d5c8e65972ef9c.xlsx"},{"id":105574826,"identity":"ed7c01c2-e8f3-4035-a9cf-6614d418c092","added_by":"auto","created_at":"2026-03-27 13:36:22","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":49688,"visible":true,"origin":"","legend":"\u003cp\u003eTables S2. Folate metabolism related differentially expressed genes.\u003c/p\u003e","description":"","filename":"TablesS2.Folatemetabolismrelateddifferentiallyexpressedgenes.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9129596/v1/5e65ffa0e1ac460afca30acf.xlsx"},{"id":105574715,"identity":"4c15be68-7ca2-4733-bbe2-de9621440d45","added_by":"auto","created_at":"2026-03-27 13:35:56","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":9885,"visible":true,"origin":"","legend":"\u003cp\u003eTables S3. The intersection between differentially expressed genes (DEGs) and folate metabolism related genes (FMRGs).\u003c/p\u003e","description":"","filename":"TablesS3.TheintersectionbetweendifferentiallyexpressedgenesDEGsandfolatemetabolismrelatedgenesFMRGs.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9129596/v1/5a96a579c26e58e706dc088d.xlsx"},{"id":105574825,"identity":"1a140c90-939b-4f58-bb6b-54497a4d3ce9","added_by":"auto","created_at":"2026-03-27 13:36:21","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":85846,"visible":true,"origin":"","legend":"\u003cp\u003eTables S4. Stratification of ccRCC patients into low- and high-risk groups based on the median FMRG score.\u003c/p\u003e","description":"","filename":"TablesS4.StratificationofccRCCpatientsintolowandhighriskgroupsbasedonthemedianFMRGscore.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-9129596/v1/80fb5a6f6ec1d53f6129423b.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrated Multi-Omics Analysis Reveals Folate Metabolism Related Genes as Prognostic Markers and Therapeutic Targets in Clear Cell Renal Cell Carcinoma","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eRenal cell carcinoma (RCC) is a common malignancy originating from the renal epithelium, accounting for approximately 434,419 new cases and 155,702 deaths worldwide in 2022, with a pronounced male predominance[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Clear cell renal cell carcinoma (ccRCC), the most frequent histological subtype and comprising roughly 75% of RCC cases, exhibits a poorer prognosis than other subtypes[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Localized ccRCC is typically amenable to surgical resection and is associated with a relatively favorable prognosis. However, once the disease progresses to a metastatic stage, it frequently demonstrates marked resistance to conventional radiotherapy and cytotoxic chemotherapy, thereby posing substantial therapeutic challenges[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Notably, approximately 20%-30% of patients present with distant metastases at the time of initial diagnosis. Furthermore, despite undergoing curative-intent surgery, nearly 30% of patients with initially localized ccRCC subsequently experience recurrence and ultimately develop metastatic disease[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Although advances in immune checkpoint inhibitors and molecularly targeted therapies have expanded the therapeutic landscape and improved clinical outcomes compared with traditional treatment modalities, considerable interindividual variability in treatment response persists. Durable remission remains difficult to achieve in a substantial proportion of patients[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Therefore, the identification of reliable prognostic biomarkers and the exploration of novel therapeutic targets continue to represent critical priorities in efforts to enhance long-term survival outcomes for patients with ccRCC.\u003c/p\u003e \u003cp\u003eMetabolic reprogramming constitutes a hallmark of ccRCC, driven predominantly by inactivation of the von Hippel-Lindau (VHL) tumor suppressor gene and consequent accumulation of hypoxia-inducible factors (HIFs)[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This pathogenic cascade promotes extensive remodeling of cellular metabolism, including aberrant accumulation of glycogen and lipid droplets, enhanced glycolytic flux, and dysregulation of glutamine and fatty acid metabolism[\u003cspan additionalcitationids=\"CR12 CR13 CR14 CR15 CR16\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. These metabolic alterations not only fuel tumor growth and survival under hypoxic and nutrient-deprived conditions but also profoundly influence the tumor microenvironment (TME) by modulating immune cell infiltration, facilitating angiogenesis, and promoting therapeutic resistance[\u003cspan additionalcitationids=\"CR12 CR13 CR14 CR15 CR16\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Consequently, targeting metabolic vulnerabilities has emerged as a promising strategy for ccRCC treatment. Folate metabolism has been implicated in ccRCC pathogenesis due to its fundamental role in one-carbon transfer reactions essential for DNA synthesis, repair, and methylation[\u003cspan additionalcitationids=\"CR19 CR20 CR21\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This has led to the hypothesis that folate status may influence cancer risk by modulating genomic stability[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, epidemiological and molecular evidence remains inconsistent. While some studies associate specific folate metabolism genes with advanced tumor stage and poor survival in ccRCC[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], others find no clear link between dietary folate intake and ccRCC risk[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Genetic polymorphisms in key enzymes like MTHFR add further complexity to this relationship[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Crucially, the systematic impact of folate metabolism on the TME and the ensuing clinical outcomes in ccRCC remains largely unexplored.\u003c/p\u003e \u003cp\u003eFor this reason, we performed an integrative investigation of folate metabolism in ccRCC. We constructed and validated a folate metabolism related gene (FMRG) signature capable of predicting patient prognosis, TME characteristics, and immunotherapeutic responsiveness. Our findings underscore the contribution of folate metabolism to interpatient heterogeneity and identify NGF as a key effector within this regulatory network. Functional experiments further demonstrated that knockdown NGF attenuates the malignant phenotypes of ccRCC cells. Given its high expression in renal tissue and its central roles in metabolic regulation, NGF represents a promising therapeutic target. Collectively, this study presents a novel integrative framework for risk stratification and highlights potential avenues for personalized therapeutic strategies in ccRCC.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eData Collection\u003c/h2\u003e\n \u003cp\u003eClear cell renal cell carcinoma (ccRCC) samples with complete RNA sequencing (RNA-seq) data, clinical annotations, and survival information were obtained from The Cancer Genome Atlas (TCGA) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/repository\u003c/span\u003e\u003c/span\u003e, n\u0026thinsp;=\u0026thinsp;532). An independent validation cohort, E-MTAB-1980 (n\u0026thinsp;=\u0026thinsp;106), was retrieved from the ArrayExpress database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ebi.ac.uk/biostudies/arrayexpress\u003c/span\u003e\u003c/span\u003e), selected on the basis of comparable gene expression profiles and available survival data. Patients lacking survival information or exhibiting low quality sequencing data were excluded. Adjacent normal tissue samples were used only for differential expression analyses and were omitted from survival analyses. Additionally, single-cell RNA-seq datasets from three ccRCC patients (GSE304466) and spatial transcriptomic data from one ccRCC patient (GSM5924030 within GSE175540) were downloaded from the Gene Expression Omnibus (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo\u003c/span\u003e\u003c/span\u003e). A curated set of 410 folate metabolism related genes (FMRGs) was compiled from the GeneCards database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.genecards.org/\u003c/span\u003e\u003c/span\u003e). Detailed gene information is provided in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eIdentification of Folate Metabolism Related Differentially Expressed Genes\u003c/h3\u003e\n\u003cp\u003eDifferential expression analysis between tumor tissues and matched adjacent normal tissues was conducted using the TCGA-KIRC dataset. The \u0026quot;limma\u0026quot; R package was employed to identify differentially expressed genes (DEGs) with statistical significance defined as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log2FC| \u0026ge; 1[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Resulting DEGs were visualized using a volcano plot generated with the \u0026quot;ggrepel\u0026quot; package. Folate metabolism related DEGs were subsequently identified by intersecting the 2288 DEGs with a curated list of 410 FMRGs.\u003c/p\u003e\n\u003ch3\u003eFunctional and Pathway Enrichment Analysis\u003c/h3\u003e\n\u003cp\u003eTo characterize the expression patterns of the 61 folate metabolism related DEGs, a heatmap was generated using the \u0026quot;pheatmap\u0026quot; R package. Functional annotation was performed through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Enrichment results were visualized with the \u0026quot;ggplot2\u0026quot; R package to display enriched biological processes and pathways.\u003c/p\u003e\n\u003ch3\u003eProtein-Protein Interaction Network Analysis\u003c/h3\u003e\n\u003cp\u003eA protein-protein interaction (PPI) network was constructed to investigate the functional relationships among the top 20 folate metabolism related DEGs. The network was generated using the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003c/span\u003e), with a confidence score threshold set at 0.4 to include both experimentally validated and predicted interactions. Hub genes within the network were identified and ranked using the \u0026quot;CytoHubba\u0026quot; plugin in Cytoscape based on the Maximal Clique Centrality (MCC) algorithm.\u003c/p\u003e\n\u003ch3\u003eConstruction and Validation of an FMRG Risk Signature\u003c/h3\u003e\n\u003cp\u003eTo develop a prognostic signature for ccRCC, we first identified 20 candidate genes from the intersection of FMRGs and DEGs in the TCGA-KIRC cohort. Univariate Cox regression analysis identified 11 genes associated with overall survival (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). To refine the model and mitigate overfitting, least absolute shrinkage and selection operator (LASSO) Cox regression with 10‑fold cross‑validation was applied[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], selecting 10 genes with the strongest prognostic value. Multivariate Cox regression was subsequently performed to confirm the independent prognostic contribution of each gene. The final FMRGs risk score was calculated for each patient as a weighted linear combination of the expression levels of the 10 selected genes, using the regression coefficients derived from the LASSO model:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003cp\u003eIn the TCGA-KIRC training cohort (n\u0026thinsp;=\u0026thinsp;532), patients were dichotomized into high risk and low risk groups based on the median FMRG score. Kaplan-Meier survival analysis and log‑rank tests were used to compare overall survival between the two groups. The predictive accuracy of the signature was evaluated using time‑dependent receiver operating characteristic (ROC) curve analysis. The prognostic performance of the FMRG score was further validated in an independent external cohort (E‑MTAB‑1980) using the same risk-stratification procedure and analytical methods.\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eDevelopment and Validation of a Prognostic Nomogram\u003c/h2\u003e\n \u003cp\u003eTo evaluate the independent prognostic value of the FMRG score in ccRCC, univariable and multivariate Cox regression analyses were performed, incorporating the FMRG score and key clinical characteristics. Variables identified as independent prognostic factors in the multivariate analysis were subsequently integrated into a nomogram to predict 1‑, 3‑, and 5‑year overall survival (OS). The predictive accuracy of the nomogram and the FMRG score was assessed and compared with that of individual clinical pathological factors using time‑dependent ROC analysis and area under the curve (AUC) values.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eAssociation of Risk Stratification with Clinicopathological Characteristics\u003c/h3\u003e\n\u003cp\u003eThe associations between the defined risk groups and key clinicopathological variables, including age, gender, grade, and TNM stage, were evaluated. The distribution of these clinical characteristics across risk strata was compared using appropriate statistical tests: Chi-square tests for categorical variables and Wilcoxon rank-sum tests for continuous variables. To assess the prognostic relevance of risk grouping within specific clinical subsets, Kaplan-Meier survival analysis was performed across strata of each clinicopathological variable, with survival curves compared using the log-rank test. All analyses were conducted in R software utilizing the \u0026quot;survival\u0026quot; and \u0026quot;survminer\u0026quot; packages.\u003c/p\u003e\n\u003ch3\u003eExploration of the Immune Landscape Across Risk Groups\u003c/h3\u003e\n\u003cp\u003eTo characterize the tumor immune microenvironment (TIME) in ccRCC, we compared immune features between the high- and low-risk groups in the TCGA-KIRC cohort. The CIBERSORT algorithm was employed to estimate the relative proportions of 22 immune cell types[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Tumor purity, stromal score, immune score, and ESTIMATE score were inferred using the ESTIMATE algorithm[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Immune cell enrichment was further evaluated via single-sample gene set enrichment analysis (ssGSEA) implemented in the \u0026quot;GSVA\u0026quot; package[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Additionally, we examined the relationship between the ten model genes and key immune checkpoint molecules. Spearman correlation analysis was used to assess the association between the FMRG score and immune infiltration levels. Intergroup differences in immune cell abundance were evaluated using the Wilcoxon rank-sum test.\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eDrug Sensitivity Analysis\u003c/h2\u003e\n \u003cp\u003eTo compare the therapeutic response to conventional chemotherapeutic agents between the high- and low-risk ccRCC groups, we profiled the half-maximal inhibitory concentration (IC\u003csub\u003e50\u003c/sub\u003e) of commonly used drugs. The IC\u003csub\u003e50\u003c/sub\u003e values were predicted in silico using the \u0026quot;pRRophetic\u0026quot; R package.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eIdentification of Core Prognostic Genes in ccRCC\u003c/h2\u003e\n \u003cp\u003eTo identify core genes with prognostic significance from the FMRGs model, we evaluated the association between each gene\u0026apos;s expression levels and patient survival outcomes. Kaplan-Meier survival analysis was conducted for all 10 model genes using the TCGA-KIRC cohort. For each gene, patients were stratified into high- and low-expression groups based on the median expression value as the cutoff. Differences in overall survival between expression groups were assessed using log-rank tests, with a p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant. This comprehensive survival profiling enabled the identification of genes most strongly associated with clinical outcomes in ccRCC.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eProcessing of Single-cell Sequencing Data\u003c/h2\u003e\n \u003cp\u003eWe analyzed single-cell RNA-seq data utilizing the R packages \u0026quot;Seurat\u0026quot; and \u0026quot;SingleR\u0026quot;[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. To maintain high quality cellular data, we focused on genes that were expressed in at least three individual cells. Additionally, we excluded cells with gene counts below 200 or above 10,000, and those where more than 20% of the genes were mitochondrial or ribosomal. To correct for batch effects between cancer and adjacent normal samples, we used the \u0026quot;harmony\u0026quot; R package[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. To cluster the integrated data, we utilized the \u0026quot;FindNeighbors\u0026quot; and \u0026quot;FindClusters\u0026quot; functions, visualizing the resulting cell groups with UMAP techniques. To pinpoint genes uniquely expressed in each cluster, we performed Wilcoxon tests between pairs of clusters, leveraging the \u0026quot;FindAllMarkers\u0026quot; and \u0026quot;FindMarkers\u0026quot; functions from the \u0026quot;scran\u0026quot; R package. The expression patterns of specific genes were illustrated using the \u0026quot;featureplot\u0026quot; function[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Cell type annotations were derived from the original literature as well as data from the tumor single-cell transcriptome database TISCH (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tisch.comp-genomics.org/\u003c/span\u003e\u003c/span\u003e). Quantitative comparisons between cell subgroups were based on the gene expression units (FPKM). The high NGF expression group and low NGF expression group were distinguished based on the median of the overall data.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eCell-Cell Communication Analysis\u003c/h2\u003e\n \u003cp\u003eIntercellular communication was modeled using the single-cell gene expression matrix with the \u0026quot;CellChat\u0026quot; R package. A normalized Seurat object was used to construct a CellChat object, referencing \u0026quot;CellChatDB.human\u0026quot; for ligand-receptor pairs. Communication probabilities were computed via \u0026quot;computeCommunProb\u0026quot; to assess interaction strength and number between cell types. Enriched ligand-receptor pairs and associated signaling genes were extracted using \u0026quot;extractEnrichedLR\u0026quot; to highlight key pathway interactions. Cell types with fewer than 10 cells were excluded, and interactions with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were deemed significant.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eProcessing of Spatial Transcriptome Sequencing Data\u003c/h2\u003e\n \u003cp\u003eThe data is analyzed in R using \u0026quot;Seurat\u0026quot;[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. UMI counts undergo normalization and scaling, with the most variable features identified through the \u0026quot;SCTransform\u0026quot; function. For unsupervised clustering analysis, dimensionality reduction is performed using \u0026quot;RunPCA\u0026quot;. The \u0026quot;FindNeighbors\u0026quot; and \u0026quot;FindClusters\u0026quot; functions are applied with default settings, focusing on the 30 most significant principal components. The \u0026quot;SpatialFeaturePlot\u0026quot; function is employed to visualize subgroups and gene expressions. The Institute of Molecular Biosciences at the University of Queensland has developed an integrated analysis tool called the stlearn package (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/BiomedicalMachineLearning/stLearn\u003c/span\u003e\u003c/span\u003e). This package utilizes gene expression data, tissue morphology, and spatial location information to initially classify cell types and then reconstruct the distribution of these cell types within tissues.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003eCell Culture and Reagents\u003c/h2\u003e\n \u003cp\u003eThe human clear cell renal cell carcinoma (ccRCC) cell lines OS-RC-2 and A-498 were obtained from the American Type Culture Collection (ATCC; Manassas, USA). Both cell lines were authenticated by short tandem repeat (STR) profiling and confirmed to be free of mycoplasma contamination before experimentation. Cells were cultured at 37 ℃ in a humidified incubator containing 5% CO\u003csub\u003e2\u003c/sub\u003e. OS-RC-2 cells were maintained in RPMI-1640 medium (Gibco, Grand Island, USA), while A-498 cells were cultured in Minimum Essential Medium (MEM; Gibco, Grand Island, USA). Both media were supplemented with 10% fetal bovine serum (FBS; Gibco, Grand Island, USA).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003eTransient Transfection of ccRCC Cells\u003c/h2\u003e\n \u003cp\u003eSmall interfering RNAs (siRNAs) targeting human NGF and a non-targeting negative control (si-NC) were designed and synthesized by GENERAL BIOL (Anhui, China). Two independent siRNA sequences were used to ensure target specificity, their sequences are as follows:\u003c/p\u003e\n \u003cp\u003esi-NGF-1: 5\u0026prime;- CCACAGACAUCAAGGGCAAdTdT-3\u0026prime; (sense), 5\u0026prime;-UUGCCCUUGAUGUCUGUGGdTdT-3\u0026prime; (antisense)\u003c/p\u003e\n \u003cp\u003esi-NGF-2: 5\u0026prime;-GACCACCGCCACAGACAUCdTdT-3\u0026prime; (sense), 5\u0026prime;-GAUGUCUGUGGCGGUGGUCdTdT-3\u0026prime; (antisense).\u003c/p\u003e\n \u003cp\u003eFor transfection, ccRCC cells were seeded into appropriate culture plates and grown to 60\u0026ndash;70% confluence. siRNA oligonucleotides were mixed with Lipofectamine 2000 (Lipo2K) Transfection Reagent (APExBIO, Houston, USA) transfection reagent at an optimized ratio (1.6 \u0026micro;g siRNA: 5 \u0026micro;L Lipo2K) in reduced serum medium (Opti-MEM; Gibco, Grand Island, USA) and incubated for 15\u0026ndash;20 minutes at room temperature. The resulting complexes were added dropwise to the cells. The culture medium was replaced 6\u0026ndash;8 hours after transfection. Knockdown efficiency was evaluated by qRT-PCR analysis 24\u0026ndash;48 hours post transfection.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003eCell Viability Assay\u003c/h2\u003e\n \u003cp\u003eCell proliferation was evaluated using the Cell Counting Kit-8 (CCK-8; APExBIO, Houston, USA). OS-RC-2 and A-498 cells were seeded into 96-well plates at a density of 2 \u0026times; 10\u003csup\u003e3\u003c/sup\u003e cells per well in 100 \u0026micro;L of complete medium. After a 24 hours incubation to allow cell attachment, the medium was replaced with 100 \u0026micro;L of fresh medium containing 10 \u0026micro;L of CCK-8 reagent per well. The plates were then incubated at 37 ℃ for 2 hours. Absorbance at 450 nm was measured daily using a Spark\u0026reg; microplate reader (TECAN, M\u0026auml;nnedorf, Switzerland) over 5 consecutive days to generate cell growth curves.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n \u003ch2\u003eColony Formation Assay\u003c/h2\u003e\n \u003cp\u003eThe clonogenic capacity of ccRCC cells was evaluated using a colony formation assay. Cells were transfected with either a non-targeting negative control (si-NC) or NGF-targeting siRNA, and 48 hours later they were trypsinized and seeded into 6-well plates at a density of 1000 cells per well. The cells were cultured in complete medium for 10 days to allow colony formation, with the medium replaced every 3\u0026ndash;4 days. Colonies were then fixed with 4% paraformaldehyde (Servicebio, Hubei, China) for 20 minutes and stained with 0.1% crystal violet (Servicebio, Hubei, China) for 30 minutes. After gentle washing and air drying, colonies containing at least 50 cells were manually counted under an inverted microscope. The colony formation rate was calculated, and results are presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) from three independent experiments.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\n \u003ch2\u003eWound Healing Assay\u003c/h2\u003e\n \u003cp\u003eCell migration was assessed using a scratch wound healing assay. Cells were seeded in 6-well plates and transfected with either NGF-targeting siRNA or a non-targeting negative control for 24 hours. When the monolayers reached approximately 95% confluence, a uniform scratch was generated using a sterile 200 \u0026micro;L pipette tip. Detached cells were removed by washing twice with phosphate-buffered saline (PBS; Servicebio, Hubei, China), after which the cells were incubated in complete medium. Wound closure was monitored, and images from three randomly selected fields per well were captured at 0 and 48 hours using an inverted phase-contrast microscope (Olympus, Tokyo, Japan). The relative wound width or closure area was quantified using ImageJ software.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003eCell Invasion and Migration Assay\u003c/h2\u003e\n \u003cp\u003eCell invasion and migration assays were conducted using 24-well transwell inserts with 8-\u0026micro;m pore membranes. Cells were trypsinized, resuspended in serum-free medium at a density of 5 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e cells/mL, and 200 \u0026micro;L of the cell suspension was added to the upper chamber. The lower chamber was loaded with 600 \u0026micro;L of medium supplemented with 20% FBS as a chemoattractant. After 36 h of incubation at 37 ℃ under 5% CO\u003csub\u003e2\u003c/sub\u003e, non-invading cells on the upper surface of the membrane were gently removed with a cotton swab. Invaded cells on the lower surface were fixed with 4% formaldehyde, stained with 0.5% crystal violet, and imaged using an inverted light microscope (Olympus, Tokyo, Japan). Quantification of invasive cells was performed with ImageJ software.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\n \u003ch2\u003eRNA Extraction and Quantitative Real-Time PCR (qRT-PCR)\u003c/h2\u003e\n \u003cp\u003eTotal RNA was extracted from cells using TRIzol reagent (YEASEN, Shanghai, China) according to the manufacturer\u0026rsquo;s instructions. RNA concentration and purity were assessed spectrophotometrically. Genomic DNA was removed, and 2 \u0026micro;g of total RNA was reverse-transcribed into complementary DNA (cDNA) using the FastKing RT Kit with gDNase (TIANGEN, Beijing, China). qRT-PCR was performed in triplicate using 2\u0026times; Universal Blue SYBR Green qPCR Master Mix (Servicebio, Hubei, China) on a StepOnePlus\u0026trade; Real-Time PCR System (Thermo Fisher Scientific, Waltham, USA). The cycling conditions were: 95 ℃ for 30 seconds, followed by 40 cycles of 95 ℃ for 15 seconds and 60 ℃ for 30 seconds. Gene-specific primers were designed and synthesized by Sengon Biotech (Shanghai, China). The primer sequences were as follows: NGF forward, 5\u0026prime;-TGAAGCTGCAGACACTCAGG-3\u0026prime;; reverse, 5\u0026prime;-AGAATTCGCCCCTGTGGAAG-3\u0026prime;; \u0026beta;-actin forward, 5\u0026prime;-CACCATTGGCAATGAGCGGTTC-3\u0026prime;; reverse, 5\u0026prime;-AGGTCTTTGCGGATGTCCACGT-3\u0026prime;. Relative mRNA expression was calculated using the 2\u003csup\u003e\u0026minus;\u0026Delta;\u0026Delta;Ct\u003c/sup\u003e method and normalized to the housekeeping gene \u0026beta;-actin.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n \u003cp\u003eAll experiments were independently performed at least three times. Data are presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). Between-group differences were analyzed using unpaired two-tailed Student\u0026rsquo;s t tests for normally distributed data or Mann-Whitney U tests for non-normal distributions. For comparisons involving more than two groups, one-way ANOVA or the Kruskal-Wallis test was applied, as appropriate. Correlations were evaluated using Pearson\u0026rsquo;s correlation analysis for normally distributed variables and Spearman\u0026rsquo;s rank correlation for non-normal variables. Survival curves were generated using the Kaplan-Meier method and compared using the log-rank test. Model predictive performance was assessed by receiver operating characteristic (ROC) curve analysis, and the area under the curve (AUC) was calculated. Statistical analyses were conducted using SPSS (version 26.0), R (version 4.5.2), and GraphPad Prism (version 10.1.2). A two-sided p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003eIdentification and Functional Enrichment of Folate Metabolism Related Differentially Expressed Genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDifferential gene expression analysis was performed between 532 tumor samples from TCGA-KIRC and 72 adjacent non-tumor tissues, identifying a total of 2,288 DEGs, including 1,057 upregulated and 1,231 downregulated genes (Fig. 1A\u0026nbsp;and Tables S2). By intersecting these DEGs with 410 known folate metabolism related genes, we identified 61 candidate genes implicated in folate metabolism for subsequent analysis (Fig. 1B\u0026nbsp;and Tables S3).\u003c/p\u003e\n\u003cp\u003eTo elucidate the functional relevance of these 61 candidate genes, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted. KEGG pathway analysis revealed significant enrichment in processes such as one-carbon pool by folate, antifolate resistance, folate transport and metabolism, and glycine, serine, and threonine metabolism (Fig. 1C, D). GO analysis further delineated their functional roles.\u0026nbsp;In biological process (BP), the genes were primarily associated with response to nutrient and vitamin, tetrahydrofolate metabolic process, folic acid-containing compound metabolic process, serine family amino acid metabolic process, and pteridine-containing compound metabolic process (Fig. 1E, F).\u0026nbsp;For cellular component (CC), significant terms included the external side of plasma membrane, cytoplasmic vesicle lumen, vesicle lumen, and brush border membrane (Fig. 1E, F).\u0026nbsp;In molecular function (MF), the most enriched terms were vitamin binding, carboxylic acid binding, organic acid binding, pyridoxal phosphate binding, and vitamin B6 binding (Fig. 1E, F).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProtein-Protein Interaction (PPI) Network Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA protein-protein interaction (PPI) network was generated from the top 20 folate metabolism related DEGs using the STRING database (Fig. 2A). Subsequent topological analysis identified ten pivotal hub genes within this network, namely ALB, ERBB2, TGFB1, PDGFRA, CDH1, CDKN2A, VIM, FASLG, NGF, and SHMT1, suggesting their central roles in the underlying molecular machinery (Fig. 2B-D).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction and Validation of a Folate Metabolism Related Prognostic Signature in ccRCC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo develop a prognostic model for clear cell renal cell carcinoma (ccRCC), we initially performed univariable Cox regression analysis, which identified 11 candidate genes associated with patient survival (p \u0026lt; 0.05). Subsequent LASSO Cox regression analysis was employed to refine the model and prevent overfitting, leading to the selection of 10 pivotal genes with the optimal penalty parameter \u0026lambda;. Expression analysis of these genes using the TCGA-KIRC dataset revealed distinct patterns: CDKN2A, VIM, FASLG, NGF, and SHMT2 were upregulated in tumor tissues, whereas ERBB2, PDGFRA, SHMT1, ABCB1, and TIMP3 were downregulated (Fig. 2D). A comprehensive risk-score model, termed FMRG score, was constructed based on the expression levels of these genes weighted by their respective regression coefficients as follows:\u003c/p\u003e\n\u003cp\u003e\u003cimg 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Ygct0kZdkXseaKS21Xdk2G4ih57y9Bp5wz/aMUF6LQjY5LAn8iPQ5iN7RUcYHzrbjs8ghxyuYA7iPexOcBwLWAttMqpewVFACyn3EcwJbSXnEdde7WAg0PSBd7VpeT//CGwwl69EFhEKxtTERjDbM83exeASKkqNgWJoCak0k4lhITwMEaIhPgIhJJSEOYRfWcpl880375QnAbQqR6QJaJQd90pBME7aZyIjKCanSaQOk8tql2cO0eZdsGXI4yBdPp5vK31eVaTPVg8lR/HIi4TYWrUlg7Q4boKYM9DqYKQYRxOOp0FbrI5hKn2q4HiLRcZUYbrkheLQ1plujUa7LR7gbBwjzhUhhuOgfiJy0siMvBEQDWMiiIMZjwpl6JmC50VR3piIE8I7yEgzXOL6AdmBfT++/2xByIsZJKqfPujZYnP16tXdmVkedosH+XglyDMvJCUpbjqBEdHfKIt4MNTmlHrMPwaLAesbKMQRCUZmYAJvMmleIyni4MnYqyuCsdx///27oxsRB29yaIzphIh3Nb62pG1vogIJbgAAEABJREFUO2ZjsSp+IYKdADgw2O37EU66xfyN+FiY8VJHXHulc2qt3XnViKcLeY7N74iDb0tyIz6u0sgvXRJx7dW4tM9xT6aNLb3XJ5IIJ2LBWxj5p7pyVhgnutY8sbi10KLfzFXvIftT1RPpMOXdpkPIEE8nfa0/5i1CVesaT60ynAhky3u0QdywgExYrIROgwPvO7kdVmY+4u06WGggbO37YElnsR8RD+9aaxk27ogfvBDiKGNM6TXzLeLiyqnFRrE5jpPEoll+RNDc5iGP/O1VHpgix4hjpJFL46X9EeeKoNPFfc+rtGGBHdEudn6HCTLP62y8HXGBGecRMjmsfBtvTpInu9ftnG3zxL35aK6a83RaxLsXh2Qbm4jXLnjQq47eOC7EMcGO06+RL670KF3Boyw9djLwHv2jjyNv/2phRW9a2Jhj/XS8wzyHkfb20/N5/hGYMxJNuVKQFICVq4nOQDGibbcoNcrOhCMkVromAC8eA21159wlLyCi0ZblxZWH0SOQSKj8PHbOV1K0hN1KkpJGCNry49ybQJQHr5P28YIw8iYFAsLwUyAmvTbyiAQh8W7kF4nTLoollJSJRZHybDIWFCJiTVFstdVWhXHTXqt5pMq7eVBgKVCSsBqnDwyJiT9VMFnHqU8ek1uAq+fZBLKiP61yVp+xdaXcXdtARii/YWUirw+M1CNEXK21tHV6N3Jgu7AvY1FmuldkiVKj5MYtK78tcTsavCdkh0zzEPGK1HohoRinTgaWwiarjOugMmSYt5ono0+0yOhmEx5ZhoscWuwgvLzQjgqYlzwsCFlbt/nAe4QEmJcWE7wr5BVJZkgiPx1xt7vdrTv7ar4iqrxrkT7fV4tZhrNtozbQAQypNnoWal0zHq0siY9g8eveuLoKIYfDysjTD/SGOfzOd76zONKDEDrSYGzljTrdC+qmT/zyA9JkrGpd01bpgjL6qb/TwdsxOEcqODvoPl40+ouDgB5W97jBFjUZMVd4sbUDISBnCHYr8xYd9D1ZYSumegf5I6s85PSx3RTzQb+nKrs+0xE79qkvX3QzQtXqs1rXjJnxHNQm+Jm/g+SrzU9PGh/HzDiAeGLZNXjIR/8i2HSAD04tNtgyY8vpZVxg2Zd/ZQPPfhvFw93cV17ecYLFPX2nX4ipcSNXxhHJHqcOeegd3MPRlT7W0ttAt9m1wE1a/OHDmUH/WSxEGXwCZ+HNtpvGNpPjWteMV+SLq10b2MKUc4VOt/CgHzjl2vGLMq7kAdfAKexgw1R8BHPEUR4OFhjVWguZiPS8LgwCc0aieRJMVOSEsPi4BuEKj1h0Tx4TlGIkZCYP403Z8NhK98wT3fdy8NDJT6h9EeysG2LquAXF7h2E1QRRdxAkSoNycH7JMQ9CLu+g4J1WwfrBc8wrxEOuHz4y8bGCLVrPypvojqCYeDx2cOCBD4JiYlJq8gqe1VNrLSaKuDYg1oxHHJlAOigmHnor9jbvfN7DleLUz/57rephO1XwSwfKUg4CkuV5nEAx1lrXOT/cL4v0kBGhn+ZZvMWDhQ4DLS6CBR5vezzP5AqL6ZTTL3JMXng4LL5GeSpG1e2cJdkeVZ4skjHzA0GK+hg+W7EWfeSfkaC0zTOeU0bBvOM5I/NRztUCx7vVy1NjLtsZ4u1STp4IvDf0A485bwvDxIh6f+QZdjVv+jJGJhHNNp7RQsyG1dPGM6iDZNq4kFEL4zb/qPswyORoVL6p0siCRRWPL70VRIhjQlkeMdcIFrfOtSLJxivih10dixqWNijegoxc2t53JtxuiQX/mrzj/0uv2eJWgkypl+5EHmBn8SdN0CfxjkF4FhB5x+iMNacDYiOeTqXzkRM6mw6GA0JmDOUZJ5i7Ub93COTSL1u4j8C2IZjj1Em+2JB+Xm3VNnq1nzbsGUbSppIvxJQDxjwzZrXW7gNUdlV5Mm1uw9d8Nl8c72BL7Y7BMOR/Ou1Tt4DMuo4bjLtFtwWVnVlt78v4VHV5pz7RL1PlteCUpyXKns0L4x3zTJxAL7L5drh5us1JOk9aP8CLk4J8S7OgJcviYEwviu8HiyqOOXOCoy24S+Qz5r6TYLfiCAncYg5EvrzOPwJzRqIZUF6l2EohRM4K97vEw2DgCTCirAwSzNtq+5wxpqBsAZvIUd7ER6IZfwTXChpZQDBDuchLoSqPVPOaiVMPxWAFR2EgWuIHBdv3hJjg+yABsYl8JnmttfDWRVx7Ndm8k5cl4vVVewi/OJOBwqfgeQjEtUEdSD8yYmXOgMLFERLKsc077J6x4oWYKvBcDatjOvHwRPCnCmFMkDR4UFr99zAsVtn9eEdTLHB4nPpp4oMIkSMECoZtPu8zNhZpzhw7q4jA8f4aY/ILYyv9ttx07/vvnao8Uo9sajMvhQ/vGL+pyg1K54Un6wzjoHRxPFCufW+yeYdE2IExTogyGTRvEChybPHr5wWVj2C+kWfz2S/kIMTmM2zFRb7+FUm3C8NLZ04iSP08/edBciaPxUAre+SLHEmbadA+9Q7y9JDFQfXGwpruaNMZxEEy3ebp3yMRdkrgyStH15kvZMTYtPl33HHH7hcfeIprHewda/PTue3zVPfG3rGEcIogmgjYVOXadHoPCQndqQ9kDckjP/R7i5Fni2x2JOoh2zyOCBdiG/H6Ix4JNO7y8eTzSnqOfFNd2ZFWjtzXWgs5cB+BjZhOvYPeq//mmYVhP32QXZDHDoX3BgkUJ9BrLXbiIlhY2e0yd9kF81WArQUz0kk+LdzYCySS/tFXOtWCJOqKq3lIn8Zz/8re9eNGPWtb7FpbdFtYjaMP2jrpdfPSvGnjB90js8iqOd6m2+lRj3PfEU8vI8BwVL8FpJ3MYR8z2zVy7DSOzsDYmNkBol/7xxG9x0KLTXJvMQx7921Qr6MyyLajHMYKH+JEaPPl/TQQmKOsc0aiTTbbIzGZKQiKv99OZ5oZOYpCXsqS8uJ5RYZ4Xq2wCHlblpG3bcyIUBoUGe+s99pmbvO6pxgIr3vvQ769xyTgBRc/KFDAlDuPpO0t3nFtltdVHQi+534weXgYtD/SeMmQCVtd4qxUeQS0RxvFtYG3z0RjPJ2D9vU0EmNRUOvUBlJdtkURoKnCbAmjdwm2t3xVP1XwYZr8xh1GfQNCuTEUgxSheMaMgldHBIZZGnkT57wlxRdkFkn1HngzCuqwULH4ch7PV9xW9sgcssITop6ZBmM9nbLIiY9QjQUyaeyQ+UEyPVW9vqQn8yFrg/IzpPDtk2hkEQk0rxhJCzYLCwbNeElHnPqeFAbZHLQgHqT8B7Uh4sg/Q8ZwmxcRP+zqGEBfxmpd84sgbTwdof3D6hkn3hyHhX5HfmSv1rrWLw1FmquxY9RaLxUDjKCQUXlmGtTjiInt5LYO+gxBdYxjXNmb7jj5sBCpMT/MG84E98hr25ZR99puccGTF/nsRNgZ4RRByshZpMWV7o5744HI0x08pq7SkDbEkt4k/+LYDzrT/bhBfnaolSX2ANFs47SbHhm33kH5tE+fW/myi2Q+DbKb6rCgYF/01bPAJtF9/fksLQIM5YE1+dR2OpvceI587G+ttfs9cTYWEUTwELfIY6Gu/CgbOt1dCjLtmwq2jkOB3nGk0bvjveNczbMY/1H5yS/ZaeXNIowzhXOrdRSIZ0NCpzojzo6xIeZE/z0Wd5wYdJs0MkvmnUvHdQbhZncbMTeXtUu5NiD2+Ijxt8sXdp3dIg8zsRVt/Xk/OwTmhESbAIgKIjKqOYTRmVH5al1DCHl7nQllwK2OKWlKvhVwE53xpyyRpHgHZcP4Mm4Rh+QyWCY7IqWscoTe5EeQI2//6uwnwsqoI1xWkQwF8iAvIqYPMXkoJd4h7ZZu4lCCysinXYgaMm6FLY80CwJtNAHEtYEXmwJDdCgFE4g3ejrkjjJETqYKLcZtGwbdw4NBMdkHpU8njpJFOCwwbMHB23hRmo7O1Lrm2AavcxgZ8sEbQNEZV2Ucn4E5z0Gta+TJxxwWW4yLMbCwcU85em+ttcA2sHGPcBkLRs1zvy/epS5KsJ8WzwySsdfOiBt1VSd5Q57tCNguZOgcN0IW7UK0ZGxUXdLICe86DxePibh+0EZnlMlVS7i0m3xF280/42MBybun7xZ+5N5Cua3XHPBrFUiB9rdp7b3tVvN+11137Yy0OnlmkEPbpwhMm3++7mFld6z/Pm01DnYGLMrMcToENryc8pNXi7qYE2QMBmEw6SYLI3OfrlKmDcbDs3yug4L3WqRYVJNtBjny8YYhs85RaoO20JOOk1mMR764kl+6AYGKuFFXY2u3APn1HgsLMuLXAXiQHW8gx6PqkEYHIyPGmA4RJyAk9CC5RQIt3sQL5iI5RRrgow5z2Za2OUou5RO0D/Eig7Wu0QPiF0MgX8am3xakTJ95F9knOLKN9Kxxll883OhIz2wX+XMsznyHC++pMUHc5DH+ns1X85r8OIJDhi0G4A9XdgaZDNlXl3lsDJA1dXFSsZm8qOphu7zPfKWL5WkD+WK7jHMbP+yefPmVGnbSrrV5QsaMMecIsqjOYeXbeOTT4sJiTb/pF/bDosu8jbz66SgZ/GHtmR4iy+aGXRa2QH71IK/sA0zFwc8OB31pvomLACNHBb0z4lwtzNgx74OPuAjGydwyr9k1dRpDH9MaH/nEOavteBedxHaZA1EXvSxfhoVBYNYkmpAjA7YbDD7hGtYVWzbOE/E2I97yOZbho0D3PBKUs8lgG9lEEG+lZWJRBCZETHzHHKw8pcUq3qS33UHwrGxNDhPDRPGRoPqGBWTZ5CXUtqSdfVOG8lHGZDDR/WqHdiO36qbspNtisXr3ixrKI0d+YJ2XiHKSB04mK+PhuR8QQhOVUuEFVD+M1dvPO5/PJq5FhC0kymI277bQ4UmiOHhdbWUZJ0QSxupGIHl8QiEhyz6Wocz8egp8jAEvBuOijGCsfJhhda9enmbKy7ljylCeNpAbyoqx4slt04wVpYh4kjlEhlLlMW/zuUd0eHwoZ89TBYbVx2I+2qKUIz85IS8U+KD3RL72al4gNuYNY4zEtenuyRBZYrDkQzrEC+aaOWxuxXEPR5+0A3kxrxlupM8cVUZgVB19Uh/D5Cp+UDB+DLfxYByNjV984DU2XwaVmY843hxjj6C177PIgIGjDM4nazPZNyfNA3kRPMeHfADo2Ry1e2RhoG9k1OLQGU9kSh4BxnQLgg1fckyu6CjpESz+vM/CyqLGeESaMff71sqbJ0iN4Dgd0sTIRl5XhIVupl88jxPoabJhbupblNEW847DYxwDjgSaQ2ROn6MeV/MfYYaj5wj0I+cKzHklyR0cjAcSZ7Eor4WMOeQdCJ5+il8sgSOGLUKQ2jbRp46lsDmO4ZAvJJbMhA7xbNFqjilL99GB5Acm5hBZcx8LIwTZvTS4sFVkjXOCvVQP++MYmzY4O07G7IYh8Z59DCcf549FFDvHlpJtxLkkBO8AABAASURBVM3VfJYnAlKvXeZTxE11JQ/KGFeyG/ntaplzbLFxjfhRV9+3sL8W/voOR/fkgbwqy077xRdx5rx+s/XOImu/Zzvk8gpkzw8e0A2wOeOMM0R3f2CF05DORdpF0o0cPOa7chZ24gULcvMHrvS7uAg4Ai7k53BhIJjDcLFgYoPMNfaHDmcXlJXOiRT6nj4Qn2H+EZg1iUZiTU4TlrEYRFSiWwwJzyzSPCifVSjPD6VAmGtd41WQ1yoMMXKmLlaKhMxK3qqNYvAeBNQK2kSyvUfZ8l4zdMiXPMMCY6QcQWZEKRrleJeU4elgUHzsQvGbnOoMjy7PinRttGI0iRnTSFcHEo5IuHruBx4WZShPyhAJd77U4qCfd76f4YwsIp6zfbexprjtRNji4k2jOI21uil4hoXS8yzAGxG0sOHFYRjgH2MvDyXPiyveGFBI5GkY3t6H0DvPa3zVEUG9tuNs35FLBoKRUibyuFKyiIuv3McdJ7JLeQ8iNfpGuYd3yTtGBXOQvDACMCPz/fzyUOQMKiPYkix95BFizPvGUT3qg6myrZdJnea+eGTIYkX+QcEuAAIgGG9YI0kWJgjToDLzEUevIDjIYv99foPXB2zmszFBMGzrRz7zXd/JbsSRZ+caySrjSS/yUEe6K4zJkYWd8vSJZ7IrPQI5Q9h9nESX0DmRBntjpnw/eD/PXOR1ReQs3OgSz+MExBbB6c8di2ALXsSK7ExVl35Z2NKldFqbn9eczkQw2njv0G9pZFt/HCXSHsdLIi99j6jCgF6AS6Qthit5sNhErvrtMe76Q78gyLbz2ZzIxyHAvnEKRZw5w0bZ2TA+bIl6Il09Fhxkkr0159VrfoYdk9eYItg+9DQ3eZ/pBTtj0gX6j00XLz95N1/ZR+ltQCrtsLLtbfyoe2TZ+PbrY9/ZcO2je0bVEWnstfnpOwI6iu734b75a27JRza8ky43t6PfFjNkWTtqXcM55DffzCU2x8Kt1jVp5mTIW5DiWmuh17wP9uRXHQJbpp+IchsvjYyrqx/CHhkDOsqzPtW6pg3G0q454m4BYR6oL8P8IzBrEk2IrFgpScFkG9YNxkgeZIRS7OdDQqQLFAShlwehpiDFI5UxKaRRoOojYJGf0mbEbIshKci0Z8RNmWGB0tFGisW7kO+WACvHMEoXKLl+unZLE6QzhMpF0E4eWF7LiGuvJgOFpr8CUk2ptHkW6p7iYewoirloA4Nq/IwRGWoxQUbJA2PdvosCs50OX15q8temuzcmDC6vtoUR+RE/KFB8xlrok1bESbukRTB2xrity1YdLyID18aPukekKOZBecixfpLHQen9ODLGG6+N+tzvh/xwYmzlEcLbJa3FgNEVF6HWWsxpZQRzLdL0QVyEQXM68pJr462dxk4Z2DJkkWcmV4abTphJWWXML/qBV9nWrrgIDF7MRXLYHy8LAXirI8rQPeqje2AFu0iLK4yduyT3cJCXXPXllFE3B8ibMY7yrp558ZXvB+MsXT6Bt4rX2Nylk8SNEyy0WjlpyzDu6tPfNn7QvQUj/LTT+Ld5yAzCNIiMe4f2Kqc8HW48WkzpCekRyGRb/2zv7a4Y45nWY+wsouzk8Ha29ZAv6fS8/sHT3I885gsZ0e+IM48RaDJDvpDCSHOVbszIgLJwUR5O0tugfvpRHjqVfLfvl1c5YyYPWezLqDx2jO1ikWtOC3HjBHO/Hcu2DLmyeGrluE0fdE+WzCttFcwPi/zIqy/su7QIcIcnGY18cTUexl5e/dImafKLg1mQfGOJL0Q84iyvQI5hyJ7UuoYEixfUoUw/WFghyspIIyPua11T3mJJvGCs51rutS3DeAjMmkSP95rMtRwQQOZ5O23D+SDO9tJy6NdM+8B4MLIwQeSGGYSZ1p/lpkYA0QvjNnXudXOQabsWPIWOAyw3mbbNbNvXcRseN/1dF4WMGYYAkoQEDUufIr4gpTznFtqO7DhKMFWZpZTuaJ/jcI6e8PxOh/QupX5mWxOBYQgkiR6GTMYPRABR9JNazsnaNhyYaYVEIicINA+OLbfZGNsVAtmi7CaPr61gRJNM+8mtRdnQaTbKMQLHofzigfPdzqRPs4rMPgcI2AF1pMxHZLb1nW+dg2oXvAoLAuelOVQs0Hhia13jKV3wxmUDEoF5QmB5kuh5Am8lvoZnhbLkXXG0ZSViEH3mBXWmze+wjjoPHPnzujgRcNTEuUIfOJLp5eKt1S/HgnyQ6HiT58U5Asu7VXSmYynOJltwLxf50i/HCsiXYxQpX8tbjrN3gxFIEj0Yl4wdgQDl6byuc1sjsi37JGcNnQNcLkZx2Q/YFB20KCLTg87nTlF0zpLnsiI7I/qjX3NZb9Y1MwQcDXE+uv2mZ2Y1LY5S5MsZY2eRF0eLshWJwPwjkCR6/jHPNyYCiUAikAgkAolAIrBcEFix/UgSvWKHPjueCCQCiUAikAgkAolAIjBTBJJEzxS5LJcILAYEsg2JQCKQCCQCiUAisCAIJIleENjzpYlAIpAIJAKJwMpFIHueCCwHBJJEL4dRzD4kAolAIpAIJAKJQCKQCMwrAkmi5xXuxfCybEMikAgkAolAIpAIJAKJwGwRSBI9WwSzfCKQCCQCicD6RyDfkAgkAonAIkMgSfQiG5BsTiKQCCQCiUAikAgkAonA4kdgHBK9+HuRLUwEEoFEIBFIBBKBRCARSATmEYEk0fMIdr4qEUgE5hOBfFcikAgkAolAIrD+EEgSvf6wzZoTgUQgEUgEEoFEIBGYHgKZe8kgkCR6yQxVNjQRSAQSgUQgEUgEEoFEYLEgkCR6sYxEtmMxIJBtSAQSgUQgEUgEEoFEYCwEkkSPBVNmSgQSgUQgEUgEFisC2a5EIBFYCASSRC8E6vnORCARSAQSgUQgEUgEEoEljUCS6FkOXxZPBBKBRCARSAQSgUQgEVh5CCSJXnljnj1OBBKBRCARSAQSgUQgEZglAkmiZwlgFk8EEoFEIBFIBBKBRCARmA8EFtc7kkQvrvHI1iQCiUAikAgkAolAIpAILAEEkkQvgUHKJiYCiwGBbEMikAgkAolAIpAIXIhAkugLsci7RCARSAQSgUQgEVheCGRvEoH1hkCS6PUGbVacCCQCiUAikAgkAolAIrBcEUgSvVxHdjH0K9uQCCQCiUAikAgkAonAMkUgSfQyHdjsViKQCCQCicDMEMhSiUAikAiMg0CS6HFQyjyJQCKQCCQCiUAikAgkAolAg8AiI9FNy/I2EUgEEoFEIBFIBBKBRCARWKQIJIlepAOTzUoEEoElhEA2NRFIBBKBRGDFIZAkesUNeXY4EUgEEoFEIBFIBBKBUhKD2SGQJHp2+GXpRCARSAQSgUQgEUgEEoEViECS6BU46NnlxYBAtiERSAQSgUQgEUgEljICSaKX8uhl2xOBRCARSAQSgflEIN+VCCQCkwgkiZ6EIm8SgUQgEUgEEoFEIBFIBBKB8RBIEj0eTosh15JvwxlnnFF+9rOflRe/+MXlu9/97oz7c95555Wjjz66/OUvfyl//OMfy3777VfOPPPMkfVJl+8Pf/hD2XPPPbvy6mkLnXjiiWWfffYpf/3rX4t8//3vf8vpp5/eZlnrPvL/4x//KCeffPJaaaPeJ/8rX/nK8p3vfGdk/WtVOI8P+vzPf/6zw6j/2sMOO6zDBj4R/vznP5f999+/nHXWWf3s3fNJJ51UYPmnP/2pKxt1B/5HHHFEFx/1uRrbfffdt5xzzjldHe0/5557bjn00EPLMccc00Yv6L0+7rrrruWFL3xh2W233SbbQi70V5/+85//lNNOO20ybdgNjPUfrgcccEDR3zbv+eefX7yPHKn33//+9zryd+yxx3bzQ3qEd73rXeVtb3tbW9Va9wcffHA3DhEJ38c97nHls5/9bDFG3htpC3nVTnNYv+BE9sy3aJP4CNHm4447bhKPv/3tb+WUU06J7OVf//pX129lQiYnEydu6Bppe+21V6HDvKstI60fDjzwwOLd5Pfwww8vxoqu0l7jO+g9E6+a1/+N72c+85nyile8opibM325PuqvOsbtHxz/97//dfrbeGgLvAa14YQTTigHHXTQQF1AX/exf/WrX11++tOfTlZlvMiM98jr2r5Pu9mk3XfffUo7Mlnperwxd7VzWAh9AEN49/PRm3R420QYhl1r89NJdFSbV71tHvcw6usZ7YCjeOn0FRsrvq1vud8niV7uI7xI+mdSf+hDHyoM+SMf+ciyxRZbzKhlFO3f//73goR+/OMfL7vsskvZdtttyze+8Y2hJM67v/jFL5bXvOY15etf/3p5+9vfXrbbbruy9957T7aBkVPnm970poIMvf/97y+Pf/zjy1ve8pZy6qmnTuZzQyn/4he/KG984xu79//gBz8oFJ80gRJq3/eOd7yjex+jIf2mN71pQU6Q6De/+c0FGRe/GAJ8f/KTn5T73e9+HQ79NiEPxm/16tXl85//fIcnrJ7//Od39/38MN5hhx06HL/85S+XT3ziE+URj3hEcY9IeB9D8NjHPrY88YlP7Mbxq1/9aiErT3va08qnPvWptaqUH0mV9q1vfWuttIV6YKBgYGH4ohe9qNz3vvftmoJ8vexlL+vk7Qtf+EK3eHzDG96wFoHrMjb//P73vy/K6Des4PK9732vyVHKkUce2eH53ve+t8Oc3Hq/dkTGH/7wh1077njHO5YIL3nJSzpiF3naK5JsHskb8Ve60pXKBz7wgW7BstNOOxVkNdKmd5273J/73OfKS1/60m7emfOwes5znlMOOeSQyZe8/vWv7/psvodBR6L07053ulOH3fHHHz+ZH5HcbLPNujKD5qL3wQXeFi8WNmTV3KVPXvCCF5Stt9667Lzzzt2cuP/9718+9rGPFfL9nve8p5AJ7yDzFlnPfOYzO9I+2YAFuIEXnH77298Wc/f2t7/9jFtB1hBX+uAjH/lIefrTn15+97vfDa3PQpLeJlPmOv1oHA844IB1ytD1L3/5y4t5M2hsLACMTRvMnbYi73nVq17V6RzzkLwY05Dn29zmNp0+NkbaT7+35ef7np3YZpttymMe85jCjpBzQZsf8IAHTNo55JcM3vOe9+xskTzk7lnPelYni3COthtvsnfnO9+5S5P305/+dHnuc59byAFdFXnpETJ7r3vdq1tAy2u8yPnrXve6yQU7vfS85z2vfPSjH+30ED3PZn7lK1+JqlbEdYMV0cvs5IwRYIRMZJNzNsqFojW5KMy73OUu5aIXveiM2sTbiTAgUxQrzxolSFHyTg6qlCJW5tGPfnRnQLfffvvOe23yR35K/0c/+lFnoF/72tcW+e9whzuUd7/73WsZPIZRXxiNG93oRp0SonA22WSTqKrzOin/qEc9qrz1rW8tjACFTUHLdJGLXKTc4ha36AwDjwnjK36coN+8cN/+9rcHek14hRgDpGGc+vp5zj777KK8NloM9NNsh0pmAAAQAElEQVQp7Jvf/OblPve5T7Hg0D/hCle4QkeQ2/w8bkgg42jRQuG/853vLP/3f/9XLnWpS3UyUGstxg8eD3nIQ4pFizEVVq1aVRi/qNNCBTFCsH/zm99MGpNIn8nVODImMykbZSyieLcQgVYO3ve+9xXjxLhYuJEH/Wd8omx75S198pOfXK561asWWMHgile8Yuc9PnDCsxl5GTVeJaQQ9ggdWYZJ5HFlcL///e+XNmiDtDaYUwieOtt498b12c9+djdWSD35Ez9O4IE1l1pjHuXoEvXxJEbcVFeeYH2++93v3hn+kBXvIU9RHvm41rWu1S20Q8/c+ta3LoI8iIB098LDH/7wco1rXKNc9rKXLQiMuAgW1HDdeOONi7G5ylWu0nlF1WWBAX96ADm3qKbfkI/rXe96xRwSZzFioaO99CiPNO8+EhTvmc6VzNJB0ynT5uVUQIrMde27+tWv3iZP696Czhy93e1uNymzxpuu46UcVJlFszln4QETizs6jX2gv5ThuECGOV3MoUEEWj6B/m1l3EJr0003ldQF43SrW92qwM08RLzNQfOWfTNOFhF0lHZbDHcFx/znxz/+ceFUGZTdvLWQGqRLB+UXd5Ob3KRo/73vfe/CrsFIsPji3LjEJS4hW7nyla9c6ExyayEkj3fd4x736BZxHBhdxol/bnazm3V1kmOYyutqDMgi58hEtu5/c56Ti05GsOWF2wMf+MDyyU9+stDnMn7ta1/rnEfsm3Z+8IMf7BxO7ul+eZZ8GKMDSaLHAGklZ0EabQ/Z9qFwZoIF8vOlL32pPOxhDysUxEzqiDIMAE8kQ8U4XfKSl+y8prZnbSdFvrhqM4J8+ctfviDFtdaOwFL6FHnkW716dbHtzkvM8K5atarzTCEYrdcKUac0eFMZ1Y0njKv8UQ+8vE957xNPGVHSDJfnCMgS7wCl3zfekWfQFWmg8Hmy++m888gbPPpp4zxTjAzZ3e52t+5oS78MEsVrSSkHcaF0BYavza+NFjA8eIg3nC5zmct03uUnPelJk1mVIyPItchaa1GfwPMnTqDskRVE5ba3va2oWQfbkI6azLQieMAcXshU1INcWJQhuJtssknZcMMNOxKGuJgLka+98gpqy1ZbbVXgJPA+MYbaGXmRaDIV7yNbN7zhDbujUpHHFVFEpNuA3Elrg/rNb/Jaa22TuvvLXe5y5cEPfnDnybNd20WO8Q8yhdyTcfO2LQIb4ziKHLX53SNK5iKsYSPOAoz33lzyLCAu5LPtq/fbdkZObnzjG8s2GRh8pPgGN7hBIduR4J6cWjAaQ3NdGt2jX4i35913373AmrwaZ3Ic7zZXEZtVE/qE/NMv6uLtIwvKTzeQhZkukr2LDkMiyZlFmriZBmSerrH7stFGGxX95I20UAiyVXr/IZ3Gy9htsMEGBe7GVJssrmTXR3MLAR608JMnwi1vecvSyrjxaftlfBFttsL40EXSjYF3RD1kxqLfwo8+iviprnQcGbcQaPPCRX2/+tWv1jmS1ebr35MLco74X/ziF59Mtjjk4JiMmLgh0/oVdvXSl750uf71r9+RW/2byNL9r07jjpgbJ5HIODlls1qSLy/PNH0QeV3Ju7xsovIWwcbsale7WjGO1772tTvbak6b+/KshJAkeiWM8gL3kYHmRXroQx8665aEQlq1atVkXYwXA8fDMRl5wQ2FQKEj0Re72MW62Fprue51r1uk8SCKZOBahYVIUo7qplzlEXgIvXvLLbcsUZ/4CMo5QyZPpNdaC8PpfX1Fi5ww8NGOqGfYtdbabdPzDlLciJeFAqXFy6V9vOCM9bA6hsVbLPH08mpQiq2BiTKMNw+T/oijfJEY7V89sRARJxgnbVMfgiEuAkPG+xPPCKfAmIo744wzyi9/+ctiUcQTIk7ghYQXXLVP3EIH/WSk7a7UeiEBRYbhdJ3rXGeyidpsEWBrFbmaTLjgxvcCbhEMVwHpY9BjkUW+9thjj26RAUd5EEreRHI+aMzkGRbUxzB7T3+c2jIIj3fwWLfxo+4ZcwveX//618XCjpzL//Of/7w74mJsp7MYYqxrrUX/ERR11VqLeVvrhdiTReQAUZJHQOiMh8VvrRfmlWbxjFirh54Sp612Wnhq3avPol0a2Q3skRiLQIRHmmCRvfnmm7stiArd0j1M/GMskRmLHh7Eiah5/5/XFPGK+TabBljQmY/0a9QDS4sti5CIa68WUGQWNuJdETTEmY4UZ8frqU99ardjBW9xMw0IYFsHXUN+EM82Xv1PeMITCvJJ93geJzhaZl7vsMMO3S6FOWiMHXUwxnYaw+EwTn12KOzQkDntQ6g9qzfkTj30Pv3DI93OXbuens0X+QTt0W/HXjyb9/SQnVaLkIiXRp7JNfnwPsRZnea+fnJAyWfcBfcCTNl6tsF4ilsJYYOV0Mns48IhYAIiZ8gohTLbliBxCKpt1aiLkTLZEcmIi6v3IywUCmUd8aFYGciIa68Mv6MWO+64Y6FspVFCvMlW3EgSBWTLnUeY4pXH+xiP/vu8m0Lsv08cssF4xwpfPaOC/jIwzvY95SlPKbZBEWhts6Xm3Nuo8oPSKEBb0wwCwiSPONc2IMYMna1Q23y8Nt6NnGy99dZdVgoadoyB+rrIIf8wDDyH3uW8ojptt9s2RMoR+iFFF0U0HMgjw9E2yNEMY81AtfG8umQEhm28ex9PuZIHV0F+xjJkAzkxj655zWtK7gJyTv69r4u44J+QV5jynCKLFyRNXuw8MKa8kmRxMqF3Y/7yfJOxXtLIR+SSXCHTyAQvpPF1hIL3sNa1Ce2oymwx280yJ/Up8GrLwMACDOlA3OUTeIPpDuSsze8eYeAJ5cWjq4yPdtoFIYNk3qLbWMjfBmkIZLvQbtPbe+TEsQ4khIfbPG7T5+se8UKyYlEwm/faxaDbLLCiHjiSV3og4tqrBQ3iF3iSO/rY2NEHbd5x7h1FcMTLODt6SO6HleMltTNGX5GFWteWP8TVfLabOKyOfrx+OMql39ttt10hExaIdBp9LL5fZtQzuaY/7LzoE+eBBV146aMsYkx2zSNxUcYRJN77VifJCxdOHHVqn+MtnC3aqM/qEMgpTC1q7BY5nmGBwA7SI/L0g/E2v+khR9bIRD/Pcn1OEr1cR3aR9ItB4qljhHl6Ztuso446alpVeD/lMp1CVt2MLoLKiEZZCouip5B4tSgVyodC+vCHP9ydB2MEeJuizFTXWmvnVURkBi0ChpVHpGxRIpkMMwLqbKIzcsPKjIrnBWUQebgZNQYeDv0yzgsywBQ37yWPlrO04niX5LegoIRtkW+88caihgZ48dwj7ne96127c6tIofKIifqHFl4ECRY/DAYi1TbHAqJv9Nr0QfdkalB8Gzeu/JtvcIQ/8stLbeEF66jPQhChtaMxDqFCfMjIdOTbu3xo56MkRtZHZ3YTXENe5Bkn8NAz+BaPvOfIBS9dWxZJIFPOjpLPCLFo6S9qjJM+mcsWxxbcyAYCYyue7vAOZMO8aN/l3set+oFkex4WkAwExPlqi29kbVje9R2vv6tWrSpzMbfo9um2d1wZHqdeOBpbC087DzCmwxDRfnm626KffnIuepjMm8uIvl22fh3Dno0/cmrObbPNNsWCjV3QvmFlhsXbKTBnN9tss0J+4eW5782240LH2A2gj7fccsvu+wd2QBtaL7HFN51uXljY6jt7xo6Q/bYtdK94Z6u9f9WqVd1Hxa7t4j3KsLGONFmQI9COM0XaSrgmiV4JozyNPvK+mAQ8AwIDwfPDq8Mwi4vgFy3Gqdoko1xqXXvVr2zUNerKuPm4RH6Tm4eDUfIcYdi11tptCVKIPHrD8kU8RessL6OJdFhZRxrF7BkBYGApLsqKh8xX5pSdvJQd5TbO++QXnEHTRvfjBkaQVwv54v2m/Mct2+bzbl/WO3NHUcIYYdGHNp97v0bg/KM+Cwi8s3POdQe5QkzU2VfOyvcD2UAseALDO+kcMKNBIffzz+bZ8Zy+nPGQ8ra08c5MjuuJYsDJdp9gWTAyWrAYt83hsTIHh5UxH72TnA7LIx5x9ZEUo8nrxutkzvCASlcezs71Gz9xEcgSskreI669Tpc4MeZIjneqEy760dY57r1FgSNC5qiFny3mtqyjAsi+X5Ahn4LFmfGwFR0YRxleQ55EXjsyr07nfM0tMunoiflAJqJMe/VLNkiJedPGt/f0FQ++s/AWoQhXmz7qnhy2sumezFpIuI/g3DaiNKquSKOXyCu5jTjXb37zmwUWUeewq/P4scDWb/2bjgPA2JMF8ui9swk8tPQEebfr5fiNBZDvJ9p66VZyQ9dII0dtev/eHOSR7cePekYw2SpHr2Csn7Wua/NG1SHNLp4jMXZdyC8yzckgrQ1kg442bo7wSTO3kGnj6FmAtV0odbBZPhDkfFG/438W0/JFcFyK/lWP9zuW4tekYGtRGfniqm6LE78KMpNd0KhnqV6TRC/VkVtP7WYweKsoHQEJ4K3iSTKBxEWgTGfbjKhr1JXhjfPUFAGy1hpySpxCHmToKBWEuO/ppVgYEt7O6AMPBeNka9L2HDIcaa48HbXWwhCHAUKqEVlGRDuizmHvG9RGdWtnreMrXP11lABZ4Hmh7Hg+eFDUN27gtVMPbzJPobO9AlKsT209vK6eeSdcBTiow/jpv7gIyEXcD7va8uT9a4lFrWtwkDas3EzieSG1sw0WBAhZG8coIFwzeUeUcRaaIbMlGnGuZMxiB2Hz3IY4H+woQsSbf4iK+sTx7sPZnLAAEcdgkwf9q3UNdsallSkk0XvDYCqPKCJ1SGaMubFEuuVDftXfD+rtx416tti22OLZcvwJiUF0LAZGlRuWpl0WXAw9r12bz1Y2bPU34mGO2Dj3CZeId9UO8bXWEnJN9i02zH/104kIkvxtMLbGAslp4/v3iLjFp2MESFYvfeQjOWxl0z2ZdXzBfQRkfiZez/blSJPjDlHnsKvxjPnql1LoUrtzURfyCS/ezohrr5w0Fv7eJZ78IoPsyXRli74V1FNrLRY+6gqHhnhzwy99OKpj52BYu+SNQE5qXTOXIm7UFenmbDKfLJi800LULsaocv00Otfcozst/KSzRewGu+dZoBs5Bdg2C1Tnly0k7IjSp/JEMD76br6YO+JrrcVcp0Oki4vgGxc2cNWEBzriXL1TfvcR6BHkmTNpqiNhUWa5XZNEL7cRXYT9oRgZG5N2ts3j3bR9jixGnc7UUshIRL9+76aceWkQEumUBqKEtDCQEccjSfkho6FAeKqcIZRHfqTZdqhngQHgleF1kBbvE9d/nwVAvE/ZNlDsPD9t3LB75IOy5llBAP3UHwXOiPo1iEEfWA6ri8eYF109zpLy1AjqoczbcuIZFwo+4il9RhVxQcrEw4Cybg2ZeGPmlyzcR0BSkBVGIOIYHsqZNybiFuvVmJEBsti2EenlEeXZjHhYM1AMfRjI96bAuQAAEABJREFUSHNF5lzh6SogeeppF3vKw4fMy8O4MpLhUWbQba2Kky4gNmQ7yA9seUaNaQQfVdVai2c7T8O8ddP5aMi84wX3Xotx3m87PI40OHc8jk5AgnnnyIW+CPoC9xYX5MGHTX1vmPJItLzkV3lBHb5nQAQ9m4O11oIMmKtIA68v8iFNnjYYS6QCwWnj23v9p1c4IZDHWmvh6VevdrV55+ve3CSv8JvtO+10kC36LuryPYkPJxGxiGuv5JcThFNGPD3pCIFx0zZx4wRjygOLNEd+c0wdHB4RR54dN+At9a2KMaPbeFzhEPnaqwUwZ0kbN+xe/y1q2ArfcphHvgOgh+nl6Oew8m28OjyTVVcBPrztPM+eBYsW+tXRKM8C+0SvcIJ5jqA8e2fOs1HitZl+oHvb+WxxIy9bGnnZSzaPjoat8oJjT352z/lrJFp+Cy+2idzLsxLCyiXRK2F0F0EfeQl4JShNk3G2TXKkZOutt+4+puMZRSB4n3iDEGyGATmwKqckvN8HJMgrBYdgUPKUqC2taA/lxUPmnJn6lRcYfApePgrY1havEoXmXc5g8nL4WSUfOzLSPG6UcLyPEmfclVVPGyh0BghhsTho0wbdIx2+HOdx4IHmOac4tQ35Z7iQFWfyBpXvxyHglLGfPqIEpSPGDDzjxOCLExAZOCJEnuHiL30hc463SBNvjChhHzzKw1AZK3gzZvJE8BNJsNJ+cbywDBACA3tx/aB9DBNS3k+b72cLNO1hfNp3I75+6ousWaSQUwYfIePllJeHkoz5qSjP5IaskidGi1zw3PIQtgsX5Z1xNh7KIYIwNIaejYc6bb8ilgybNpiDzm3KMyjoh3htde0HpAU5inHup/efvdsY8r46pwkTecQhrgiG8RY3Klgw8MY5ZkH+zWHeYvWtXr16sqgjX+Zkfx4hrPpOF0xmnrjhSTZPLIQmHrufwLRosYD0TFaRskFkyvxwdMT7ePjlHxQsGsx/42gb3XjzovNWzoU+HPTOqeKQIfPHHJsq71TpdggtGI0HUuWcLX1sIeI7CeUdK4IpXeDZMQIkjkxyCMAH6fI7/i1Jk1cw/mQSGfQcQX0WaD5sJhPejyjzzGpX5KMPOQg4CuzYCY6uWcTRv5EvrmRS2we1JfK0V95tc1lbLITZAD87SbfCwtibg22ZYffaxBlhnkUeONGf2iROmxF09s3cEifwSmuzM9KeI9AldDnbUGvt/hKts+MW+D4YNI8iL32l/fF+toGsIsbsTTv3fZdgjliw+xaHbJNxu07kK+pc7tck0ct9hBe4f7XW7ndAKU3KcLbNoSQYJt6MzTffvNi2RV4ZZN5jRhb5oIxC6SIgVvIUKYVBmSJ/Jny0B5nTPqtwZSNQ7q0XynuQbMcoKDuExiqc8vasPgTS+ygk7/MuH0G1XgP5BEYY2aB89U3cVAGWzqh5dxBfZXhfHMnw/nEMpD9ywIMCL21FdiloCjY8xowNA6ZeCtM9RYq4S0NAkDkEjvLVDt4NP/NFqW666abdmXQeK4aOIpZH8CU9socQ8qKrkzLm1bdQccRAvgiU/oMe9KBuy9Zih4ePxwa2lH3km88rsoBoIQ/texE2feVxhbP+ycPz7l5eCxRyxsh5RtYsPJASR4TINzlzbhHO8giOW+gz7ybMLAC9BzmSTl59FIs4qotc+E1048prJE8/kAN5zB9kBiFp88BXfd7Zxo+6R8rtUGibeRB5kVyYmMMxRyNt0BUujLo+w2GTTTbpiADZsUBXBlG2QDCfLF7NdfGwQcDJtXSkTbxFrUWdZ32ym+U9zhaTP9485aQ7c2phrpygXwgSYgEv88AcltYGJA8pNxd5PY21YAGunLFr88/XvQU/mSN/s32nxRtdgTSRawT5Gc94RiH7oZtgbTwsPLyPfrJQhiusLfYQUDqk1jVHKLSPg4CXVD4k1cKLzraYU4+jLmQCiSZfSKTdBZg79iSPcfRufaU/4E9W6A/6OnSWvIIxM3fNPc/jBHqTjncsKupztfNgYUc/kMup6rIYtuAgixxC5EPg7SX35rU6OA+8z8KdY0b/xJvrdADiTtbZCf1h79TpGJD6LOTZOR+jw1hZASbyqpeeldcuC71P/5snYaPoB0eILA7YDrgKxsmOEblQ50oISaJXwijPoo+11sJQMVwUw0yqso3Ey2bFPpPy/TIUpr+2xOPGQDFojLV8jhL48pqRQuDE1VoLBWyyW5HzHvPuhZKXB+lVph8oCaRFHoESoWiceVYXZUShIY7ShVprR/QcA5GHcuJ9at8nn2CVz0OA6HueKtRaC2ODuA+qD0HxsQ2DMlVd+ky5I0gIOeyMsV/9EA8Lxh8J4/FjDJARfRKcyfPHEHhIal1j/OKdFD6Sy6jJy4ipC3GKPLzX3qPOyIdoKsfbXevadTL+FjsMtjLKMhJ+ScVCKuqdzhUJZ3ymU6bNa9tfQNYQrjYNlv6srsUZWeVRCjmVT/9hjFB4FvSDTFhwMJZkmVGXFoHxZ1iRWtiql9GtdQ1eZAAR5imFK2MKewSx1jV5oq64MqbmkvZYFLXtFKd/PKethy/KDrvyCCKf/fbLb76Yw7agPY8KyJl6GG79Nf7kjl6KcgiCxaC2Wvia79IcEUByxDt+EXMGXnZ0xJuffRlAgMiWdHJv4aw+Af7mDhmUzisYOynSI5AtmMnTDz7ktWMTeadzVW87PtMpKy+yB3/HhrRL3GyC3RgLCnKG4FrAkf2o0/wky7Eopmd4g+0E0K880RbZtV4omxZNFnaOLZjnsLZrgCAjgeq2e8epQQ+FXFhAsVW1rqnLOCqrn/1gd0Rb1CV4j4UmIjquPlaO7uQwqHXNO8UJtdbu14a0Cd7iRgUOBHpCe81bfRLIL73HTihvPli06Q+dE7Jea+3+3LqF6fbbb985L8gKe9fWieRqk/Fo56aFh8VLm5fs+3jXPGrHlPzRL9rQD8bJ+GlrLyzLxyTRy3JY565TlAwCZRVqNTyTmq1KnROzcmWgZ1LHcixD+fOm+bWPvhFfjv1djH1C5HjNZtq2WmvxqwC8i0jZTOtZzOUssiyAbcm3xHUxt3k5t43MTmcx08cC4beDYmGC8PTTV+qzhSb75IgDm7VScch+Tw+BJNHTwytzzxAB3g9HHpxPo7yttGdY1ZIvxrNga9GWtu1IXur12qmsfL0iYOsXqbEtbft4vb5snivn8XI0h+eJx63Wtb1t89ycfN0cIGA3zS4Iz6MFPI/0HFS7ZKugjx2lsAPmD4uEZ3fJdigbPq8IJImeV7hX7stsoyKLzmE5F2qLaKWiYfvXuTVnRXn3bL+vVCyWQ7+Nn4/lHCuyde1c83LoFwLNY+n8PXLhqMly6Ff2oRTfj/gAzC/8kFnnXhcjLvPRJsf8HEdxfMRi2O7rfLw337E8EEgSvTzGcUn0otZafE1MWSGPS6LR66GRzun5pQYfbdSanr31APG8V1lrLc5GWyQ6jzzvDVgPL7TtH+dV2/OQ6+FVWeUCIGBMfaPgSNlmm222AC1YHK/0izjOQrdnqRdHy7IVSwGBJNFLYZTmvY35wkQgEUgEEoFEIBFIBBKBUQgkiR6FTqYlAolAIpAILB0EsqWJQCKQCMwjAkmi5xHsfFUikAgkAolAIpAIJAKJwPJAYK5I9PJAI3uRCCQCiUAikAgkAolAIpAIjIFAkugxQMosiUAisFwRyH4lAolAIpAIJAIzQyBJ9Mxwy1KJQCKQCCQCiUAikAgsDAL51kWBQJLoRTEM2YhEIBFIBBKBRCARSAQSgaWEQJLopTRa2dbFgEC2IRFIBBKBRCARSAQSgZIkOoUgEUgEEoFEIBFY9ghkBxOBRGCuEUgSPdeIZn2JQCKQCCQCiUAikAgkAssegSTR8zDE+YpEIBFIBBKBRCARSAQSgeWFQJLo5TWe2ZtEIBFIBOYKgawnEUgEEoFEYAQCSaJHgJNJiUAikAgkAolAIpAIJAJLCYH5a2uS6PnDOt+UCCQCiUAikAgkAolAIrBMEEgSvUwGMruRCCwGBLINiUAikAgkAonASkEgSfRKGensZyKQCCQCiUAikAgMQiDjEoEZIZAkekawZaFEIBFIBBKBRCARSAQSgZWMQJLolTz6i6Hv2YZEIBFIBBKBRCARSASWIAJJopfgoGWTE4FEIBFIBBYWgXx7IpAIJAJJolMGEoFEIBFIBBKBRCARSAQSgWkisARJ9DR7mNkTgUQgEUgEEoFEIBFIBBKBOUYgSfQcA5rVJQKJQCIwEIGMTAQSgUQgEVhWCCSJXlbDmZ1JBBKBRCARSAQSgURg7hDImoYjkCR6ODaZkggkAolAIpAIJAKJQCKQCAxEIEn0QFgyMhFYDAhkGxKBRCARSAQSgURgsSKQJHqxjky2KxFIBBKBRCARWIoIZJsTgRWCQJLoFTLQ2c1EIBFIBBKBRCARSAQSgblDIEn03GG5GGrKNiQCiUAikAgkAolAIpAIzAMCSaLnAeR8RSKQCCQCicAoBDItEUgEEoGlh0CS6KU3ZtniRCARSAQSgUQgEUgEEoEFRmCDBX5/vj4RSAQSgUQgEUgEEoFEIBFYcgikJ3rJDVk2OBFIBEopCUIikAgkAolAIrCgCCSJXlD48+WJQCKQCCQCiUAisHIQyJ4uJwSSRC+n0VwCfTnmmGPK29/+9vKDH/xgCbR2/TTxX//6V3nnO99ZDjrooPXzgqx1QRE488wzyw9/+MOy4447Lmg75vLlf/nLX8q73/3ucuihh85ltVnXEkPgpJNOKh/84AfLzjvvvMRaPry53/rWt8ouu+xSTjzxxOGZMiURGIJAkughwGT03CJw/vnnl3/+85/lnve8Z7n0pS9dNt988xm/4OSTTy4f/ehHy9WvfvVyyUtesjzhCU8o++yzz9D6vHvvvfcuT37yk8tGG21UNtlkk/KRj3yknHrqqeuUkfff//53edrTnjawznPPPbd8//vfL7e73e3KxS9+8XK3u92t/O53vyvKtZV53+rVq7v3Xec61ykf/vCHJ993wxvesNzgBjcoD3jAAwpy0i9b5vk/7//HP/5RtFefhJvc5CblzW9+82RLdpkwMle4whVKrbXc9a53nSRTb33rW8tFLnKRssEGG5QttthiMv8vf/nLcr3rXa/Lf8c73rEcfPDBk2luTjnllHKve92rK3e/+92vHHLIIWW33Xbrnr1fUKe6jdnFLnaxLu1HP/qR4l3QboT1pS99afnSl77UxS30P+TjTW96U/nMZz5TnvGMZ0w2x+LxjW98YyczV7ziFct2221Xjj766Mn0QTcWWc9+9rO7MmTofe97XznvvPPWySpujz32KFtvvfU6cijzUUcdVbTpmte8ZieP5s0b3vCGIl56G2B62GGHlR122KHc9KY3La95zWu65Nve9rblxje+cbn97W9fjIF8XcIC/fOVr3ylXOUqV+nki6yQkWtf+9rl6U9/ejnuuOPWapU5SlT8XucAABAASURBVJY23HDDrv/KGZs//elPA/HSt//+97/lxS9+cadf1H+Na1yjvOpVryrnnHPOZN0PfehDu/eHjGqD4PlZz3pWOe2007q8f/zjH8tFL3rRTn6lC9e61rXKC1/4wnLGGWd0eQb9ox0veMELunHYf//9B2WZ1zg69h73uEdHNh/96EdPvptTwPNlLnOZQrd98pOfHCinUQCGP/vZz8pd7nKXcolLXKLc+c53Xkum9Jvsmy93v/vduzEjs3SN+R71tFdldpxYtNITbXzcS9d+9uevf/1rRHfX+973vp1D46lPfWo54YQTuriF/OflL395JytkhqwIZJBcPeUpTxnYNP37+Mc/Xi51qUuVP//5z+vkYes+8IEPlFvf+tYdnnSQfv/tb3/r8h555JGd/q61FuNoodQlDPiHXH/uc5/rdAH7651w/cMf/jAg9/KP2mD5dzF7OBsETM599923/Oc//ykIwkzromgpgBe96EUFMaAYZlIXBfyxj32sfP7zny8U8YEHHljOPvvsjpQMIgXeccABBxTGiCE8/fTTO0L7nve8p3zxi1+UPBkoEvU+6UlPKr/61a8m49sbZIVxRTKQIISS8m0V1wET72Mgr3a1qxXvQ/jf+973dm1WF2P+sIc9rOjHYx/72KJO8eOGY489thw40e9B44FQIfCU5rj1UX5IxS1vecuC7MGRUjz88MMnq0DQ9JXShB0sJb7kJS8pDOgd7nCHbodBnMD4Pf7xjy+UNcOHNIsXjBfSi6gwFO9///sLgveLX/yiUOz/+9//inCLW9yiPPe5z+2Ixje+8Y0uDyOgDl4j3t5HPOIR5dOf/rSoWYXjjz9+2uPQfyHZfNvb3tYZMQsQ4y8P791rX/vagkyRGf10/7rXvU7ywGBR8bjHPa4jbcqQoXe9612dN7gtYHHC4Bm//fbbr03q7hm8Jz7xiYWxJGdIG7nnfWOsze8u48Q/8iJAxpoh/clPflKQ7Ymk7v8HPvCB5Te/+U0X9+Mf/3hsfeAd+nPEEUd09fT/8V5tJyf9tGHP5s+9733vjuQi/TCGEXJNJttyxoIcmtvmo7mNlJBP921e92QNnle96lWLuukFhBmx0xd5BPNAPRafcI3w4Ac/uFtAmivy3fzmNy/Pf/7zuzgkjRMACfnpT39anve858kyMMDYOJVSRpLSgYWbSNgbf/OuiZ7WrfGh08jSK1/5yg53FcBG/291q1sV93YZLbx23nnnofJB36iDHqWnHvnIR3b6m2zBl34zhuTVYhRmr3jFKzr9ggh6bxvovJ///Oflq1/9ajdf2jT35rYxHabXjZO5cJvb3Kbon/zKjRvoMfI3KD9M2M9BacPiyIpFK+cAPUeu6GLOhjvd6U4Di7HP+i9v3y6YXxYY3/nOdwqi7ZkOINsIsArJuvdd7nKXK+YWmya+H9gGMkt+LerpdTLqOl3c+nUv1ecNlmrDs93zgwBiYMIhfNMxcm3rTFpEB0nj4W3TpnvPCDKGiBuvx5WudKWCbCAlPN39+ijYX//614Vi5qWqtXbeY97RViFTgp/97Ge7fPe///371Uw+v/rVry4II4V22ctetvMmMQS8qIwUI/Db3/628zLqa61r3sfbguxMVjRxg5Q+5jGPKYwxojQRNeX/6kfYX/ayl5U999xznfz6yuAw+OskDoiAD7JkUQNT5IkiRSIYlChCMfPwUe76H/HaY4ufV+n6179+RHdXfUKC4QP/LnLiH0YFebGYQJ55OCeiO+/+pz71qcLraiHCgNznPveRVG50oxuVTTfdtGy88caFHGrzXnvtVYYp+67QNP4hO9tss800Sqyb1a4C0rn99tt3fYgcPGCIM+NIZvQX0fr6179eGMfI114RKKQTKVFms8026zx2vHOBpSsZthCxM9KWj3uLkd///veFTMf4kDvy+7Wvfa0YP3nJLuLD2G677baTi05pbbjuda9byCx9wKC2acPuyc53v/vdgqiQlX6+b37zm93xpukYYXIrv34jyDyQdnaQOaSmfYe8MNT2WmsnS3YCyLl51Ob9+9//Xng81UV2V61aVVZNBPfPec5zOo9y5Cef7jfv7aohI8ZXmgBbJENbeRPJvUXqJhM7YnSZPP1g7tBHFv/k3dzr5xn32c7BW97yloK8j1umzXfWWWcVJBSGcGjTPvGJTxS60y4fMrrlllt23k7jTT7bvHFPzuxSmdt2CJBoixEyz96ox0LOghRGG03sHj7kIQ/pdkLsAkY9cYWNeLYAvhEfV3KOMNJRsI/49kp+7B7w+FvUt2lT3TuaZx4i+21e8mkx8IUvfKGNnvIeBsgwJ4J2KWAXUPvMY89tIB+OR9o5sstr0dSmkyXjZ97aSYI5Mm53ioc/8tLT5gp7CvOIjysZJtv0rgU9PNVFn9BT5l7kXUnXJNGjRjvT5gQBJILXhSdgthXyqCAeJj+FWWvtPDy2aJHXfv0UEg+HdMpZOqJI4WgXQieO0qDMGYn/+7//E7VOQEgQJYqd0peBAecddRyCkfI+RNb7YpVPsXmfdwnKRXjmM59ZGH39irhR11prQWT1AdFlwCI/Dw9SiSBpY8SPuiJRyIC2M/aRl8FvyTLMGYmHP/zhkaW7MhSOvzj+YWu2i5z4R7soW0ZV3UG4ECpGDRk2HozjRPbuf3nDww1PRjgICmJtsSEj0khxG6u2vLSFDB/60IcKot+XH9vHDB2Z0D7tZyAZdB40cW1grCyUVq1a1RE4aeRTn42B+sSRLws1coCYi+sH5YyrhWykMZTiLExqrV20nSLEGNHnidK2LmHAP3YZLNIsdAYkrxNlniKlvOYWaox1ZKIXjKsdCHMp4qe6kifywTve5jVHyVUbp2/kB8mKeMcpLMId6UAyIh65NlbbbLNNd+ws4o2XhXCta/ASb97CEiHxHMHxkZvd7Gbx2B3j0nckA+mQ4B3GQLs8t8Ec2XXXXcuVr3zlbsEuH5lo88znPay//e1vlwc96EHd0aL23XYuzPuYt9LsENCtiKvnfrBboG8WMdJcYUN/66vx22qrrQrPcK1r8KZX4WLMlGnD9773ve74g3lX65r8bTriaZ4YF7LYprX35M+48+i2MtHmGXTP2aCM3TXtlIdOff3rX18shO3Aihs3eDe5suit9cL+mJeD5IXMWyhvPrGYo1ssKtp3aRM9TSerV5oFkfpg7Vmgcy2YLPA89wNHEblEpFv5Nv6O5sXOW7/ccn9OEr3cR3gR9I+xrbUWhmu2zeHJouycf4y6EAVElbKPuLhSGuJ5PuSJ+Mtf/vLdWV4KS5wVP+JJCXkeFGzNiafcXYVaa+EloDQpIO+Tj9dr0Pu0RbkI+oJcBTGK+FFXZIDHU/3ICTKBDDgbTjEiVRTbqDoijVFHzhBhXhNkGbGO9Lg6IoLAIVB2AyLsvvvuhRG0SKj1QoXPo2NcbGUbd/ioC6HhNbErgdj1SZA8cGSEGVaLBXEWLcbQPUOIhOu/58UQ4MaL6XgJWWrbZMzJA5mLeEZMn/peI+kIFqx5jTxHICvuQ2bhi1TAQ3wb4t7ihgfKtjjvv3FDOnj9bLvXWjtvtKMxZMBCl9GVjzcMcYm64gp7pGnQtwCRp73WWjvPPMLlGBKPGnnQBsTMsQoLqFHEva3PveMF5N6C0bN22laG29Zbby1qMhgXOx4t/uQeITFfkQuZERHyDAPb2+JGBYtlsg2LIEu+feiXQWrsdFj8GivvNF+NvZ2tfn46Tlt4BKeDSb+euXp2xAvWvuOo9cI5Djfx4ZGM95FTaeZ3xMVVfv1v9YU+mufeIy3yxpU+spMDL7s8Ee9K1zoqh/yqQ1w/sBXj6kP61Pyajj7Wf0dT7GBYFFvw8E5bIDo+QT76bRr1TDYQUnbFPHTsC3EdVAZe5pWz6jCVB16uEci9hY35btfH/I60uCpjniDHFucRH1ftcITD4oc+ifi8lpIkOqVgvSJgwlopM0rtqnemL3X2azplvZ/BnU6ZYXnHqYdSGydfvIMxRw6RRoYn4qe6UrK2CRFJxI0H7VGPelR3bnCqsm16rbU7h2xrj6HnVUOyENnIp0+8yq4UOi9RBN7gVatWdeeVI78rDBC4Wmt3fhLJYCQRKMQBQRN48eVvA0OrPHLfxi/me55G4zeoPxaR02k7Ty3iPZ0yo/L6BgGxcZzCuO2www7dkQbeVeUQWgTaItK2+LbbblucAXaExPERxFS+COYxY+o4EeMb8VNdLSR8cIokIoiOUtiyd9Z2qrJtundajMFbXbykSItfD7GA9J1Bm987ed3JacQj3bz9FiI8fuKRHvG84p5HBZggHcrD1By0ZT7Ii2cBysNKD1pAOkvqw2ZkpE/4vfPLX/5yN2fMH6TbPJlLefCO6QR6waKvxU952MPL/bgBFuPmjXx2/5A/ZBkhjnjjbyePlzkIZKTN9IpE2jkjX+PWYYyMJXnWHrrYcTMLKnNq3Hoin4UcJ0TMQ/XYGYz09urYDPkmf2xJrbXQnW0eZNw3GRYIFojmW3+XwM6SuPYYUluH9shjwdvGr4f7JVdlkuglN2Trt8EUNmVlxS/4AITHyES1khUXATEapzUIGe+cSd7PH3WNujLkFLmyvHfI3LjKu9ZavBuZVk4dMw3eraz+uA4L8T7GfliefjziyAPZjx/1zMNhy5CRoQBtlY/KPywNKUK0eE14Gf0KAdIc+XlWLF6QWiSrDbw/DJu2RH5jozyDZMwZEgTauUzGwLaq8URevC/KxRUWlPYgQhp5Znsl063MMVQ8hm2ce2f5x3kXmdDvQR6vkIdx5Y9R5p1T5zjvHpWHNxBp5rF2jMbY+QCPB885RjLKy6Tv5MCYifcREu+eM9I8vIPewagqPyhtWBwi7cgRj51+rl69eljWofH6RB55l8mRBQDvmd0ZBNV9FOb5RkD7JEv/kTCyGHnNI2PVHo3xqzTkQECAYwzJJ5m2qISps/xbbLFFd7Qs6osrjz0iw8PpYzkfOtrNQrr6O1+ItrPyiLm5BS8EXFujvqmujhVpbwT1OS5mLCPOFW5T1SWdXiKP/bZ6rrV2H/7KN06ALwz1aZz8jhjQ/xY28KVPlKPPeWDJkOMU4iKQD7sExifipnPVX/p0OmX0y04gh4h22XVwtKvWOp1qug/RHT80R8iVeUgP2rnrV6SN5raz+pwqHFXwGNRvC2ZnvY27c/i+zzHvo07zxKKIVz3i2qudNvJLf0c8+SH/6nRGmiMh0lbSdYOV1Nns69QIUJa2fqxsIzCsjLCjAhHnaht/6hpH51DPVMGWrzPQamLgeM5MaM9tsLXaPrunVJA4RtMCQVwbEMD2edS9ehClQZ5FRyyQbEpePoa7VVJRry3kuG+v2qbuNm6qe0rLr534SBIZRYKnKjMsfcMNNyy8aLb9eYtsKUZeSpl3oyUc0uRDpHiv9V2cwOBLo7hrXfMhFyLmzDaiAxfl+scVlBVgx9PHE+d5fQQy3crdZptt1v2pApRLAAAQAElEQVQ8VBvn3sJhtu835hacFiP9uiwk+nHILHmyO9FP89wuWDyPCgwdj6lzoQytvM662/5lTJHgCAwkWbK7wcPK60SeEVXl+mFQf/p5+s/6hOgiRMiOBXs/z1TPZAPZpBcQUR52OyKOGtW6NmlxhIUHlf5q60VUfJ/gY+OItwOA3Lay7DgSzzDyy/te65r6nYc2ps6hKo88WXy0ZcULFgzmiHYismQKOTQPpEfwft5HC1BXYzHMMxhlBl3JMtmNgNBZcPh+IOJcQx4G1TFOHN1JPixo+vktGiwC+vEWM/rtaEY/jVyrL+LVSwbJrnPF7eLIIsY5ZMdefKAIK4tD5JocOFphYRh1TedKD2vjdMpY8PqmgN7z6xWOn2ibuTWdeuzusAVBms1D/erLr/fZzTA37cDov4WGs8/D3odkOxbi6JSFHZsYedkSiwfyHnHt1fsEOj3iyY9xRM7JrjGPtJV0TRK9kkZ7jL4inSaKjzQERIcCRtLci4swyOs2xivWyhJ1jboybM51KWhVbrsN0fIsuDeZKWjPbeAtQbytpCknaRQbBW3SBzkXP1WI+hFKdchPafPyIPDID/yQP+8LRSwvMqMPw94njTdDneMEJJTn2Tt97OHrcD/5R4Fr0zh1MBaBSeTXD4ZMPyKOZ9j7EM+Ic0XO5EUQPEewwIE7mRHH+CEcFLiFF1KOwJAn6W2AlfOIDKrxadPm8p5MtzJnbBGgNs69YwCzfa86GCxyG3Xpv8UgghNxcUVi9V8eXruId/zAuPQNaqQPuhpf8mCsI10dZE2aOO9jEBlC+IsTlHNt4zxHIMu1riGVETfsqg5zDoF01h2BRw7sTght+4bVEfFINBKF9JO/iB90dTQJmSPXkW5uOt+pHRwDEV9r7X5DN55djQ+vKWJKXmtdc4bcQpqXmDzLB0/vqHVtPPTLPCFf2kHGLFYdFyATygrwsTWPfCHRyBQSJlismH/yjRPMU7IbQTsd59GXiHO1uBinvsijjXHvqi8WZMbDnBcnmL9kY9D8ZTMQNTqbvMmvf3QqvUlviIONBZKzxo6A0SHmQuhUc8BCiN6HkeAohfLmiSM+5p26phuMpQXTuOXMEzsWvLvk2k6D4x0+0iXzfdxG1cvRYD6ywZHPAk2b4tmVTPFS++UVCzL9F4wz+ZFHMMfh5l6AvwW6eSOIQ57ZJ3IT8ize7rPFi/sIxjzuyY/3qc/xLGMUaSvpmiR6JY32AvTVRKVMrZCtZGfbBMSMEeLdYQwpVYqHwkSwKSzkw1YYjyjCYIvK1pefwdMG3gIeMUcY+u1RXp2UD+XSplMwPFeUNCWmLit6StTXyRRMvA9p5M2Vx7sExqCtL+6RK0qb1yHiRl0ZDueKGUcEmnFGCChwxxSkMUyj6pDmnCEjGAYQXowPY2bhIg8FbJsbHpSlOAE2PkartRbKVFwEBIOXkTEw/ryejKFxgG/gz0MbZeLqPYiEcR5HKcMO/gyyuqOe+b46M2r8yVn/3QgUo48kwcWihMzyHBlD+WFCZs0Tu0FkHHbOvZNH263k1yIJ4VUmAqJGzow5whfxrjzd5IpHNjxzCCjD6+xwrbVou7HRJvHwNA5+RYE8a4u6IkjXpmh7xA+7GhekkbeYB5T3mTEnF2SI3CLUZGpYHW08gmkBYs608f17WCMZvHoWqZ557pAdWCHvsI5ytt+dR6U/YCoPL7T+8vRFPvPFWFiI1bo2aY48cbWzA18f5UWc+UVObJ9HHBKpXTyobV7yrS1xjfzzeYWz9xvz/nvpNDhZwJM9usmvbPDQ83zK75meNEc98y4jbXQo2UA0YcqjbDzUQ/YQabrNvBDstI2SOeOkfrLr2g/G3/vouX5aPFsQkE1jG3FTXZ1/doTDToRfcDHfEGg7JX7NqCW1o+qCr5+ThQGbOSwvvMmVOdvKpfL6pw/0gSDdTyW6hw89Yo5bRPmg3TvUZ16QS2RdHeSThzl2H+kc+bUPvuoyT+wGKEfPqGslhiTRK3HU56TP41WCCCAQPA8U5Xilhuei4Hg0KQqeCve2VpE0SpvBoYApMIap1lp4ShlwhtovULzjHe8oDCtl3r4JIfVzW7blGDhn8Zx1RCIjnzPiPNs+1PBOHi2/5UpZyVNrLe69Tz3eR7nyvjgzJ08bKCNGRDrl26YNuqfgEBJK1kdZjEzkQ3yd7eW1sj0X8cOuiIj32zq21axPPq5yzg2hUE7f9QPeCIU4AdlGwuHNIyJOMM7wdSZRXeJ4wJzd5rWgeKVT3OqV3gZtYJApdWS6TWvvLVCQDsdZGHi/PUvpW0S0+ebr3hEeRqbFIt5tsUBWeM6017cFtdZCdiMPskxm9VucLWmGGOFU1oefPKHkT3oExhvxQjJgS97IpEWbPBYjyKK5oB5zBv7kx3Ota0igYwM+OrIA82GU9+iLM5fIvroiIPpkjCGudU35SBt0JSNIvD55t92TyCdOm8xh9Ub8sCtC4jgIIgQzdQ/KS77gQMbVTb6d3fR+ceYkuWzL+kDNz1yaD8aGLkA4jJ+FUOT1U2a8dBbTFgER378ij7batcVxkNB/iCAM6COLJ/HGSH0cA0iKupBI4+mevCOe7uc7IPUWOMac/mnfT254IelJbSUvyDBdxyssL3mCJ2Lm2eKN/tVnY48QOteMFNZau9/Yp4st6pwNNy8EhH0YWYMdvUEefGRKPrxLMA7G0vcNyCQZIEM85pwE8kQwrhY9of8iftTVPLG4hYVFp7zqgIeFIl0pblQw5uTBriZ58JGqvgwqQ+9ZeMpHtuShA8Xrt8UyubTjZ/Hot7y1xRg4u2wHh24Je2MRYJHj3ea/MfHxobgg0WweT79xocPYCPOPA6XdzdGWlRaSRK+0EV+A/jJAlB8SMdvX11q7Pxfr4xnGzodJSIlz3JQ2JSaOAuad9j4eZF5nihRRQHopCluO0iMwqn4yi3LXViSDEmFEI49tdsSGsfAHBigdBoBRjDwUKIXfvo8RVzbyxNV7tBtBsuCI+GHXWtcsCrx3UH084ggCQz2sjojnJXFWmYJFHnxI5syyn53TJvnU4+wdEgBXcQKPi61yBsqvgogT9H2nnXYqCLYtcHFtQDQpcuMD3zbNPYPMGDIS3iFuUEBa9ZXxouzViexZjAzKv77j9BsO+s14te/j3XEOkScNUfMLEmS2NdR2OGAScbzwDJ1tYluq7pWBX1s3EuqsqwUJokq+LRrbfLy/8CH32miBZ4zs3ERddhmQHbJvcUmmyS/Dy8Me+Vx5snm97H7UOjWJNieRIXKmX+pog2NJCK5FVhs/6J6H0NElCywfVA2bM9pMHsin+Uq+ya95s9OEfFpw9svaNle3xTOip4z+8+RJi/aYH2TfB3vqifj+lc5DLnm3EZBY8NInyIiFioW/eGMjH9x5ItVFppQjF9rluIj4+Q68kBwD2tEndoiYD5HJl7YjWJ71K9qJxJH9aL8rOXM+PMaejMNBGXKA5HpfP8BTnn6AqXGmC+g17Yo8xoEsOGZBHhBARN+Z7lrXll96x6KhPU4R9Qy7+uNW5hWZa/PAwALXR4Jt/KB7Y26hQK45Anjy+/IZ5ZBWH7nqBxkRr7/0AD2NQCPK5jTnAscKvUC30pfkzuJaOcHiBdFnNxxDMSbsozj6Xx7OK/bBQsRYmRuuPNHqlWelhiTRK3Xkx+y3yc3wUJItURyzeJcNWUUirJR5ArrIWfyD4FHEFAXyiUyKU2WttVD6jim07XW0AJkXT0FaQde6tgKliJEweSIg4pS6uoVaa/dHEJAdeRjXvvKUb5z38QJTsoh7S2iUHxUoTgqy1rXbr0yttcCDUvU8KvBS6J/Fg75oA+WK9EQ5fZcmwC/ikRlxgjoi3nvFCfCM+LhquzTBeyM+rkiJNME7Ir5/1X/tkS+CZ33v5x3n2WKJt3acvMPyMFSIAMPV93Ah0oy5tpIdJLc1kuaINHIT9TNczimKD5mNtLjCnldPnghIAJwjj7mBMMBbHnUa1/b98iK40uQxDtqrrLQISBQvGVIiPeJHXWutxXjBZlA+88ecpWsGpbdxPOjaJ+h3revOAfnVhQDIF0H/tbnFRt42OLpgHihjm5qegLH6Ih98pQujZNQY00/ykU1zQx3665kcaIt4fZGPxy/mn/Gn48TLyxmg/HQDEmcbHnbTLSs/DOhvHldefHFtILOhN109t+n6Ckv9FF9rLeQvcLZTqa/SBPiQd/3uB/jI0w/ky/jKT4bNt8hjHLQrxsJVHm2o9UL5scvBK8uR0JaPeoZdyYA2D0qnY83tQWltnDGHg/YLZLfWC9vW5oWlPPoU7yWfsBFPZuhBfeBsaLH0DvO8rY8tVa4fyJx6I6/xi3fQD95hLpCPyLMSr/NJolcivtnnCxCwwvVREU+cbec+ybgg24q46LvjIjwxDA4P3Yro+DLvJELEE+0cLk8jb+1y6rKjOo4nuPKmt8RnOfUz+7IuAjy3PPB22BwrsZhaN9fSjNEXBNoOBC8tO7U0e5KtXggEkkQvBOor8J0IBq8rb4GtZx/mrEAYui47W2ibnqfcNqdtvy4h/1mkCIzfLOeEHbvw8ZuznuOXXNw5nbF2xth5S7tSPFyLu8XZurlEgPfSFr8jVLb0d9ttt7msfkHrcqTO7oo/MOTY1II2Jl++5BBIEr3khmzpNtjWlvOYzjLaBlq6PZldy20Z+/DG+UHbkLOrLUsvNgQcDXFe3zGdxda2mbbHQs85SOcwHQ2ZaT1Zbuki4DiJs/vObzuKsXR7snbLHY9w9txfj2yPL6yda4k+ZbPXOwJJotc7xPmCFgFKyllZ59Ha+JV0z6uDiDhjtpL6vVL66qwx+Xaucbn02XlmMhtnMJdLv7If00PAGXlngJ1hn17JxZubU0N/9G3xtjJbtlgRSBK9WEcm27WUEci2JwKJQCKQCCQCicAyRyBJ9DIf4OxeIpAIJAKJQCIwHgKZKxFIBKaDQJLo6aCVeROBRCARSAQSgUQgEUgEEoEJBJJET4CwGP7PNiQCiUAikAgkAolAIpAILB0EkkQvnbHKliYCiUAisNgQyPYkAolAIrBiEUgSvWKHPjueCCQCiUAikAgkAonASkRgbvqcJHpucMxaEoFEIBFIBBKBRCARSARWEAJJolfQYGdXE4HFgEC2IRFIBBKBRCARWA4IJIleDqOYfUgEEoFEIBFIBBKB9YlA1p0IrINAkuh1IMmIRCARSAQSgUQgEUgEEoFEYDQCSaJH45OpiwGBbEMikAgkAolAIpAIJAKLDIEk0YtsQLI5iUAikAgkAssDgexFIpAILG8EkkQv7/HN3iUCiUAikAgkAolAIpAIrAcElimJXg9IZZWJQCKQCCQCiUAikAgkAonABQgkib4AiLwkAolAIrDgCGQDEoFEIBFIBJYMAkmil8xQZUMTgUQgEUgEEoFEIBFYfAis1BYliV6pI5/9TgQSgUQgEUgEEoFEIBGYMQJJomcMXRZMSW1QGgAAD+RJREFUBBYDAtmGRCARSAQSgUQgEVgIBJJELwTq+c5EIBFIBBKBRGAlI5B9TwSWAQJJopfBIGYXEoFEIBFIBBKBRCARSATmF4Ek0fOL92J4W7YhEUgEEoFEIBFIBBKBRGCWCCSJniWAWTwRSAQSgURgPhDIdyQCiUAisLgQSBK9uMYjW5MIJAKJQCKQCCQCiUAisAQQGItEL4F+ZBMTgUQgEUgEEoFEIBFIBBKBeUMgSfS8QZ0vSgQSgXlGIF+XCCQCiUAikAisNwSSRK83aLPiRCARSAQSgUQgEUgEpotA5l8qCCSJXiojle1MBBKBRCARSAQSgUQgEVg0CCSJXjRDkQ1ZDAhkGxKBRCARSAQSgUQgERgHgSTR46CUeRKBRCARSAQSgcWLQLYsEUgEFgCBJNELAHq+MhFIBBKBRCARSAQSgURgaSOQJHq245flE4FEIBFIBBKBRCARSARWHAJJolfckC9sh88777xy0kknlTPOOGNhG7KC3n7uueeW0047rcD+/PPPLyeeeGI566yzVhAC2dVBCGRcIpAIJAKJwOwQSBI9O/yy9DQQOPnkk8uXvvSl8qY3van89re/nUbJ4VkPP/zw8sMf/rAcd9xxwzM1Kcj7P/7xj/Ld73637L333k3KhbennHJK+eMf/1hcL4xdc4eI/uEPfyhf/vKXB4af//znazKup39h+Mtf/rJ79w9+8INyxBFHrPUm7fvxj3/cpUcbP/rRj5Y3vOEN5cgjjyznnHNOec973lM+/vGPl/3333+tsvP98NOf/nSynX/605/K2Wef3TXhv//972T8V7/61S7OP3vuuedkfJv/+OOPL9/73ve6tDa/MoJ6f//733fpP/nJT3IBB5QMiUAikAgsTQQWVauTRC+q4Vi+jUFIkedvfvOb5QlPeEK5xz3uMevOnn766WX77bcv2223Xdlvv/2mrG+fffYpL3nJS8qnP/3pLv+ZZ565ThmkHOF82ctetg5BlZkn99///nd54Qtf2C0IEDgBQX31q19dPvWpT8m2XoL+vuhFL+oI8CGHHFJ22WWX8qxnPasceOCBk+9DGF/xileUrbfeejI897nPLXvttVeXZ8MNNyzPec5zCu/0jjvuWA499NAufiH+2Xfffbs26hPiDFvBwsAYrF69uhizaJuF0vOe97yy7bbblv/85z9FXmm86hZnT3ziE8vjH//4dUiyOuAEE+NroaFchkQgEUgEEoFEYDYIbDCbwll2+SPAc/nhD3+4vOUtbylI3Ex7jDzz0n7gAx8ot7jFLcpFLnKRmVY1WY7XkVd2MmLEDeL7pCc9qSDOCPKzn/3srh1tER5cJOzXv/51QbbatLjXbu1ftWpVR9iQMwG522yzzcr97ne/yDrn18985jMdcdd+RBJp/853vlNg2r7sete7Xud11ecIvNFXvvKVu2yuT3/60wtCbTHQRY7zz0Sef/7zn+Ud73jHOkR1Iqnz3MPi4IMP9jhlQJKvda1rlZvc5CZlq622Khe72MVKrbXc6EY3Kte//vWLxYAFUlQEX8RZ/kc+8pFdfmlXucpVyo1vfONyq1vdqiPW7aICYeaRR8ylP+5xjyuXvOQlFcuQCCQCiUAikAjMCoEk0bOCb/kXRlqOPfbYctRRR3UEZSY9VvZrX/taeexjH1sQnpnU0S/zr3/9qyCGb3vb2/pJ6zxr/w477FA23njj8olPfKJc9apX7QhkmxHJQsi/8Y1vFCS1TevfO9N90YtetNzsZjebTNpggw06DzFv6GTkHN7wHPO23u52t+vIf5D5+9///t3RGO2P1yGjm2yySUE2I1zzmtcs2hx5LnGJS5RnPOMZ3bEWRxwifqorT/fHPvaxjrg7GhP5LU5gvMcee3RnryN+1FU7tU+73EdeY0vunvzkJ681TrzmvP53v/vdy0YbbRTZu/Pehx12WEGQleOxjsS//vWvxZgaq9vf/vZzsniLuvOaCCQCSwOBbGUisL4QSBK9vpDNeicRsPX+v//9b868tM4F77zzzh0JvPSlLz35nkE3SJXz187EjvK6bjxBsB0h4GEeVE/E8Ww6HnCFK1yhXPayl+0Io7O6+hh51sf1gAMO6LzjPK5t/be5zW3KCSecUJDINn6c+zvc4Q6dx/f73//+ONm7PLe+9a27M+28u9/+9re7OB8qvv3tby8w/tGPflSuc53rdPHj/nPqqacWCx35fQCp7i233LIg/uIiOMteay13vetdI6q7KmvHhAdeRJz1dsTG0RXece+44x3vKDlDIpAIJAKJQCIwJwgkiZ4TGLOSwQiUjmT6gO9KV7pSucxlLjMs29jxSOxuu+1WkKIHPvCBU5bjOXWMxBEGRzR22mmn7hyxK4/nlBX0MvAIO9LAK+o4BYL2/Oc/vyOQvaxz+ogo8vbe9KY3XateHmVtQiIjATY8/y94wQuKYytf/OIXCw9upLdXnuw///nPQ9PbvO5rrQXu+v2ud72rfOELXyjvf//7y29+85sC02tf+9qyjR0sCoxReLUd+0HKH/rQh67jNf7FL35RePyR//YFsOGZd1zDoumggw7qfn3EsZN73vOe5eY3v3m3AOlj19aR94lAIpAIJAKJwHQRSBI9XcQy/7QQQHod5+Dhbbfsp1VJk5mn0rGGhz3sYQUxb5IG3iKYvOA+PHRW98EPfnDZZpttCq/mox/96Gl/WIfw8bgq68wuTyfidsMb3nDg++cqUr9bojys3lpr5wlGGBHoO9/5zsWRlze/+c2FB79f7opXvGIXb4z6acOenSn2cahz4D5a/PrXv9593Nn3EA8r38Zf/epXn3x0ltlHho7T9McWyXZW3TvsAkwWmrgxrki0hRLCjIQ7muNjxde+9rWFZ1p9jvFMZM//E4GpEcgciUAikAiMgUCS6DFAWklZEC0fx13jGtcogq15JMw52Bvc4AZdnHhh3CMMSGattftorI+leqYKjhD4GTtlHbngkUVaeZZ5IRFb3sd73etesqwVkHhHHXyI9sxnPrOoy7Y+Iu3srOMHaxWY4gGZQ8o33XTT7mM2Z3p98BZHCYYV56l1JneqvvqgTv39ehBXZ4f1t59Wa+08tGXiPwuV9773vd1CAZFGdp0tdhbcQmIiyzr/I+iOhKyTMCKCR9i5ZNg7Iy7UWkeUGJ1kgeBIyItf/OLi3Hc/t7YfffTRpT/GxtfOANm000Fe/fTfV77yle64D9x42n2s2Cff/XfkcyKQCCQCiUAiMB0EFhuJnk7bM+96QAAR8SsVjisIBx54YPehnY/QnAUWFwGBnG0Toq5R17/97W9liy226F6F+DoH7SjFS1/60u5DQWdfkfyrXe1qXZ72H2TPx4wIqPtIQ3ovdalLdV7YiBvnyhvq/DSyKz8i+apXvao7k2wBIm5QuMtd7lL8tvGofkrzs28IX78Ov2Khvb/73e/WSuJB5uXXprUSLnjQZ/U5zmHxcUH0WhdeXL/UsVbkiAe/0uLcssWVoxw+6EPSeehHFBuYxEOMCP/sZz8rPo7k2R6Ukewh+o6ftOkWaI4LxXjAWV7k2b108oNck+22bN4nAolAIpAIJAKzQSBJ9GzQy7JjIYCg8VgiS2MVGJHJHwn5/Oc/XyK8/OUv7z5Ae+Mb39jF9YsizzzAPNXOCke6xQEP7CDiHXkGXXmuHYHgUY50PyPnZwCRvIib66ujCn5hwjGUY445pqsembXg8ZN7FgoifSSoLfD2jETK7yiD89Pi+gHhvPzlL9+PHvisPn+oxu9hO3rBo897fNvb3rZY2PD6Diw4JJIH2dg4W/20pz2tWCD1szqS8/e//71c/OIX73YS2nQLF6TZzoR4ZNoREcdteJ55r3n2vUd5edZPyFoTgUQgEUgEVhoCSaJX2ojPc3+RWF5AfxwE6Zvr1yPG6vXHXNSNcPnpO95X5BaBf9CDHlS83+8sy8Pj+6EPfaggW85Ii4uA6MvrqAgvb8THdddddy2IN1IqTp53vvOd3U+xXe5ylxO1XgIced4RYh/MeS8i6/wv761jHF7sJ90ci/DLGf4ICfL5kY98pDzqUY8qxkGeNvjri35lBElv44fdq09//c60IzLyIeDOMavD0RFnlMWPG/TJzoDz24PK6Ku/IGlsjHWbRz9hEHEPechDir+EGB8f+sM6juDwtkeevCYCiUAikAhcgEBeZoVAkuhZwZeFx0GAFxC5dVRhnPzj5tl99927vxzomIJzwIivsrywvMxIl+c73elO3e8a+3UHP5t23/vetyBtzs7ywsojOJbhlyf8ZUVb/7yZ/kCLD+eQN97uWmtxpMOvUKiLB9ZvIyPtjluoZ30FxNC7vN97Xf3yiHPe8U6/xY0wa6s+OhOtD345A1mOfK5I9q9+9atikTHIAyxPP9Rau7846T0WKJGuvF0C55l5qyN+qivvsPF5ylOeUgYtQoyhv8zoKIz79mcKfTz4yle+svt5PGTZQoq33dEXiw4Lng9+8IOdd9vOxVzL31R9y/REIBFIBBKB5Y1AkujlPb6z7p0ztY4LIG0z9eYhmJtvvnnxFwZn3aCmAh/08RoLCBMPsTb61QhxfqkhsvtVB3/dT7yftuOtdR430l0RTR8wyhOB99pf00POHFeI+P512223Xecn2dQ5IswoST+QR+93BKJ/Lh1xfutb31p4pOXZa6+9up/06xNoL7c4QGIf8IAHeBwr3PKWtyzwHZTZwgPhtcAYlD4ozrllH10O80KTv+23377bSdAf9Uc9/uiKOEGfEflIc334wx8+Wc4flHHUQ3yGRCARSAQSgURgLhBIEj0XKC7jOpBSf/iCRxPhmklXkRvb/I4OzDWRnkl7skwpzgnz3voJOL/ikZgkAolAIjAeApkrEUgEAoEk0YFEXtcrAjzZjlz4vWJnlh3BWK8vzMoHIuCohb+wuN122xWLI8coBmbMyEQgEUgEEoFEIBEYiUCS6JHwLK7EpdwaHm3b784h+2UMRyuWcn+WatudF//kJz9ZHJHws4XxQeJS7U+2OxFIBBKBRCARWCgEkkQvFPIr9L1+1cKHe/HLDisUhgXrNtLst52dRfbx3YI1JF+8khDIviYCiUAisCwRSBK9LIc1O5UIJAKJQCKQCCQCiUAiMHMEpi6ZJHpqjDJHIpAIJAKJQCKQCCQCiUAisBYCSaLXgiMfEoFEYDEgkG1IBBKBRCARSAQWOwJJohf7CGX7EoFEIBFIBBKBRGApIJBtXGEIJIleYQOe3U0EEoFEIBFIBBKBRCARmD0CSaJnj2HWsBgQyDYkAolAIpAIJAKJQCIwjwgkiZ5HsPNViUAikAgkAolAi0DeJwKJwNJFIEn00h27bHkikAgkAolAIpAIJAKJwAIhsIJJ9AIhnq9NBBKBRCARSAQSgUQgEVjyCCSJXvJDmB1IBBKBFYVAdjYRSAQSgURgUSDw/wAAAP//o+LblwAAAAZJREFUAwAwIJnkeeDCHQAAAABJRU5ErkJggg==\"\u003e\u003c/p\u003e\n\u003cp\u003eUsing the median FMRG score as a cutoff, patients were stratified into low-risk (n = 266) and high-risk (n = 266) groups (Tables S4). This stratification effectively distinguished patients with distinct clinical outcomes: higher FMRG scores were associated with shorter overall survival and increased mortality (Fig. 3A, C). Kaplan-Meier analysis confirmed that patients in the low-risk group had better overall survival than those in the high-risk group (log-rank p \u0026lt; 0.001; Fig. 3E). The time-dependent receiver operating characteristic (ROC) analysis demonstrated predictive accuracy of the FMRG score, with area under the curve (AUC) values of 0.752, 0.698, and 0.725 for 1-, 3-, and 5-year survival, respectively (Fig. 3G).\u003c/p\u003e\n\u003cp\u003eThe prognostic performance of the FMRG score was further validated in an independent external cohort (E-MTAB-1980). Consistent with the training set, high-risk patients exhibited worse overall survival (log-rank p = 0.001; Fig. 3F), and the model maintained substantial predictive power across multiple timepoints (Fig. 3H). Based on the strong prognostic association of the FMRG score, we integrated it with key clinical parameters to construct a predictive nomogram for estimating 1-, 3-, and 5-year overall survival probabilities (Fig. 4A). Receiver operating characteristic (ROC) analysis and calibration curves showed excellent concordance between nomogram-predicted outcomes and actual observed survival (Fig. 4B, C), supporting the clinical utility of this integrated tool.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation of the FMRG-based Signature with Clinical Characteristics and Prognosis in ccRCC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnivariable and multivariate Cox regression analyses identified the FMRG score, along with Grade and T, N, and M stage, as independent prognostic factors for ccRCC (Fig. 4D, E). Specifically, the FMRG score was associated with a hazard ratio (HR) of 1.20 (95% CI: 1.13-1.28, p \u0026lt; 0.001). To further validate the predictive accuracy of the FMRG score, Kaplan-Meier analyses were performed across subgroups stratified by various clinical features. The FMRG score was elevated in patients with more advanced disease (Fig. 4F-I), showing a positive correlation with disease progression across all stage components, though no significant difference was observed between Grade 1 and 2. Stratified survival analysis confirmed that patients with a high FMRG score had worse overall survival (OS), particularly in advanced TNM stages (Fig. 4J-R). This prognostic value of the FMRG score was consistently observed in both young and elderly patient subgroups (Fig. 4J, K), underscoring its broad clinical applicability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe FMRG score Reshapes the Immune Landscape and Predicts Drug Sensitivity in ccRCC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe composition of the tumor microenvironment (TME) is a key determinant of immunotherapy response. To investigate this relationship in ccRCC, we evaluated immune profiles according to FMRG score stratification. Analysis of 22 immune cell subsets revealed elevated infiltration of CD8+ T cells, follicular helper T cells, regulatory T cells (Tregs), and M0 macrophages in the high FMRG score group, whereas resting CD4+ memory T cells, resting NK cells, M1 macrophages, resting dendritic cells, and resting mast cells were substantially decreased (Fig. 5E). Furthermore, the high FMRG score group exhibited elevated stromal, immune, and ESTIMATE scores, along with reduced tumor purity (Fig. 5A-D). We also assessed the correlation between the ten model genes and immune cell abundance, finding that most immune cells showed significant associations with all genes (Fig. 5F). To explore the clinical relevance of the FMRG signature, we compared the sensitivity of high- and low-risk groups to conventional ccRCC chemotherapeutic agents. Patients with high FMRG scores demonstrated lower IC\u003csub\u003e50\u003c/sub\u003e values for Sorafenib, Sunitinib, Temsirolimus, and Nilotinib (Fig. 5G-J), suggesting a potential role of the FMRG score in guiding personalized therapy. Collectively, these results indicate that the FMRG score is closely linked to both the immune contexture and drug susceptibility in ccRCC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNGF Serves as a Key Prognostic Factor and is Associated with an Immunosuppressive Microenvironment in ccRCC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSurvival analysis of the 10 FMRG model genes revealed distinct prognostic associations: low expression of TIMP3, SHMT1, ERBB2, and ABCB1 correlated with poor overall survival (OS), whereas high expression of SHMT2, PDGFRA, NGF, FASLG, CDKN2A, and VIM was associated with reduced OS (Fig. 6A-J).\u003c/p\u003e\n\u003cp\u003eGiven its prognostic significance, we investigated the relationship between NGF expression and the tumor immune microenvironment. Analysis of immune cell infiltration demonstrated that high NGF expression was characterized by a altered immune landscape. Specifically, it was associated with elevated infiltration of B cells naive and NK cells resting, concurrent with reduced infiltration of macrophages M2, Dendritic cells activated, and neutrophils (Fig. 6K). This profile suggests that VIM is linked to a distinct, potentially immunosuppressive, immune contexture in ccRCC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNGF\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Expression Signature in ccRCC via Integrated Single-cell and Spatial Transcriptomics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe established prognostic significance of NGF in ccRCC through bulk transcriptomics necessitates a deeper investigation into its cell-type-specific expression and spatial distribution, dimensions obscured by tissue-level averaging. To this end, we integrated single-cell RNA sequencing (scRNA-seq) and spatial transcriptomic (ST) data, enabling the precise mapping of NGF expression across cellular subpopulations and its direct visualization within the native tissue architecture. Analysis of the scRNA-seq dataset (GSE304466) identified 22 distinct cell clusters, which were annotated into 7 major cell types, including B cell, endothelial cells, epithelial cells, macrophage, monocyte, T cells, tissue stem cells (Fig. 7A, B). Interrogation of NGF expression revealed its predominant enrichment in specific populations, including tissue stem cells and endothelial cells (Fig. 7C, D). CellChat analysis was employed to investigate global intercellular communication patterns in ccRCC based on single-cell gene expression profiles. The results revealed distinct differences in communication intensity and interaction strength among cell types. Specifically, B cells and monocytes exhibited significantly higher outgoing signal strength and number of interactions, whereas epithelial cells and macrophages showed pronounced reductions in these measures (Fig. 7E, F). Spatial transcriptomic analysis further contextualized this expression. NGF was highly expressed within the core tumor region, as demarcated by the tumor marker CA9, but was notably absent in the adjacent normal tissue defined by UMOD expression (Fig. 7G-H). This pattern underscores a tumor-specific upregulation of NGF and its potential role in the core tumor niche.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNGF Promotes the Malignant Phenotype of ccRCC Cells In Vitro\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo functionally validate the oncogenic role of NGF in ccRCC, we performed loss-of-function assays in two representative cell lines (OS-RC-2 and A-498). Cells were transfected with NGF-targeting siRNA (si-NGF-1 and si-NGF-2) or a non-targeting control (si-NC), and efficient knockdown was confirmed by qRT-PCR (Fig. 8A). Functional analysis revealed that NGF silencing suppressed the malignant phenotype of ccRCC cells. Cell viability assessed by CCK-8 assay was reduced in si-NGF groups (Fig. 8B). The long-term clonogenic potential was also impaired, as evidenced by a significant decrease in both the number and size of colonies formed in the colony formation assay upon NGF knockdown (Fig. 8C). Furthermore, wound healing demonstrated that NGF depletion attenuated the migratory capacities of both cell lines (Fig. 8D). In addition, Transwell invasion and migration assays showed that NGF knockdown reduced the number of cells through Matrigel-coated membranes (Fig. 8E, F). Collectively, these in vitro results demonstrate that NGF is a critical regulator of ccRCC cell proliferation, clonogenicity, migration, and invasion, substantiating its role in driving tumor progression.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe high recurrence and metastasis rates of clear cell renal cell carcinoma (ccRCC) remain a major therapeutic hurdle. Although immune checkpoint inhibitors combined with targeted agents constitute first-line therapy for advanced disease, their clinical benefit is frequently constrained by intrinsic and acquired resistance, highlighting the urgent need to identify novel mechanisms underlying disease progression and therapeutic failure. Metabolic reprogramming is a defining feature of clear cell renal cell carcinoma (ccRCC), primarily driven by inactivation of the von Hippel-Lindau (VHL) gene and subsequent accumulation of hypoxia-inducible factors (HIFs) [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This process induces profound metabolic alterations, including glycogen and lipid accumulation, enhanced glycolysis, and disrupted glutamine and fatty acid metabolism[\u003cspan additionalcitationids=\"CR12 CR13 CR14 CR15 CR16\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Beyond sustaining tumor growth under hypoxic and nutrient-limited conditions, these changes reshape the tumor microenvironment (TME) by affecting immune infiltration, angiogenesis, and therapeutic resistance. Consequently, targeting metabolic dependencies represents a promising therapeutic strategy in ccRCC.Folate metabolism, essential for one-carbon transfer reactions involved in DNA synthesis, repair, and methylation, has been implicated in ccRCC pathogenesis[\u003cspan additionalcitationids=\"CR19 CR20 CR21\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Although altered folate related genes have been associated with advanced stage and poor prognosis, epidemiological data on dietary folate intake and ccRCC risk remain inconsistent, further complicated by polymorphisms in enzymes such as MTHFR[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Notably, the comprehensive role of folate metabolism in modulating the TME and clinical outcomes in ccRCC has yet to be systematically defined.\u003c/p\u003e \u003cp\u003eTo this end, we retrieved 410 folate metabolism related genes (FMRGs) from the GeneCards database and intersected this set with differentially expressed genes (DEGs) identified in the TCGA-KIRC cohort. Functional enrichment analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, were subsequently conducted on the overlapping gene set. KEGG pathway analysis revealed significant enrichment in key biological processes, including the one-carbon pool by folate, antifolate resistance, folate transport and metabolism, and glycine, serine, and threonine metabolism. GO analysis further elucidated the functional annotations of these genes. Within the biological process (BP) domain, the genes were associated with nutrient and vitamin responses, tetrahydrofolate metabolism, folic acid-containing compound metabolism, serine family amino acid metabolism, and pteridine-containing compound metabolism. In the cellular component (CC) category, enriched terms included the external side of the plasma membrane, cytoplasmic vesicle lumen, vesicle lumen, and brush border membrane. For molecular function (MF), the most highly enriched terms comprised vitamin binding, carboxylic acid binding, organic acid binding, pyridoxal phosphate binding, and vitamin B6 binding. A protein-protein interaction (PPI) network was subsequently constructed using the STRING database, focusing on the top 20 differentially expressed FMRGs. To investigate the potential clinical relevance of FMRGs in ccRCC, we developed a prognostic model via LASSO Cox regression analysis. Using this model, an individualized FMRG score was calculated for each patient, and ccRCC cases were stratified into high- and low-score groups based on the median score. Kaplan-Meier survival analysis demonstrated that patients in the high-score group exhibited worse overall survival (OS) compared to those in the low-score group, a finding consistently validated in both the TCGA-KIRC cohort and the independent external validation set, E-MTAB-1980. Given the prognostic performance of the FMRG score, we integrated it with key clinicopathological variables to construct a predictive nomogram for estimating 1-, 3-, and 5-year overall survival probabilities. Receiver operating characteristic (ROC) curves and calibration plots revealed excellent concordance between nomogram-predicted and observed survival outcomes, supporting the clinical applicability of this integrative tool. Univariate and multivariate Cox regression analyses further confirmed that the FMRG score served as an independent prognostic risk factor in the TCGA-KIRC cohort, with hazard ratios (HR) greater than 1 and p-values below 0.05. To elucidate the mechanisms underlying the association between elevated FMRG score and poor prognosis, we next examined the correlation between FMRG score and clinicopathological characteristics. The results demonstrated a positive association between FMRG score and disease progression across all tumor stage components; however, no statistically significant difference was observed between Grade 1 and Grade 2. Stratified survival analysis further confirmed that patients with a high FMRG score exhibited worse OS, particularly in advanced TNM stages. Of note, this prognostic value was consistently observed across both younger and elderly patient subgroups, underscoring the broad clinical applicability of the FMRG score. This multi-gene score serves as a independent prognostic indicator and a multi-faceted predictor of tumor microenvironment (TME) composition and therapy response in ccRCC. The high-risk group was associated with an immunosuppressive, pro-fibrotic TME, characterized by elevated infiltration of regulatory T cells (Tregs) and M0 macrophages, higher stromal scores, and lower tumor purity. These findings suggest that dysregulated folate metabolism may contribute to establishing an immune-cold microenvironment, potentially explaining the limited efficacy of immunotherapy in a subset of patients[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. This aligns with the established concept that metabolic reprogramming is a key facilitator of immune evasion in cancer[\u003cspan additionalcitationids=\"CR37\" citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Drug sensitivity analysis revealed that patients with high FMRG scores demonstrated lower IC\u003csub\u003e50\u003c/sub\u003e values for Sorafenib, Sunitinib, Temsirolimus, and Nilotinib, suggesting a potential role of the FMRG score in guiding personalized therapy.\u003c/p\u003e \u003cp\u003eOur integrated multi-omics approach confirmed the clinical utility of the FMRG score and identified NGF as a pivotal effector within this metabolic network. A key contribution of this study is the precise dissection of NGF expression at cellular and spatial resolution. While bulk analyses linked high NGF to poor prognosis, our single-cell RNA sequencing data delineated its specific cellular origins, revealing predominant enrichment in distinct subsets of tissue stem cells and endothelial cells. This cell-type specificity implies that NGF's function extends beyond its canonical role as a neurotrophic factor, suggesting compartment-specific roles in both tumor stemness and vascular microenvironment regulation. Spatial transcriptomics provided the critical topological context for these observations. We demonstrated that NGF expression is not diffuse but is highly concentrated within the CA9-positive tumor core, while being absent from adjacent UMOD-positive normal tissue. This spatial restriction to the tumor core unveils a previously underappreciated architectural role, positing that NGF-high cells might constitute or modulate a central niche vital for maintaining tumor integrity, stemness, or a specialized microenvironment conducive to tumor growth.The functional relevance of this spatially defined role was corroborated by our in vitro assays. NGF knockdown significantly impaired ccRCC cell proliferation, clonogenicity, migration, and invasion, providing causal evidence that NGF is a functional driver of aggressive behaviors likely originating from the tumor core. These findings collectively suggest that NGF functions as an active executor within a critical tumor core niche, where it propels malignant phenotypes.\u003c/p\u003e \u003cp\u003eFurthermore, the FMRG score itself demonstrated significant clinical translational potential. This signature not only served as an independent prognostic factor beyond traditional TNM stage and grade but also maintained robust stratification capability across various clinical subgroups. Notably, although patients in the high-risk group exhibited worse prognosis, they showed lower IC\u003csub\u003e50\u003c/sub\u003e values for conventional ccRCC targeted therapies including Sorafenib and Sunitinib, suggesting that these patients might benefit from more aggressive initial treatment strategies. Additionally, the immune landscape characterized by increased regulatory T cell infiltration and decreased M1 macrophages in the high-risk group raises intriguing questions regarding their potential response to immune checkpoint inhibitors that warrant further investigation. Together, these insights redefine NGF from a passive correlative biomarker to an active executor within a critical tumor core niche, where it drives malignant phenotypes and likely contributes to organizing a permissive tumor microenvironment. This integrated view positions NGF as a compelling therapeutic target, as its inhibition could simultaneously disrupt cell-intrinsic oncogenic pathways and the integrity of the tumor core ecosystem.\u003c/p\u003e \u003cp\u003eBeyond NGF, the prognostic model comprised nine additional folate metabolism related genes, including ERBB2, PDGFRA, CDKN2A, VIM, FASLG, SHMT1, SHMT2, ABCB1, and TIMP3. ERBB2 expression was significantly downregulated in ccRCC tumor tissues compared to normal controls in our cohort, consistent with previous reports demonstrating reduced ERBB2 mRNA and protein levels in ccRCC[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. This downregulation may appear counterintuitive given ERBB2's well-established oncogenic role in breast and gastric cancers; however, in ccRCC, lower ERBB2 expression has been associated with aggressive clinicopathological features including advanced T stage, M stage, and higher histological grade[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Mechanistically, ERBB2 expression in ccRCC correlates positively with immune cell infiltration, immune checkpoint molecules (TIGIT, LAG3), and PTEN expression, suggesting its downregulation may contribute to immune dysregulation and tumor progression[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Our univariable Cox analysis identifying ERBB2 as a protective factor (HR\u0026thinsp;\u0026lt;\u0026thinsp;1) aligns with these observations, supporting its context-dependent tumor-suppressive role in ccRCC. PDGFRA, encoding platelet-derived growth factor receptor alpha, was upregulated in our high-risk group and associated with poor prognosis. Intra-tumoral molecular heterogeneity of PDGFRA expression has been documented in ccRCC, with variable expression levels between primary tumors and metastatic lesions potentially influencing differential responses to tyrosine kinase inhibitors including sorafenib and sunitinib[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. This heterogeneity may partially explain the diversity of clinical outcomes observed in ccRCC patients receiving targeted therapies. CDKN2A, a critical cell cycle regulator, demonstrated significant upregulation in tumor tissues and high-risk patients in our study, correlating with reduced overall survival. This finding corroborates comprehensive TCGA analyses demonstrating that CDKN2A alterations, including DNA hypermethylation and somatic changes, associate with decreased survival across all major RCC histologic subtype[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. The consistent association of CDKN2A dysregulation with poor outcomes underscores its fundamental role in RCC progression independent of histologic subtype. VIM, a canonical mesenchymal marker, was elevated in our high-risk group and associated with adverse prognosis. Recent spatial proteomic analyses using 33-marker immunofluorescence imaging have demonstrated that ccRCC cells losing epithelial markers show increased expression of mesenchymal markers including vimentin, indicating epithelial-to-mesenchymal transition (EMT)[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. FASLG, a member of the tumor necrosis factor superfamily, mediates apoptosis through FAS receptor binding and is essential for immune system regulation including activation-induced cell death of T cells. Our observation of FASLG upregulation in high-risk patients suggests its potential role in tumor immune evasion through induction of T lymphocyte apoptosis, a mechanism implicated in the progression of several cancers. SHMT1 and SHMT2, encoding cytoplasmic and mitochondrial serine hydroxymethyltransferase isoenzymes respectively, exhibited opposing expression patterns in our ccRCC cohort. SHMT1 was downregulated in tumor tissues and low expression correlated with poor survival, whereas SHMT2 was upregulated and high expression associated with reduced overall survival. These findings align with comprehensive analyses of SHMT family members in RCC, demonstrating significant SHMT1 downregulation and SHMT2 upregulation in ccRCC patients, with SHMT1 high expression correlating with longer survival periods[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. ABCB1, a key ATP-binding cassette transporter, was downregulated in ccRCC tumor tissues compared to normal kidney cortex in our study. Previous investigations have confirmed significantly lower ABCB1 mRNA and protein expression in clear cell RCC compared to adjacent normal cortex tissue[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. This downregulation may influence both the elimination of xenobiotics and the intrinsic chemotherapy resistance of ccRCC, potentially contributing to inter-individual differences in drug disposition and treatment outcomes. TIMP3, a key regulator of extracellular matrix remodeling, demonstrated downregulation in ccRCC tissues and was associated with poor prognosis in our model. TIMP3 exerts antitumor effects through both matrix metalloproteinase (MMP)-dependent and MMP-independent pathways[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Due to promoter methylation and miRNA binding, TIMP3 expression is frequently decreased in various cancers, leading to increased cancer cell migration and invasion[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The downregulation observed in our high-risk patients aligns with this tumor-suppressive role and supports TIMP3 as a potential biomarker in ccRCC. The convergence of these nine genes within a single prognostic signature reflects the multifaceted nature of ccRCC pathogenesis, encompassing growth factor signaling, cell cycle regulation, EMT and cytoskeletal remodeling, immune modulation, metabolic reprogramming, drug transport, and extracellular matrix homeostasis. Their coordinated dysregulation underscores the complex interplay between folate metabolism and fundamental oncogenic pathways in ccRCC.\u003c/p\u003e \u003cp\u003eHowever, several limitations of this study should be acknowledged. First, the FMRG score was constructed based on retrospective data and requires validation in large-scale prospective cohorts prior to clinical application. Second, the specific molecular mechanisms through which NGF functions in tissue stem cells and endothelial cells, as well as its regulatory relationship with folate metabolism pathways, warrant further in-depth investigation. Finally, the drug sensitivity predictions derived from the FMRG score are primarily based on in vitro data and require confirmation in in vivo models and clinical trials.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eIn summary, this study establishes the folate metabolism related gene (FMRG) score as a novel, integrative biomarker for prognostic stratification and therapy response prediction in ccRCC. More importantly, by leveraging single-cell and spatial transcriptomic technologies, we have defined a spatially resolved role for its core component, NGF. We demonstrate that NGF is a functionally critical driver specifically enriched within the architecturally central tumor core niche. Functional validation through in vitro loss-of-function assays confirmed that NGF knockdown suppresses key malignant phenotypes, including proliferation, clonogenicity, migration, and invasion in ccRCC cells. This work underscores the power of spatial biology in elucidating the precise ecological context of oncogenic drivers. Targeting NGF presents a promising strategy to disrupt this core tumor niche and its associated immunosuppressive microenvironment, potentially overcoming a key mechanism of treatment resistance in ccRCC.\u003c/p\u003e"},{"header":"ABBREVIATIONS","content":"\u003cp\u003eATCC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;American Type Culture Collection\u003c/p\u003e\n\u003cp\u003eAUC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Area under the curve\u003c/p\u003e\n\u003cp\u003eBP\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Biological process\u003c/p\u003e\n\u003cp\u003eCC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Cellular component\u003c/p\u003e\n\u003cp\u003eCCK-8\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Cell Counting Kit-8\u003c/p\u003e\n\u003cp\u003eccRCC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Clear cell renal cell carcinoma\u003c/p\u003e\n\u003cp\u003ecDNA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Complementary DNA\u003c/p\u003e\n\u003cp\u003eDEGs\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Differentially expressed genes\u003c/p\u003e\n\u003cp\u003eFBS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Fetal bovine serum\u003c/p\u003e\n\u003cp\u003eFMRG\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Folate metabolism related gene\u003c/p\u003e\n\u003cp\u003eFMRGs\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Folate metabolism related genes\u003c/p\u003e\n\u003cp\u003eGEO\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Gene Expression Omnibus\u003c/p\u003e\n\u003cp\u003eGO\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Gene Ontology\u003c/p\u003e\n\u003cp\u003eHIFs\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Hypoxia-inducible factors\u003c/p\u003e\n\u003cp\u003eHR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Hazard ratio\u003c/p\u003e\n\u003cp\u003eIC50\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Half-maximal inhibitory concentration\u003c/p\u003e\n\u003cp\u003eKEGG\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Kyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n\u003cp\u003eLASSO\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Least absolute shrinkage and selection operator\u003c/p\u003e\n\u003cp\u003eLipo2K\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Lipofectamine 2000\u003c/p\u003e\n\u003cp\u003eMCC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Maximal Clique Centrality\u003c/p\u003e\n\u003cp\u003eMEM\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Minimum Essential Medium\u003c/p\u003e\n\u003cp\u003eMF\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Molecular function\u003c/p\u003e\n\u003cp\u003eOS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Overall survival\u003c/p\u003e\n\u003cp\u003ePBS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Phosphate-buffered saline\u003c/p\u003e\n\u003cp\u003ePPI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Protein-protein interaction\u003c/p\u003e\n\u003cp\u003eRCC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Renal cell carcinoma\u003c/p\u003e\n\u003cp\u003eRNA-seq\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;RNA sequencing\u003c/p\u003e\n\u003cp\u003eROC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Receiver operating characteristic\u003c/p\u003e\n\u003cp\u003escRNA-seq\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Single-cell RNA sequencing\u003c/p\u003e\n\u003cp\u003eSD\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Standard deviation\u003c/p\u003e\n\u003cp\u003esiRNAs\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Small interfering RNAs\u003c/p\u003e\n\u003cp\u003essGSEA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Single-sample gene set enrichment analysis\u003c/p\u003e\n\u003cp\u003eST\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Spatial transcriptomic\u003c/p\u003e\n\u003cp\u003eSTR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Short tandem repeat\u003c/p\u003e\n\u003cp\u003eTCGA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;The Cancer Genome Atlas\u003c/p\u003e\n\u003cp\u003eTIME\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Tumor immune microenvironment\u003c/p\u003e\n\u003cp\u003eTME\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Tumor microenvironment\u003c/p\u003e\n\u003cp\u003eTregs\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Regulatory T cells\u003c/p\u003e\n\u003cp\u003eVHL\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Von Hippel-Lindau\u003c/p\u003e\n\u003cp\u003eVIM\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Vimentin\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed during this study are publicly available. The transcriptomic and clinical data for ccRCC were sourced from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/). The independent validation dataset E-MTAB-1980 was obtained from the ArrayExpress database (https://www.ebi.ac.uk/biostudies/arrayexpress). The single-cell RNA-seq (GSE304466) and spatial transcriptomic (GSM5924030) data were acquired from the Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/). The curated list of folate metabolism related genes was compiled from the GeneCards database (https://www.genecards.org/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from the National Natural Science Foundation of China (No. 82203365) and the Natural Science Foundation of Jiangxi Province (No. S2023ZRMSL0904).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJinkang Lin, Sheng Li, and Yunxin Zhou conceived and designed the study, performed the majority of the experiments and data analysis, and wrote the original draft. Zecao Han, Weilin Chen, Huanhui Zheng, Shun Liu, Jiabiao Dai, Wei Cheng, and Chen Fu performed parts of the molecular biology experiments. Haibo Xi, Wen Deng, and Jin Zeng contributed to the study design and critically reviewed and revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the contributors of The Cancer Genome Atlas (TCGA), the ArrayExpress database, the Gene Expression Omnibus (GEO), and the GeneCards database for making their valuable data and resources publicly available. We also gratefully acknowledge the support from the Jiangxi Provincial Key Laboratory of Urinary System Diseases.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray F, Laversanne M, Sung H, Ferlay J, Siegel RL, Soerjomataram I, Jemal A. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 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Int J Mol Sci 2024, 25(6).\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":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Clear cell renal cell carcinoma, Folate metabolism, Multi-omics, Prognostic signature, Tumor microenvironment, Immunotherapy","lastPublishedDoi":"10.21203/rs.3.rs-9129596/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9129596/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eClear cell renal cell carcinoma (ccRCC) is an aggressive tumor with high metastatic potential and therapeutic resistance, yet the role of folate metabolism in its pathogenesis and immune evasion remains unclear. This study aims to develop and validate a folate metabolism related gene (FMRG) scoring system to stratify patients by prognostic risk and immune phenotypes, and to explore the functional role of key FMRGs in ccRCC progression.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUsing transcriptomic and clinical data from The Cancer Genome Atlas (TCGA), we developed a folate metabolism related gene (FMRG) scoring system via integrative machine learning and validated it in an external cohort. We analyzed associations of the FMRG score with clinicopathological features, biological pathways, immune infiltration, therapeutic responsiveness, and drug sensitivity. Single-cell RNA sequencing and spatial transcriptomics mapped candidate gene expression, and in vitro experiments validated the functional role of NGF.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA ten gene prognostic model based on the FMRG score stratified ccRCC patients into groups with distinct clinical outcomes, immune profiles, and therapeutic responses. NGF was upregulated in ccRCC, with heterogeneous spatial expression. Functional assays showed that NGF enhances proliferation, migration, and invasion, and contributes to an immunosuppressive tumor microenvironment.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis folate metabolism-based scoring framework facilitates prognostic stratification, tumor microenvironment characterization, and prediction of immunotherapy response in ccRCC. NGF is identified as a functional mediator of tumor progression and immune modulation, offering potential therapeutic targets and insights into metabolic-immune crosstalk.\u003c/p\u003e","manuscriptTitle":"Integrated Multi-Omics Analysis Reveals Folate Metabolism Related Genes as Prognostic Markers and Therapeutic Targets in Clear Cell Renal Cell Carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-27 13:12:00","doi":"10.21203/rs.3.rs-9129596/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-03-26T06:11:48+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-17T06:48:47+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-17T06:24:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-17T06:24:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Cancer","date":"2026-03-15T14:54:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-cancer","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bcan","sideBox":"Learn more about [BMC Cancer](http://bmccancer.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bcan/default.aspx","title":"BMC Cancer","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6ca4202b-96d2-4d0e-b0ba-5573f3460238","owner":[],"postedDate":"March 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-27T13:12:00+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-27 13:12:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9129596","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9129596","identity":"rs-9129596","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}