A DNA Methylation–LDHA–Deoxycarnitine Regulatory Axis Promotes Tumorigenesis in Clear Cell Renal Cell Carcinoma

A DNA Methylation–LDHA–Deoxycarnitine Regulatory Axis Promotes Tumorigenesis 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 A DNA Methylation–LDHA–Deoxycarnitine Regulatory Axis Promotes Tumorigenesis in Clear Cell Renal Cell Carcinoma Chen Wang, Qifa Zhang, Yelong Wang, Qiang Li, Xin Chen, Tao Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6616219/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Clear cell renal cell carcinoma (ccRCC) is the predominant histological subtype of renal cancer, characterized by high recurrence and metastasis rates. Despite advances in targeted therapies, treatment resistance and poor prognosis persist, necessitating the identification of novel molecular mechanisms. Methods: We systematically investigated the role of the DNA methylation–LDHA–deoxycarnitine axis in ccRCC progression by integrating two-sample Mendelian randomization (MR), summary-data-based Mendelian randomization (SMR), and mediation analyses. Cis-eQTL, mQTL, and metabolomic GWAS datasets were utilized to establish causal relationships between DNA methylation, LDHA expression, deoxycarnitine levels, and ccRCC risk. Results: Our findings demonstrate that elevated LDHA expression, driven by hypomethylation at specific CpG sites (cg02232751, cg15700009, cg19631472), causally promotes ccRCC development. LDHA overexpression was associated with reduced deoxycarnitine levels, reflecting disrupted mitochondrial metabolism. Mediation analyses revealed that DNA methylation regulates ccRCC risk predominantly through modulation of LDHA expression and subsequent metabolic reprogramming. Furthermore, deoxycarnitine emerged as a significant metabolic mediator linking LDHA activity to tumor progression. Conclusions: This study identifies a novel epigenetic–metabolic pathway wherein DNA methylation regulates LDHA expression, reshapes deoxycarnitine metabolism, and drives ccRCC progression. These insights illuminate potential biomarkers for early detection and highlight LDHA and its metabolic network as promising therapeutic targets. Future validation in functional models and diverse populations will be critical to translate these findings into clinical applications. DNA Methylation LDHA Deoxycarnitine Clear Cell Renal Cell Carcinoma (ccRCC) Epigenetic–Metabolic Regulation Key Points 1.DNA methylation regulates LDHA expression and promotes ccRCC progression through metabolic reprogramming. 2.Integrative MR, SMR, and mediation analyses confirm a causal link between epigenetic alteration and ccRCC risk. 3.Specific CpG sites (cg02232751, cg15700009, cg19631472) mediate LDHA expression and influence tumor development. 4.Deoxycarnitine reduction acts as a key metabolic mediator in LDHA-driven ccRCC progression. 5.The DNA methylation–LDHA–deoxycarnitine axis represents a promising biomarker and therapeutic target in ccRCC. 1. Introduction Clear cell renal cell carcinoma (ccRCC) is the most common histological subtype of renal cancer[ 1 ], accounting for 70–85% of all cases, and is associated with high rates of recurrence and metastasis​[ 2 ]. In the United States alone, kidney and renal pelvis cancers are projected to cause 81,610 new cases and 14,390 deaths in 2024, underscoring their substantial impact on public health​[ 3 ]. Although localized ccRCC can be effectively treated via surgical resection, up to 30% of patients present with distant metastases at diagnosis or experience relapse within two years after nephrectomy, resulting in poor overall survival rates below 10% in advanced stages​[ 4 , 5 ]. Standard therapies, including tyrosine kinase inhibitors (TKIs) such as sunitinib and sorafenib, temporarily extend survival by targeting the VEGF/VEGFR axis but are frequently compromised by resistance and adverse effects​[ 6 ]. The complexity of the tumor microenvironment (TME), including the presence of vasculogenic mimicry (VM), further contributes to therapeutic failure, as VM provides an alternative blood supply pathway resistant to anti-angiogenic agents​[ 7 ]. Mechanistically, inactivating mutations in the von Hippel–Lindau (VHL) gene—found in approximately 90% of ccRCC cases—lead to stabilization of hypoxia-inducible factor-1α (HIF-1α), which activates oncogenic pathways such as PDGFRβ signaling, histone lactylation, and metabolic reprogramming​[ 8 ]. Recent studies have further identified novel molecular drivers, including ISG15 and KMT5C, which promote tumor growth through the JAK2/STAT3 and glycolysis pathways, respectively, reinforcing the need to explore upstream epigenetic regulators and downstream metabolic effectors in ccRCC​[ 4 , 9 ]. Collectively, these findings underscore the pressing need to delineate the molecular mechanisms underlying ccRCC progression to identify actionable biomarkers and therapeutic vulnerabilities. DNA methylation plays a central role in cancer development by regulating gene expression without altering the underlying DNA sequence, often leading to the silencing of tumor suppressor genes or the activation of oncogenes through aberrant CpG island methylation. In hepatocellular carcinoma, epigenetic inactivation of key tumor suppressors such as CDH1, RASSF1A, GSTP1, and PTEN is frequently observed, contributing to uncontrolled cell proliferation, apoptosis evasion, and hepatocyte transformation[ 10 ]​. S Similarly, in colorectal cancer, genes such as MUTYH, KLF4/6, and WNT1 exhibit abnormal promoter methylation patterns that disrupt DNA repair pathways, enhance the accumulation of oxidative DNA damage, and drive tumor initiation​[ 11 ]. These methylation-driven regulatory disruptions are not limited to digestive tract tumors; in prostate cancer, hypermethylation of regulatory genes including GSTP1, MGMT, and TMPRSS2 correlates with tumor aggressiveness, cell cycle deregulation, and impaired apoptotic signaling​[ 12 ]. The complexity and context dependency of DNA methylation patterns underscore the necessity for integrative multi-omics approaches to identify functionally relevant epigenetic drivers across cancer types. Metabolic reprogramming is a defining feature of ccRCC, characterized by enhanced glycolysis, suppressed mitochondrial respiration, and profound alterations in fatty acid metabolism. Untargeted metabolomic profiling of 60 tissue samples revealed significant differences in 344 out of 516 metabolites between ccRCC and normal kidney tissues, including elevated glucose, glucose phosphates, and glycolytic intermediates such as glucose-6-phosphate and fructose-6-phosphate, alongside increased lactate levels, indicating activation of the Warburg effect​[ 13 ]. At the enzyme level, lactate dehydrogenase A (LDHA)—a key regulator of anaerobic glycolysis—was found to be significantly overexpressed in ccRCC tumors and positively associated with tumor size, stage, and poorer overall survival​[ 14 ]. In contrast, LDHB exhibited an inverse pattern, suggesting a loss of oxidative metabolic capacity in advanced disease. Importantly, BBOX1, a succinyltransferase and a key enzyme in carnitine biosynthesis, was shown to be epigenetically silenced in ccRCC, and its re-expression suppressed mTORC1 activity and glycolysis through TBK1 inhibition, highlighting a regulatory axis linking carnitine metabolism and glucose utilization​[ 15 ]. Moreover, deoxycarnitine—an intermediate in carnitine biosynthesis—has been identified as a genetically regulated metabolite through Mendelian analysis, and population studies have confirmed its inverse correlation with cardiovascular risk, supporting its systemic metabolic relevance​[ 16 ]. Together, these findings reveal that the LDHA–deoxycarnitine axis is tightly integrated into the metabolic plasticity of ccRCC, offering potential metabolic vulnerabilities for therapeutic targeting. Mendelian randomization (MR) represents an innovative analytical methodology that employs single nucleotide polymorphisms (SNPs) as naturally occurring instrumental variables to investigate potential causal relationships between exposures and outcomes. This approach has been widely adopted across various disease domains to identify risk factors and elucidate underlying disease mechanisms[ 17 – 19 ]. Integrative multi-omics MR pipelines collectively offer a viable strategy for elucidating epigenetic–metabolic pathways, such as methylation-regulated LDHA or deoxycarnitine, in ccRCC. Furthermore, these frameworks provide scalable and replicable platforms for disentangling causal relationships among noncoding variants, methylation profiles, and metabolic reprogramming. 2. Method and Materials We applied two-sample Mendelian randomization and mediation analyses to explore the causal relationship between succinylation and ccRCC, and to elucidate the mechanistic pathways through which succinylation may drive tumor progression (Supplementary Fig. 1). 2.1 Selection of Succinylation Genes A total of 19 succinylation genes were collected for analysis in this study[ 20 – 24 ], and the detailed gene set is provided in Supplementary Table 1. 2.2 eQTL dataset Expression quantitative trait locus (eQTL) data for all genes analyzed in this study were obtained from the eQTLGen database ( https://www.eqtlgen.org/cis-eqtls.html )[ 25 ]. All data utilized were cis-eQTLs derived from blood samples. SNPs were selected according to the following criteria: p-value < 5 × 10⁻⁸, clumping window of 10,000 kb, and r² < 0.1. This filtering process yielded cis-eQTL data for 15,695 genes. Intersecting these genes with a predefined set of 19 succinylation-related genes resulted in a final list of 10 succinylation genes possessing cis-eQTL data. 2.3 Outcome dataset In this study, genome-wide association study (GWAS) data for ccRCC were obtained from the FinnGen database, a collaborative initiative between public and private sectors aimed at investigating genotype–phenotype associations within the Finnish founder population (website: https://www.finngen.fi/en )[ 26 ]. The specific dataset ID was FinnGen_R10_C3_KIDNEY_CLEAR_CELL_CARCINOMA_EXALLC.gz, comprising 944 ccRCC cases and 314,193 control samples. Among the participants, 38.3% were female and 61.7% were male, with a median age of 67.48 years. The diagnosis of ccRCC was based on the International Classification of Diseases (ICD) criteria. 2.4 Two-sample Mendelian randomization analysis The TwoSampleMR package (version 0.5.11) was employed to perform two-sample MR analyses. Cis-eQTLs associated with succinylation-related genes were used as exposures, and ccRCC was designated as the outcome. To mitigate potential bias arising from weak instrumental variables, SNPs with F-statistics less than 10 were excluded prior to analysis. The inverse variance weighted (IVW) method was utilized as the primary MR approach, and exposures for which IVW estimates could not be generated were omitted from further analysis. To assess potential horizontal pleiotropy, the MR-Egger intercept test was conducted. Cochran’s Q test was applied to evaluate heterogeneity. In addition, leave-one-out sensitivity analysis was performed to examine the robustness of the MR results[ 27 , 28 ]. 2.5 SMR (Summary-data-based Mendelian Randomization) analysis SMR leverages summary statistics from GWAS and eQTL studies to investigate pleiotropic associations between protein expression levels and complex traits of interest[ 29 ]. To assess potential horizontal pleiotropy within colocalized signals, the Heterogeneity in Dependent Instruments (HEDI) test is employed. The null hypothesis of the HEDI test asserts the absence of horizontal pleiotropy, defined as the scenario in which a single genetic variant influences multiple traits through pathways independent of the primary trait under investigation. Collectively, SMR and HEDI facilitate the differentiation of whether the effect of a SNP on a phenotype is mediated through protein expression or via alternative biological mechanisms. Detailed descriptions of the SMR algorithm are available on the official SMR website, which may assist in manuscript preparation. In this study, SMR analyses were performed using the smr-1.3.1-macos-arm64 software tool, downloaded from the official SMR website. All analyses were conducted using the default parameters provided by the SMR platform. 2.6 Differential expression analysis Microarray expression data for ccRCC were downloaded from the Gene Expression Omnibus (GEO) database, specifically the dataset GSE15641[ 30 ]. Differential expression analysis was performed by comparing tumor tissues with normal (control) tissues. This dataset includes a total of 55 samples, comprising 32 tumor tissues and 23 normal tissues, based on our specific grouping criteria. Differential expression analysis was conducted using the limma package in R. Genes were considered differentially expressed if they exhibited either upregulation or downregulation in tumor tissues relative to normal tissues, with a p-value less than 0.05. 2.7 Mediation analysis The mediation analysis was divided into two parts: (1) DNA methylation–succinylation–ccRCC relationship and (2) succinylation–metabolite–ccRCC relationship. 2.7.1 Mediation Analysis of the DNA Methylation–Succinylation–ccRCC Relationship Methylation quantitative trait loci (mQTL) data were downloaded from the GoDMC database ( http://www.GoDMC.org.uk/ ) [ 31 ]. Information on DNA methylation sites for succinylation-related genes was obtained from https://ngdc.cncb.ac.cn/ewas/datahub/exploration . Methylation sites associated with the LDHA gene were extracted, and SNPs were selected based on the following criteria: p-value < 5 × 10⁻⁸, clumping window of 10,000 kb, and r² < 0.1. After filtering, four mQTL sites were retained as exposures. Initially, the effect sizes of mQTLs on ccRCC were estimated and denoted as β_all. Subsequently, we calculated the effect sizes of mQTLs on LDHA expression eQTL, referred to as β1, and the effect sizes of LDHA eQTLs on ccRCC, referred to as β2. The mediation effect (β12) was then computed as β1 × β2. The mediation proportion (β12_p) was calculated using the formula: β12_p = (β12 / β_all) × 100%. Methylation sites were considered to meet the mediation criteria if they exhibited a mediation proportion greater than 40% and the direction of β12 was consistent with that of β_all. 2.7.2 Mediation Analysis of the Succinylation–Metabolite–ccRCC Relationship A two-step MR mediation analysis was performed to investigate whether LDHA promotes the development of ccRCC by regulating specific metabolites as intermediaries. GWAS summary statistics for 1,400 circulating metabolites were retrieved from the publicly available GWAS Catalog ( https://www.ebi.ac.uk/gwas/ ), corresponding to accession numbers GCST90199621–GCST90201020[ 32 ]. SNPs were selected using the following criteria: p-value < 5 × 10⁻⁸, clumping window of 10,000 kb, and r² < 0.1. Following this selection, 106 metabolite datasets were retained for further analysis. Initially, we estimated the effect size of LDHA eQTLs on ccRCC (β_all). Subsequently, we calculated the effect size of LDHA eQTLs on each individual metabolite (β1) and the effect size of each metabolite on ccRCC (β2). Significant mediators were identified according to the following criteria: a p-value less than 0.05, a mediation proportion greater than 10%, and concordance in the directionality of the mediation effect and the total effect. 2.8 Statistical Analysis All statistical analyses in this study were conducted using the open-source R software (version 4.3.2; https://www.r-project.org/ ). A p-value of less than 0.05 was considered the threshold for statistical significance. Our analyses were conducted in accordance with the three core assumptions of MR[ 33 ] and complied with the guidelines outlined in the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist[ 34 ]. 3. Results 3.1 The causal relationships between succinylation and ccRCC Through IVW analysis, only LDHA was found to have a significant association with ccRCC (odds ratio [OR] = 2.04, p = 0.0008; P heterogeneity = 0.495; P pleiotropy = 0.73), as shown in Supplementary Fig. 2. The corresponding forest plot, scatter plot, and leave-one-out sensitivity analysis are presented in Supplementary Fig. 3. 3.2 SMR Analysis The SMR analysis similarly indicated that LDHA was the only gene significantly associated with ccRCC (β-SMR = 0.761, p-SMR = 4.744 × 10⁻³), suggesting that LDHA may contribute to ccRCC pathogenesis, as shown in Supplementary Table 2. To exclude the potential influence of pleiotropy in the genetic instruments, we conducted the HEIDI test. The HEIDI test yielded a p-value greater than 0.05, suggesting that the observed association is unlikely to be driven by horizontal pleiotropy, thereby reinforcing the robustness of our conclusions. 3.3 Differential expression analysis Analysis of gene expression data from the GSE15641 dataset revealed that LDHA is significantly overexpressed in tumor tissues compared to normal controls, with results reaching statistical significance (Supplementary Fig. 4). 3.4 Mediation Analysis of the DNA Methylation–Succinylation–ccRCC Relationship During the calculation of mQTL effect sizes on ccRCC, three loci were identified as having a significant impact on ccRCC risk: cg02232751 (OR = 0.677, 95% confidence interval [CI]: 0.565–0.811, p = 2.29 × 10⁻⁵), cg15700009 (OR = 0.743, 95% CI: 0.635–0.868, p = 1.86 × 10⁻⁴), and cg19631472 (OR = 0.702, 95% CI: 0.593–0.831, p = 4.01 × 10⁻⁵). The effects of these loci on LDHA expression were assessed using the IVW method, yielding the following results: cg02232751 (β = − 0.265, p = 0.014), cg15700009 (β = − 0.329, p = 3.49 × 10⁻¹⁸), and cg19631472 (β = − 0.216, p = 0.022). By evaluating the causal effect of LDHA as a mediator, we determined that these three methylation sites provided in Supplementary Table 3 indirectly facilitated ccRCC progression through modulation of LDHA expression, with mediation effect proportions of 48.51%, 78.92%, and 43.55%, respectively. 3.5 Mediation Analysis of the Succinylation–Metabolite–ccRCC Relationship In assessing the effect sizes of LDHA gene eQTLs on 1,400 circulating metabolites, eight metabolites demonstrated significant associations. Subsequently, when treating the 1,400 metabolites as exposures and ccRCC as the outcome, 106 metabolites were identified as significantly associated with ccRCC. Ultimately, deoxycarnitine was the only metabolite found to mediate the promotive effect of LDHA on ccRCC, with a mediation proportion of 12.46% (p = 0.025), which can be found in Supplementary Table 4. 4. Discussion In this study, we systematically elucidated the role of the DNA methylation–LDHA–deoxycarnitine axis in the progression of ccRCC by integrating two-sample MR, SMR, and mediation analyses. Our results provide compelling evidence that elevated LDHA expression is causally linked to an increased risk of ccRCC. Additionally, we identified specific CpG methylation sites negatively regulating LDHA expression, implicating epigenetic modifications as key drivers of metabolic alterations favoring tumorigenesis. Notably, elevated LDHA expression correlated with markedly reduced deoxycarnitine levels, suggesting impaired mitochondrial metabolism contributes to ccRCC pathogenesis. Mediation analyses further demonstrated that DNA methylation influences ccRCC risk primarily through the modulation of LDHA expression and subsequent metabolic dysregulation. Collectively, these findings offer novel mechanistic insights into the interplay between epigenetic regulation and metabolic reprogramming in ccRCC, thereby identifying promising biomarkers and therapeutic targets for early diagnosis and clinical intervention. LDHA plays a pivotal role in tumor metabolic reprogramming by catalyzing the conversion of pyruvate to lactate, a hallmark feature of the Warburg effect[ 35 ]. Elevated LDHA expression has consistently been observed across multiple malignancies, including ccRCC[ 36 ], and is associated with aggressive tumor behavior and poor clinical outcomes[ 37 , 38 ]. Specifically, in ccRCC, high LDHA expression positively correlates with tumor size, histological grade, TNM stage, and metastatic potential[ 39 ]. Mechanistic studies have demonstrated that LDHA enhances glycolytic metabolism and promotes epithelial–mesenchymal transition (EMT), thereby facilitating cell migration, invasion, and metastatic progression. Conversely, pharmacological inhibition of LDHA activity with agents such as oxamate significantly suppresses these malignant processes[ 40 ]. Additional evidence from other cancer models reinforces LDHA's oncogenic role, demonstrating that its overexpression sustains aerobic glycolysis, preserves mitochondrial function under metabolic stress, and contributes to therapeutic resistance[ 41 – 43 ]. Small-molecule inhibitors, such as PSTMB, effectively block LDHA activity, reduce lactate production, impair tumor growth, and induce apoptosis via oxidative stress pathways[ 42 ]. Thus, LDHA emerges as a central regulator of tumor metabolism and aggressiveness in ccRCC, underscoring its potential as a therapeutic target. Deoxycarnitine, a key intermediate in the carnitine biosynthesis pathway, is crucial for mitochondrial fatty acid oxidation and cellular energy homeostasis[ 44 ]. In ccRCC, BBOX1—the enzyme responsible for converting deoxycarnitine to carnitine—functions as a tumor suppressor, with markedly reduced expression observed during malignant transformation. Restoration of BBOX1 expression suppresses mTORC1 signaling and glycolytic pathways, consequently inhibiting tumor viability and growth in xenograft models[ 15 ]. Similar findings in hepatocellular carcinoma (HCC) demonstrate that CRIP1-mediated ubiquitination and subsequent degradation of BBOX1 reduce carnitine levels, promoting cancer stemness through activation of the Wnt/β-catenin pathway[ 45 ]. Beyond cancer, carnitine and its derivatives are recognized regulators of mitochondrial function, preventing metabolic inflexibility and supporting cellular detoxification[ 46 ]. Although large-scale metabolomic analyses have highlighted deoxycarnitine's systemic metabolic importance, MR analyses have yet to establish a causal association between deoxycarnitine and cardiovascular outcomes[ 16 ]. DNA methylation, a critical epigenetic modification, regulates gene expression by silencing promoter activity without altering the DNA sequence itself. Aberrant methylation patterns frequently contribute to cancer progression[ 47 ]. Our MR and mediation analyses specifically identified CpG methylation sites upstream of the LDHA gene significantly influencing its expression, thereby modulating tumor risk. Typically, CpG island hypermethylation in promoter regions leads to transcriptional repression of tumor suppressors, while global hypomethylation can activate oncogenic pathways[ 47 , 48 ]​. Beyond individual gene silencing, methylation modifications also reshape chromatin architecture, affecting broader gene regulatory networks​[ 49 ]. For instance, aberrant promoter hypermethylation of differentiation-associated genes, such as the ion channel gene KCNA5 in Ewing sarcoma, promotes tumor proliferation and stemness​[ 50 ]. Analogously, methylation at LDHA regulatory regions demonstrated strong mediation effects (> 40%) on ccRCC risk, highlighting DNA methylation’s critical role in epigenetically activating LDHA-driven metabolic reprogramming. These insights substantiate promoter CpG methylation as a crucial mechanism regulating key metabolic enzymes and support targeting epigenetic dysregulation as a therapeutic strategy in ccRCC. The identification of the DNA methylation–LDHA–deoxycarnitine axis in ccRCC highlights novel opportunities for early detection and targeted therapy. Blood-based methylation profiling presents a minimally invasive approach for disease monitoring. For instance, hypermethylation at ACTB CpG sites has been linked to increased coronary heart disease risk, achieving significant discrimination capabilities (AUC values up to 0.77)[ 51 ]. Similarly, exploratory studies in pancreatic cancer identified CpG04969764 within LAMA5 as a potential methylation biomarker[ 52 ], underscoring the clinical feasibility of peripheral blood methylation markers. Furthermore, LDHA’s involvement in regulating mitophagy and activating the JAK–STAT signaling pathway implies that targeting the LDHA–lactate axis might not only correct metabolic dysfunction but also enhance antitumor immunity[ 53 ]. The present study has several strengths enhancing the robustness of its conclusions, although limitations should be acknowledged. By integrating MR, SMR, and mediation analyses, we minimized potential confounding and strengthened causal inference regarding the DNA methylation–LDHA–deoxycarnitine axis. Utilizing large-scale genome-wide datasets and multi-level analytical methodologies further reinforced the validity and generalizability of our findings. However, limitations include the predominantly European ancestry of the study population, potentially restricting generalizability to other ethnic groups. Furthermore, while genetic approaches provide strong causal evidence, in vitro and in vivo functional validation experiments remain essential to fully elucidate the biological mechanisms involved. Lastly, environmental and lifestyle factors that independently influence DNA methylation were not assessed. Future studies integrating functional assays, diverse ethnic cohorts, and comprehensive environmental covariates are warranted to confirm and expand upon our findings. 5. Conclusion In conclusion, this study identifies a novel epigenetic–metabolic pathway wherein DNA methylation regulates LDHA expression, thereby altering deoxycarnitine metabolism and driving the progression of ccRCC. Through the integration of MR, SMR, and mediation analyses, our findings robustly support the causal role of the DNA methylation–LDHA–deoxycarnitine axis in ccRCC pathogenesis. These insights significantly enhance our understanding of the complex interplay between epigenetic modifications and metabolic reprogramming events that underlie renal carcinogenesis. Additionally, our study highlights promising biomarkers and potential therapeutic targets with significant translational relevance. Future functional validation through cellular and animal models will be crucial to confirm the biological mechanisms suggested by genetic analyses. Moreover, expanding research to diverse populations and incorporating environmental and lifestyle factors will be essential for fully elucidating the intricate relationship between epigenetics, metabolism, and tumor progression. Ultimately, harnessing these insights could advance the development of innovative early detection strategies and personalized therapeutic interventions targeting epigenetic and metabolic vulnerabilities in ccRCC. Abbreviations ccRCC Clear cell renal cell carcinoma TKIs Tyrosine kinase inhibitors TME. Tumor microenvironment VM Vasculogenic mimicry VHL. Von Hippel–Lindau HIF-1α. Hypoxia-inducible factor-1α LDHA. Lactate dehydrogenase A MR Mendelian randomization SNP Single nucleotide polymorphism eQTL. Expression quantitative trait locus GWAS Genome-wide association study ICD International Classification of Diseases IVW Inverse variance weighted HEDI. Heterogeneity in Dependent Instruments SMR Summary-data-based Mendelian Randomization GEO Gene Expression Omnibus mQTL. Methylation quantitative trait loci OR. Odds ratio STROBE Strengthening the Reporting of Observational Studies in Epidemiology EMT Epithelial–mesenchymal transition HCC hepatocellular carcinoma Declarations Consent to Participate Not applicable. Consent to Publish Not applicable. Clinical trial number Not applicable Ethics Approval This study was conducted solely using publicly available summary statistics without accessing individual-level data; therefore, ethical approval was not required. Conflict of Interest The authors declare no competing interests. Funding This research was generously Funded by Special Disease Construction Project of Jiading District Health System (NO.ZK2024A08). Author Contribution The study was designed by Chen Wang. Qifa Zhang, Qiang Li, and Yelong Wang downloaded and analyzed the data. 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ANXA2P2/miR-9/LDHA axis regulates Warburg effect and affects glioblastoma proliferation and apoptosis. Cell Signal. 2020;74:109718. Paul HS, Sekas G, Adibi SA. Carnitine biosynthesis in hepatic peroxisomes. Demonstration of gamma-butyrobetaine hydroxylase activity. Eur J Biochem. 1992;203(3):599–605. Wang J, et al. CRIP1 suppresses BBOX1-mediated carnitine metabolism to promote stemness in hepatocellular carcinoma. EMBO J. 2022;41(15):e110218. Virmani MA, Cirulli M. The Role of l-Carnitine in Mitochondria, Prevention of Metabolic Inflexibility and Disease Initiation. Int J Mol Sci, 2022. 23(5). Puneet, et al. Epigenetic Mechanisms and Events in Gastric Cancer-Emerging Novel Biomarkers. Pathol Oncol Res. 2018;24(4):757–70. Hirst M, Marra MA. Epigenetics and human disease. Int J Biochem Cell Biol. 2009;41(1):136–46. Oshikawa D et al. CpG Methylation Altered the Stability and Structure of the i-Motifs Located in the CpG Islands. Int J Mol Sci, 2022. 23(12). Ryland KE, et al. Promoter Methylation Analysis Reveals That KCNA5 Ion Channel Silencing Supports Ewing Sarcoma Cell Proliferation. Mol Cancer Res. 2016;14(1):26–34. Jin J, et al. The association between ACTB methylation in peripheral blood and coronary heart disease in a case-control study. Front Cardiovasc Med. 2022;9:972566. Jansen RJ, et al. A Pilot Study of Blood-Based Methylation Markers Associated With Pancreatic Cancer. Front Genet. 2022;13:849839. Zeng S et al. LDHA-lactate axis modulates mitophagy inhibiting CSFV replication. J Virol, 2025: p. e0026825. Additional Declarations No competing interests reported. Supplementary Files SupplementaryTable1.xls SupplementaryTable2.xlsx SupplementaryTable3.xls SupplementaryTable4.xls SupplementaryFig.1.pdf SupplementaryFig.2.pdf SupplementaryFig.3.pdf SupplementaryFig.4.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6616219","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":470328974,"identity":"aa48ca1e-fd63-47fc-9e27-3847989a9b2f","order_by":0,"name":"Chen Wang","email":"","orcid":"","institution":"The Second Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chen","middleName":"","lastName":"Wang","suffix":""},{"id":470328976,"identity":"b6f77e49-65a2-4333-8854-2f3cf5e6dbdb","order_by":1,"name":"Qifa Zhang","email":"","orcid":"","institution":"Ruijin Hospital, Shanghai Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Qifa","middleName":"","lastName":"Zhang","suffix":""},{"id":470328978,"identity":"c7170f1a-a815-4c71-9022-06ca4d609926","order_by":2,"name":"Yelong Wang","email":"","orcid":"","institution":"Ruijin Hospital, Shanghai Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Yelong","middleName":"","lastName":"Wang","suffix":""},{"id":470328979,"identity":"894e3305-5574-4a22-a79f-fab1618f8267","order_by":3,"name":"Qiang Li","email":"","orcid":"","institution":"Ruijin Hospital, Shanghai Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Qiang","middleName":"","lastName":"Li","suffix":""},{"id":470328981,"identity":"49c65c61-6f09-429a-8131-701e9d01a37c","order_by":4,"name":"Xin Chen","email":"","orcid":"","institution":"The Second Affiliated Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Chen","suffix":""},{"id":470328983,"identity":"aebc7fe5-fedb-44bf-8b75-f4ce730a8251","order_by":5,"name":"Tao Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYDACCWQGD4MNDz9/AzFaEuBa0mQkZxwgTcthG4OGBPw65Gc3P3v49YedHP/s7sQHb9vO8xgwHGD88DEHtxbGOcfMjWUSko0l7pzdbDi37TaPOXMDs+TMbbi1MEskmElLJDAnNtzI3SbNC9Ri2XCAjZkXjxY2ifRvQC31ifNv5G7/zdt2jsfgQAJ+LTwSOWaSHxIOJ24A2sLM23aAsBYJiZwyaYa048aGN3I3S845l8wjOeNgM16/yM9I3yb5w6ZaTu5G7sYPb8rs7Pn5mw9++IhHCzgIeFD5jA341YOU/CCoZBSMglEwCkY0AACQ/VEJOl7M3QAAAABJRU5ErkJggg==","orcid":"","institution":"The Second Affiliated Hospital of Anhui Medical University","correspondingAuthor":true,"prefix":"","firstName":"Tao","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-05-08 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08:29:17","extension":"pdf","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":20412,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFig.3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6616219/v1/f2cb39dea5b7e8a6d03f35a1.pdf"},{"id":84681769,"identity":"90c06398-c43c-45ab-9518-d035593ca95b","added_by":"auto","created_at":"2025-06-16 08:29:17","extension":"pdf","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":201092,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFig.4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6616219/v1/2397a2a1ca928f6deb8033b2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A DNA Methylation–LDHA–Deoxycarnitine Regulatory Axis Promotes Tumorigenesis in Clear Cell Renal Cell Carcinoma","fulltext":[{"header":"Key Points","content":"\u003cp\u003e1.DNA methylation regulates LDHA expression and promotes ccRCC progression through metabolic reprogramming.\u003c/p\u003e\u003cp\u003e2.Integrative MR, SMR, and mediation analyses confirm a causal link between epigenetic alteration and ccRCC risk.\u003c/p\u003e\u003cp\u003e3.Specific CpG sites (cg02232751, cg15700009, cg19631472) mediate LDHA expression and influence tumor development.\u003c/p\u003e\u003cp\u003e4.Deoxycarnitine reduction acts as a key metabolic mediator in LDHA-driven ccRCC progression.\u003c/p\u003e\u003cp\u003e5.The DNA methylation\u0026ndash;LDHA\u0026ndash;deoxycarnitine axis represents a promising biomarker and therapeutic target in ccRCC.\u003c/p\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eClear cell renal cell carcinoma (ccRCC) is the most common histological subtype of renal cancer[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], accounting for 70\u0026ndash;85% of all cases, and is associated with high rates of recurrence and metastasis​[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In the United States alone, kidney and renal pelvis cancers are projected to cause 81,610 new cases and 14,390 deaths in 2024, underscoring their substantial impact on public health​[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Although localized ccRCC can be effectively treated via surgical resection, up to 30% of patients present with distant metastases at diagnosis or experience relapse within two years after nephrectomy, resulting in poor overall survival rates below 10% in advanced stages​[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Standard therapies, including tyrosine kinase inhibitors (TKIs) such as sunitinib and sorafenib, temporarily extend survival by targeting the VEGF/VEGFR axis but are frequently compromised by resistance and adverse effects​[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The complexity of the tumor microenvironment (TME), including the presence of vasculogenic mimicry (VM), further contributes to therapeutic failure, as VM provides an alternative blood supply pathway resistant to anti-angiogenic agents​[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Mechanistically, inactivating mutations in the von Hippel\u0026ndash;Lindau (VHL) gene\u0026mdash;found in approximately 90% of ccRCC cases\u0026mdash;lead to stabilization of hypoxia-inducible factor-1α (HIF-1α), which activates oncogenic pathways such as PDGFRβ signaling, histone lactylation, and metabolic reprogramming​[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Recent studies have further identified novel molecular drivers, including ISG15 and KMT5C, which promote tumor growth through the JAK2/STAT3 and glycolysis pathways, respectively, reinforcing the need to explore upstream epigenetic regulators and downstream metabolic effectors in ccRCC​[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Collectively, these findings underscore the pressing need to delineate the molecular mechanisms underlying ccRCC progression to identify actionable biomarkers and therapeutic vulnerabilities.\u003c/p\u003e \u003cp\u003eDNA methylation plays a central role in cancer development by regulating gene expression without altering the underlying DNA sequence, often leading to the silencing of tumor suppressor genes or the activation of oncogenes through aberrant CpG island methylation. In hepatocellular carcinoma, epigenetic inactivation of key tumor suppressors such as CDH1, RASSF1A, GSTP1, and PTEN is frequently observed, contributing to uncontrolled cell proliferation, apoptosis evasion, and hepatocyte transformation[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]​. S Similarly, in colorectal cancer, genes such as MUTYH, KLF4/6, and WNT1 exhibit abnormal promoter methylation patterns that disrupt DNA repair pathways, enhance the accumulation of oxidative DNA damage, and drive tumor initiation​[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These methylation-driven regulatory disruptions are not limited to digestive tract tumors; in prostate cancer, hypermethylation of regulatory genes including GSTP1, MGMT, and TMPRSS2 correlates with tumor aggressiveness, cell cycle deregulation, and impaired apoptotic signaling​[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The complexity and context dependency of DNA methylation patterns underscore the necessity for integrative multi-omics approaches to identify functionally relevant epigenetic drivers across cancer types.\u003c/p\u003e \u003cp\u003eMetabolic reprogramming is a defining feature of ccRCC, characterized by enhanced glycolysis, suppressed mitochondrial respiration, and profound alterations in fatty acid metabolism. Untargeted metabolomic profiling of 60 tissue samples revealed significant differences in 344 out of 516 metabolites between ccRCC and normal kidney tissues, including elevated glucose, glucose phosphates, and glycolytic intermediates such as glucose-6-phosphate and fructose-6-phosphate, alongside increased lactate levels, indicating activation of the Warburg effect​[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. At the enzyme level, lactate dehydrogenase A (LDHA)\u0026mdash;a key regulator of anaerobic glycolysis\u0026mdash;was found to be significantly overexpressed in ccRCC tumors and positively associated with tumor size, stage, and poorer overall survival​[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In contrast, LDHB exhibited an inverse pattern, suggesting a loss of oxidative metabolic capacity in advanced disease. Importantly, BBOX1, a succinyltransferase and a key enzyme in carnitine biosynthesis, was shown to be epigenetically silenced in ccRCC, and its re-expression suppressed mTORC1 activity and glycolysis through TBK1 inhibition, highlighting a regulatory axis linking carnitine metabolism and glucose utilization​[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Moreover, deoxycarnitine\u0026mdash;an intermediate in carnitine biosynthesis\u0026mdash;has been identified as a genetically regulated metabolite through Mendelian analysis, and population studies have confirmed its inverse correlation with cardiovascular risk, supporting its systemic metabolic relevance​[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Together, these findings reveal that the LDHA\u0026ndash;deoxycarnitine axis is tightly integrated into the metabolic plasticity of ccRCC, offering potential metabolic vulnerabilities for therapeutic targeting.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) represents an innovative analytical methodology that employs single nucleotide polymorphisms (SNPs) as naturally occurring instrumental variables to investigate potential causal relationships between exposures and outcomes. This approach has been widely adopted across various disease domains to identify risk factors and elucidate underlying disease mechanisms[\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Integrative multi-omics MR pipelines collectively offer a viable strategy for elucidating epigenetic\u0026ndash;metabolic pathways, such as methylation-regulated LDHA or deoxycarnitine, in ccRCC. Furthermore, these frameworks provide scalable and replicable platforms for disentangling causal relationships among noncoding variants, methylation profiles, and metabolic reprogramming.\u003c/p\u003e"},{"header":"2. Method and Materials","content":"\u003cp\u003eWe applied two-sample Mendelian randomization and mediation analyses to explore the causal relationship between succinylation and ccRCC, and to elucidate the mechanistic pathways through which succinylation may drive tumor progression (Supplementary Fig.\u0026nbsp;1).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Selection of Succinylation Genes\u003c/h2\u003e \u003cp\u003eA total of 19 succinylation genes were collected for analysis in this study[\u003cspan additionalcitationids=\"CR21 CR22 CR23\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], and the detailed gene set is provided in Supplementary Table\u0026nbsp;1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 eQTL dataset\u003c/h2\u003e \u003cp\u003eExpression quantitative trait locus (eQTL) data for all genes analyzed in this study were obtained from the eQTLGen database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.eqtlgen.org/cis-eqtls.html\u003c/span\u003e\u003cspan address=\"https://www.eqtlgen.org/cis-eqtls.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. All data utilized were cis-eQTLs derived from blood samples. SNPs were selected according to the following criteria: p-value\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10⁻⁸, clumping window of 10,000 kb, and r\u0026sup2; \u0026lt; 0.1. This filtering process yielded cis-eQTL data for 15,695 genes. Intersecting these genes with a predefined set of 19 succinylation-related genes resulted in a final list of 10 succinylation genes possessing cis-eQTL data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Outcome dataset\u003c/h2\u003e \u003cp\u003eIn this study, genome-wide association study (GWAS) data for ccRCC were obtained from the FinnGen database, a collaborative initiative between public and private sectors aimed at investigating genotype\u0026ndash;phenotype associations within the Finnish founder population (website: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.finngen.fi/en\u003c/span\u003e\u003cspan address=\"https://www.finngen.fi/en\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The specific dataset ID was FinnGen_R10_C3_KIDNEY_CLEAR_CELL_CARCINOMA_EXALLC.gz, comprising 944 ccRCC cases and 314,193 control samples. Among the participants, 38.3% were female and 61.7% were male, with a median age of 67.48 years. The diagnosis of ccRCC was based on the International Classification of Diseases (ICD) criteria.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Two-sample Mendelian randomization analysis\u003c/h2\u003e \u003cp\u003eThe TwoSampleMR package (version 0.5.11) was employed to perform two-sample MR analyses. Cis-eQTLs associated with succinylation-related genes were used as exposures, and ccRCC was designated as the outcome. To mitigate potential bias arising from weak instrumental variables, SNPs with F-statistics less than 10 were excluded prior to analysis. The inverse variance weighted (IVW) method was utilized as the primary MR approach, and exposures for which IVW estimates could not be generated were omitted from further analysis. To assess potential horizontal pleiotropy, the MR-Egger intercept test was conducted. Cochran\u0026rsquo;s Q test was applied to evaluate heterogeneity. In addition, leave-one-out sensitivity analysis was performed to examine the robustness of the MR results[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 SMR (Summary-data-based Mendelian Randomization) analysis\u003c/h2\u003e \u003cp\u003eSMR leverages summary statistics from GWAS and eQTL studies to investigate pleiotropic associations between protein expression levels and complex traits of interest[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. To assess potential horizontal pleiotropy within colocalized signals, the Heterogeneity in Dependent Instruments (HEDI) test is employed. The null hypothesis of the HEDI test asserts the absence of horizontal pleiotropy, defined as the scenario in which a single genetic variant influences multiple traits through pathways independent of the primary trait under investigation. Collectively, SMR and HEDI facilitate the differentiation of whether the effect of a SNP on a phenotype is mediated through protein expression or via alternative biological mechanisms. Detailed descriptions of the SMR algorithm are available on the official SMR website, which may assist in manuscript preparation. In this study, SMR analyses were performed using the smr-1.3.1-macos-arm64 software tool, downloaded from the official SMR website. All analyses were conducted using the default parameters provided by the SMR platform.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Differential expression analysis\u003c/h2\u003e \u003cp\u003eMicroarray expression data for ccRCC were downloaded from the Gene Expression Omnibus (GEO) database, specifically the dataset GSE15641[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Differential expression analysis was performed by comparing tumor tissues with normal (control) tissues. This dataset includes a total of 55 samples, comprising 32 tumor tissues and 23 normal tissues, based on our specific grouping criteria. Differential expression analysis was conducted using the limma package in R. Genes were considered differentially expressed if they exhibited either upregulation or downregulation in tumor tissues relative to normal tissues, with a p-value less than 0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Mediation analysis\u003c/h2\u003e \u003cp\u003eThe mediation analysis was divided into two parts: (1) DNA methylation\u0026ndash;succinylation\u0026ndash;ccRCC relationship and (2) succinylation\u0026ndash;metabolite\u0026ndash;ccRCC relationship.\u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e\u003cb\u003e2.7.1 Mediation Analysis of the DNA Methylation\u0026ndash;Succinylation\u0026ndash;ccRCC Relationship\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eMethylation quantitative trait loci (mQTL) data were downloaded from the GoDMC database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.GoDMC.org.uk/\u003c/span\u003e\u003cspan address=\"http://www.GoDMC.org.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Information on DNA methylation sites for succinylation-related genes was obtained from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ngdc.cncb.ac.cn/ewas/datahub/exploration\u003c/span\u003e\u003cspan address=\"https://ngdc.cncb.ac.cn/ewas/datahub/exploration\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Methylation sites associated with the LDHA gene were extracted, and SNPs were selected based on the following criteria: p-value\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10⁻⁸, clumping window of 10,000 kb, and r\u0026sup2; \u0026lt; 0.1. After filtering, four mQTL sites were retained as exposures.\u003c/p\u003e \u003cp\u003eInitially, the effect sizes of mQTLs on ccRCC were estimated and denoted as β_all. Subsequently, we calculated the effect sizes of mQTLs on LDHA expression eQTL, referred to as β1, and the effect sizes of LDHA eQTLs on ccRCC, referred to as β2. The mediation effect (β12) was then computed as β1\u0026thinsp;\u0026times;\u0026thinsp;β2. The mediation proportion (β12_p) was calculated using the formula: β12_p = (β12 / β_all) \u0026times; 100%. Methylation sites were considered to meet the mediation criteria if they exhibited a mediation proportion greater than 40% and the direction of β12 was consistent with that of β_all.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e\u003cb\u003e2.7.2 Mediation Analysis of the Succinylation\u0026ndash;Metabolite\u0026ndash;ccRCC Relationship\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eA two-step MR mediation analysis was performed to investigate whether LDHA promotes the development of ccRCC by regulating specific metabolites as intermediaries. GWAS summary statistics for 1,400 circulating metabolites were retrieved from the publicly available GWAS Catalog (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ebi.ac.uk/gwas/\u003c/span\u003e\u003cspan address=\"https://www.ebi.ac.uk/gwas/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), corresponding to accession numbers GCST90199621\u0026ndash;GCST90201020[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. SNPs were selected using the following criteria: p-value\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10⁻⁸, clumping window of 10,000 kb, and r\u0026sup2; \u0026lt; 0.1. Following this selection, 106 metabolite datasets were retained for further analysis.\u003c/p\u003e \u003cp\u003eInitially, we estimated the effect size of LDHA eQTLs on ccRCC (β_all). Subsequently, we calculated the effect size of LDHA eQTLs on each individual metabolite (β1) and the effect size of each metabolite on ccRCC (β2). Significant mediators were identified according to the following criteria: a p-value less than 0.05, a mediation proportion greater than 10%, and concordance in the directionality of the mediation effect and the total effect.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Statistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses in this study were conducted using the open-source R software (version 4.3.2; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org/\u003c/span\u003e\u003cspan address=\"https://www.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). A p-value of less than 0.05 was considered the threshold for statistical significance. Our analyses were conducted in accordance with the three core assumptions of MR[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] and complied with the guidelines outlined in the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1 The causal relationships between succinylation and ccRCC\u003c/h2\u003e \u003cp\u003eThrough IVW analysis, only LDHA was found to have a significant association with ccRCC (odds ratio [OR]\u0026thinsp;=\u0026thinsp;2.04, p\u0026thinsp;=\u0026thinsp;0.0008; P\u003csub\u003eheterogeneity\u003c/sub\u003e = 0.495; P\u003csub\u003epleiotropy\u003c/sub\u003e = 0.73), as shown in Supplementary Fig.\u0026nbsp;2. The corresponding forest plot, scatter plot, and leave-one-out sensitivity analysis are presented in Supplementary Fig.\u0026nbsp;3.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2 SMR Analysis\u003c/h2\u003e \u003cp\u003eThe SMR analysis similarly indicated that LDHA was the only gene significantly associated with ccRCC (β-SMR\u0026thinsp;=\u0026thinsp;0.761, p-SMR\u0026thinsp;=\u0026thinsp;4.744 \u0026times; 10⁻\u0026sup3;), suggesting that LDHA may contribute to ccRCC pathogenesis, as shown in Supplementary Table\u0026nbsp;2. To exclude the potential influence of pleiotropy in the genetic instruments, we conducted the HEIDI test. The HEIDI test yielded a p-value greater than 0.05, suggesting that the observed association is unlikely to be driven by horizontal pleiotropy, thereby reinforcing the robustness of our conclusions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Differential expression analysis\u003c/h2\u003e \u003cp\u003eAnalysis of gene expression data from the GSE15641 dataset revealed that LDHA is significantly overexpressed in tumor tissues compared to normal controls, with results reaching statistical significance (Supplementary Fig.\u0026nbsp;4).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Mediation Analysis of the DNA Methylation\u0026ndash;Succinylation\u0026ndash;ccRCC Relationship\u003c/h2\u003e \u003cp\u003eDuring the calculation of mQTL effect sizes on ccRCC, three loci were identified as having a significant impact on ccRCC risk: cg02232751 (OR\u0026thinsp;=\u0026thinsp;0.677, 95% confidence interval [CI]: 0.565\u0026ndash;0.811, p\u0026thinsp;=\u0026thinsp;2.29 \u0026times; 10⁻⁵), cg15700009 (OR\u0026thinsp;=\u0026thinsp;0.743, 95% CI: 0.635\u0026ndash;0.868, p\u0026thinsp;=\u0026thinsp;1.86 \u0026times; 10⁻⁴), and cg19631472 (OR\u0026thinsp;=\u0026thinsp;0.702, 95% CI: 0.593\u0026ndash;0.831, p\u0026thinsp;=\u0026thinsp;4.01 \u0026times; 10⁻⁵).\u003c/p\u003e \u003cp\u003eThe effects of these loci on LDHA expression were assessed using the IVW method, yielding the following results: cg02232751 (β = \u0026minus;\u0026thinsp;0.265, p\u0026thinsp;=\u0026thinsp;0.014), cg15700009 (β = \u0026minus;\u0026thinsp;0.329, p\u0026thinsp;=\u0026thinsp;3.49 \u0026times; 10⁻\u0026sup1;⁸), and cg19631472 (β = \u0026minus;\u0026thinsp;0.216, p\u0026thinsp;=\u0026thinsp;0.022).\u003c/p\u003e \u003cp\u003eBy evaluating the causal effect of LDHA as a mediator, we determined that these three methylation sites provided in Supplementary Table\u0026nbsp;3 indirectly facilitated ccRCC progression through modulation of LDHA expression, with mediation effect proportions of 48.51%, 78.92%, and 43.55%, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Mediation Analysis of the Succinylation\u0026ndash;Metabolite\u0026ndash;ccRCC Relationship\u003c/h2\u003e \u003cp\u003eIn assessing the effect sizes of LDHA gene eQTLs on 1,400 circulating metabolites, eight metabolites demonstrated significant associations. Subsequently, when treating the 1,400 metabolites as exposures and ccRCC as the outcome, 106 metabolites were identified as significantly associated with ccRCC. Ultimately, deoxycarnitine was the only metabolite found to mediate the promotive effect of LDHA on ccRCC, with a mediation proportion of 12.46% (p\u0026thinsp;=\u0026thinsp;0.025), which can be found in Supplementary Table\u0026nbsp;4.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, we systematically elucidated the role of the DNA methylation\u0026ndash;LDHA\u0026ndash;deoxycarnitine axis in the progression of ccRCC by integrating two-sample MR, SMR, and mediation analyses. Our results provide compelling evidence that elevated LDHA expression is causally linked to an increased risk of ccRCC. Additionally, we identified specific CpG methylation sites negatively regulating LDHA expression, implicating epigenetic modifications as key drivers of metabolic alterations favoring tumorigenesis. Notably, elevated LDHA expression correlated with markedly reduced deoxycarnitine levels, suggesting impaired mitochondrial metabolism contributes to ccRCC pathogenesis. Mediation analyses further demonstrated that DNA methylation influences ccRCC risk primarily through the modulation of LDHA expression and subsequent metabolic dysregulation. Collectively, these findings offer novel mechanistic insights into the interplay between epigenetic regulation and metabolic reprogramming in ccRCC, thereby identifying promising biomarkers and therapeutic targets for early diagnosis and clinical intervention.\u003c/p\u003e \u003cp\u003eLDHA plays a pivotal role in tumor metabolic reprogramming by catalyzing the conversion of pyruvate to lactate, a hallmark feature of the Warburg effect[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Elevated LDHA expression has consistently been observed across multiple malignancies, including ccRCC[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], and is associated with aggressive tumor behavior and poor clinical outcomes[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Specifically, in ccRCC, high LDHA expression positively correlates with tumor size, histological grade, TNM stage, and metastatic potential[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Mechanistic studies have demonstrated that LDHA enhances glycolytic metabolism and promotes epithelial\u0026ndash;mesenchymal transition (EMT), thereby facilitating cell migration, invasion, and metastatic progression. Conversely, pharmacological inhibition of LDHA activity with agents such as oxamate significantly suppresses these malignant processes[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Additional evidence from other cancer models reinforces LDHA's oncogenic role, demonstrating that its overexpression sustains aerobic glycolysis, preserves mitochondrial function under metabolic stress, and contributes to therapeutic resistance[\u003cspan additionalcitationids=\"CR42\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Small-molecule inhibitors, such as PSTMB, effectively block LDHA activity, reduce lactate production, impair tumor growth, and induce apoptosis via oxidative stress pathways[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Thus, LDHA emerges as a central regulator of tumor metabolism and aggressiveness in ccRCC, underscoring its potential as a therapeutic target.\u003c/p\u003e \u003cp\u003eDeoxycarnitine, a key intermediate in the carnitine biosynthesis pathway, is crucial for mitochondrial fatty acid oxidation and cellular energy homeostasis[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. In ccRCC, BBOX1\u0026mdash;the enzyme responsible for converting deoxycarnitine to carnitine\u0026mdash;functions as a tumor suppressor, with markedly reduced expression observed during malignant transformation. Restoration of BBOX1 expression suppresses mTORC1 signaling and glycolytic pathways, consequently inhibiting tumor viability and growth in xenograft models[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Similar findings in hepatocellular carcinoma (HCC) demonstrate that CRIP1-mediated ubiquitination and subsequent degradation of BBOX1 reduce carnitine levels, promoting cancer stemness through activation of the Wnt/β-catenin pathway[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Beyond cancer, carnitine and its derivatives are recognized regulators of mitochondrial function, preventing metabolic inflexibility and supporting cellular detoxification[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Although large-scale metabolomic analyses have highlighted deoxycarnitine's systemic metabolic importance, MR analyses have yet to establish a causal association between deoxycarnitine and cardiovascular outcomes[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDNA methylation, a critical epigenetic modification, regulates gene expression by silencing promoter activity without altering the DNA sequence itself. Aberrant methylation patterns frequently contribute to cancer progression[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Our MR and mediation analyses specifically identified CpG methylation sites upstream of the LDHA gene significantly influencing its expression, thereby modulating tumor risk. Typically, CpG island hypermethylation in promoter regions leads to transcriptional repression of tumor suppressors, while global hypomethylation can activate oncogenic pathways[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]​. Beyond individual gene silencing, methylation modifications also reshape chromatin architecture, affecting broader gene regulatory networks​[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. For instance, aberrant promoter hypermethylation of differentiation-associated genes, such as the ion channel gene KCNA5 in Ewing sarcoma, promotes tumor proliferation and stemness​[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Analogously, methylation at LDHA regulatory regions demonstrated strong mediation effects (\u0026gt;\u0026thinsp;40%) on ccRCC risk, highlighting DNA methylation\u0026rsquo;s critical role in epigenetically activating LDHA-driven metabolic reprogramming. These insights substantiate promoter CpG methylation as a crucial mechanism regulating key metabolic enzymes and support targeting epigenetic dysregulation as a therapeutic strategy in ccRCC.\u003c/p\u003e \u003cp\u003eThe identification of the DNA methylation\u0026ndash;LDHA\u0026ndash;deoxycarnitine axis in ccRCC highlights novel opportunities for early detection and targeted therapy. Blood-based methylation profiling presents a minimally invasive approach for disease monitoring. For instance, hypermethylation at ACTB CpG sites has been linked to increased coronary heart disease risk, achieving significant discrimination capabilities (AUC values up to 0.77)[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Similarly, exploratory studies in pancreatic cancer identified CpG04969764 within LAMA5 as a potential methylation biomarker[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], underscoring the clinical feasibility of peripheral blood methylation markers. Furthermore, LDHA\u0026rsquo;s involvement in regulating mitophagy and activating the JAK\u0026ndash;STAT signaling pathway implies that targeting the LDHA\u0026ndash;lactate axis might not only correct metabolic dysfunction but also enhance antitumor immunity[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe present study has several strengths enhancing the robustness of its conclusions, although limitations should be acknowledged. By integrating MR, SMR, and mediation analyses, we minimized potential confounding and strengthened causal inference regarding the DNA methylation\u0026ndash;LDHA\u0026ndash;deoxycarnitine axis. Utilizing large-scale genome-wide datasets and multi-level analytical methodologies further reinforced the validity and generalizability of our findings. However, limitations include the predominantly European ancestry of the study population, potentially restricting generalizability to other ethnic groups. Furthermore, while genetic approaches provide strong causal evidence, in vitro and in vivo functional validation experiments remain essential to fully elucidate the biological mechanisms involved. Lastly, environmental and lifestyle factors that independently influence DNA methylation were not assessed. Future studies integrating functional assays, diverse ethnic cohorts, and comprehensive environmental covariates are warranted to confirm and expand upon our findings.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, this study identifies a novel epigenetic\u0026ndash;metabolic pathway wherein DNA methylation regulates LDHA expression, thereby altering deoxycarnitine metabolism and driving the progression of ccRCC. Through the integration of MR, SMR, and mediation analyses, our findings robustly support the causal role of the DNA methylation\u0026ndash;LDHA\u0026ndash;deoxycarnitine axis in ccRCC pathogenesis. These insights significantly enhance our understanding of the complex interplay between epigenetic modifications and metabolic reprogramming events that underlie renal carcinogenesis. Additionally, our study highlights promising biomarkers and potential therapeutic targets with significant translational relevance. Future functional validation through cellular and animal models will be crucial to confirm the biological mechanisms suggested by genetic analyses. Moreover, expanding research to diverse populations and incorporating environmental and lifestyle factors will be essential for fully elucidating the intricate relationship between epigenetics, metabolism, and tumor progression. Ultimately, harnessing these insights could advance the development of innovative early detection strategies and personalized therapeutic interventions targeting epigenetic and metabolic vulnerabilities in ccRCC.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eccRCC \u0026nbsp; \u0026nbsp;Clear cell renal cell carcinoma\u003c/p\u003e\n\u003cp\u003eTKIs \u0026nbsp; \u0026nbsp; \u0026nbsp;Tyrosine kinase inhibitors\u003c/p\u003e\n\u003cp\u003eTME. \u0026nbsp; \u0026nbsp; Tumor microenvironment\u003c/p\u003e\n\u003cp\u003eVM \u0026nbsp; \u0026nbsp; \u0026nbsp; Vasculogenic mimicry\u003c/p\u003e\n\u003cp\u003eVHL. \u0026nbsp; \u0026nbsp; Von Hippel\u0026ndash;Lindau\u003c/p\u003e\n\u003cp\u003eHIF-1\u0026alpha;. \u0026nbsp; Hypoxia-inducible factor-1\u0026alpha;\u003c/p\u003e\n\u003cp\u003eLDHA. \u0026nbsp; \u0026nbsp;Lactate dehydrogenase A\u003c/p\u003e\n\u003cp\u003eMR \u0026nbsp; \u0026nbsp; \u0026nbsp; Mendelian randomization\u003c/p\u003e\n\u003cp\u003eSNP \u0026nbsp; \u0026nbsp; \u0026nbsp;Single nucleotide polymorphism\u003c/p\u003e\n\u003cp\u003eeQTL. \u0026nbsp; \u0026nbsp; Expression quantitative trait locus\u003c/p\u003e\n\u003cp\u003eGWAS \u0026nbsp; \u0026nbsp;Genome-wide association study\u003c/p\u003e\n\u003cp\u003eICD \u0026nbsp; \u0026nbsp; \u0026nbsp; International Classification of Diseases\u003c/p\u003e\n\u003cp\u003eIVW \u0026nbsp; \u0026nbsp; \u0026nbsp;Inverse variance weighted \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHEDI. \u0026nbsp; \u0026nbsp; Heterogeneity in Dependent Instruments\u003c/p\u003e\n\u003cp\u003eSMR \u0026nbsp; \u0026nbsp; \u0026nbsp;Summary-data-based Mendelian Randomization\u003c/p\u003e\n\u003cp\u003eGEO \u0026nbsp; \u0026nbsp; \u0026nbsp;Gene Expression Omnibus\u003c/p\u003e\n\u003cp\u003emQTL. \u0026nbsp; \u0026nbsp;Methylation quantitative trait loci\u003c/p\u003e\n\u003cp\u003eOR. \u0026nbsp; \u0026nbsp; \u0026nbsp; Odds ratio\u003c/p\u003e\n\u003cp\u003eSTROBE \u0026nbsp;Strengthening the Reporting of Observational Studies in Epidemiology\u003c/p\u003e\n\u003cp\u003eEMT \u0026nbsp; \u0026nbsp; \u0026nbsp;Epithelial\u0026ndash;mesenchymal transition\u003c/p\u003e\n\u003cp\u003eHCC \u0026nbsp; \u0026nbsp; \u0026nbsp;hepatocellular carcinoma\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConsent to Participate\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eConsent to Publish\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eClinical trial number\u003c/h2\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003ch2\u003eEthics Approval\u003c/h2\u003e\n\u003cp\u003eThis study was conducted solely using publicly available summary statistics without accessing individual-level data; therefore, ethical approval was not required.\u003c/p\u003e\n\u003ch2\u003eConflict of Interest\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis research was generously Funded by Special Disease Construction Project of Jiading District Health System (NO.ZK2024A08).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eThe study was designed by Chen Wang. Qifa Zhang, Qiang Li, and Yelong Wang downloaded and analyzed the data. Chen Wang drafted the manuscript, which was reviewed by Xin Chen and Tao Zhang. The final version of the manuscript has been reviewed and approved for submission by all the authors. All the authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eData is provided within the manuscript and supplementary information files.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eYoshida GJ. Metabolic reprogramming: the emerging concept and associated therapeutic strategies. 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LDHA-lactate axis modulates mitophagy inhibiting CSFV replication. J Virol, 2025: p. e0026825.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"DNA Methylation, LDHA, Deoxycarnitine, Clear Cell Renal Cell Carcinoma (ccRCC), Epigenetic–Metabolic Regulation","lastPublishedDoi":"10.21203/rs.3.rs-6616219/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6616219/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 the predominant histological subtype of renal cancer, characterized by high recurrence and metastasis rates. Despite advances in targeted therapies, treatment resistance and poor prognosis persist, necessitating the identification of novel molecular mechanisms.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eWe systematically investigated the role of the DNA methylation\u0026ndash;LDHA\u0026ndash;deoxycarnitine axis in ccRCC progression by integrating two-sample Mendelian randomization (MR), summary-data-based Mendelian randomization (SMR), and mediation analyses. Cis-eQTL, mQTL, and metabolomic GWAS datasets were utilized to establish causal relationships between DNA methylation, LDHA expression, deoxycarnitine levels, and ccRCC risk.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003eOur findings demonstrate that elevated LDHA expression, driven by hypomethylation at specific CpG sites (cg02232751, cg15700009, cg19631472), causally promotes ccRCC development. LDHA overexpression was associated with reduced deoxycarnitine levels, reflecting disrupted mitochondrial metabolism. Mediation analyses revealed that DNA methylation regulates ccRCC risk predominantly through modulation of LDHA expression and subsequent metabolic reprogramming. Furthermore, deoxycarnitine emerged as a significant metabolic mediator linking LDHA activity to tumor progression.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e \u003cp\u003eThis study identifies a novel epigenetic\u0026ndash;metabolic pathway wherein DNA methylation regulates LDHA expression, reshapes deoxycarnitine metabolism, and drives ccRCC progression. These insights illuminate potential biomarkers for early detection and highlight LDHA and its metabolic network as promising therapeutic targets. Future validation in functional models and diverse populations will be critical to translate these findings into clinical applications.\u003c/p\u003e","manuscriptTitle":"A DNA Methylation–LDHA–Deoxycarnitine Regulatory Axis Promotes Tumorigenesis in Clear Cell Renal Cell Carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-16 08:29:12","doi":"10.21203/rs.3.rs-6616219/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f17b776e-c9de-46e3-841c-4716b8030d00","owner":[],"postedDate":"June 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-30T13:39:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-16 08:29:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6616219","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6616219","identity":"rs-6616219","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}