Integrating HCY Metabolic Enzyme Gene Polymorphisms and Expression Data Reveals Key Genes in Ovarian Cancer

Integrating HCY Metabolic Enzyme Gene Polymorphisms and Expression Data Reveals Key Genes in Ovarian Cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Article Integrating HCY Metabolic Enzyme Gene Polymorphisms and Expression Data Reveals Key Genes in Ovarian Cancer Yu Zhang, Yuan Liu, Shan Gao, Yaqiong Guo, XueLing Wei, He Xu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5328638/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 This study investigates the role of homocysteine (HCY) metabolic enzyme variants in ovarian cancer. HCY is a sulfur-containing non-protein amino acid and an important metabolic intermediate in the human body, with elevated HCY levels being linked to cancer by many researchers. We analyzed SNPs of six HCY metabolic enzymes using eQTL data to determine their effects on gene expression. By matching ovarian cancer gene expression data from the TCGA database and performing differential analysis using the GEO dataset GSE12470, we identified nine genes (MTHFR, PLOD1, U2AF, EIF2S2, EDEM2, MTR, WDR4, CHMP4B, AGTRAP) that were significantly differentially expressed in ovarian cancer patients. These findings suggest that SNPs of HCY metabolic enzymes influence the progression of ovarian cancer and hyperhomocysteinemia through genetic and epigenetic mechanisms. Biological sciences/Biochemistry Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Biological sciences/Genetics Health sciences/Biomarkers Health sciences/Oncology HCY metabolic enzymes ovarian cancer single nucleotide polymorphism expression quantitative trait loci differentially expressed genes clinical biomarkers. Figures Figure 1 1. Introduction Ovarian cancer (OV) is one of the most common gynecological cancers and has the highest mortality rate, with epithelial ovarian cancer being the most common type 1 . Unlike many cancers that spread through hematogenous metastasis, epithelial cancer is believed to spread by the direct migration of tumor cells into the peritoneal cavity and omentum through peritoneal fluid 2 . Homocysteine (HCY) is a common sulfur-containing amino acid in the human body, formed during the conversion of methionine to cysteine. Its metabolism occurs through two pathways: remethylation to form methionine and trans-sulfuration to generate cysteine 3 . Several key enzymes play crucial roles in this metabolic process. Adenosylhomocysteinase (AHCY) reversibly catalyzes the breakdown of S-adenosylhomocysteine (SAH) to generate HCY 4 . SAH is an important inhibitor of methylation reactions, and its accumulation can affect the methylation levels of DNA, RNA, and proteins, thereby influencing gene expression and cellular functions. The remethylation of HCY is mainly carried out by betaine-homocysteine methyltransferase (BHMT) in the liver and kidneys. BHMT transfers a methyl group from betaine to HCY, generating methionine and maintaining the balance of methyl donors in the body 5 . In the trans-sulfuration pathway, HCY is condensed with serine by cystathionine β-synthase (CBS) to form cystathionine, which is subsequently cleaved by cystathionine γ-lyase (CSE) to produce cysteine. This process is crucial for the synthesis of cysteine and glutathione, affecting cellular antioxidant capacity and sulfur metabolism 6 . Methylenetetrahydrofolate reductase (MTHFR) is an essential enzyme in folate and HCY metabolism, catalyzing the reduction of 5,10-methylenetetrahydrofolate to 5-methyltetrahydrofolate, which provides a methyl donor for HCY remethylation 7 . MTHFR gene polymorphism is considered the most common cause of elevated HCY levels and may be associated with an increased risk of cardiovascular diseases, neurodegenerative diseases, and certain cancers 8 . Methionine synthase (MTR) is responsible for remethylating HCY to methionine, a process that requires 5-methyltetrahydrofolate as a cofactor. Methionine synthase reductase (MTRR) participates in the reductive methylation cycle of MTR by sequentially transferring electrons from NADPH to FAD, FMN, and cob (II)alamin, ensuring the activity of MTR 9 . In summary, these six enzymes play key roles in HCY metabolism. Their functional, structural abnormalities or genetic polymorphisms can significantly affect HCY levels, thereby impacting cellular methylation status, oxidative stress response, and overall metabolic function. Therefore, studying mutations in HCY-related enzymes is of great importance for understanding the occurrence and development of ovarian cancer. 2. Material and methods As this study utilized data solely from publicly available databases that do not involve personally identifiable information, ethical committee approval was not required. First, AHCY, BHMT, CBS, MTHFR, MTR, and MTRR were used as keywords to search in the GWAS database, and a total of 155 SNPs of the 6 HCY-related enzymes were identified. In order to capture all possible genetic variations that may influence gene expression through eQTL, and to comprehensively explore the regulatory role of HCY metabolic enzyme gene polymorphisms on gene expression in ovarian cancer, minor allele frequency (MAF) and linkage disequilibrium (R²) were not filtered in this study as they do not affect gene expression levels. eQTL data was obtained from the publicly available expression quantitative trait loci (eQTLs) dataset provided by the eQTLGen consortium, which includes eQTL data for 31,684 blood and PBMC samples from 37 individual cohorts 10 . All SNPs of HCY-related enzymes were matched with the Significant cis-eQTLs dataset, resulting in 149 matches and mapping to 149 gene names. The OV dataset was obtained from the TCGA database through the GDC website, and the RNA-seq data from TCGA were normalized and processed using the TCGAbiolinks package. The expression genes of HCY-related enzymes were matched with the expression genes in the TCGA OV dataset to ensure the presence of these genes in OV, and TCGA's large dataset ensures that the corresponding expression genes can be fully matched. A total of 37 intersecting genes were identified. In this study, the differential expression dataset for ovarian cancer and normal tissues was obtained from GSE12470 (platform ID: GPL887 Agilent-012097 Human 1A Microarray (V2) G4110B), which includes 43 serous ovarian cancer samples and 10 normal peritoneal samples 11 . The grouping matrix and comparison matrix of GSE12470 were extracted using RStudio. Based on the GPL887 platform, all negative values in the expression matrix were set to 0.01, followed by logarithmic transformation and quantile normalization. Differentially expressed genes (DEGs) analysis was conducted using the "limma" package in R, with criteria of |log2(FC)| ≥ 1.0 and P-value < 0.05 for DEG selection. The volcano plot of the differential expression results was created using the R package "ggplot2", as shown in Fig. 1 . 3. Results A total of 9 genes, including MTHFR, PLOD1, U2AF1, EIF2S2, EDEM2, MTR, WDR4, CHMP4B, and AGTRAP, were found to exhibit significant differential expressions in OV patients. The differential expression results of these 9 genes are shown in Table 1 . Table 1 Differential expression of HCY metabolic enzyme SNP-mapped genes in OV. No Gene logFC adj.P.Val Expression Change 1 EDEM2 -3.659677 2.484485e-12 Down 2 CHMP4B 2.491556 1.534226e-09 Up 3 WDR4 -3.573825 1.682130e-08 Down 4 MTHFR 4.943933 9.775868e-05 Up 5 U2AF1 2.576555 1.891855e-04 Up 6 PLOD1 -4.750785 2.343971e-03 Down 7 AGTRAP 2.532727 8.959617e-03 Up 8 MTR 3.190320 2.554631e-02 Up 9 EIF2S2 -1.554833 2.641425e-02 Down 4. Discussion SNPs are the most common form of variation in the human genome, and certain functional SNPs can significantly affect gene expression and protein function, thereby leading to an increased incidence of diseases. This study selected SNPs of six HCY metabolism-related enzymes, AHCY, BHMT, CBS, MTHFR, MTR, and MTRR, because HCY metabolism plays an important role in the one-carbon cycle, which is involved in and influences crucial biological processes such as DNA methylation and nucleotide synthesis. Any mutation in these enzymes can lead to functional abnormalities, resulting in cardiovascular diseases, neurodegenerative diseases, and cancer. SNPs can affect different functional regions of genes, thereby altering gene expression levels, which is crucial for the six enzymes in the metabolic pathway, as their expression levels directly affect enzyme activity levels and metabolite concentrations. SNPs in the coding region can alter the amino acid sequence, thereby affecting the normal function of the metabolic pathway. The advantages of selecting SNPs as research targets include SNP mutations are closely related to the enzyme, independent of other confounding factors, and are not influenced by other pathways. If only the mRNA expression of enzymes such as AHCY is studied, attention may be limited to these enzymes themselves, ignoring the genetic influence on the expression of other related genes. By using eQTL analysis, the scope of research can be expanded, and more disease-related genes can be identified. For CBS and MTHFR, there have been numerous previous studies, and this research will not elaborate further. However, there has been limited research on the AGTRAP and PLOD genes in ovarian cancer, so we focus on discussing these two genes. AGTRAP is a candidate gene of the renin-angiotensin system (RAS), involved in the proliferation of hematopoietic progenitor cells. In 2019, this gene was identified as being significantly expressed in tongue squamous cell carcinoma through a weighted gene co-expression network 12 , 13 . In this study, AGTRAP was identified as significantly upregulated in ovarian cancer, suggesting that the gene might be upregulated in tumor tissues due to HCY metabolic enzyme mutations, which, through certain signaling pathways, may lead to angiogenesis and other changes in tumor tissues. This finding provides clearer insight into how genetic factors influence tumor growth and also reveals why angiogenesis inhibitors are needed in the treatment of ovarian cancer patients. PLOD is involved in the synthesis and modification of collagen, which is a major component of the extracellular matrix. Its expression is regulated by various cytokines, transcription factors, and microRNAs (miRNAs). Overexpression of PLOD is associated with enriched intercellular junctions, and PLOD1 has been relatively less studied 14 , 15 . In this study, we found that PLOD1 expression was downregulated in ovarian cancer, leading to decreased collagen synthesis, and reduced intercellular adhesion, directly demonstrating the high metastatic potential of tumor tissues in ovarian cancer. Combined with the findings regarding AGTRAP, we can also understand how genetics influence tumor invasion and metastasis. In this study, eQTL analysis was used to match SNPs with differentially expressed genes, directly evaluating the relationship between SNPs and gene expression levels. This analysis revealed how genetic variations influence cellular functions and diseases by affecting gene expression, further helping us understand how these variations impact ovarian cancer development through the HCY metabolic pathway. Declarations Author Contributions Statement Yu Zhang, Yuan Liu, and Shan Gao contributed to the conception and design of the project; Yu Zhang, Yaqiong Guo, and XueLing Wei contributed to the analysis and interpretation of the data; Yu Zhang and He Xu contributed to the data acquisition and provided statistical analysis support; Yu Zhang and Yuan Liu drafted the article. All authors have read and agreed to the published version of the manuscript. Competing interests The authors declare no competing interests. AI and AI-assisted Technologies in Authorship Declaration Declaration: During the preparation of this work, the authors used ChatGPT to eliminate grammatical errors. After using this tool, the authors reviewed and edited the content as needed and took full responsibility for the content of the publication. Data availability statement The datasets analyzed in the current study are available in the GEO repository (https://www.ebi.ac.uk/gwas), the TCGA database (https://portal.gdc.cancer.gov), the eQTdata(https://www.eqtlgen.org),and the GSE12470 dataset (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE12470). Ethics declarations Ethical approval was not required for this study as all data was obtained from publicly available databases, which do not contain identifiable personal information. References Gaona-Luviano, P. & Medina-Gaona, L. A. Magaña-Pérez, K. Epidemiology of ovarian cancer. Chin. Clin. Oncol. 9 , 47–47 (2020). Certelli, C. et al. Minimally Invasive Secondary Cytoreduction in Recurrent Ovarian Cancer. Cancers (Basel) . 15 , 4769 (2023). Hermann, A., Sitdikova, G. & Homocysteine Biochemistry, Molecular Biology and Role in Disease. Biomolecules . 11 , 737 (2021). Vizán, P., Di Croce, L. & Aranda, S. Functional and Pathological Roles of AHCY. Front. Cell. Dev. Biol. 9 , (2021). Hannibal, L. & Blom, H. J. Homocysteine and disease: Causal associations or epiphenomenons? Mol. Aspects Med. 53 , 36–42 (2017). Rehman, T. et al. Cysteine and homocysteine as biomarker of various diseases. Food Sci. Nutr. 8 , 4696–4707 (2020). Yuan, H., Fu, M., Yang, X., Huang, K. & Ren, X. Single nucleotide polymorphism of MTHFR rs1801133 associated with elevated HCY levels affects susceptibility to cerebral small vessel disease. PeerJ . 8 , e8627 (2020). Yuan, X. et al. Associations of homocysteine status and homocysteine metabolism enzyme polymorphisms with hypertension and dyslipidemia in a Chinese hypertensive population. Clin. Exp. Hypertens. 42 , 52–60 (2020). Zhang, J. et al. The N-terminus of MTRR plays a role in MTR reactivation cycle beyond electron transfer. Bioorg. Chem. 100 , 103836 (2020). Võsa, U. et al. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. Nat. Genet. 53 , 1300–1310 (2021). Yoshihara, K. et al. Gene expression profiling of advanced-stage serous ovarian cancers distinguishes novel subclasses and implicates ZEB2 in tumor progression and prognosis. Cancer Sci. 100 , 1421–1428 (2009). Kwiatkowski, B. A. & Richard, R. E. Angiotensin II Receptor-Associated Protein (AGTRAP) Synergizes with Mpl Signaling to Promote Survival and to Increase Proliferation Rate of Hematopoietic Cells. Blood . 114 , 3606–3606 (2009). Zeng, H., Li, H., Zhao, Y., Chen, L. & Ma, X. Transcripto-based network analysis reveals a model of gene activation in tongue squamous cell carcinomas. Head Neck . 41 , 4098–4110 (2019). Qi, Y. & Xu, R. Roles of PLODs in Collagen Synthesis and Cancer Progression. Front. Cell. Dev. Biol. 6 , (2018). Guo, T., Gu, C., Li, B. & Xu, C. PLODs are overexpressed in ovarian cancer and are associated with gap junctions via connexin 43. Lab. Invest. 101 , 564–569 (2021). Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies 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-5328638","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":384459844,"identity":"dfcb9060-72eb-4738-94d2-c767e0250427","order_by":0,"name":"Yu Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYBACNmb+h49/VDAkgHk8xGjhZ+dhNmY4Q4oWyX4eNmnGNlK0GBzmPWxcOO9wnu6MBMYHb9sY5M0Ja+FLfDxzW1qx2Y0EZsO5bQyGOxsIamEwNuDdZpO47UYCmzQv0IUGBwhrMZPgnSMB0sL+mygtks08ZtK8DRBbmInSws/Mlmw44xjQL2ceNkvOOSdhuIGQFjb+wwcffKg5nGd2PPnghzdlNvIEbUECjA1AQoJ49aNgFIyCUTAKcAMA5B4/f4FGcEQAAAAASUVORK5CYII=","orcid":"","institution":"The Second People's Hospital of Gansu Province （ Affiliated Hospital of Northwest Minzu University）","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Zhang","suffix":""},{"id":384459845,"identity":"e4baf1dc-4ffe-46f8-b832-a2bc4ca0ba6e","order_by":1,"name":"Yuan Liu","email":"","orcid":"","institution":"The Second People's Hospital of Gansu Province （ Affiliated Hospital of Northwest Minzu University）","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Liu","suffix":""},{"id":384459846,"identity":"abd68954-ac1b-4f1f-ac58-0386b205e7e3","order_by":2,"name":"Shan Gao","email":"","orcid":"","institution":"The Second People's Hospital of Gansu Province （ Affiliated Hospital of Northwest Minzu University）","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shan","middleName":"","lastName":"Gao","suffix":""},{"id":384459847,"identity":"cf15c8af-6306-4e8b-8736-95b16bfcf529","order_by":3,"name":"Yaqiong Guo","email":"","orcid":"","institution":"The Second People's Hospital of Gansu Province （ Affiliated Hospital of Northwest Minzu University）","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yaqiong","middleName":"","lastName":"Guo","suffix":""},{"id":384459848,"identity":"5274fd38-f39f-4032-afba-5ca502a6d950","order_by":4,"name":"XueLing Wei","email":"","orcid":"","institution":"The Second People's Hospital of Gansu Province （ Affiliated Hospital of Northwest Minzu University）","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"XueLing","middleName":"","lastName":"Wei","suffix":""},{"id":384459849,"identity":"5fe0a33a-1df4-43e1-b269-30d85b3dcb49","order_by":5,"name":"He Xu","email":"","orcid":"","institution":"Xinjiang Gem Flower Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"He","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2024-10-25 00:53:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5328638/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5328638/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71612472,"identity":"e5d7108d-95a2-4acc-b3f0-09087346b916","added_by":"auto","created_at":"2024-12-17 07:01:25","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":219970,"visible":true,"origin":"","legend":"\u003cp\u003eThe 37 intersecting expressed genes were matched with the DEGs identified in GSE12470.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5328638/v1/bd748fa286853239ba8dc541.jpeg"},{"id":83671610,"identity":"59a20f94-25ac-4ade-b003-c2d7a8a4ea61","added_by":"auto","created_at":"2025-05-30 13:32:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":617851,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5328638/v1/e54dd5d6-3eb7-4736-99a0-11f5dc54cb43.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integrating HCY Metabolic Enzyme Gene Polymorphisms and Expression Data Reveals Key Genes in Ovarian Cancer","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eOvarian cancer (OV) is one of the most common gynecological cancers and has the highest mortality rate, with epithelial ovarian cancer being the most common type\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Unlike many cancers that spread through hematogenous metastasis, epithelial cancer is believed to spread by the direct migration of tumor cells into the peritoneal cavity and omentum through peritoneal fluid\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHomocysteine (HCY) is a common sulfur-containing amino acid in the human body, formed during the conversion of methionine to cysteine. Its metabolism occurs through two pathways: remethylation to form methionine and trans-sulfuration to generate cysteine\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Several key enzymes play crucial roles in this metabolic process.\u003c/p\u003e \u003cp\u003eAdenosylhomocysteinase (AHCY) reversibly catalyzes the breakdown of S-adenosylhomocysteine (SAH) to generate HCY\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. SAH is an important inhibitor of methylation reactions, and its accumulation can affect the methylation levels of DNA, RNA, and proteins, thereby influencing gene expression and cellular functions.\u003c/p\u003e \u003cp\u003eThe remethylation of HCY is mainly carried out by betaine-homocysteine methyltransferase (BHMT) in the liver and kidneys. BHMT transfers a methyl group from betaine to HCY, generating methionine and maintaining the balance of methyl donors in the body\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn the trans-sulfuration pathway, HCY is condensed with serine by cystathionine β-synthase (CBS) to form cystathionine, which is subsequently cleaved by cystathionine γ-lyase (CSE) to produce cysteine. This process is crucial for the synthesis of cysteine and glutathione, affecting cellular antioxidant capacity and sulfur metabolism\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMethylenetetrahydrofolate reductase (MTHFR) is an essential enzyme in folate and HCY metabolism, catalyzing the reduction of 5,10-methylenetetrahydrofolate to 5-methyltetrahydrofolate, which provides a methyl donor for HCY remethylation\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. MTHFR gene polymorphism is considered the most common cause of elevated HCY levels and may be associated with an increased risk of cardiovascular diseases, neurodegenerative diseases, and certain cancers\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMethionine synthase (MTR) is responsible for remethylating HCY to methionine, a process that requires 5-methyltetrahydrofolate as a cofactor. Methionine synthase reductase (MTRR) participates in the reductive methylation cycle of MTR by sequentially transferring electrons from NADPH to FAD, FMN, and cob (II)alamin, ensuring the activity of MTR\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn summary, these six enzymes play key roles in HCY metabolism. Their functional, structural abnormalities or genetic polymorphisms can significantly affect HCY levels, thereby impacting cellular methylation status, oxidative stress response, and overall metabolic function. Therefore, studying mutations in HCY-related enzymes is of great importance for understanding the occurrence and development of ovarian cancer.\u003c/p\u003e"},{"header":"2. Material and methods","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAs this study utilized data solely from publicly available databases that do not involve personally identifiable information, ethical committee approval was not required.\u003c/p\u003e \u003cp\u003eFirst, AHCY, BHMT, CBS, MTHFR, MTR, and MTRR were used as keywords to search in the GWAS database, and a total of 155 SNPs of the 6 HCY-related enzymes were identified. In order to capture all possible genetic variations that may influence gene expression through eQTL, and to comprehensively explore the regulatory role of HCY metabolic enzyme gene polymorphisms on gene expression in ovarian cancer, minor allele frequency (MAF) and linkage disequilibrium (R\u0026sup2;) were not filtered in this study as they do not affect gene expression levels.\u003c/p\u003e \u003cp\u003eeQTL data was obtained from the publicly available expression quantitative trait loci (eQTLs) dataset provided by the eQTLGen consortium, which includes eQTL data for 31,684 blood and PBMC samples from 37 individual cohorts\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. All SNPs of HCY-related enzymes were matched with the Significant cis-eQTLs dataset, resulting in 149 matches and mapping to 149 gene names.\u003c/p\u003e \u003cp\u003eThe OV dataset was obtained from the TCGA database through the GDC website, and the RNA-seq data from TCGA were normalized and processed using the TCGAbiolinks package. The expression genes of HCY-related enzymes were matched with the expression genes in the TCGA OV dataset to ensure the presence of these genes in OV, and TCGA's large dataset ensures that the corresponding expression genes can be fully matched. A total of 37 intersecting genes were identified.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn this study, the differential expression dataset for ovarian cancer and normal tissues was obtained from GSE12470 (platform ID: GPL887 Agilent-012097 Human 1A Microarray (V2) G4110B), which includes 43 serous ovarian cancer samples and 10 normal peritoneal samples \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. The grouping matrix and comparison matrix of GSE12470 were extracted using RStudio. Based on the GPL887 platform, all negative values in the expression matrix were set to 0.01, followed by logarithmic transformation and quantile normalization. Differentially expressed genes (DEGs) analysis was conducted using the \"limma\" package in R, with criteria of |log2(FC)| \u0026ge; 1.0 and P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for DEG selection. The volcano plot of the differential expression results was created using the R package \"ggplot2\", as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003eA total of 9 genes, including MTHFR, PLOD1, U2AF1, EIF2S2, EDEM2, MTR, WDR4, CHMP4B, and AGTRAP, were found to exhibit significant differential expressions in OV patients. The differential expression results of these 9 genes are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifferential expression of HCY metabolic enzyme SNP-mapped genes in OV.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003elogFC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eadj.P.Val\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExpression Change\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEDEM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.659677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.484485e-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHMP4B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.491556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.534226e-09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWDR4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.573825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.682130e-08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMTHFR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.943933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.775868e-05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eU2AF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.576555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.891855e-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePLOD1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-4.750785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.343971e-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAGTRAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.532727\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.959617e-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.190320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.554631e-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUp\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEIF2S2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.554833\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.641425e-02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDown\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eSNPs are the most common form of variation in the human genome, and certain functional SNPs can significantly affect gene expression and protein function, thereby leading to an increased incidence of diseases. This study selected SNPs of six HCY metabolism-related enzymes, AHCY, BHMT, CBS, MTHFR, MTR, and MTRR, because HCY metabolism plays an important role in the one-carbon cycle, which is involved in and influences crucial biological processes such as DNA methylation and nucleotide synthesis. Any mutation in these enzymes can lead to functional abnormalities, resulting in cardiovascular diseases, neurodegenerative diseases, and cancer.\u003c/p\u003e \u003cp\u003eSNPs can affect different functional regions of genes, thereby altering gene expression levels, which is crucial for the six enzymes in the metabolic pathway, as their expression levels directly affect enzyme activity levels and metabolite concentrations. SNPs in the coding region can alter the amino acid sequence, thereby affecting the normal function of the metabolic pathway. The advantages of selecting SNPs as research targets include SNP mutations are closely related to the enzyme, independent of other confounding factors, and are not influenced by other pathways.\u003c/p\u003e \u003cp\u003eIf only the mRNA expression of enzymes such as AHCY is studied, attention may be limited to these enzymes themselves, ignoring the genetic influence on the expression of other related genes. By using eQTL analysis, the scope of research can be expanded, and more disease-related genes can be identified.\u003c/p\u003e \u003cp\u003eFor CBS and MTHFR, there have been numerous previous studies, and this research will not elaborate further. However, there has been limited research on the AGTRAP and PLOD genes in ovarian cancer, so we focus on discussing these two genes.\u003c/p\u003e \u003cp\u003eAGTRAP is a candidate gene of the renin-angiotensin system (RAS), involved in the proliferation of hematopoietic progenitor cells. In 2019, this gene was identified as being significantly expressed in tongue squamous cell carcinoma through a weighted gene co-expression network \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. In this study, AGTRAP was identified as significantly upregulated in ovarian cancer, suggesting that the gene might be upregulated in tumor tissues due to HCY metabolic enzyme mutations, which, through certain signaling pathways, may lead to angiogenesis and other changes in tumor tissues. This finding provides clearer insight into how genetic factors influence tumor growth and also reveals why angiogenesis inhibitors are needed in the treatment of ovarian cancer patients.\u003c/p\u003e \u003cp\u003ePLOD is involved in the synthesis and modification of collagen, which is a major component of the extracellular matrix. Its expression is regulated by various cytokines, transcription factors, and microRNAs (miRNAs). Overexpression of PLOD is associated with enriched intercellular junctions, and PLOD1 has been relatively less studied \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. In this study, we found that PLOD1 expression was downregulated in ovarian cancer, leading to decreased collagen synthesis, and reduced intercellular adhesion, directly demonstrating the high metastatic potential of tumor tissues in ovarian cancer. Combined with the findings regarding AGTRAP, we can also understand how genetics influence tumor invasion and metastasis.\u003c/p\u003e \u003cp\u003eIn this study, eQTL analysis was used to match SNPs with differentially expressed genes, directly evaluating the relationship between SNPs and gene expression levels. This analysis revealed how genetic variations influence cellular functions and diseases by affecting gene expression, further helping us understand how these variations impact ovarian cancer development through the HCY metabolic pathway.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYu Zhang, Yuan Liu, and Shan Gao contributed to the conception and design of the project; Yu Zhang, Yaqiong Guo, and XueLing Wei contributed to the analysis and interpretation of the data; Yu Zhang and He Xu contributed to the data acquisition and provided statistical analysis support; Yu Zhang and Yuan Liu drafted the article. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAI and AI-assisted Technologies in Authorship Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDeclaration: During the preparation of this work, the authors used ChatGPT to eliminate grammatical errors. After using this tool, the authors reviewed and edited the content as needed and took full responsibility for the content of the publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed in the current study are available in the GEO repository (https://www.ebi.ac.uk/gwas), the TCGA database (https://portal.gdc.cancer.gov), the eQTdata(https://www.eqtlgen.org),and the GSE12470 dataset (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE12470).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was not required for this study as all data was obtained from publicly available databases, which do not contain identifiable personal information.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGaona-Luviano, P. \u0026amp; Medina-Gaona, L. A. Maga\u0026ntilde;a-P\u0026eacute;rez, K. Epidemiology of ovarian cancer. \u003cem\u003eChin. Clin. Oncol.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, 47\u0026ndash;47 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCertelli, C. et al. Minimally Invasive Secondary Cytoreduction in Recurrent Ovarian Cancer. \u003cem\u003eCancers (Basel)\u003c/em\u003e. \u003cb\u003e15\u003c/b\u003e, 4769 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHermann, A., Sitdikova, G. \u0026amp; Homocysteine Biochemistry, Molecular Biology and Role in Disease. \u003cem\u003eBiomolecules\u003c/em\u003e. \u003cb\u003e11\u003c/b\u003e, 737 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eViz\u0026aacute;n, P., Di Croce, L. \u0026amp; Aranda, S. Functional and Pathological Roles of AHCY. \u003cem\u003eFront. Cell. Dev. Biol.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHannibal, L. \u0026amp; Blom, H. J. Homocysteine and disease: Causal associations or epiphenomenons? \u003cem\u003eMol. Aspects Med.\u003c/em\u003e \u003cb\u003e53\u003c/b\u003e, 36\u0026ndash;42 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRehman, T. et al. Cysteine and homocysteine as biomarker of various diseases. \u003cem\u003eFood Sci. Nutr.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e, 4696\u0026ndash;4707 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan, H., Fu, M., Yang, X., Huang, K. \u0026amp; Ren, X. Single nucleotide polymorphism of \u003cem\u003eMTHFR\u003c/em\u003e rs1801133 associated with elevated HCY levels affects susceptibility to cerebral small vessel disease. \u003cem\u003ePeerJ\u003c/em\u003e. \u003cb\u003e8\u003c/b\u003e, e8627 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan, X. et al. Associations of homocysteine status and homocysteine metabolism enzyme polymorphisms with hypertension and dyslipidemia in a Chinese hypertensive population. \u003cem\u003eClin. Exp. Hypertens.\u003c/em\u003e \u003cb\u003e42\u003c/b\u003e, 52\u0026ndash;60 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, J. et al. The N-terminus of MTRR plays a role in MTR reactivation cycle beyond electron transfer. \u003cem\u003eBioorg. Chem.\u003c/em\u003e \u003cb\u003e100\u003c/b\u003e, 103836 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eV\u0026otilde;sa, U. et al. Large-scale cis- and trans-eQTL analyses identify thousands of genetic loci and polygenic scores that regulate blood gene expression. \u003cem\u003eNat. Genet.\u003c/em\u003e \u003cb\u003e53\u003c/b\u003e, 1300\u0026ndash;1310 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoshihara, K. et al. Gene expression profiling of advanced-stage serous ovarian cancers distinguishes novel subclasses and implicates \u003cem\u003eZEB2\u003c/em\u003e in tumor progression and prognosis. \u003cem\u003eCancer Sci.\u003c/em\u003e \u003cb\u003e100\u003c/b\u003e, 1421\u0026ndash;1428 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKwiatkowski, B. A. \u0026amp; Richard, R. E. Angiotensin II Receptor-Associated Protein (AGTRAP) Synergizes with Mpl Signaling to Promote Survival and to Increase Proliferation Rate of Hematopoietic Cells. \u003cem\u003eBlood\u003c/em\u003e. \u003cb\u003e114\u003c/b\u003e, 3606\u0026ndash;3606 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeng, H., Li, H., Zhao, Y., Chen, L. \u0026amp; Ma, X. Transcripto-based network analysis reveals a model of gene activation in tongue squamous cell carcinomas. \u003cem\u003eHead Neck\u003c/em\u003e. \u003cb\u003e41\u003c/b\u003e, 4098\u0026ndash;4110 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQi, Y. \u0026amp; Xu, R. Roles of PLODs in Collagen Synthesis and Cancer Progression. \u003cem\u003eFront. Cell. Dev. Biol.\u003c/em\u003e \u003cb\u003e6\u003c/b\u003e, (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo, T., Gu, C., Li, B. \u0026amp; Xu, C. PLODs are overexpressed in ovarian cancer and are associated with gap junctions via connexin 43. \u003cem\u003eLab. Invest.\u003c/em\u003e \u003cb\u003e101\u003c/b\u003e, 564\u0026ndash;569 (2021).\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":"HCY metabolic enzymes, ovarian cancer, single nucleotide polymorphism, expression quantitative trait loci, differentially expressed genes, clinical biomarkers.","lastPublishedDoi":"10.21203/rs.3.rs-5328638/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5328638/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the role of homocysteine (HCY) metabolic enzyme variants in ovarian cancer. HCY is a sulfur-containing non-protein amino acid and an important metabolic intermediate in the human body, with elevated HCY levels being linked to cancer by many researchers. We analyzed SNPs of six HCY metabolic enzymes using eQTL data to determine their effects on gene expression. By matching ovarian cancer gene expression data from the TCGA database and performing differential analysis using the GEO dataset GSE12470, we identified nine genes (MTHFR, PLOD1, U2AF, EIF2S2, EDEM2, MTR, WDR4, CHMP4B, AGTRAP) that were significantly differentially expressed in ovarian cancer patients. These findings suggest that SNPs of HCY metabolic enzymes influence the progression of ovarian cancer and hyperhomocysteinemia through genetic and epigenetic mechanisms.\u003c/p\u003e","manuscriptTitle":"Integrating HCY Metabolic Enzyme Gene Polymorphisms and Expression Data Reveals Key Genes in Ovarian Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-17 07:01:20","doi":"10.21203/rs.3.rs-5328638/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":"bccb41fe-4ad0-4f52-b570-c4ea5ce1f83a","owner":[],"postedDate":"December 17th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":40962702,"name":"Biological sciences/Biochemistry"},{"id":40962703,"name":"Biological sciences/Cancer"},{"id":40962704,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":40962705,"name":"Biological sciences/Genetics"},{"id":40962706,"name":"Health sciences/Biomarkers"},{"id":40962707,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2025-05-30T13:24:01+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-17 07:01:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5328638","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5328638","identity":"rs-5328638","version":["v1"]},"buildId":"cTy_lsJlmDsVRNrSptgXS","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}