Prognostic biomarker GOLM1 correlated with immune infiltrates in Endometrial cancer

Prognostic biomarker GOLM1 correlated with immune infiltrates in Endometrial 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 Research Article Prognostic biomarker GOLM1 correlated with immune infiltrates in Endometrial cancer Hui Wang, Xiaodong Luo, Jianguo Hu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5399574/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 Endometrial cancer (UCEC) is a prevalent gynecological cancer that affects women’s reproductive organs. While studies indicate that GOLM1 may influence the immune response through the tumor microenvironment, its exact roles and mechanisms in UCEC are still not well understood. This study examines the varied expression levels of GOLM1 in UCEC and investigates the relationship between GOLM1 expression and the prognosis of patients with UCEC. Methods In this study, we analyzed data from the TCGA database. We utilized several bioinformatics methods, such as differential gene expression analysis, Kaplan-Meier survival analysis, gene set enrichment analysis, and immune cell infiltration analysis. These bioinformatics methods systematically integrate clinical data, gene expression, and immune information, offering a comprehensive view of the molecular mechanisms underlying UCEC.This study employed the software tools TCGA, GEPIA, TIMER, and MethSurv. Results GOLM1 mRNA levels were significantly elevated in UCEC tumor tissues. A strong association was found between GOLM1 expression and overall survival in UCEC patients. Enrichment analysis showed that GOLM1 is linked to several biological pathways, including dynein-bound intraflagellar transporters, fatty acid metabolism, and the transcriptional regulation of pluripotent stem cells. Based on these associations, GOLM1 likely plays an important role in the immune microenvironment of UCEC. Furthermore, GOLM1 expression may be influenced by DNA methylation. Conclusions GOLM1 is crucial for UCEC, as its levels significantly influence both the occurrence and progression of the disease. GOLM1 and its related genes are potential immunotherapeutic targets for UCEC. Additionally, DNA methylation may regulate the expression of GOLM1 in patients with UCEC. GOLM1 Gene expression endometrial cancer UCEC TCGA Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Uterine endometrial cancer (UCEC) is recognized as one of the most prevalent malignancies affecting the female reproductive system, posing significant threats to patient health and imposing substantial economic burdens on society. The incidence of UCEC has steadily increased, especially among postmenopausal women. This rise has led to a decline in their quality of life and increased healthcare costs for managing the disease. Current treatments, such as surgery, radiotherapy, and chemotherapy, have limitations, especially in late-stage cases. These treatments often lack effectiveness, and recurrence rates are high. This highlights an urgent need for the identification of novel prognostic biomarkers and therapeutic targets that could potentially improve patient outcomes and therapeutic strategies. Recent studies have revealed the roles of various biomarkers in cancer progression and prognosis, yet a significant gap remains in understanding how specific genes affect the pathophysiology of UCEC. Among these, Golgi membrane protein 1 (GOLM1) has become an important gene due to its roles in tumor biology, such as cell proliferation, migration, and modulating the tumor microenvironment. Previous studies have shown that GOLM1 expression is frequently elevated in several types of cancers, correlating with poor prognostic outcomes and changes in the immune landscape of tumors. These findings underscore the necessity of further investigating GOLM1's potential as a biomarker in UCEC, especially concerning its influence on immune cell infiltration and overall patient survival. Our research uses comprehensive datasets from The Cancer Genome Atlas (TCGA). We specifically focus on gene expression profiles, immune infiltration patterns, and clinical outcomes in UCEC patients. This approach integrates extensive clinical and genomic data, creating a strong foundation for analyzing potential biomarkers. The main goal of this study is to clarify how GOLM1 expression levels relate to survival outcomes in UCEC patients, and to investigate its role in influencing immune cell infiltration and the epigenetic regulation of DNA methylation. In the context of cancer research, bioinformatics analysis has become an indispensable tool for dissecting the complexities of tumor biology. Consequently, we employ advanced statistical methods and computational models to gain meaningful insights into the relationship between GOLM1 expression and various clinical parameters. Our aim is to determine if GOLM1 can be an independent prognostic marker and to outline its impact on the tumor immune microenvironment, essential for creating new therapeutic strategies for UCEC. In summary, the present study aims to fill existing gaps in the understanding of GOLM1's role in UCEC. By investigating GOLM1's expression in relation to patient survival and immune response, this research seeks to identify GOLM1 as a potential biomarker and contribute to the broader understanding of the molecular mechanisms driving UCEC progression. This research is essential for advancing personalized and effective treatment options for patients diagnosed with this increasingly prevalent malignancy. Material and methods Evidence from the TCGA database We accessed the TCGA database for UCEC ( https://portal.gdc.cancer.gov ) to gather data on immune system infiltration, gene expression (grade 3 HTSeq-FPKM), and clinical information, which included 35 normal and 554 tumor samples. Based on this data, we performed differential gene expression analysis between the high and low expression groups of GOLM1 in UCEC patients. We conducted Kaplan-Meier (K-M) survival analysis to examine the relationship between GOLM1 expression and the survival status of UCEC patients. Gene set enrichment analysis RNA-Seq data obtained from TCGA were normalized for GSEA ( https://www.gsea-msigdb.org/gsea/msigdb/collections.jsp ) analysis. We conducted GO and KEGG pathway enrichment analyses to explore the potential biological functions of GOLM1. GO terms were categorized into three groups: biological processes, molecular functions, and cellular components, alongside the KEGG pathways. A result is deemed statistically significant if it satisfies two criteria: a false discovery rate (FDR) below 0.05 and a nominal p-value below 0.05. Tumor invasion level as analyzed by immune cells TIMER is a detailed resource for researching the molecular signatures of tumor-immune interactions across different types of cancer ( https://cistrome.shinyapps.io/timer/ ). TIMER uses deconvolution statistics to estimate the abundance of six types of tumor-infiltrating immune cells: B cells, CD4 T cells, CD8 T cells, macrophages, neutrophils, and DCs, based on data from the Cancer Genome Atlas (TCGA). This section analyzes GOLM1 expression across different cancer types and its correlation with immune cell infiltration. The correlation module explores the relationship between GOLM1 expression and markers of tumor invasion. GEPIA ( http://gepia.cancer-pku.cn/?from=timeline&isappinstalled=0 ) was used to further validate the genes that showed significant associations in TIMER. Analysis of GOLM1 regulated by DNA methylation MethSurv is a web tool that facilitates multivariable survival analysis using DNA methylation data, enabling researchers to study methylation signatures in various diseases. In this context, the tool was utilized to analyze the DNA methylation regulation of GOLM1 in UCEC and the associated survival curves. Statistical analysis Univariate and multivariate models of Cox analysis were used to calculate 95% confidence intervals (CIs) and hazard ratios (HRs). We conducted univariate survival analyses to compare different clinical features with survival outcomes. Logistic regression analysis was employed to evaluate the correlation between clinical features and GOLM1 expression. All statistical analyses were performed using R software version 4.2.1.Statistical significance was expressed as *p < 0.05; **p < 0.01; and ***p < 0.005. Results GOLM1 mRNA expression was elevated in UCEC tumors We analyzed the correlation between GOLM1 and UCEC using the TCGA database to compare GOLM1 mRNA levels in normal and tumor tissues. We also used GEPIA2 to compare GOLM1 expression levels in normal and tumor tissues. Unpaired data showed that GOLM1 expression was significantly higher in UCEC tumor tissues than in normal tissues (Fig. 1A). Paired samples showed high expression of GOLM1 in UCEC tumor tissues relative to normal tissues (Fig. 1B and Fig. 1C). We investigated the relationship between GOLM1 expression levels and survival in UCEC patients. Figure 1D shows a significant correlation between GOLM1 expression and UCEC prognosis (HR = 0.47, P < 0.001). In the GEPIA online database, GOLM1 expression significantly correlated with disease-free survival (DFS) in UCEC patients (HR = 0.5, P = 0.045). Next, we analyzed the correlation between UCEC overall survival and multivariate characteristics. Univariate analysis showed that age (HR = 1.85, P = 0.009), clinical stage (HR = 3.553, P < 0.001), tissue grade (HR = 3.298, P < 0.001), residual tumor (HR = 3.112, P < 0.001), tumor invasion (HR = 2.825, P < 0.001), radiotherapy (HR = 0.596, P = 0.019), and GOLM1 expression (HR = 0.47, P < 0.001) were significantly correlated with OS (Table 1). Multivariate analyses indicated that GOLM1 expression (HR = 1.022, P = 0.944) was not an independent prognostic factor (Fig. 1F and Table 1). We examined the distribution of GOLM1 expression, survival status, and risk scores in UCEC patients. Figure 1G illustrates that the low-risk group had lower GOLM1 expression levels and higher survival rates compared to the high-risk group. We further evaluated the correlation between GOLM1 expression levels and various clinicopathological factors in UCEC patients. Elevated GOLM1 expression was significantly correlated with clinical stage (P < 0.005, Fig. 1H), tissue grade (P < 0.005, Fig. 1I), and OS events (P < 0.005, Fig. 1J). In conclusion, our findings indicate that high GOLM1 expression is associated with advanced clinical stages and higher tissue grades in UCEC, potentially influencing tumorigenesis and progression. High levels of GOLM1 may influence tumorigenesis and progression of UCEC. Identification and enrichment analysis of differentially expressed genes We identified 4,386 differentially expressed genes (DEGs) between the high and low GOLM1 groups, consisting of 654 up-regulated genes and 3,732 down-regulated genes, as shown in the volcano plot (Fig. 2A). We performed Gene Set Enrichment Analysis (GSEA) to identify significant differences in the enrichment of GO and KEGG pathways related to GOLM1 between the high and low expression groups. GOLM1-related genes were linked to 86 KEGG pathways (|NES|> 1, p 1) and 19 pathways with negative correlations (NES< -1). The KEGG analysis identified five categories positively correlated with high GOLM1 levels: intraflagellar transporters binding to dynein, genes involved in primary ciliary development, reactions for ethanol oxidation, fatty acid metabolism, and protein output. KEGG analysis also revealed five classes with negative correlations: transcriptional regulation of pluripotent stem cells, formation of keratinizing envelopes, sleep regulation, collagen degradation, and voltage-gated potassium ion channels (Figs. 2B, 2C). The GO pathway analysis revealed significant enrichment of related genes in 157 pathways, which included 57 pathways with positive correlations (NES > 1) and 23 pathways with negative correlations (NES < − 1). The five pathways most strongly correlated with GOLM1 expression include the chromosome centromere region, condensed chromosome centromeric region, nuclear chromosome, chromosomal region, and central spindle region. The five most negatively correlated pathways are: collagen containing extracellular interstitium, basement membrane, external encapsulation structure, collagen trimer complex, and collagen (Fig. 2D-2G, Table 3). These results suggest that pathways regulating protein trafficking, fatty acid metabolism, and chromosome signaling are crucial in UCEC patients and are closely related to GOLM1 expression. Correlation between GOLM1 expression and tumor invasion levels of immune cells in UCEC This study aims to evaluate the correlation between GOLM1 expression and immune cell infiltration in uterine corpus endometrial carcinoma (UCEC). We used TIMER to analyze the correlation between GOLM1 expression and tumor-infiltrating immune cells (TIICs) in UCEC. The results showed that GOLM1 expression correlated with CD8 T cell levels (p < 0.001) and CD4 T cell levels (p = 0.02), but not with B cell levels (p = 0.68) (Fig. 3A and 3B). These findings were validated in the TCGA database, where GOLM1 expression was positively correlated with T cells and Th cells among the 24 immune-infiltrating cell subtypes (p < 0.01 by Spearman, Fig. 3C, Table 4). These results demonstrate that GOLM1 plays an important role in the immune infiltration of UCEC. To assess the immune microenvironment in UCEC patients with high and low GOLM1 expression, We generated heat maps of infiltrating immune cells in tumor samples to reveal correlations between different types of TIICs (Fig. 3D). we also compared the levels of 24 immune-infiltrating cell subtypes between the two groups (Fig. 3E). The results indicated that GOLM1 significantly affected the levels of activated dendritic cells (aDC), eosinophils, immature dendritic cells (iDC), macrophages, NK CD56bright cells, T helper cells, central memory T cells (Tcm), Th1 cells, and Th17 cells. In the next step, we continued to verify the correlation between GOLM1 expression and immune marker genes in different immune cells, including Th1, Th17, T helper, TCM, M1, M2, and eosinophils. Our findings revealed a significant correlation between GOLM1 expression and various immune markers in different T cell subtypes(Table 5). Additionally, we validated the relationship between GOLM1 expression and different immune cell markers (Figs. 4A and 4B). The correlation between GOLM1 expression and related genes in UCEC ranked the top 15 The correlation heatmap displays the top 15 genes linked to GOLM1 expression in UCEC patients (R > 0.3, p < 0.001 by Spearman, Fig. 5A and Table 6). We examined the expression profiles of the top 15 genes associated with GOLM1 and their corresponding normal controls in UCEC using the TCGA database, as shown in Fig. 5B (unpaired samples) and Fig. 5C (paired samples). The analysis of unpaired samples revealed significant elevation of several genes, including C9orf152, ELAPOR1, ALG2, BMPR1B, MANSC1, TFF3, MFAP3L, SPDEF, and ARFGEF3, in tumor groups. In the paired sample analysis, we found that the expression of C9orf152, AC084866.1, ELAPOR1, SPDEF, and ARFGEF3 was significantly higher in the tumor group than in the normal controls. Lastly, we explored the prognostic significance of the top 15 GOLM1-related genes that exhibited notable expression differences in UCEC, based on data from the TCGA database, as recorded in the TCGA database. The results indicated that C9orf152, ELAPOR1, ALG2, BMPR1B, TFF3, MFAP3L, and SPDEF were significantly associated with overall survival (OS) events (Supplementary Fig. 1). Correlation analysis between GOLM1 expression and DNA methylation in UCEC We presented a correlation heatmap (Fig. 6a, Table 7) illustrating the DNA methylation sites associated with GOPM1 expression in UCEC patients. We examined changes in DNA methylation sites in UCEC patients through the TCGA database. The results indicated that CG12410273, CG13737695, CG13850654, and CG14574047 exhibited significant expression levels. Furthermore, we explored the prognostic significance of the identified methylation sites in UCEC. The results indicated that CG12410273, CG13737695, and CG13850654 were significantly associated with overall survival (OS) in UCEC patients. Discussion Endometrial cancer (UCEC) represents one of the most prevalent malignancies affecting the female reproductive system, posing significant health risks and economic burdens on society. The incidence of UCEC has been steadily rising, particularly among postmenopausal women. This increase results in a lower quality of life and higher healthcare costs for patients. Current treatment modalities, including surgical intervention, radiotherapy, and chemotherapy, exhibit limitations, particularly in advanced cases, where efficacy is often insufficient and recurrence rates remain high. Therefore, it is crucial to identify new prognostic biomarkers and therapeutic targets to improve treatment outcomes and increase survival rates for UCEC patients [ 1 ]. This study focuses on the expression and role of the Golgi membrane protein 1 (GOLM1) gene in UCEC. Previous research has indicated a correlation between GOLM1 expression and the progression of various tumors, particularly in relation to tumor cell proliferation and metastasis. Elevated GOLM1 levels have been linked to tumor immune microenvironments and patient prognosis. This investigation aims to clarify the potential of GOLM1 as a prognostic biomarker in uterine cancer (UCEC). It focuses on immune infiltration and survival analysis, using bioinformatics methods with data from The Cancer Genome Atlas (TCGA) [2][3][4]. This study offers insights into the role of GOLM1 in UCEC and emphasizes its importance for future therapeutic strategies. In this study, we investigated the expression of GOLM1 in uterine cancer and its potential implications in disease prognosis and immune response.Our analysis showed that GOLM1 mRNA levels were significantly higher in uterine cancer (UCEC) tissues than in normal endometrial tissues (HR = 0.47, P < 0.001).. This aligns with previous findings that GOLM1 is involved in various cancers and may serve as a crucial biomarker for tumor progression [2]. The elevated levels of GOLM1 highlight its importance in the pathogenesis of uterine cancer and suggest that it could be leveraged as a predictive marker for patient outcomes. Moreover, we observed that GOLM1 expression was significantly correlated with the infiltration levels of CD8 + T cells (p < 0.001) and CD4 + T cells (p = 0.02), indicating that GOLM1 may play a role in modulating the immune microenvironment in UCEC. This is consistent with the findings that GOLM1 can influence immune responses within tumors, potentially affecting therapeutic outcomes [3]. The connection between GOLM1 expression and immune cell infiltration highlights the necessity of incorporating the tumor immune microenvironment into the evaluation of treatment strategies for UCEC. Regarding survival analysis, our results indicated that while GOLM1 expression correlated with overall survival in univariate analyses (HR = 0.47, p < 0.001), it did not remain an independent prognostic factor in multivariate analyses (HR = 1.022, p = 0.944). This indicates that although GOLM1 may influence survival outcomes, other factors likely have a greater impact on prognosis [5]. Further investigation is warranted to elucidate the interplay between GOLM1 expression and other clinical factors, which could enhance the understanding of UCEC progression and inform treatment decisions. Our pathway analysis showed that GOLM1 expression correlates with various signaling pathways, particularly those related to lipid metabolism and cellular signaling. This underscores the multifaceted roles of GOLM1 in cellular processes and its potential as a therapeutic target [ 1 ]. The exploration of these pathways could provide insights into novel treatment strategies that target GOLM1-related mechanisms in uterine cancer. Finally, our DNA methylation analysis revealed significant correlations between GOLM1 expression and specific methylation sites, suggesting that epigenetic modifications may regulate GOLM1 levels in UCEC [6]. Understanding how DNA methylation influences GOLM1 expression could facilitate the development of epigenetic therapies to reverse abnormal GOLM1 expression in cancer. In summary, our findings indicate that GOLM1 is a promising biomarker in UCEC, with implications for patient prognosis and immune response modulation. Future research should focus on validating these results in larger cohorts and exploring the potential for GOLM1-targeted therapies in improving treatment outcomes for patients with uterine cancer. The limitations of this study warrant careful consideration. Firstly, the lack of wet laboratory validation poses a significant challenge, as the findings rely solely on bioinformatics analyses derived from the TCGA database. This dependence on computational data, while informative, restricts the confirmation of GOLM1’s role in UCEC through experimental approaches. Moreover, the small sample size could affect the reliability and generalizability of our results, as it may not fully capture the diversity of UCEC. Furthermore, we did not perform an extensive analysis of clinical features correlated with GOLM1 expression, which could provide deeper insights into the clinical implications of our findings. Lastly, variations in data collection methodologies across different studies may introduce batch effects, complicating the interpretation of our results. In conclusion, this research highlights the significant association between GOLM1 expression and patient prognosis in UCEC, alongside its impact on immune infiltration. Our findings suggest that GOLM1 could serve as a promising biomarker for prognosis and therapeutic targeting in UCEC. Future investigations should aim to validate these findings through experimental studies and explore the broader implications of GOLM1 in various UCEC subtypes. This work not only contributes to the current understanding of UCEC biology but also paves the way for potential clinical applications that could enhance patient management and treatment outcomes. Abbreviations UCEC: Endometrial cancer GEPIA: Gene Expression Profiling Interactive Analysis GO: Gene Ontology GSEA: Gene Set Enrichment Analysis GOLM1: I Golgi membrane protein 1 KEGG: Kyoto Encyclopedia of Genes and Genomes K-M survival: Kaplan-Meier survival NES: Normalized enrichment score TCGA: The Cancer Genome Atlas TIIC: Tumor-infiltrating immune cells TIMER: Tumor immune estimation resource Declarations Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Acknowledgments Not applicable Funding Funding for this project was provided by the Kuanren Talents Program and Senior Medical Talents Program of the second affiliated hospital of Chongqing Medical University, as well as the National Natural Science Foundation of China. Author information Hui Wang、Xiaodong Luo and Jianguo Hu contributed equally to this work. Authors and Affiliations Department of Obstetrics and Gynecology, Second Affiliated Hospital, Chongqing Medical University, Linjiang Road, No. 76, Chongqing 400010, China; Hui Wang & Jianguo Hu Authors' contributions Hui Wang contributed to the conception and design of the study. Hui Wang acquisition of data, analysis, and interpretation of data. Hui Wang wrote the main manuscript text, prepared figures, and statistical analysis. Xiaodong Luo and Jianguo Hu revised the manuscript and supervised the project. All authors read and approved the final manuscript. Ethics declarations This study did not require ethical approval since it was not directly involving the use of human or animal subjects. Consent for publication The publication of the article has the agreement of all authors. Competing interests The authors declare that they have no competing of interests References Lee EK, Liu JF. Uterine serous carcinoma and uterine carcinosarcoma: molecular features, clinical advances, and emerging therapies. Clin Adv Hematol Oncol. 2024;22(6):301-310. Li W, Wang X, Li B, Lu J, Chen G. Diagnostic significance of overexpression of Golgi membrane protein 1 in prostate cancer. Urology. 2012;80(4):952.e1-952.e9527. doi:10.1016/j.urology.2012.06.017 Song Q, He X, Xiong Y, et al. The functional landscape of Golgi membrane protein 1 (GOLM1) phosphoproteome reveal GOLM1 regulating P53 that promotes malignancy. Cell Death Discov. 2021;7(1):42. Published 2021 Mar 1. doi:10.1038/s41420-021-00422-2 Aruna, Li LM. Overexpression of golgi membrane protein 1 promotes non-small-cell carcinoma aggressiveness by regulating the matrix metallopeptidase 13. Am J Cancer Res. 2018;8(3):551-565. Published 2018 Mar 1. Nithin KU, Sridhar MG, Srilatha K, Habebullah S. CA 125 is a better marker to differentiate endometrial cancer and abnormal uterine bleeding. Afr Health Sci. 2018;18(4):972-978. doi:10.4314/ahs.v18i4.17 Dahl C, Guldberg P. DNA methylation analysis techniques. Biogerontology. 2003;4(4):233-250. doi:10.1023/a:1025103319328 Tables Table 1 is available in the Supplementary Files section. Table 2 Signaling pathways most significantly correlated with GOLM1 expression based on their NES and FDR KEGG ID NES P .adj FDR Positive intraflagellar transport proteins binding to dynein 2.752 0.000 0.000 genes related to primary cilium development based on crispr 2.519 0.000 0.000 reactome ethanol oxidation 2.284 0.012 0.011 fatty acid metabolism 2.204 0.001 0.001 protein exportT 2.075 0.039 0.038 Negative transcriptional regulation of pluripotent stem cells 1.761 0.013 0.012 formation of the cornified envelope 1.700 0.000 0.000 sleep regulation 1.682 0.029 0.028 collagen degradation 1.678 0.008 0.008 voltage gated potassium channels 1.645 0.042 0.041 GO ID NES P .adj FDR Positive chromosome centromeric region 2.615 0.000 0.000 condensed chromosome centromeric region 2.612 0.000 0.000 nuclear chromosome 2.556 0.000 0.000 chromosomal region 2.502 0.000 0.000 spindle midzone 2.484 0.000 0.000 Negative collagen containing extracellular matrix 2.226 0.000 0.000 basement membrane 2.148 0.000 0.000 external encapsulating structure 2.147 0.000 0.000 complex of collagen trimers 2.089 0.000 0.000 collagen trime 2.058 0.000 0.000 Table 3 is available in the Supplementary Files section. Table 4 Correlation analysis between GOLM1 and immune cells Table 5 Correlation analysis between GOLM1 and relate markers of immune cells Table 6 Correlation analysis between GOLM1 and relate genes top 15 Gene name Gene biotype Pearson Spearman R. P. R. P. C9orf152 Protein coding 0.604 0.000 0.614 0.000 HSDL2 Protein coding 0.546 0.000 0.582 0.000 MRAP2 Protein coding 0.579 0.000 0.580 0.000 AC084866.1 lncRNA 0.560 0.000 0.574 0.000 ELAPOR1 Protein coding 0.552 0.000 0.570 0.000 ALG2 Protein coding 0.527 0.000 0.563 0.000 BMPR1B Protein coding 0.542 0.000 0.562 0.000 MANSC1 Protein coding 0.533 0.000 0.557 0.000 TFF3 Protein coding 0.534 0.000 0.546 0.000 MFAP3L Protein coding 0.521 0.000 0.545 0.000 SPDEF Protein coding 0.518 0.000 0.540 0.000 PRRT1B Protein coding 0.510 0.000 0.535 0.000 PBLD Protein coding 0.512 0.000 0.535 0.000 SPATA18 Protein coding 0.535 0.000 0.533 0.000 ARFGEF3 Protein coding 0.524 0.000 0.532 0.000 Table 7 Prognostic Value of single CpG of the GOLM1 gene family in UCEC by Methsurv paltform Additional Declarations No competing interests reported. Supplementary Files Supplementalfigure1.pdf Table1.docx Table3.docx 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 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-5399574","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":375232838,"identity":"6be7235e-66ce-42af-8900-66cb91508cd6","order_by":0,"name":"Hui Wang","email":"","orcid":"","institution":"Chongqing Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Wang","suffix":""},{"id":375232839,"identity":"82786ff7-452a-4a31-ab3c-86fd7cf406b0","order_by":1,"name":"Xiaodong Luo","email":"","orcid":"","institution":"Chongqing Medical 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06:01:57","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":447626,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementalfigure1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5399574/v1/296bf03671d3bd6a342ec0ab.pdf"},{"id":71003241,"identity":"74739c9a-5a5a-460c-9d83-a6c3da10b9b0","added_by":"auto","created_at":"2024-12-10 06:01:57","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":22421,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-5399574/v1/8d0f80f95faa77c4cf0e5d8b.docx"},{"id":71004677,"identity":"88047044-233d-4b06-bbb5-35cd0e72a14a","added_by":"auto","created_at":"2024-12-10 06:09:58","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":23775,"visible":true,"origin":"","legend":"","description":"","filename":"Table3.docx","url":"https://assets-eu.researchsquare.com/files/rs-5399574/v1/398ff2ae2be95537c1bcd14c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prognostic biomarker GOLM1 correlated with immune infiltrates in Endometrial cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUterine endometrial cancer (UCEC) is recognized as one of the most prevalent malignancies affecting the female reproductive system, posing significant threats to patient health and imposing substantial economic burdens on society. The incidence of UCEC has steadily increased, especially among postmenopausal women. This rise has led to a decline in their quality of life and increased healthcare costs for managing the disease. Current treatments, such as surgery, radiotherapy, and chemotherapy, have limitations, especially in late-stage cases. These treatments often lack effectiveness, and recurrence rates are high. This highlights an urgent need for the identification of novel prognostic biomarkers and therapeutic targets that could potentially improve patient outcomes and therapeutic strategies.\u003c/p\u003e \u003cp\u003eRecent studies have revealed the roles of various biomarkers in cancer progression and prognosis, yet a significant gap remains in understanding how specific genes affect the pathophysiology of UCEC. Among these, Golgi membrane protein 1 (GOLM1) has become an important gene due to its roles in tumor biology, such as cell proliferation, migration, and modulating the tumor microenvironment. Previous studies have shown that GOLM1 expression is frequently elevated in several types of cancers, correlating with poor prognostic outcomes and changes in the immune landscape of tumors. These findings underscore the necessity of further investigating GOLM1's potential as a biomarker in UCEC, especially concerning its influence on immune cell infiltration and overall patient survival.\u003c/p\u003e \u003cp\u003eOur research uses comprehensive datasets from The Cancer Genome Atlas (TCGA). We specifically focus on gene expression profiles, immune infiltration patterns, and clinical outcomes in UCEC patients. This approach integrates extensive clinical and genomic data, creating a strong foundation for analyzing potential biomarkers. The main goal of this study is to clarify how GOLM1 expression levels relate to survival outcomes in UCEC patients, and to investigate its role in influencing immune cell infiltration and the epigenetic regulation of DNA methylation.\u003c/p\u003e \u003cp\u003eIn the context of cancer research, bioinformatics analysis has become an indispensable tool for dissecting the complexities of tumor biology. Consequently, we employ advanced statistical methods and computational models to gain meaningful insights into the relationship between GOLM1 expression and various clinical parameters. Our aim is to determine if GOLM1 can be an independent prognostic marker and to outline its impact on the tumor immune microenvironment, essential for creating new therapeutic strategies for UCEC.\u003c/p\u003e \u003cp\u003eIn summary, the present study aims to fill existing gaps in the understanding of GOLM1's role in UCEC. By investigating GOLM1's expression in relation to patient survival and immune response, this research seeks to identify GOLM1 as a potential biomarker and contribute to the broader understanding of the molecular mechanisms driving UCEC progression. This research is essential for advancing personalized and effective treatment options for patients diagnosed with this increasingly prevalent malignancy.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003eEvidence from the TCGA database\u003c/p\u003e \u003cp\u003eWe accessed the TCGA database for UCEC (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to gather data on immune system infiltration, gene expression (grade 3 HTSeq-FPKM), and clinical information, which included 35 normal and 554 tumor samples. Based on this data, we performed differential gene expression analysis between the high and low expression groups of GOLM1 in UCEC patients. We conducted Kaplan-Meier (K-M) survival analysis to examine the relationship between GOLM1 expression and the survival status of UCEC patients.\u003c/p\u003e \u003cp\u003eGene set enrichment analysis\u003c/p\u003e \u003cp\u003eRNA-Seq data obtained from TCGA were normalized for GSEA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/gsea/msigdb/collections.jsp\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/gsea/msigdb/collections.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) analysis. We conducted GO and KEGG pathway enrichment analyses to explore the potential biological functions of GOLM1. GO terms were categorized into three groups: biological processes, molecular functions, and cellular components, alongside the KEGG pathways. A result is deemed statistically significant if it satisfies two criteria: a false discovery rate (FDR) below 0.05 and a nominal p-value below 0.05.\u003c/p\u003e \u003cp\u003eTumor invasion level as analyzed by immune cells\u003c/p\u003e \u003cp\u003eTIMER is a detailed resource for researching the molecular signatures of tumor-immune interactions across different types of cancer (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cistrome.shinyapps.io/timer/\u003c/span\u003e\u003cspan address=\"https://cistrome.shinyapps.io/timer/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). TIMER uses deconvolution statistics to estimate the abundance of six types of tumor-infiltrating immune cells: B cells, CD4 T cells, CD8 T cells, macrophages, neutrophils, and DCs, based on data from the Cancer Genome Atlas (TCGA). This section analyzes GOLM1 expression across different cancer types and its correlation with immune cell infiltration. The correlation module explores the relationship between GOLM1 expression and markers of tumor invasion. GEPIA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gepia.cancer-pku.cn/?from=timeline\u0026amp;isappinstalled=0\u003c/span\u003e\u003cspan address=\"http://gepia.cancer-pku.cn/?from=timeline\u0026amp;isappinstalled=0\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to further validate the genes that showed significant associations in TIMER.\u003c/p\u003e \u003cp\u003eAnalysis of GOLM1 regulated by DNA methylation\u003c/p\u003e \u003cp\u003eMethSurv is a web tool that facilitates multivariable survival analysis using DNA methylation data, enabling researchers to study methylation signatures in various diseases. In this context, the tool was utilized to analyze the DNA methylation regulation of GOLM1 in UCEC and the associated survival curves.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eUnivariate and multivariate models of Cox analysis were used to calculate 95% confidence intervals (CIs) and hazard ratios (HRs). We conducted univariate survival analyses to compare different clinical features with survival outcomes. Logistic regression analysis was employed to evaluate the correlation between clinical features and GOLM1 expression. All statistical analyses were performed using R software version 4.2.1.Statistical significance was expressed as *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; and ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.005.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eGOLM1 mRNA expression was elevated in UCEC tumors\u003c/h2\u003e \u003cp\u003eWe analyzed the correlation between GOLM1 and UCEC using the TCGA database to compare GOLM1 mRNA levels in normal and tumor tissues. We also used GEPIA2 to compare GOLM1 expression levels in normal and tumor tissues. Unpaired data showed that GOLM1 expression was significantly higher in UCEC tumor tissues than in normal tissues (Fig.\u0026nbsp;1A). Paired samples showed high expression of GOLM1 in UCEC tumor tissues relative to normal tissues (Fig.\u0026nbsp;1B and Fig.\u0026nbsp;1C). We investigated the relationship between GOLM1 expression levels and survival in UCEC patients. Figure\u0026nbsp;1D shows a significant correlation between GOLM1 expression and UCEC prognosis (HR\u0026thinsp;=\u0026thinsp;0.47, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In the GEPIA online database, GOLM1 expression significantly correlated with disease-free survival (DFS) in UCEC patients (HR\u0026thinsp;=\u0026thinsp;0.5, P\u0026thinsp;=\u0026thinsp;0.045). Next, we analyzed the correlation between UCEC overall survival and multivariate characteristics. Univariate analysis showed that age (HR\u0026thinsp;=\u0026thinsp;1.85, P\u0026thinsp;=\u0026thinsp;0.009), clinical stage (HR\u0026thinsp;=\u0026thinsp;3.553, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), tissue grade (HR\u0026thinsp;=\u0026thinsp;3.298, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), residual tumor (HR\u0026thinsp;=\u0026thinsp;3.112, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), tumor invasion (HR\u0026thinsp;=\u0026thinsp;2.825, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), radiotherapy (HR\u0026thinsp;=\u0026thinsp;0.596, P\u0026thinsp;=\u0026thinsp;0.019), and GOLM1 expression (HR\u0026thinsp;=\u0026thinsp;0.47, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were significantly correlated with OS (Table\u0026nbsp;1). Multivariate analyses indicated that GOLM1 expression (HR\u0026thinsp;=\u0026thinsp;1.022, P\u0026thinsp;=\u0026thinsp;0.944) was not an independent prognostic factor (Fig.\u0026nbsp;1F and Table\u0026nbsp;1). We examined the distribution of GOLM1 expression, survival status, and risk scores in UCEC patients. Figure\u0026nbsp;1G illustrates that the low-risk group had lower GOLM1 expression levels and higher survival rates compared to the high-risk group. We further evaluated the correlation between GOLM1 expression levels and various clinicopathological factors in UCEC patients. Elevated GOLM1 expression was significantly correlated with clinical stage (P\u0026thinsp;\u0026lt;\u0026thinsp;0.005, Fig.\u0026nbsp;1H), tissue grade (P\u0026thinsp;\u0026lt;\u0026thinsp;0.005, Fig.\u0026nbsp;1I), and OS events (P\u0026thinsp;\u0026lt;\u0026thinsp;0.005, Fig.\u0026nbsp;1J). In conclusion, our findings indicate that high GOLM1 expression is associated with advanced clinical stages and higher tissue grades in UCEC, potentially influencing tumorigenesis and progression. High levels of GOLM1 may influence tumorigenesis and progression of UCEC.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIdentification and enrichment analysis of differentially expressed genes\u003c/h3\u003e\n\u003cp\u003eWe identified 4,386 differentially expressed genes (DEGs) between the high and low GOLM1 groups, consisting of 654 up-regulated genes and 3,732 down-regulated genes, as shown in the volcano plot (Fig.\u0026nbsp;2A). We performed Gene Set Enrichment Analysis (GSEA) to identify significant differences in the enrichment of GO and KEGG pathways related to GOLM1 between the high and low expression groups. GOLM1-related genes were linked to 86 KEGG pathways (|NES|\u0026gt; 1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), including 15 pathways with positive correlations (NES\u0026thinsp;\u0026gt;\u0026thinsp;1) and 19 pathways with negative correlations (NES\u0026lt; -1). The KEGG analysis identified five categories positively correlated with high GOLM1 levels: intraflagellar transporters binding to dynein, genes involved in primary ciliary development, reactions for ethanol oxidation, fatty acid metabolism, and protein output. KEGG analysis also revealed five classes with negative correlations: transcriptional regulation of pluripotent stem cells, formation of keratinizing envelopes, sleep regulation, collagen degradation, and voltage-gated potassium ion channels (Figs.\u0026nbsp;2B, 2C). The GO pathway analysis revealed significant enrichment of related genes in 157 pathways, which included 57 pathways with positive correlations (NES\u0026thinsp;\u0026gt;\u0026thinsp;1) and 23 pathways with negative correlations (NES\u0026thinsp;\u0026lt;\u0026thinsp;\u0026minus;\u0026thinsp;1). The five pathways most strongly correlated with GOLM1 expression include the chromosome centromere region, condensed chromosome centromeric region, nuclear chromosome, chromosomal region, and central spindle region. The five most negatively correlated pathways are: collagen containing extracellular interstitium, basement membrane, external encapsulation structure, collagen trimer complex, and collagen (Fig.\u0026nbsp;2D-2G, Table\u0026nbsp;3). These results suggest that pathways regulating protein trafficking, fatty acid metabolism, and chromosome signaling are crucial in UCEC patients and are closely related to GOLM1 expression.\u003c/p\u003e\n\u003ch3\u003eCorrelation between GOLM1 expression and tumor invasion levels of immune cells in UCEC\u003c/h3\u003e\n\u003cp\u003eThis study aims to evaluate the correlation between GOLM1 expression and immune cell infiltration in uterine corpus endometrial carcinoma (UCEC). We used TIMER to analyze the correlation between GOLM1 expression and tumor-infiltrating immune cells (TIICs) in UCEC. The results showed that GOLM1 expression correlated with CD8 T cell levels (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and CD4 T cell levels (p\u0026thinsp;=\u0026thinsp;0.02), but not with B cell levels (p\u0026thinsp;=\u0026thinsp;0.68) (Fig.\u0026nbsp;3A and 3B). These findings were validated in the TCGA database, where GOLM1 expression was positively correlated with T cells and Th cells among the 24 immune-infiltrating cell subtypes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01 by Spearman, Fig.\u0026nbsp;3C, Table\u0026nbsp;4). These results demonstrate that GOLM1 plays an important role in the immune infiltration of UCEC. To assess the immune microenvironment in UCEC patients with high and low GOLM1 expression, We generated heat maps of infiltrating immune cells in tumor samples to reveal correlations between different types of TIICs (Fig.\u0026nbsp;3D). we also compared the levels of 24 immune-infiltrating cell subtypes between the two groups (Fig.\u0026nbsp;3E). The results indicated that GOLM1 significantly affected the levels of activated dendritic cells (aDC), eosinophils, immature dendritic cells (iDC), macrophages, NK CD56bright cells, T helper cells, central memory T cells (Tcm), Th1 cells, and Th17 cells. In the next step, we continued to verify the correlation between GOLM1 expression and immune marker genes in different immune cells, including Th1, Th17, T helper, TCM, M1, M2, and eosinophils. Our findings revealed a significant correlation between GOLM1 expression and various immune markers in different T cell subtypes(Table\u0026nbsp;5). Additionally, we validated the relationship between GOLM1 expression and different immune cell markers (Figs.\u0026nbsp;4A and 4B).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eThe correlation between GOLM1 expression and related genes in UCEC ranked the top 15\u003c/h2\u003e \u003cp\u003eThe correlation heatmap displays the top 15 genes linked to GOLM1 expression in UCEC patients (R\u0026thinsp;\u0026gt;\u0026thinsp;0.3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 by Spearman, Fig.\u0026nbsp;5A and Table\u0026nbsp;6). We examined the expression profiles of the top 15 genes associated with GOLM1 and their corresponding normal controls in UCEC using the TCGA database, as shown in Fig.\u0026nbsp;5B (unpaired samples) and Fig.\u0026nbsp;5C (paired samples). The analysis of unpaired samples revealed significant elevation of several genes, including C9orf152, ELAPOR1, ALG2, BMPR1B, MANSC1, TFF3, MFAP3L, SPDEF, and ARFGEF3, in tumor groups. In the paired sample analysis, we found that the expression of C9orf152, AC084866.1, ELAPOR1, SPDEF, and ARFGEF3 was significantly higher in the tumor group than in the normal controls. Lastly, we explored the prognostic significance of the top 15 GOLM1-related genes that exhibited notable expression differences in UCEC, based on data from the TCGA database, as recorded in the TCGA database. The results indicated that C9orf152, ELAPOR1, ALG2, BMPR1B, TFF3, MFAP3L, and SPDEF were significantly associated with overall survival (OS) events (Supplementary Fig.\u0026nbsp;1).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCorrelation analysis between GOLM1 expression and DNA methylation in UCEC\u003c/h3\u003e\n\u003cp\u003eWe presented a correlation heatmap (Fig.\u0026nbsp;6a, Table\u0026nbsp;7) illustrating the DNA methylation sites associated with GOPM1 expression in UCEC patients. We examined changes in DNA methylation sites in UCEC patients through the TCGA database. The results indicated that CG12410273, CG13737695, CG13850654, and CG14574047 exhibited significant expression levels. Furthermore, we explored the prognostic significance of the identified methylation sites in UCEC. The results indicated that CG12410273, CG13737695, and CG13850654 were significantly associated with overall survival (OS) in UCEC patients.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eEndometrial cancer (UCEC) represents one of the most prevalent malignancies affecting the female reproductive system, posing significant health risks and economic burdens on society. The incidence of UCEC has been steadily rising, particularly among postmenopausal women. This increase results in a lower quality of life and higher healthcare costs for patients. Current treatment modalities, including surgical intervention, radiotherapy, and chemotherapy, exhibit limitations, particularly in advanced cases, where efficacy is often insufficient and recurrence rates remain high. Therefore, it is crucial to identify new prognostic biomarkers and therapeutic targets to improve treatment outcomes and increase survival rates for UCEC patients [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study focuses on the expression and role of the Golgi membrane protein 1 (GOLM1) gene in UCEC. Previous research has indicated a correlation between GOLM1 expression and the progression of various tumors, particularly in relation to tumor cell proliferation and metastasis. Elevated GOLM1 levels have been linked to tumor immune microenvironments and patient prognosis. This investigation aims to clarify the potential of GOLM1 as a prognostic biomarker in uterine cancer (UCEC). It focuses on immune infiltration and survival analysis, using bioinformatics methods with data from The Cancer Genome Atlas (TCGA) [2][3][4]. This study offers insights into the role of GOLM1 in UCEC and emphasizes its importance for future therapeutic strategies.\u003c/p\u003e \u003cp\u003eIn this study, we investigated the expression of GOLM1 in uterine cancer and its potential implications in disease prognosis and immune response.Our analysis showed that GOLM1 mRNA levels were significantly higher in uterine cancer (UCEC) tissues than in normal endometrial tissues (HR\u0026thinsp;=\u0026thinsp;0.47, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).. This aligns with previous findings that GOLM1 is involved in various cancers and may serve as a crucial biomarker for tumor progression [2]. The elevated levels of GOLM1 highlight its importance in the pathogenesis of uterine cancer and suggest that it could be leveraged as a predictive marker for patient outcomes.\u003c/p\u003e \u003cp\u003eMoreover, we observed that GOLM1 expression was significantly correlated with the infiltration levels of CD8\u0026thinsp;+\u0026thinsp;T cells (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and CD4\u0026thinsp;+\u0026thinsp;T cells (p\u0026thinsp;=\u0026thinsp;0.02), indicating that GOLM1 may play a role in modulating the immune microenvironment in UCEC. This is consistent with the findings that GOLM1 can influence immune responses within tumors, potentially affecting therapeutic outcomes [3]. The connection between GOLM1 expression and immune cell infiltration highlights the necessity of incorporating the tumor immune microenvironment into the evaluation of treatment strategies for UCEC.\u003c/p\u003e \u003cp\u003eRegarding survival analysis, our results indicated that while GOLM1 expression correlated with overall survival in univariate analyses (HR\u0026thinsp;=\u0026thinsp;0.47, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), it did not remain an independent prognostic factor in multivariate analyses (HR\u0026thinsp;=\u0026thinsp;1.022, p\u0026thinsp;=\u0026thinsp;0.944). This indicates that although GOLM1 may influence survival outcomes, other factors likely have a greater impact on prognosis [5]. Further investigation is warranted to elucidate the interplay between GOLM1 expression and other clinical factors, which could enhance the understanding of UCEC progression and inform treatment decisions.\u003c/p\u003e \u003cp\u003eOur pathway analysis showed that GOLM1 expression correlates with various signaling pathways, particularly those related to lipid metabolism and cellular signaling. This underscores the multifaceted roles of GOLM1 in cellular processes and its potential as a therapeutic target [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The exploration of these pathways could provide insights into novel treatment strategies that target GOLM1-related mechanisms in uterine cancer.\u003c/p\u003e \u003cp\u003eFinally, our DNA methylation analysis revealed significant correlations between GOLM1 expression and specific methylation sites, suggesting that epigenetic modifications may regulate GOLM1 levels in UCEC [6]. Understanding how DNA methylation influences GOLM1 expression could facilitate the development of epigenetic therapies to reverse abnormal GOLM1 expression in cancer.\u003c/p\u003e \u003cp\u003eIn summary, our findings indicate that GOLM1 is a promising biomarker in UCEC, with implications for patient prognosis and immune response modulation. Future research should focus on validating these results in larger cohorts and exploring the potential for GOLM1-targeted therapies in improving treatment outcomes for patients with uterine cancer.\u003c/p\u003e \u003cp\u003eThe limitations of this study warrant careful consideration. Firstly, the lack of wet laboratory validation poses a significant challenge, as the findings rely solely on bioinformatics analyses derived from the TCGA database. This dependence on computational data, while informative, restricts the confirmation of GOLM1\u0026rsquo;s role in UCEC through experimental approaches. Moreover, the small sample size could affect the reliability and generalizability of our results, as it may not fully capture the diversity of UCEC. Furthermore, we did not perform an extensive analysis of clinical features correlated with GOLM1 expression, which could provide deeper insights into the clinical implications of our findings. Lastly, variations in data collection methodologies across different studies may introduce batch effects, complicating the interpretation of our results.\u003c/p\u003e \u003cp\u003eIn conclusion, this research highlights the significant association between GOLM1 expression and patient prognosis in UCEC, alongside its impact on immune infiltration. Our findings suggest that GOLM1 could serve as a promising biomarker for prognosis and therapeutic targeting in UCEC. Future investigations should aim to validate these findings through experimental studies and explore the broader implications of GOLM1 in various UCEC subtypes. This work not only contributes to the current understanding of UCEC biology but also paves the way for potential clinical applications that could enhance patient management and treatment outcomes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eUCEC:\u003c/strong\u003e Endometrial cancer\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGEPIA:\u003c/strong\u003eGene Expression Profiling Interactive Analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGO:\u003c/strong\u003eGene Ontology\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGSEA:\u003c/em\u003e\u003c/strong\u003eGene Set Enrichment Analysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGOLM1:\u003c/strong\u003eI\u0026nbsp;Golgi membrane protein 1\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKEGG:\u003c/strong\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eK-M survival:\u003c/strong\u003eKaplan-Meier survival\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNES:\u003c/strong\u003eNormalized enrichment score\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTCGA:\u003c/strong\u003eThe Cancer Genome Atlas\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTIIC:\u003c/strong\u003eTumor-infiltrating immune cells\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTIMER:\u003c/strong\u003eTumor immune estimation resource\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp;\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003eFunding for this project was provided by the Kuanren Talents Program and Senior Medical Talents Program of the second affiliated hospital of Chongqing Medical University, as well as the National Natural Science Foundation of China.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHui Wang、Xiaodong Luo and Jianguo Hu contributed equally to this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDepartment of Obstetrics and Gynecology, Second Affiliated Hospital, Chongqing Medical University, Linjiang Road, No. 76, Chongqing 400010, China;\u0026nbsp;Hui Wang\u0026nbsp;\u0026amp;\u0026nbsp;Jianguo Hu\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp;\u003c/strong\u003eHui Wang contributed to the conception and design of the study. Hui Wang acquisition of data, analysis, and interpretation of data. Hui Wang wrote the main manuscript text, prepared figures, and statistical analysis. Xiaodong Luo and Jianguo Hu revised the manuscript and supervised the project. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not require ethical approval since it was not directly involving the use of human or animal subjects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe publication of the article has the agreement of all authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing of interests\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLee EK, Liu JF. Uterine serous carcinoma and uterine carcinosarcoma: molecular features, clinical advances, and emerging therapies. Clin Adv Hematol Oncol. 2024;22(6):301-310. \u003c/li\u003e\n\u003cli\u003eLi W, Wang X, Li B, Lu J, Chen G. Diagnostic significance of overexpression of Golgi membrane protein 1 in prostate cancer. Urology. 2012;80(4):952.e1-952.e9527. doi:10.1016/j.urology.2012.06.017 \u003c/li\u003e\n\u003cli\u003eSong Q, He X, Xiong Y, et al. The functional landscape of Golgi membrane protein 1 (GOLM1) phosphoproteome reveal GOLM1 regulating P53 that promotes malignancy. Cell Death Discov. 2021;7(1):42. Published 2021 Mar 1. doi:10.1038/s41420-021-00422-2 \u003c/li\u003e\n\u003cli\u003eAruna, Li LM. Overexpression of golgi membrane protein 1 promotes non-small-cell carcinoma aggressiveness by regulating the matrix metallopeptidase 13. Am J Cancer Res. 2018;8(3):551-565. Published 2018 Mar 1. \u003c/li\u003e\n\u003cli\u003eNithin KU, Sridhar MG, Srilatha K, Habebullah S. CA 125 is a better marker to differentiate endometrial cancer and abnormal uterine bleeding. Afr Health Sci. 2018;18(4):972-978. doi:10.4314/ahs.v18i4.17 \u003c/li\u003e\n\u003cli\u003eDahl C, Guldberg P. DNA methylation analysis techniques. Biogerontology. 2003;4(4):233-250. doi:10.1023/a:1025103319328 \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 Signaling pathways most significantly correlated with GOLM1 expression based on their NES and FDR\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"643\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 411px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKEGG \u0026nbsp;ID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNES\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;.adj\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFDR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 411px;\"\u003e\n \u003cp\u003eintraflagellar transport proteins binding to dynein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2.752\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 411px;\"\u003e\n \u003cp\u003egenes related to primary cilium development based on crispr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2.519\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 411px;\"\u003e\n \u003cp\u003ereactome ethanol oxidation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2.284\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.012\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.011\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 411px;\"\u003e\n \u003cp\u003efatty acid metabolism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2.204\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 411px;\"\u003e\n \u003cp\u003eprotein exportT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2.075\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.039\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.038\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 411px;\"\u003e\n \u003cp\u003etranscriptional regulation of pluripotent stem cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.761\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.013\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 411px;\"\u003e\n \u003cp\u003eformation of the cornified envelope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.700\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 411px;\"\u003e\n \u003cp\u003esleep regulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.682\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.029\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 411px;\"\u003e\n \u003cp\u003ecollagen degradation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.678\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.008\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.008\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 411px;\"\u003e\n \u003cp\u003evoltage gated potassium channels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e1.645\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.042\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 411px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGO \u0026nbsp;ID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNES\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;.adj\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFDR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 411px;\"\u003e\n \u003cp\u003echromosome centromeric region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2.615\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 411px;\"\u003e\n \u003cp\u003econdensed chromosome centromeric region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2.612\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 411px;\"\u003e\n \u003cp\u003enuclear chromosome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2.556\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 411px;\"\u003e\n \u003cp\u003echromosomal region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2.502\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 411px;\"\u003e\n \u003cp\u003espindle midzone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2.484\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 411px;\"\u003e\n \u003cp\u003ecollagen containing extracellular matrix\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2.226\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 411px;\"\u003e\n \u003cp\u003ebasement membrane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2.148\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 411px;\"\u003e\n \u003cp\u003eexternal encapsulating structure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2.147\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 411px;\"\u003e\n \u003cp\u003ecomplex of collagen trimers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2.089\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 411px;\"\u003e\n \u003cp\u003ecollagen trime\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e2.058\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 3 is available in the Supplementary Files section.\u003c/p\u003e\n\u003cp\u003eTable 4 \u003cstrong\u003eCorrelation analysis between GOLM1 and immune cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003cimg src=\"https://myfiles.space/user_files/122228_c8a1650c59388082/122228_custom_files/img1732545288.png\"\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Table 5 \u003cstrong\u003eCorrelation analysis between GOLM1 and relate markers of immune cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cimg src=\"https://myfiles.space/user_files/122228_c8a1650c59388082/122228_custom_files/img1732545289.png\"\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6 Correlation analysis between GOLM1 and relate genes top 15\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"568\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 122px;\"\u003e\n \u003cp\u003eGene name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 151px;\"\u003e\n \u003cp\u003eGene biotype\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 131px;\"\u003e\n \u003cp\u003ePearson\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 146px;\"\u003e\n \u003cp\u003eSpearman\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eR.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003eP.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 90px;\"\u003e\n \u003cp\u003eR.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003eP.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 122px;\"\u003e\n \u003cp\u003eC9orf152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eProtein coding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.614\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 122px;\"\u003e\n \u003cp\u003eHSDL2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eProtein coding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.582\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 122px;\"\u003e\n \u003cp\u003eMRAP2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eProtein coding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 122px;\"\u003e\n \u003cp\u003eAC084866.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003elncRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.560\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 122px;\"\u003e\n \u003cp\u003eELAPOR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eProtein coding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 122px;\"\u003e\n \u003cp\u003eALG2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eProtein coding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 122px;\"\u003e\n \u003cp\u003eBMPR1B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eProtein coding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.542\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 122px;\"\u003e\n \u003cp\u003eMANSC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eProtein coding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 122px;\"\u003e\n \u003cp\u003eTFF3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eProtein coding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 122px;\"\u003e\n \u003cp\u003eMFAP3L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eProtein coding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 90px;\"\u003e\n \u003cp\u003e0.545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 1px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 122px;\"\u003e\n \u003cp\u003eSPDEF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eProtein coding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 122px;\"\u003e\n \u003cp\u003ePRRT1B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eProtein coding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 122px;\"\u003e\n \u003cp\u003ePBLD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eProtein coding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 122px;\"\u003e\n \u003cp\u003eSPATA18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eProtein coding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 122px;\"\u003e\n \u003cp\u003eARFGEF3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 151px;\"\u003e\n \u003cp\u003eProtein coding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 76px;\"\u003e\n \u003cp\u003e0.524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 55px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 57px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 7 \u003cstrong\u003ePrognostic Value of single CpG of the GOLM1 gene family in UCEC by Methsurv paltform\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cimg src=\"https://myfiles.space/user_files/122228_c8a1650c59388082/122228_custom_files/img1732545345.png\"\u003e\u003c/strong\u003e\u003cbr\u003e\u003c/p\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":"GOLM1, Gene expression, endometrial cancer, UCEC, TCGA","lastPublishedDoi":"10.21203/rs.3.rs-5399574/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5399574/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eEndometrial cancer (UCEC) is a prevalent gynecological cancer that affects women\u0026rsquo;s reproductive organs. While studies indicate that GOLM1 may influence the immune response through the tumor microenvironment, its exact roles and mechanisms in UCEC are still not well understood. This study examines the varied expression levels of GOLM1 in UCEC and investigates the relationship between GOLM1 expression and the prognosis of patients with UCEC.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this study, we analyzed data from the TCGA database. We utilized several bioinformatics methods, such as differential gene expression analysis, Kaplan-Meier survival analysis, gene set enrichment analysis, and immune cell infiltration analysis. These bioinformatics methods systematically integrate clinical data, gene expression, and immune information, offering a comprehensive view of the molecular mechanisms underlying UCEC.This study employed the software tools TCGA, GEPIA, TIMER, and MethSurv.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eGOLM1 mRNA levels were significantly elevated in UCEC tumor tissues. A strong association was found between GOLM1 expression and overall survival in UCEC patients. Enrichment analysis showed that GOLM1 is linked to several biological pathways, including dynein-bound intraflagellar transporters, fatty acid metabolism, and the transcriptional regulation of pluripotent stem cells. Based on these associations, GOLM1 likely plays an important role in the immune microenvironment of UCEC. Furthermore, GOLM1 expression may be influenced by DNA methylation.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eGOLM1 is crucial for UCEC, as its levels significantly influence both the occurrence and progression of the disease. GOLM1 and its related genes are potential immunotherapeutic targets for UCEC. Additionally, DNA methylation may regulate the expression of GOLM1 in patients with UCEC.\u003c/p\u003e","manuscriptTitle":"Prognostic biomarker GOLM1 correlated with immune infiltrates in Endometrial cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-10 06:01:50","doi":"10.21203/rs.3.rs-5399574/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":"86ccfbf6-312a-4513-8017-874f2a4d7656","owner":[],"postedDate":"December 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-10T06:01:54+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-10 06:01:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5399574","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5399574","identity":"rs-5399574","version":["v1"]},"buildId":"veTbxFhMMB0_faC6-Wkog","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}