Pan-cancer analysis of UDP-glucose 6-dehydrogenase and its carcinogenesis in hepatocellular carcinoma

Pan-cancer analysis of UDP-glucose 6-dehydrogenase and its carcinogenesis in hepatocellular carcinoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Pan-cancer analysis of UDP-glucose 6-dehydrogenase and its carcinogenesis in hepatocellular carcinoma Xu Cao, Size Li, Baiquan Xue, Li Hou, Shihao Zheng, Jiaxin Zhang, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4632654/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 Backgrounds: Abnormalities in glycometabolism lead to carcinogenesis. UDP-glucose 6-dehydrogenase (UGDH) is the key enzyme of glucuronic acid metabolism and acts as a key mediator in several cancer developmental signaling pathways. In this study, our objective is to offer a more systematic and comprehensive elucidation of the involvement of UGDH in the onset and advancement of various malignancies via an in-depth analysis of UGDH in cancer contexts. Method: We investigated the role of UGDH in cancers using the Human Protein Atlas (HPA), The Cancer Genome Atlas (TCGA), and Genotype-Tissue Expression (GTEx) databases. And analyzed data using various R packages and websites, including TISIDB, cBioPortal, STRING, Cytoscape, GSCALite, and CancerSEA. A rat hepatocellular carcinoma (HCC) model was established using intraperitoneal injection of diethylnitrosamine. Hematoxylin-Eosin (HE) staining, MASSON staining, and KI67 immunohistochemistry of liver tissues were performed. Real-time quantitative PCR (qRT-PCR) and western blotting (WB) were used to detect the expression of UGDH. UGDH gene was knocked down in Huh7 cells, and CCK8 and nude mice tumor xenograft assays were further performed. Results: UGDH high expression is associated with poor clinical outcomes in hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, and sarcoma. And differentially expressed across molecular and immune subtypes. UGDH was primarily involved in the pentose and glucuronate interconversion pathway. Its expression positively correlated with T helper, Tcm, and Th2 cells in most cancers. Moreover, experimental results demonstrated that UGDH expression is elevated in liver cancer and promotes the proliferation of HCC. Conclusions: Our study elucidates that UGDH could be used as a valuable prognostic biomarker and potential therapeutic target in many cancers, especially liver and lung cancer. UGDH could promote the proliferation of HCC cells, possibly by modulating the pentose and glucuronate interconversion pathway. UGDH hepatocellular carcinoma bioinformatics pan-cancer pentose and glucuronate interconversion Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 INTRODUCTION Cancer has been recognized as one of the leading causes of death worldwide [ 1 ]. With the continuous development of chemotherapy drugs, the problem of tumor drug resistance has become increasingly prominent [ 2 ]. Metabolic reprogramming is one of the typical features of cancer, where cancer cells need to change their metabolic state in response to proliferative signals delivered by oncogene signaling pathways [ 3 ]. Tumor is also a metabolic disease, and the specific microenvironment and metabolites further affect the metabolic phenotype of tumor cells, thereby influencing tumor progression, treatment, and prognosis [ 4 ]. Current research indicates that the metabolism of uridine diphosphate glucuronic acid (UDPGA) is important during cancer development, and disruption of sugar nucleotide clearance is a therapeutic vulnerability of cancer cells [ 5 ]. UDP-glucose 6-dehydrogenase (UGDH) is strongly associated with tumor drug resistance [ 6 ]. The protein encoded by this gene is a rate-limiting enzyme that converts UDP-glucose to UDP-glucuronate, thereby participating in the biosynthesis of glycosaminoglycans such as hyaluronan, chondroitin sulfate, and heparan sulfate. These glycosylated compounds are common components of the extracellular matrix and likely play roles in signal transduction, cell migration, cancer growth, and cancer metastasis [ 7 ]. The role of UGDH in cancer has received extensive attention from researchers in recent years. Abnormally elevated UGDH expression has been reported to be associated with the development of malignant tumors such as lung cancer [ 8 ], hepatocellular carcinoma (HCC) [ 9 ], pancreatic ductal adenocarcinoma [ 10 ], breast cancer [ 11 ], ovarian cancer [ 12 ], prostate cancer [ 13 ], and glioblastoma [ 14 ], as well as being correlated with poor prognosis of a variety of cancers. UGDH has broad and diverse regulatory effects in cancer and is closely associated with tumor drug resistance [ 15 ], epithelial-mesenchymal transition [ 16 ], and cellular localization [ 17 ], participating in pathways such as the sugar nucleotide biosynthetic [ 5 ] and TGFβ signaling [ 18 ] pathways, which is of great significance for cancer treatment. Therefore, UGDH is an attractive potential target for tumor diagnosis and therapy. It has been found that tumors from different organs but of the same histological type and tumors with anatomical structures belonging to the same system have strong molecular similarities [ 19 ]. Therefore, exploring the phenotypic characteristics of pan-cancer molecules will help to elucidate their commonalities in tumors and their intrinsic regulatory mechanisms. In this study, we observed the characterization of 33 cancers in a pan-cancer analysis using The Cancer Genome Atlas (TCGA), the Genotype Tissue Expression (GTEx), and the Human Protein Atlas (HPA) databases. We used bioinformatics to comprehensively analyze the expression, diagnostic value, clinical prognosis, and functional enrichment of UGDH. The relationship between UGDH and tumor-infiltrating lymphocytes, major histocompatibility complex (MHC) molecules, immunostimulators, immunoinhibitors, chemokines, and chemokine receptors was then further explored. Through experiments of western blot and Quantitative Real-time Polymerase Chain Reaction, we detected UGDH expression in the liver tissue of HCC rats. Following the UGDH knockdown of Huh7 cells, we employed CCK8 and nude mice tumor xenograft assays to evaluate the effect of UGDH on HCC cell proliferation. MATERIALS AND METHODS Expression Analysis and Datasets Sources of UGDH We systematically queried the Human Protein Atlas (HPA) database ( https://www.proteinatlas.org/ ) to gather comprehensive data on UGDH RNA and protein expression in human tissues and cell lines. Additionally, we utilized The Cancer Genome Atlas (TCGA) ( https://cancergenome.nih.gov ) and the Genotype-Tissue Expression (GTEx) project ( https://gtexportal.org/ ) to acquire detailed information on UGDH mRNA expression in tumor samples, corresponding paracancerous tissues, and normal controls. Samples with 0 gene expression values were excluded. Only paired samples were used for paired sample analyses. RNA sequencing data, originally in Fragments Per Kilobase per Million (FPKM) format, were converted and normalized through the Toil process to transcripts per million reads and log2-transformed for subsequent analysis. Statistical analyses were conducted using R software (version 4.2.1), “stats” (version 4.2.1), and “car” (version 3.1-0). The UGDH gene expression across the 33 cancer types was visualized using the “ggplot2” package (version 3.3.6). The median expression method was adopted to set cutoff values. To assess the expression differences between groups, the Wilcoxon rank-sum test was applied. Receiver Operator Characteristic (ROC) Curve Analysis of UGDH In the study of 33 cancer types, ROC curves were employed to evaluate the diagnostic value of UGDH. These ROC curves were constructed using mRNA expression data of UGDH in cancerous and corresponding normal tissues from TCGA and GTEx databases. The ROC analysis was performed using the “pROC” package (version 1.18.0) in R, and the curves were plotted using the “ggplot2” package (version 3.3.6). Diagnostic metrics calculated included the Area Under the Curve (AUC), cutoff values, sensitivity, specificity, positive predictive value, and negative predictive value. The AUC value close to 1 indicates superior diagnostic accuracy. Survival Analysis of UGDH Kaplan-Meier (K-M) survival analysis was conducted using the “survival” package (version 3.3.1) in R (version 4.2.1). This analysis compared overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) rates between high and low UGDH gene expression groups across the 33 cancer types. We also went on to observe the relationship between UGDH and common clinical parameters to tumor types influenced by UGDH on OS prognosis. Survival differences were statistically evaluated using Cox regression analysis. Forest plots illustrating hazard ratios (HR), 95% confidence intervals, and p-values were generated and visualized with the “survminer” (version 3.3.1) and “ggplot2” packages (version 3.3.6). Different Molecular and Immune Subtypes of Cancers in UGDH Expression To investigate the associations between UGDH expression and molecular or immune subtypes in these cancers, we utilized the “subtype” module of the TISIDB database ( http://cis.hku.hk/TISIDB/ ). This database integrates various datasets to assess interactions between cancer and the immune system. We analyzed UGDH mRNA expression across different immune subtypes: C1 (wound healing), C2 (IFN-g dominant), C3 (inflammatory), C4 (lymphocyte deplete), C5 (immunologically quiet), and C6 (TGF-b dominant). Different tumors have their specific molecular types. Genetic Alteration Analysis of UGDH The cBioPortal ( https://www.cbioportal.org/ ) was accessed to gather genetic alteration data on UGDH. We included all TCGA PanCancer Atlas Studies in our search. The frequency of somatic mutations and genomic information regarding UGDH mutations in cancers were examined using the “cancer types summary and mutations” and “mRNA vs. study” modules. Mutation sites were identified through the “mutations” module. Network Analysis and Functional Enrichment Analysis of UGDH Protein interactions of UGDH were investigated using the STRING database ( https://string-db.org/ ). The collected data were used for a Protein-Protein Interaction (PPI) network, setting a confidence score threshold of > 0.7 for significance. This data was then imported into Cytoscape (3.8.0) for visualization and further analysis. The cytoHubba plugins identified key modules and the top 10 nodes, ranked by the Maximal Clique Centrality of cytoHubba, were presented as hub genes. We explored the correlation between hub gene expression and UGDH. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of UGDH PPI-related genes using the DAVID database ( https://david.ncifcrf.gov/ ), with a significance threshold set at a p-value of < 0.05. Results were displayed as a bubble chart using the “ggplot2” package (version 3.3.6). Besides, we used the GSCALite database ( https://guolab.wchscu.cn/GSCA/ ) to focus on identifying the activation or inhibition of key cancer-related pathways in the 33 cancer types involving UGDH. These pathways include TSC/mTOR, RTK, RAS/MAPK, PI3K/AKT, Hormone ER, Hormone AR, Epithelial-Mesenchymal Transition (EMT), DDR, Cell Cycle, and Apoptosis. Gene Set Enrichment Analysis (GSEA) of UGDH GSEA was conducted using the “clusterProfiler” package (version 4.4.4) to elucidate biological pathway variations between high- and low-UGDH expression groups. Pathways with a false discovery rate (FDR) < 0.25 and an adjusted p-value < 0.05 were deemed significantly altered. We performed gene set permutation 1,000 times for each analysis. The top 5 enriched pathways are presented as mountain maps, visualized using the “ggplot2” package (version 3.3.6). Functional States Analysis of UGDH in CancerSEA The functional status of UGDH in various cancers was examined using the CancerSEA database. We investigated the average correlation between UGDH and functional states in 18 cancers, including invasion, metastasis, proliferation, EMT, angiogenesis, apoptosis, cell cycle, differentiation, DNA damage, DNA repair, hypoxia, inflammation, quiescence, and stemness. A correlation strength > 0.3 absolute value and a p-value < 0.05 were set as thresholds for significance in correlating UGDH with cancer functional states. Immunogenomic Analyses of UGDH The correlation between UGDH expression and tumor-infiltrating lymphocytes, MHC molecules, immunostimulators, immunoinhibitors, chemokines, and chemokine receptors in 33 cancers was assessed using the “GSVA” package (version 1.44.5) with the ssGSEA algorithm. Statistical significance was determined using Spearman’s correlation, with p-values < 0.05 indicating significant correlation. The “ggplot2” package (version 3.3.6) was utilized to visualize these correlations as heatmaps. Animals of Experiment 4-week-old male BALB/C nude mice and 6-week-old male Wistar rats were purchased from Beijing Vital River Laboratory Animal Technology Co. Ltd (license: SCXK (Beijing) 2016-0006). All animals were raised in the Barrier Environmental Animal Laboratory of Dongzhimen Hospital of Beijing University of Chinese Medicine (license: SYXK (Beijing) 2015-0001) and maintained under the National Standards for Laboratory Animals of China (GB14925-2010). Our study was carried out in compliance with the ARRIVE guidelines. This study was approved by the Ethics Committee of Laboratory Animals of Dongzhimen Hospital of Beijing University of Chinese Medicine (No.21-10-01 and 21-46-01). Animals were kept separately in an SPF laboratory, with the breeding environment: temperature 25 ± 1°C, humidity 50 ± 10%, free of food and drinking water, 12-hour day and night alternation, as well as adaptable feeding for 5 days. Design of Rat Hepatocellular Carcinoma Experiment The hepatocellular carcinoma rat model was constructed by intraperitoneal injection of diethylnitrosamine (50 mg/kg/week, Psaitong, N60001, CN) for sixteen consecutive weeks. At the end of the 16th week, after 12 hours of fasting, the experimental animals were taken to observe the condition of the liver. The rats were anesthetized by intraperitoneal injection at 0.2 ml/100g of 3% pentobarbital sodium solution and the largest lobe of liver tissue was taken and kept in tissue fixation fluid, the rest of the liver tissue was placed in -80 ℃ refrigerator for storage. Cell Line of Experiment Hepatoma cell lines including Huh7 were purchased from the American Type Culture Collection (Manassas, VA, USA) and maintained in Dulbecco’s modified eagle medium (DMEM) supplemented with 10% fetal bovine serum (FBS), 100 U/mL penicillin, 100 µg/mL streptomycin and 5% CO 2 . Knockdown of UGDH 24 hours before transfection, 2×10 5 cells were seeded in 400 µL of antibiotic-free medium at a cell confluency of 60–80% for transfection. siRNA was transfected into wells of a plate using Lipo2000 transfection reagent, and diluted to a final concentration of 50 nM. 50 µL of Opti-MEM was used to dilute the siRNA, gently pipetting 3–5 times to mix. The 1.0 µL transfection reagent was diluted in 50 µL of Opti-MEM, mixed gently by pipetting 3–5 times, and left at room temperature for 5 minutes. The siRNA was combined with Lipofectamine 2000, gently mixed by pipetting 3–5 times and incubated at room temperature for 20 minutes. The transfection complex (100 µL per well) was added evenly across the cell plate. The si-RNA used in this study is shown in Table 1 . Table 1 The si-RNA sequence of UGDH gene iUGDH-1 sense GAUGUCAAUGAAUCAAGAATT antisense UUCUUGAUUCAUUGACAUCTT iUGDH-2 sense CAGCAUUAACUCCAUAAGUTT antisense ACUUAUGGAGUUAAUGCUGTT iUGDH-3 sense GAUUAUGAACGCAUUCAUATT antisense UAUGAAUGCGUUCAUAAUCTT Cell Viability Assay After knocking down the UGDH according to the instruction manual, 5×10 3 cells were seeded in 96-well plates with six duplications, after being incubated for 24, 48, and 72 hours. The CCK-8 assay kit (Solarbio, CN) was carried out to assess the ability of cell growth by measuring the absorbance at the wavelength of 450 nm by the TECAN infinite M200 Multimode microplate reader (Tecan, Mechelen, Belgium). Design of Nude Mice Tumor Xenograft Assay The detailed protocol for model construction of nude mice tumor xenograft was described in the previous study [ 20 ]. Injected the amount of 5 × 10 6 Huh7 cells into the underarm skin of 4-week-old male BLAB/c nude mice. After 2 weeks of injection, the transplanted tumor formed. The mice were sacrificed and tumors were obtained after 2 weeks of regular feeding. Quantitative Real-time Polymerase Chain Reaction (qRT-PCR) The reverse transcription of total RNA to cDNA was performed with a qPCR RT Master Mix kit (TOYOBO, JAN). qRT-PCR was performed using the Real-time PCR Detection System (Agilent Technologies, US) with the SYBR Green Real-time PCR Master Mix (TOYOBO, JAN). We used GAPDH and Actb as an internal control gene. The experiments were performed in triplicate and repeated 3 times. The primers used in this study are shown in Table 2 . Table 2 The primers of genes UGDH Forward Primer CCCTGTGTGCTGTATATGAGC Reverse Primer TGCTTATTCTCTGGGCAAGAAAA GAPDH Forward Primer GGAGCGAGATCCCTCCAAAAT Reverse Primer GGCTGTTGTCATACTTCTCATGG Ugdh Forward Primer ACTTGAATCTACAGGTTCTGTC Reverse Primer CTCTGTCTGGGTTCTTTAGG Actb Forward Primer CTTCCTGGGTATGGAATCCT Reverse Primer TCTTTACGGATGTCAACGTC Western blot (WB) The protein lysates were separated by 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) (Epizyme Biomedical Technology, PG112, CN) and then electrophoretically transferred onto the polyvinylidene fluoride membranes (Epizyme Biomedical Technology, WJ001, CN). The primary antibodies we used were anti-UGDH (Proteintech, 67360-1-Ig, 1:1000, US) and anti-GAPDH (MBL, M171-3, 1:5000, JPN). The second antibody we used was HRP-conjugated Affinipure Goat Anti-Mouse IgG (H + L) (Proteintech, SA00001-1, 1:8000, US). The Image J software was used to analyze the integrated density of the protein bands. RESULTS Expression Landscape of UGDH The mRNA and protein of UGDH were widely expressed in various organs and tissues, and most significantly expressed in the Liver, Gallbladder, and Gastrointestinal tract (Fig. 1 A). The result obtained from the consensus dataset, which included 375 normal tissues in the HPA database and 13,084 samples in GTEx, showed the top 10 mRNA of UGDH expressed primarily in the Liver, Colon, Rectum, Urinary bladder, Adipose tissue, Stomach, Duodenum, Small intestineine, Gallbladder, and Placenta (Fig. 1 B). The protein of UGDH data was acquired from the HPA database which has 144 individuals corresponding to 44 samples of different normal tissue types. The protein of UGDH is mainly expressed in the Bronchus, Esophagus, Stomach, Colon, Liver, Prostate, Endommetrium, and Appendix (Fig. 1 C). UGDH mRNA expression in different tissues and cell lines was shown (Fig. 1 D), and the highest expression was found in Liver cancer, Gallbladder cancer, Kidney cancer, Non-cancerous, and Lung cancer. Expression of UGDH in the 33 Cancers The cancer types and its abbreviation were shown in Table 3. The UGDH mRNA expression was evaluated in the 33 cancer types from TCGA in cancer and normal tissues with statistically significant differences in BRCA, COAD, GBM, HNSC, KICH, KIRC, KIRP, LIHC, LUAD, LUSC, PCPG, PRAD, READ, STAD, THCA, and UCEC (Fig. 2 A). The UGDH mRNA expression was evaluated in the 33 cancer types from TCGA and GTEx in cancer and normal tissues with statistically significant differences in ACC, BRCA, COAD, DLBC, ESCA, GBM, HNSC, KICH, LAML, LGG, LIHC, LUAD, LUSC, OV, PAAD, PCPG, PRAD, READ, SKCM, STAD, TGCT, THYM, UCEC, and UCS with statistical differences (Fig. 2 B). MESO and UVM could not be analyzed due to the lack of sufficient non-cancerous tissue samples. Statistically significant differences in UGDH mRNA expression in BRCA, COAD, HNSC, KICH, LIHC, LUAD, LUSC, PRAD, READ, THCA, and UCEC were assessed in 23 carcinomas versus paracarcinomas by paired-sample analysis (Fig. 2 C). Table 3 | The abbreviation 33 types of cancers in TCGA Diagnostic Value of UGDH in the 33 Cancers As shown in Fig. 3 A–F, UGDH exhibited significant diagnostic potential across a diverse array of cancers. Its AUC was greater than 0.7 in 12 cancers (Figure supplement 1) and even more than 0.9 in 6 cancers, including GBM (AUC = 0.978), LAML (AUC = 0.997), LGG (AUC = 0.952), LUAD (AUC = 0.911), PCPG (AUC = 0.964) and TGCT (AUC = 0.925), indicating its high diagnostic value. It is worth noting that the number of cases in the PCPG was too low (N = 3) and its results could be biased. While in ACC, BRCA, DLBC, ESCA, KICH, LUSC, PAAD, PRAD, SARC, THYM, UCEC, and UCS with AUC of the ROC exceeded 0.7, which also has some diagnostic implications (Figure Supplement 1). Survival Analysis of UGDH in the 33 Cancers To evaluate the prognosis value of UGDH, we carried out the K-M analysis in 33 cancers. The types of cancers in which UGDH expression affects OS in 10 cancers, and another 23 cancer types whose effects were not apparent. Highly expressed UGDH in ACC, KIRC, KIRP, LICH, LUAD, and SARC had a lower survival rate, and lowly expressed UGDH in BRCA, LAML, SKCM, and UCS had a higher survival rate (Fig. 4 A-J). The forest plot showed DSS and PFI information for 10 cancers in which UGDH expression was significantly associated with OS, based on Cox regression analyses of the cancers (Fig. 4 K). For LAML, DSS and PFI lack of information. Among them, BRCA, LUAD, and SKCM were not statistically different in DSS, and LUAD and SKCM were still not statistically different in progression-free intervals. The relationship between UGDH and common clinical parameters tumors is shown in Figure Supplement 2. UGDH Expression in Different Immune and Molecular Subtypes of the 33 Cancers From previous results, we found that high or low levels of UGDH expression had an impact on OS in 10 cancers. Therefore, we analyzed UGDH expression in immune and molecular subtypes of 10 cancers. The results showed that UGDH was significantly differentially expressed in 9 of the 10 immune subtypes of cancer, including ACC, BRCA, KIRC, KIRP, LIHC, LUAD, SARC, SKCM, and UCS (Fig. 5 A-I). For molecular subtypes, UGDH was significantly differentially expressed in 5 cancer types, including ACC, BRCA, KIRP, LIHC, and SKCM (Fig. 6 A-E). Genetic Alteration of UGDH Mutations in UGDH expressed in cancer were analyzed by cBioPortal. We identified 84 mutation sites between amino acids 0 and 494, including 67 missense mutations, 12 truncating mutations, 1 in-frame mutation, 2 splices, and 2 fusions, with M432Cfs and Nfs being the most common mutation sites (Fig. 7A). UGDH mutations were most common in the top 10 cancers of UCEC, CHOL, STAD, ACC, LUAD, LIHC, SKCM, UCS, PAAD, and SARC (Fig. 7B). Among the 32 types of cancers, almost all cancers had shallow deletions in the expression of UGDH mRNA, gain is often present in Adrenocortical cancer, while amplification is more frequent in hepatobiliary cancer (Fig. 7C). FIGURE 7 | Genetic alteration of UGDH in pan-cancers. (A) Mutation diagram of UGDH across protein domains. (B) Bar chart of UGDH mutations in 32 cancers. (C) Mutation counts and types of UGDH in 32 cancers. The PPI, Functional Enrichment, and GSEA of UGDH in Cancers A total of 32 genes closely related to UGDH were obtained from the String and the PPI network was constructed (Fig. 8 A). The top 10 pivotal genes were UGDH, UGT1A1, UGT1A3, UGT1A4, UGT1A6, UGT1A7, UGT1A8, UGT1A9, UGT1A10 and GALE, respectively (Fig. 8 B). The first 10 hub genes were mainly associated with BRCA, KIRC, KIRP, LIHC, and LUAD where UGDH expression affected OS prognosis (p < 0.05) (Fig. 8 C). These genes were subjected to GO and KEGG enrichment to analyze function in three categories including biological process (BP), molecular function (MF), and cellular component (CC). The top GO terms for BP were cellular glucuronidation, xenobiotic glucuronidation, and flavonoid glucuronidation. MF was glucuronosyltransferase activity, UDP-glycosyltransferase activity, and enzyme binding. Moreover, CC was the endoplasmic reticulum membrane, an integral component of the membrane, and endoplasmic reticulum. The major pathways of KEGG were Pentose and glucuronate interconversion, Ascorbate and alternate metabolism, and porphyrin metabolism (Fig. 8 D). Besides, UGDH was active in the DNA damage response, EMT, Hormone AR, Hormone ER, P13K/AKT, RAS/MAPK, and TSC/mTOR pathway were both activated and repressed (Fig. 8 E). The GSEA enrichment results for the 10 cancer types were shown in Fig. 9 , and common themes included pathways associated with cell cycle regulation, immune response, metabolism, and cell signaling. These common pathways suggested that UGDH expression has a coordinated role in specific cellular processes across cancer types. Functional States of UGDH in scRNA-Seq Datasets We investigated the functional status of UGDH across various cancer types using CancerSEA, analyzing its correlation with multiple functional states of cancer cells at the single-cell level. The findings indicated that UGDH expression impacted functional status varies widely across various cancers (Fig. 10 A). Further analysis of UGDH's correlation with specific cancers indicated LUAD is positively correlated with DNA repair and negatively correlated with quiescence, metastasis, angiogenesis, and differentiation. ALL and HNSCC are positively correlated with stemness, while NSCLC is negatively correlated with stemness. AML shows a positive correlation with quiescence, differentiation, inflammation, and proliferation. RCC and PC are positively correlated with hypoxia, whereas RB is positively correlated with differentiation, inflammation, and angiogenesis, and negatively correlated with DNA repair, Cell Cycle, and DNA damage. UM is negatively correlated with DNA damage and DNA repair. (Fig. 10 B-I). Immunogenomic Analyses of UGDH in the 33 Cancers To assess the relationship between UGDH expression and immune infiltration and regulation, we generated heatmaps of UGDH with markers of immune cells or factors. Our findings indicated that UGDH expression is positively correlated with the infiltration levels of T helper cells, Tcm, and Th2 across 33 cancer types, and negatively correlated with the infiltration levels of pDC and Cytotoxic cells (Fig. 11 A). UGDH showed positive correlations with most MHC molecules in KICH, PCPG, and UVM, but negative correlations with most MHC molecules in LUAD, LUSC, and TGCT (Fig. 11 B). Interestingly, in terms of immune activation and suppression, there was a positive correlation in ACC, KICH, and UVM, and a negative correlation with LUAD and LUSC. The immunosuppressive factors TGFBR1 and KDR were positively correlated with the expression of UGDH in almost all cancers (Fig. 11 D). Most cytokines in KICH, KIRC, PCPG, and UVM were positively correlated with UGDH, while negative correlations were observed in BRCA, LUAD, and LUSC (Fig. 11 E). Regarding cytokine receptors, UGDH showed positive correlations with most cytokine receptors in BLCA, DLBC, KICH, KIRC, PCPG, PRAD, and UVM, and negative correlations in LUAD and LUSC (Fig. 11 F). UGDH depletion inhibits the proliferation of liver cancer cells The results in Fig. 12 A showed that the model group exhibited disrupted liver tissue structure, characterized by hepatocellular nodules displaying abnormal cellular proliferation and significant fibrous deposition, which revealed the modeling of rat hepatocellular carcinoma was successful. qRT-PCR and WB analyses showed elevated expression of UGDH in rat HCC tissues compared with the control group (Fig. 12 B and 12 C). Further protein blotting analysis showed that UGDH was successfully knocked down in Huh7 cells using different siRNAs compared with the non-targeting control siRNA (Si-NC), and the UGDH protein level was significantly reduced (Fig. 12 D). siRNA-mediated knockdown resulted in a reduction of UGDH mRNA level in Huh7 cells, which enhanced the effectiveness of the gene silencing (Fig. 12 E). Cell viability assay (CCK-8 analysis) demonstrated that the knockdown of UGDH significantly inhibited the proliferative activity of Huh7 cells at 24 and 72 hours. Photographs of xenograft tumors from nude mice showed that siRNA-mediated knockdown of UGDH resulted in a significant reduction in tumor size, illustrating the potential of targeting UGDH in reducing tumor load (Figs. 12 F and 12 G). Taken together, these results suggest that the UGDH plays an important role in the progression of hepatocellular carcinoma and that targeting UGDH may be a viable therapeutic strategy. DISCUSSION UGDH plays a key role in xenobiotic metabolism via the glucuronidation pathway, sugar metabolism, production of ECM precursors, and proteoglycan synthesis, which suggests that it may be a potential therapeutic target for a variety of diseases [ 21 , 6 ]. Previous studies have shown that the expression and localization of UGDH is an early serum diagnostic marker for lung cancer patients, as well as a prognostic indicator [ 17 ]. UDPGA was shown to be a precursor for the synthesis of glycosaminoglycans and proteoglycans that promote the progression of invasive prostate cancer, and the UGDH content in the prostate tip serves as a new candidate biomarker [ 13 ]. The above suggests that UGDH may become a new diagnostic marker in clinical practice. Previous studies have shown that UGDH is positively associated with the development of epirubicin resistance and regulation of the ECM in breast cancer [ 15 ]. Upregulation of UGDH has been associated with metastasis in ovarian cancer, where it regulates tumor-initiating cells affecting the tumor microenvironment [ 12 ]. And it may be related to melanoma development and progression [ 22 ]. Therefore, UGDH has a certain prognostic predictive value. Regarding common clinicopathological parameters of tumors, UGDH expression existed gender difference, pathologic T and M stage in LUAD. Past research has also shown that phosphorylation of UGDH at tyrosine 473 is associated with metastatic recurrence and poor prognosis in lung cancer patients [ 8 ]. Elevated UGDH expression correlated with male gender, vascular invasion, and histologic grade in LIHC. Investigations have shown that UGDH-mediated activation of UDPGA activates TGFβ/Smad signaling thereby promoting the migration of hepatocellular carcinoma cells [ 18 ], and it is associated with sorafenib resistance [ 9 ]. Moreover, the UGDH was correlated with age and cytogenetic risk in LAML, pathologic T stage in SKCM, age in SARC, pathologic stage and gender in KIRP, and histologic grade and pathologic T and M stage in KIRC. UGDH expression differed between various molecular or immune subtypes of cancers, with differences in immune subtypes present in 30 cancer types and differences in molecular subtypes present in 17 cancers. Except for LAML, the cancer types with differences in UGDH expression and immune subtypes were consistent with the cancer types whose expression affected survival prognosis, suggesting that UGDH may intervene in cancer prognosis by affecting immune subtypes. Moreover, in subsequent studies, we focused on analyzing the role of UGDH expression on immunomodulatory factors and major histocompatibility complex molecules and targeting immune lymphocytes. However, it has also been pointed out that genes are aberrantly expressed in specific subtypes of cancer, and the results may not be reflected in the overall patients of that cancer. Thus, differences in gene expression do not affect cancer survival in certain cancers, whereas its varied expression in different molecular or immune subtypes might play different roles in the prognosis of various cancers [ 23 ]. Genetic mutations could induce abnormal gene expression and tumorigenesis. We found that UGDH mutation sites mainly include missense, truncating, inframe, splice, and fusion in the TCGA pan-cancer Atlas study. UGDH has undergone genetic alteration in 24 types of cancer, and the cancer types with alteration frequency greater than 2% include UCEC, CHOL, STAD, ACC, and LUAD. In most cancers, the main type of UGDH gene alteration is mutation, followed by amplification. It is worth noting that the UGDH gene abnormalities in LICH are mainly amplification. To further explore the biological functions of UGDH, we established a PPI network and identified hub genes, and we showed the relationship between hub genes and cancer types in which UGDH expression affects the prognosis of cancer. For the hub genes obtained, GALE encodes UDP-galactose-4-epimerase. Previous studies have shown that it is associated with the differentiation grade of gastric cancer [ 24 ] promotes the proliferation and migration of glioblastoma cells [ 25 ], and is a potential marker for papillary thyroid carcinoma [ 26 , 27 ]. The other hub genes belong to the UDP glucuronosyltransferase family, which encodes UDP- glucuronosyltransferase, an enzyme of the glucuronidation pathway that transforms small lipophilic molecules, such as steroids, bilirubin, hormones, and drugs, into water-soluble, excretable metabolites [ 28 ]. However, these enzymes have different preferences for substrates, and the preferred substrate of UGT1A1 is bilirubin. Substrates of UGT1A3 include estrone, 2-hydroxy estrone, and metabolites of benzo alpha-pyrene. Although UGT1A4 is more active on amines, steroids, and sapogenins. UGT1A6, UGT1A7, and UGT1A9 are active on phenols. UGT1A8 and UGT1A10 have glucuronidase activity on drugs such as coumarins [ 29 ]. Meanwhile, we performed GO and KEGG analysis of UGDH and its related genes. The most prominent BP enrichment result was cellular glucuronidation, followed by xenobiotic glucuronidation. For the MF analysis results, the top rank was glucuronosyltransferase activity, followed by UDP-glycosyltransferase activity. The CC enrichment results mainly included the endoplasmic reticulum membrane and its membrane, an integral component of membrane and intracellular membrane-bounded organelle. Whereas KEGG analysis was enriched to the most significant pathway was pentose and glucuronate interconversion, suggesting a high degree of correlation between the results of the GO enrichment analysis. It is known that UDP- glucuronosyltransferase is localized to the endoplasmic reticulum, and the active transport of the metabolite UDPGA involved in UGDH is thought to occur in hepatocytes [ 30 ], so the role of UGDH in hepatocellular carcinoma should be taken seriously. Through CancerSEA analysis results, the expression of UGDH exhibited different functional states at the single-cell level in various cancers. The expression of UGDH is generally positively correlated with cell apoptosis, invasion, and proliferation, while negatively correlated with cell cycle. GSEA functional enrichment analysis was applied to cancer types in which UGDH expression affects overall prognosis. Analysis results were repeated more than twice and were considered common enrichment pathways. Most of the common enrichment pathways are immunologically related. Immunoregulatory interactions between a lymphoid and a nonlymphoid cell pathway participated adaptive Immune system [ 31 ]. The two pathways related to B cells analyzed are antigen activates B cell receptor BCR leading to generation of second messengers and CD22 mediated BCR regulation pathway, CD22 is an inhibitory B cell coreceptor that regulates B cell development and activation by downregulating BCR signaling through activation of protein tyrosine phosphatase-1 [ 32 , 33 ]. There are also 3 pathways related to complement, namely Initial triggering of complement, complement cascade, and Creation of C4 and C2 activators. Creation of C4 and C2 activators pathway participated initial triggering of complement. Current research shows that the complement system is one of the inflammatory mechanisms activated in the tumor microenvironment, beside exerting anti-tumor mechanisms such as complement-dependent cytotoxicity and phagocytosis induced by therapeutic monoclonal antibodies, the complement system may promote immunosuppression and tumor growth and invasiveness [ 34 , 35 ]. The analysis results also included Fc-gamma receptors (FCGR) activation and the Role of LAT2 Non-T cell activation linker (NTAL) lab on calcium mobilization pathway, the research suggests that FCGR are expressed on immune cells, bind to antibodies, and trigger antibody-induced cell-mediated antitumor responses when tumor-reactive antibodies are present [ 36 ]. The lipid raft resident adaptor molecules LAT1 and NTAL, also known as linkers for activation of B cells (LAB)/LAT2 are participants in the regulation of mast cell calcium responses [ 37 ]. In addition, the role of phospholipids in phagocytosis, scavenging of heme from plasma, and formation of the cornified envelope role in tumors still needs further investigation. To observe the immune status of UGDH and pan-cancer in-depth, we assessed the impact of UGDH on immune infiltration by analyzing its correlation with immune lymphocytes and immunomodulatory factors in the 33 cancers. UGDH expression was positively correlation with T helper cells, Tcm, and Th2 cells in most cancers, and negatively correlated with pDC and Cytotoxic cells. Primary T cells become effector T cells and memory T cells after being activated. Effector T cells include cytotoxic T cells and helper T (Th) cells. T cells are activated by dendritic cells (DC) and differentiate into various subtypes of Th cells, including Th2 cells [ 38 ]. Th2 cells induce an immunosuppressive protumorigenic response. Th2 cell infiltration in the tumor microenvironment is commonly associated with poor clinical outcomes in human cancers [ 39 ]. The Research that Tcm cells conferred superior anti-tumor immunity compared with effector memory T cells and effector T cells in CAR-T therapy [ 40 ]. For other immune characteristics, UGDH preferred some correlation with the type of cancer. Thus, the role of UGDH in cancer immunity is complex, and its positive correlation with Th2 cells and negative correlation with cytotoxic cells might be associated with poor prognosis of some tumors. Finally, we experimentally observed the expression and effect of UGDH in HCC. Compared with the normal group, the expression of UGDH was elevated in the liver tissue of HCC rats. Our previous experimental results also showed that the key genes AKR1B10 involved in the pentose and glucuronate interconversions pathway was consistent with the expression pattern of UGDH [ 41 ]. Subsequently, we knocked down the expression of UGDH in Huh7 cell lines and found that the knockdown of UGDH could inhibit the cell viability and in vitro tumourigenic ability of Huh7 cells. Of course, this study has some limits and did not explore the role of UGDH in more tumor types. CONCLUSIONS In conclusion, our study elucidates the role of UGDH in pan-cancer from multiple perspectives, including its associated biological functions, mutations, and immunomodulation. UGDH might be a potential diagnostic marker for lung adenocarcinoma, testicular cancer, and gliomas. And it could be a potential prognostic marker for HCC, lung cancers, and sarcomas. Inhibition of UGDH expression inhibits the proliferation of HCC cells. Declarations Ethics approval and consent to participate(Not applicable) The animal research ethics board of Beijing University of Chinese Medicine Dongzhimen Hospital approved this study. Consent for publication(Not applicable) Availability of data and materials(Not applicable) Competing interests The authors declare that the research was conducted in the absence of any commercial or fnancial relationships that could be construed as potential conficts of interest. Funding(Not applicable) Authors' contributions Cao Xu: Data curation, Formal analysis, Investigation, Methodology, Project administration, Validation, Visualization, Writing-original draft, Writing-review & editing. Size Li: Data curation, Formal analysis, Investigation, Methodology, Project administration, Validation, Visualization, Writing-original draft, Writing-review & editing. Yongan Ye: Validation, Supervision. Liping Zhang: Funding acquisition, Validation. Xiaobin Zao: Validation, Supervision. Baiquan Xue: Conceptualization, Data curation. Li Hou: Conceptualization, Data curation. Shihao Zheng: Conceptualization, Data curation. Jiaxin Zhang: Formal analysis. Xiaoke Li: Investigation. Hongbo Du: Validation. References Zhou M, Wang H, Zeng X, Yin P, Zhu J, Chen W, et al. Mortality, Morbidity, and Risk Factors in China and Its Provinces, 1990–2017: A Systematic Analysis for the Global Burden of Disease Study 2017. Lancet. 2019;394(10204):1145–58. 10.1016/s0140-6736(19)30427-1 . Vasan N, Baselga J, Hyman DM. A View on Drug Resistance in Cancer. Nature. 2019;575(7782):299–309. 10.1038/s41586-019-1730-1 . Hanahan D, Weinberg RA. Hallmarks of Cancer: The Next Generation. Cell. 2011;144(5):646–74. 10.1016/j.cell.2011.02.013 . Lane AN, Higashi RM, Fan TWM. Metabolic reprogramming in tumors: Contributions of the tumor microenvironment. Genes Dis (. 2020;7(2):185–98. 10.1016/j.gendis.2019.10.007 . Doshi MB, Lee N, Tseyang T, Ponomarova O, Goel HL, Spears M, et al. Disruption of Sugar Nucleotide Clearance Is a Therapeutic Vulnerability of Cancer Cells. Nature. 2023;623(7987):625–32. 10.1038/s41586-023-06676-3 . Zimmer BM, Barycki JJ, Simpson MA. Integration of Sugar Metabolism and Proteoglycan Synthesis by Udp-Glucose Dehydrogenase. J Histochem Cytochem. 2021;69(1):13–23. 10.1369/0022155420947500 . Price MJ, Nguyen AD, Byemerwa JK, Flowers J, Baëta CD, Goodwin CR. Udp-Glucose Dehydrogenase (Ugdh) in Clinical Oncology and Cancer Biology. Oncotarget. 2023;14:843–57. 10.18632/oncotarget.28514 . Wang X, Liu R, Zhu W, Chu H, Yu H, Wei P, et al. Udp-Glucose Accelerates Snai1 Mrna Decay and Impairs Lung Cancer Metastasis. Nature. 2019;571(7763):127–31. 10.1038/s41586-019-1340-y . Guo B, Xu X, Shao M, Yang X, He G, Qi K, et al. Udp-Glucose 6-Dehydrogenase Lessens Sorafenib Sensitivity Via Modulating Unfolded Protein Response. Biochem Biophys Res Commun. 2022;613:207–13. 10.1016/j.bbrc.2022.05.048 . Fan M, Huo S, Guo Y, Wang R, Hao W, Zhang Z, et al. Udp-Glucose Dehydrogenase Supports Autophagy-Deficient Pdac Growth Via Increasing Hyaluronic Acid Biosynthesis. Cell Rep. 2024;43(2):113808. 10.1016/j.celrep.2024.113808 . Teoh ST, Ogrodzinski MP, Lunt SY. Udp-Glucose 6-Dehydrogenase Knockout Impairs Migration and Decreases in Vivo Metastatic Ability of Breast Cancer Cells. Cancer Lett. 2020;492:21–30. 10.1016/j.canlet.2020.07.031 . Harrington BS, Kamdar R, Ning F, Korrapati S, Caminear MW, Hernandez LF, et al. Ugdh Promotes Tumor-Initiating Cells and a Fibroinflammatory Tumor Microenvironment in Ovarian Cancer. J Exp Clin Cancer Res. 2023;42(1):270. 10.1186/s13046-023-02820-z . Huang D, Casale GP, Tian J, Lele SM, Pisarev VM, Simpson MA, et al. Udp-Glucose Dehydrogenase as a Novel Field-Specific Candidate Biomarker of Prostate Cancer. Int J Cancer. 2010;126(2):315–27. 10.1002/ijc.24820 . Oyinlade O, Wei S, Lal B, Laterra J, Zhu H, Goodwin CR, et al. Targeting Udp-Α-D-Glucose 6-Dehydrogenase Inhibits Glioblastoma Growth and Migration. Oncogene. 2018;37(20):2615–29. 10.1038/s41388-018-0138-y . Vitale DL, Caon I, Parnigoni A, Sevic I, Spinelli FM, Icardi A, et al. Initial Identification of Udp-Glucose Dehydrogenase as a Prognostic Marker in Breast Cancer Patients, Which Facilitates Epirubicin Resistance and Regulates Hyaluronan Synthesis in Mda-Mb-231 Cells. Biomolecules. 2021;11(2). 10.3390/biom11020246 . Wang Q, Karvelsson ST, Johannsson F, Vilhjalmsson AI, Hagen L, de Miranda Fonseca D, et al. Udp-Glucose Dehydrogenase Expression Is Upregulated Following Emt and Differentially Affects Intracellular Glycerophosphocholine and Acetylaspartate Levels in Breast Mesenchymal Cell Lines. Mol Oncol. 2022;16(9):1816–40. 10.1002/1878-0261.13172 . Hagiuda D, Nagashio R, Ichinoe M, Tsuchiya B, Igawa S, Naoki K, et al. Clinicopathological and Prognostic Significance of Nuclear Ugdh Localization in Lung Adenocarcinoma. Biomed Res. 2019;40(1):17–27. 10.2220/biomedres.40.17 . Gao Q, Cheng B, Chen C, Lei C, Lin X, Nie D, et al. Dysregulated Glucuronic Acid Metabolism Exacerbates Hepatocellular Carcinoma Progression and Metastasis through the Tgfβ Signalling Pathway. Clin Transl Med. 2022;12(8):e995. 10.1002/ctm2.995 . Shen S, Li H, Liu J, Sun L, Yuan Y. The Panoramic Picture of Pepsinogen Gene Family with Pan-Cancer. Cancer Med. 2020;9(23):9064–80. 10.1002/cam4.3489 . Zao X, Cheng J, Shen C, et al. NFATc3 inhibits hepatocarcinogenesis and HBV replication via positively regulating RIG-I-mediated interferon transcription. Oncoimmunology. 2021;10(1):1869388. 10.1080/2162402X.2020.1869388 . Egger S, Chaikuad A, Kavanagh KL, Oppermann U, Nidetzky B. Udp-Glucose Dehydrogenase: Structure and Function of a Potential Drug Target. Biochem Soc Trans. 2010;38(5):1378–85. 10.1042/bst0381378 . Deen AJ, Arasu UT, Pasonen-Seppänen S, Hassinen A, Takabe P, Wojciechowski S, et al. Udp-Sugar Substrates of Has3 Regulate Its O-Glcnacylation, Intracellular Traffic, Extracellular Shedding and Correlate with Melanoma Progression. Cell Mol Life Sci. 2016;73(16):3183–204. 10.1007/s00018-016-2158-5 . Liang X, Li L, Fan Y, Diagnostic. Prognostic, and Immunological Roles of Hells in Pan-Cancer: A Bioinformatics Analysis. Front Immunol. 2022;13:870726. 10.3389/fimmu.2022.870726 . de Souza MFD, da Silva Filho AF, de Barros Albuquerque AP, Quirino MWL, de Souza Albuquerque MS, Cordeiro MF, et al. Overexpression of Udp-Glucose 4-Epimerase Is Associated with Differentiation Grade of Gastric Cancer. Dis Markers. 2019;2019:6325326. 10.1155/2019/6325326 . Sun X, Xue H, Xiong Y, Yu R, Gao X, Qian M, et al. Gale Promotes the Proliferation and Migration of Glioblastoma Cells and Is Regulated by Mir-Let-7i-5p. Cancer Manag Res. 2019;11:10539–54. 10.2147/cmar.S221585 . Ye G, Zhang X, Li M, Lin Z, Xu Y, Dong H, et al. Integrated Analysis of Circulating and Tissue Proteomes Reveals That Fibronectin 1 Is a Potential Biomarker in Papillary Thyroid Cancer. BMC Cancer. 2023;23(1):412. 10.1186/s12885-023-10839-w . Fujarewicz K, Jarzab M, Eszlinger M, Krohn K, Paschke R, Oczko-Wojciechowska M, et al. A Multi-Gene Approach to Differentiate Papillary Thyroid Carcinoma from Benign Lesions: Gene Selection Using Support Vector Machines with Bootstrapping. Endocr Relat Cancer. 2007;14(3):809–26. 10.1677/erc-06-0048 . Meech R, Mackenzie PI. Structure and Function of Uridine Diphosphate Glucuronosyltransferases. Clin Exp Pharmacol Physiol. 1997;24(12):907–15. 10.1111/j.1440-1681.1997.tb02718.x . King CD, Rios GR, Green MD, Tephly TR. Udp-Glucuronosyltransferases. Curr Drug Metab. 2000;1(2):143–61. 10.2174/1389200003339171 . Bock KW, Köhle C. Topological Aspects of Oligomeric Udp-Glucuronosyltransferases in Endoplasmic Reticulum Membranes: Advances and Open Questions. Biochem Pharmacol. 2009;77(9):1458–65. 10.1016/j.bcp.2008.12.004 . Nedvetzki S, Sowinski S, Eagle RA, Harris J, Vély F, Pende D, et al. Reciprocal Regulation of Human Natural Killer Cells and Macrophages Associated with Distinct Immune Synapses. Blood. 2007;109(9):3776–85. 10.1182/blood-2006-10-052977 . Akatsu C, Alborzian Deh Sheikh A, Matsubara N, Takematsu H, Schweizer A, Abdu-Allah HHM, et al. The Inhibitory Coreceptor Cd22 Restores B Cell Signaling by Developmentally Regulating Cd45(-/-) Immunodeficient B Cells. Sci Signal. 2022;15(723):eabf9570. 10.1126/scisignal.abf9570 . Alborzian Deh Sheikh A, Akatsu C, Abdu-Allah HHM, Suganuma Y, Imamura A, Ando H, et al. The Protein Tyrosine Phosphatase Shp-1 (Ptpn6) but Not Cd45 (Ptprc) Is Essential for the Ligand-Mediated Regulation of Cd22 in Bcr-Ligated B Cells. J Immunol. 2021;206(11):2544–51. 10.4049/jimmunol.2100109 . Magrini E, Minute L, Dambra M, Garlanda C. Complement Activation in Cancer: Effects on Tumor-Associated Myeloid Cells and Immunosuppression. Semin Immunol. 2022;60:101642. 10.1016/j.smim.2022.101642 . Kolev M, Das M, Gerber M, Baver S, Deschatelets P, Markiewski MM. Inside-out of Complement in Cancer. Front Immunol. 2022;13:931273. 10.3389/fimmu.2022.931273 . Erbe AK, Wang W, Goldberg J, Gallenberger M, Kim K, Carmichael L, et al. Fcgr Polymorphisms Influence Response to Il2 in Metastatic Renal Cell Carcinoma. Clin Cancer Res. 2017;23(9):2159–68. 10.1158/1078-0432.Ccr-16-1874 . Tessarz AS, Weiler S, Zanzinger K, Angelisová P, Horejsí V, Cerwenka A. Non-T Cell Activation Linker (Ntal) Negatively Regulates Trem-1/Dap12-Induced Inflammatory Cytokine Production in Myeloid Cells. J Immunol. 2007;178(4):1991–9. 10.4049/jimmunol.178.4.1991 . Basu A, Ramamoorthi G, Albert G, Gallen C, Beyer A, Snyder C, et al. Differentiation and Regulation of T(H) Cells: A Balancing Act for Cancer Immunotherapy. Front Immunol. 2021;12:669474. 10.3389/fimmu.2021.669474 . Renaude E, Kroemer M, Borg C, Peixoto P, Hervouet E, Loyon R, et al. Epigenetic Reprogramming of Cd4(+) Helper T Cells as a Strategy to Improve Anticancer Immunotherapy. Front Immunol. 2021;12:669992. 10.3389/fimmu.2021.669992 . Klebanoff CA, Gattinoni L, Torabi-Parizi P, Kerstann K, Cardones AR, Finkelstein SE, et al. Central Memory Self/Tumor-Reactive Cd8 + T Cells Confer Superior Antitumor Immunity Compared with Effector Memory T Cells. Proc Natl Acad Sci U S A. 2005;102(27):9571–6. 10.1073/pnas.0503726102 . Zao X, Cao X, Liang Y, Zhang J, Chen H, Zhang N, et al. The Chinese herbal KangXianYiAi formula inhibits hepatocellular carcinoma by reducing glutathione and inducing ferroptosis. Pharmacol Res - Mod Chin Med. 2023;8:100276. 10.1016/j.prmcm.2023.100276 . Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterials.zip 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4632654","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":322934842,"identity":"4b8c085c-9bc2-4926-b65c-67b1a4ea0996","order_by":0,"name":"Xu Cao","email":"","orcid":"","institution":"Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xu","middleName":"","lastName":"Cao","suffix":""},{"id":322934843,"identity":"63fe26f3-443a-4168-885a-f0959b8485da","order_by":1,"name":"Size Li","email":"","orcid":"","institution":"Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Size","middleName":"","lastName":"Li","suffix":""},{"id":322934844,"identity":"71aed802-4cc3-45f4-b90a-d71c15d7649b","order_by":2,"name":"Baiquan Xue","email":"","orcid":"","institution":"The First People’s Hospital of Jinzhou District","correspondingAuthor":false,"prefix":"","firstName":"Baiquan","middleName":"","lastName":"Xue","suffix":""},{"id":322934845,"identity":"5ccfb544-e026-4ddf-be6d-7db440968055","order_by":3,"name":"Li Hou","email":"","orcid":"","institution":"Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Hou","suffix":""},{"id":322934846,"identity":"d415d17f-8aad-4535-b3a5-aad46182926e","order_by":4,"name":"Shihao Zheng","email":"","orcid":"","institution":"Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Shihao","middleName":"","lastName":"Zheng","suffix":""},{"id":322934847,"identity":"d24340a7-591a-42c5-8492-603ddc256721","order_by":5,"name":"Jiaxin Zhang","email":"","orcid":"","institution":"Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jiaxin","middleName":"","lastName":"Zhang","suffix":""},{"id":322934848,"identity":"d8122bf5-0237-4ec5-b7be-6042470750c8","order_by":6,"name":"Xiaoke Li","email":"","orcid":"","institution":"Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xiaoke","middleName":"","lastName":"Li","suffix":""},{"id":322934849,"identity":"a6fd1faf-6f9b-4512-bc3e-3573e3f76b17","order_by":7,"name":"Hongbo Du","email":"","orcid":"","institution":"Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Hongbo","middleName":"","lastName":"Du","suffix":""},{"id":322934850,"identity":"2989f3f3-8249-4292-9c56-50449422861f","order_by":8,"name":"Liping Zhang","email":"","orcid":"","institution":"Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Liping","middleName":"","lastName":"Zhang","suffix":""},{"id":322934851,"identity":"8d743346-51b7-48b1-b0e6-0f226a1467e8","order_by":9,"name":"Xiaobin Zao","email":"","orcid":"","institution":"Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xiaobin","middleName":"","lastName":"Zao","suffix":""},{"id":322934852,"identity":"7f8f8e3e-0edb-4e13-a39d-cb076ca2026e","order_by":10,"name":"Yong’an Ye","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYDADfgbGBgYGAxs74rVINoC0FKQlE6/F4ACI/HAIpJGAyuM9ho8LftnlGd9IbnsA1MjMwH746Aa8Ws6cMTae2ZdcbHYjsd2AweAOHwNPWtoNvFpu5JhJ8/YwJ247c7BNgsHgGTODBI8Zfi3334C01Cdu7gFrOczYQFDLDR4zaZ4fhxM3sDcSqUXyTFqxMW/D8cQZxxvbDRIM0pLZCPmF7/jhjY95/lQn9jezP3vw4Y+NHT/74WN4tSgc4DBgYGwDs9kYEsAkASDfwP6AgeEPVMsoGAWjYBSMAmwAANFrTipT9xyZAAAAAElFTkSuQmCC","orcid":"","institution":"Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Yong’an","middleName":"","lastName":"Ye","suffix":""}],"badges":[],"createdAt":"2024-06-25 00:56:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4632654/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4632654/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60701183,"identity":"4ce19fee-3ee6-4b40-9e97-96880ce8af69","added_by":"auto","created_at":"2024-07-19 18:04:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1176892,"visible":true,"origin":"","legend":"\u003cp\u003eExpression of UGDH. (A) The summary of UGDH mRNA and protein expression in human organs and tissues. (B) Situation of UGDH mRNA expression summary in human organs and tissues. (C) Situation of UGDH protein expression summary in human organs and tissues. （D）Situation of UGDH mRNA expression in cell lines.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4632654/v1/3c3117c2578628a4472f4796.png"},{"id":60701178,"identity":"dd840e46-8954-48c8-930d-aaa053947ea2","added_by":"auto","created_at":"2024-07-19 18:04:30","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1324294,"visible":true,"origin":"","legend":"\u003cp\u003eThe expression of UGDH mRNA in pan-cancer. (A) Expression of UGDH between the 33 cancers and normal tissues in unpaired sample analysis. (B) Expression of UGDH between the 33 cancers and normal tissues in unpaired sample analysis. (C) Paired sample analysis of UGDH mRNA expression between 23 cancers and paracancerous tissues. *p \u0026lt;0.05, **p \u0026lt;0.01, ***p \u0026lt;0.001, ns: Not Significant.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4632654/v1/806278d3115931884637c150.jpeg"},{"id":60701186,"identity":"2c399edb-0e4d-4263-b76b-c7d963575e8e","added_by":"auto","created_at":"2024-07-19 18:04:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":222200,"visible":true,"origin":"","legend":"\u003cp\u003eROC curve in 6 Cancers. (A) GBM. (B) LAML. (C) LGG. (D) LUAD. (E) PCPG. (F) TGCT. Cancers with AUC \u0026gt; 0.9 for UGDH.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4632654/v1/5fe12c547aaf3d85eede998a.png"},{"id":60701716,"identity":"2a24148f-a76a-44d3-b129-2788621a37af","added_by":"auto","created_at":"2024-07-19 18:12:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":485836,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations between UGDH and prognosis in pan-cancers. (A) ACC. (B) BRCA. (C) KIRC. (D) KIRP. (E) LAML. (F) LIHC. (G) LUAD. (H) SARC. (I) SKCM. (J) UCS. The OS K-M curve for UGDH in 10 cancers. The unit of the X-axis is a month. (K) Forest plot of UGDH OS, DSS, PFI in 10 cancers.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4632654/v1/1aa4f4819640189f0905ddcd.png"},{"id":60701185,"identity":"bc380508-fa5b-4ee7-852f-b73a9e25cc54","added_by":"auto","created_at":"2024-07-19 18:04:30","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1395378,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations between UGDH expression and immune subtypes in 9 cancers. (A) ACC. (B) BRCA. (C) KIRC. (D) KIRP. (E) LIHC. (F) LUAD. (G) SARC. (H) SKCM. (I) UCS. C1 (wound healing), C2 (IFN-g dominant), C3 (inflammatory), C4 (lymphocyte deplete), C5 (immunologically quiet), and C6 (TGF-b dominant).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4632654/v1/ac1e2d1dd774f67c92deaff2.png"},{"id":60701715,"identity":"5913ef02-ac31-4713-8945-d184ecf296a2","added_by":"auto","created_at":"2024-07-19 18:12:30","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":885724,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations between UGDH expression and molecular subtypes in 5 cancers. (A) ACC. (B) BRCA. (C) KIRP. (D) LIHC. (E) SKCM.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4632654/v1/f2f6891e84c0747b35ce814c.png"},{"id":60701182,"identity":"7081745b-3b68-45d6-82ec-b57c01315b6f","added_by":"auto","created_at":"2024-07-19 18:04:30","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1528450,"visible":true,"origin":"","legend":"\u003cp\u003eGenetic alteration of UGDH in pan-cancers. (A) Mutation diagram of UGDH across protein domains. (B) Bar chart of UGDH mutations in 32 cancers. (C) Mutation counts and types of UGDH in 32 cancers.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4632654/v1/8a5079294412b29f3d664810.png"},{"id":60701188,"identity":"0fa24f63-f9b6-463d-876d-eaa4fd2f20e2","added_by":"auto","created_at":"2024-07-19 18:04:31","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":801305,"visible":true,"origin":"","legend":"\u003cp\u003e(A) The PPI network of UGDH. (B) The top 10 hub genes of the PPI network. (C) The association hub gene with UGDH in 10 cancers is present as a heatmap. **p \u0026lt; 0.01, *p \u0026lt; 0.05. (D) GO/KEGG pathway enrichment for UGDH-related genes in PPI. (E) UGDH with pathway activity or inhibition.\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-4632654/v1/d49a7226bbf3dd1ac2d12b4d.png"},{"id":60701184,"identity":"a1623b1e-c4e5-412b-8b4a-7bd67cd91a9d","added_by":"auto","created_at":"2024-07-19 18:04:30","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":635985,"visible":true,"origin":"","legend":"\u003cp\u003eGSEA functional enrichment analysis of UGDH expression in 10 cancers. (A) ACC.(B) BRCA. (C) KIRC. (D) KIRP. (E) LAML. (F) LIHC. (G) LUAD. (H) SARC. (I) SKCM. (J) UCS. The Y-axis represents one gene set and the X-axis is the distribution of logFC corresponding to the core molecules in each gene set.\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-4632654/v1/33356deca5477231194f0889.png"},{"id":60701189,"identity":"cd60cdb1-6ab6-497a-9a8a-726ef42a2b29","added_by":"auto","created_at":"2024-07-19 18:04:31","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":568782,"visible":true,"origin":"","legend":"\u003cp\u003eThe correlation of UGDH with functional state in cancers. (A) The interactive bubble chart presents the correlation of UGDH with functional state in 19 cancers. (B) AML. (C) ALL. (D) LUAD. (E) NSCLC. (F) RCC. (G) PC. (H) HNSCC. (I) RB. (J) UM. X-axis represents different gene sets, ***p \u0026lt; 0.001, **p \u0026lt; 0.01, *p \u0026lt; 0.05.\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-4632654/v1/60ca377a34be6729644b76d9.png"},{"id":60701187,"identity":"73ea5f17-9a26-49df-a9b7-c0c3b3025a87","added_by":"auto","created_at":"2024-07-19 18:04:31","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":1048379,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation of UGDH with TILs and immunoregulation-related genes in 33 cancers. (A) TILs. (B) MHC Molecules. (C) Immunostimulators. (D) Immunoinhibitors. (E) Chemokines. (F) Chemokine receptors. *p \u0026lt; 0.05, ** p \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-4632654/v1/c52c394d30854620138e3db7.png"},{"id":60701717,"identity":"0f0f8b2e-288e-49ef-aef7-a92b5e6e825b","added_by":"auto","created_at":"2024-07-19 18:12:31","extension":"jpeg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":832081,"visible":true,"origin":"","legend":"\u003cp\u003eExperimental results of UGDH in liver cancer. (A) Ultrasound, morphology, and histology of liver cancer rats. (B) Ugdh mRNA expression of qRT-PCR analysis. (C) Ugdh protein expression of WB analysis. (D) UGDH protein expression of siRNA Knockdown in Huh7 Cells. (E) UGDH mRNA expression of Post-Knockdown in Huh7 Cells. (F) Cell Viability Assay of UGDH Post-Knockdown. (G) Tumor Xenografts Assay of UGDH Post-Knockdown.\u003c/p\u003e","description":"","filename":"floatimage12.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4632654/v1/bff94a962fe2b0c43810a04e.jpeg"},{"id":60702389,"identity":"943d9dfb-30f3-46e5-b6b9-f24a3db88ad7","added_by":"auto","created_at":"2024-07-19 18:28:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11240766,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4632654/v1/7a2c272a-2c6f-4e17-be90-c309f4fea236.pdf"},{"id":60701190,"identity":"f928ffbe-30d5-40ad-99cf-631c365cfaa4","added_by":"auto","created_at":"2024-07-19 18:04:31","extension":"zip","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":38271160,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterials.zip","url":"https://assets-eu.researchsquare.com/files/rs-4632654/v1/6c0fbb403c5fcc171ced3bdd.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Pan-cancer analysis of UDP-glucose 6-dehydrogenase and its carcinogenesis in hepatocellular carcinoma","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eCancer has been recognized as one of the leading causes of death worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. With the continuous development of chemotherapy drugs, the problem of tumor drug resistance has become increasingly prominent [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Metabolic reprogramming is one of the typical features of cancer, where cancer cells need to change their metabolic state in response to proliferative signals delivered by oncogene signaling pathways [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Tumor is also a metabolic disease, and the specific microenvironment and metabolites further affect the metabolic phenotype of tumor cells, thereby influencing tumor progression, treatment, and prognosis [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Current research indicates that the metabolism of uridine diphosphate glucuronic acid (UDPGA) is important during cancer development, and disruption of sugar nucleotide clearance is a therapeutic vulnerability of cancer cells [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUDP-glucose 6-dehydrogenase (UGDH) is strongly associated with tumor drug resistance [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The protein encoded by this gene is a rate-limiting enzyme that converts UDP-glucose to UDP-glucuronate, thereby participating in the biosynthesis of glycosaminoglycans such as hyaluronan, chondroitin sulfate, and heparan sulfate. These glycosylated compounds are common components of the extracellular matrix and likely play roles in signal transduction, cell migration, cancer growth, and cancer metastasis [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The role of UGDH in cancer has received extensive attention from researchers in recent years. Abnormally elevated UGDH expression has been reported to be associated with the development of malignant tumors such as lung cancer [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], hepatocellular carcinoma (HCC) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], pancreatic ductal adenocarcinoma [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], breast cancer [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], ovarian cancer [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], prostate cancer [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and glioblastoma [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], as well as being correlated with poor prognosis of a variety of cancers. UGDH has broad and diverse regulatory effects in cancer and is closely associated with tumor drug resistance [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], epithelial-mesenchymal transition [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and cellular localization [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], participating in pathways such as the sugar nucleotide biosynthetic [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] and TGFβ signaling [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] pathways, which is of great significance for cancer treatment. Therefore, UGDH is an attractive potential target for tumor diagnosis and therapy.\u003c/p\u003e \u003cp\u003eIt has been found that tumors from different organs but of the same histological type and tumors with anatomical structures belonging to the same system have strong molecular similarities [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Therefore, exploring the phenotypic characteristics of pan-cancer molecules will help to elucidate their commonalities in tumors and their intrinsic regulatory mechanisms. In this study, we observed the characterization of 33 cancers in a pan-cancer analysis using The Cancer Genome Atlas (TCGA), the Genotype Tissue Expression (GTEx), and the Human Protein Atlas (HPA) databases. We used bioinformatics to comprehensively analyze the expression, diagnostic value, clinical prognosis, and functional enrichment of UGDH. The relationship between UGDH and tumor-infiltrating lymphocytes, major histocompatibility complex (MHC) molecules, immunostimulators, immunoinhibitors, chemokines, and chemokine receptors was then further explored. Through experiments of western blot and Quantitative Real-time Polymerase Chain Reaction, we detected UGDH expression in the liver tissue of HCC rats. Following the UGDH knockdown of Huh7 cells, we employed CCK8 and nude mice tumor xenograft assays to evaluate the effect of UGDH on HCC cell proliferation.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eExpression Analysis and Datasets Sources of UGDH\u003c/h2\u003e \u003cp\u003eWe systematically queried the Human Protein Atlas (HPA) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.proteinatlas.org/\u003c/span\u003e\u003cspan address=\"https://www.proteinatlas.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to gather comprehensive data on UGDH RNA and protein expression in human tissues and cell lines. Additionally, we utilized The Cancer Genome Atlas (TCGA) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cancergenome.nih.gov\u003c/span\u003e\u003cspan address=\"https://cancergenome.nih.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and the Genotype-Tissue Expression (GTEx) project (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gtexportal.org/\u003c/span\u003e\u003cspan address=\"https://gtexportal.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to acquire detailed information on UGDH mRNA expression in tumor samples, corresponding paracancerous tissues, and normal controls. Samples with 0 gene expression values were excluded. Only paired samples were used for paired sample analyses. RNA sequencing data, originally in Fragments Per Kilobase per Million (FPKM) format, were converted and normalized through the Toil process to transcripts per million reads and log2-transformed for subsequent analysis. Statistical analyses were conducted using R software (version 4.2.1), \u0026ldquo;stats\u0026rdquo; (version 4.2.1), and \u0026ldquo;car\u0026rdquo; (version 3.1-0). The UGDH gene expression across the 33 cancer types was visualized using the \u0026ldquo;ggplot2\u0026rdquo; package (version 3.3.6). The median expression method was adopted to set cutoff values. To assess the expression differences between groups, the Wilcoxon rank-sum test was applied.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eReceiver Operator Characteristic (ROC) Curve Analysis of UGDH\u003c/h2\u003e \u003cp\u003eIn the study of 33 cancer types, ROC curves were employed to evaluate the diagnostic value of UGDH. These ROC curves were constructed using mRNA expression data of UGDH in cancerous and corresponding normal tissues from TCGA and GTEx databases. The ROC analysis was performed using the \u0026ldquo;pROC\u0026rdquo; package (version 1.18.0) in R, and the curves were plotted using the \u0026ldquo;ggplot2\u0026rdquo; package (version 3.3.6). Diagnostic metrics calculated included the Area Under the Curve (AUC), cutoff values, sensitivity, specificity, positive predictive value, and negative predictive value. The AUC value close to 1 indicates superior diagnostic accuracy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eSurvival Analysis of UGDH\u003c/h2\u003e \u003cp\u003eKaplan-Meier (K-M) survival analysis was conducted using the \u0026ldquo;survival\u0026rdquo; package (version 3.3.1) in R (version 4.2.1). This analysis compared overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) rates between high and low UGDH gene expression groups across the 33 cancer types. We also went on to observe the relationship between UGDH and common clinical parameters to tumor types influenced by UGDH on OS prognosis. Survival differences were statistically evaluated using Cox regression analysis. Forest plots illustrating hazard ratios (HR), 95% confidence intervals, and p-values were generated and visualized with the \u0026ldquo;survminer\u0026rdquo; (version 3.3.1) and \u0026ldquo;ggplot2\u0026rdquo; packages (version 3.3.6).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eDifferent Molecular and Immune Subtypes of Cancers in UGDH Expression\u003c/h2\u003e \u003cp\u003eTo investigate the associations between UGDH expression and molecular or immune subtypes in these cancers, we utilized the \u0026ldquo;subtype\u0026rdquo; module of the TISIDB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://cis.hku.hk/TISIDB/\u003c/span\u003e\u003cspan address=\"http://cis.hku.hk/TISIDB/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). This database integrates various datasets to assess interactions between cancer and the immune system. We analyzed UGDH mRNA expression across different immune subtypes: C1 (wound healing), C2 (IFN-g dominant), C3 (inflammatory), C4 (lymphocyte deplete), C5 (immunologically quiet), and C6 (TGF-b dominant). Different tumors have their specific molecular types.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eGenetic Alteration Analysis of UGDH\u003c/h2\u003e \u003cp\u003eThe cBioPortal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cbioportal.org/\u003c/span\u003e\u003cspan address=\"https://www.cbioportal.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was accessed to gather genetic alteration data on UGDH. We included all TCGA PanCancer Atlas Studies in our search. The frequency of somatic mutations and genomic information regarding UGDH mutations in cancers were examined using the \u0026ldquo;cancer types summary and mutations\u0026rdquo; and \u0026ldquo;mRNA vs. study\u0026rdquo; modules. Mutation sites were identified through the \u0026ldquo;mutations\u0026rdquo; module.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eNetwork Analysis and Functional Enrichment Analysis of UGDH\u003c/h2\u003e \u003cp\u003eProtein interactions of UGDH were investigated using the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The collected data were used for a Protein-Protein Interaction (PPI) network, setting a confidence score threshold of \u0026gt;\u0026thinsp;0.7 for significance. This data was then imported into Cytoscape (3.8.0) for visualization and further analysis. The cytoHubba plugins identified key modules and the top 10 nodes, ranked by the Maximal Clique Centrality of cytoHubba, were presented as hub genes. We explored the correlation between hub gene expression and UGDH. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of UGDH PPI-related genes using the DAVID database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david.ncifcrf.gov/\u003c/span\u003e\u003cspan address=\"https://david.ncifcrf.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), with a significance threshold set at a p-value of \u0026lt;\u0026thinsp;0.05. Results were displayed as a bubble chart using the \u0026ldquo;ggplot2\u0026rdquo; package (version 3.3.6). Besides, we used the GSCALite database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://guolab.wchscu.cn/GSCA/\u003c/span\u003e\u003cspan address=\"https://guolab.wchscu.cn/GSCA/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to focus on identifying the activation or inhibition of key cancer-related pathways in the 33 cancer types involving UGDH. These pathways include TSC/mTOR, RTK, RAS/MAPK, PI3K/AKT, Hormone ER, Hormone AR, Epithelial-Mesenchymal Transition (EMT), DDR, Cell Cycle, and Apoptosis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eGene Set Enrichment Analysis (GSEA) of UGDH\u003c/h2\u003e \u003cp\u003eGSEA was conducted using the \u0026ldquo;clusterProfiler\u0026rdquo; package (version 4.4.4) to elucidate biological pathway variations between high- and low-UGDH expression groups. Pathways with a false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.25 and an adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were deemed significantly altered. We performed gene set permutation 1,000 times for each analysis. The top 5 enriched pathways are presented as mountain maps, visualized using the \u0026ldquo;ggplot2\u0026rdquo; package (version 3.3.6).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eFunctional States Analysis of UGDH in CancerSEA\u003c/h2\u003e \u003cp\u003eThe functional status of UGDH in various cancers was examined using the CancerSEA database. We investigated the average correlation between UGDH and functional states in 18 cancers, including invasion, metastasis, proliferation, EMT, angiogenesis, apoptosis, cell cycle, differentiation, DNA damage, DNA repair, hypoxia, inflammation, quiescence, and stemness. A correlation strength\u0026thinsp;\u0026gt;\u0026thinsp;0.3 absolute value and a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were set as thresholds for significance in correlating UGDH with cancer functional states.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eImmunogenomic Analyses of UGDH\u003c/h2\u003e \u003cp\u003eThe correlation between UGDH expression and tumor-infiltrating lymphocytes, MHC molecules, immunostimulators, immunoinhibitors, chemokines, and chemokine receptors in 33 cancers was assessed using the \u0026ldquo;GSVA\u0026rdquo; package (version 1.44.5) with the ssGSEA algorithm. Statistical significance was determined using Spearman\u0026rsquo;s correlation, with p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicating significant correlation. The \u0026ldquo;ggplot2\u0026rdquo; package (version 3.3.6) was utilized to visualize these correlations as heatmaps.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAnimals of Experiment\u003c/h2\u003e \u003cp\u003e4-week-old male BALB/C nude mice and 6-week-old male Wistar rats were purchased from Beijing Vital River Laboratory Animal Technology Co. Ltd (license: SCXK (Beijing) 2016-0006). All animals were raised in the Barrier Environmental Animal Laboratory of Dongzhimen Hospital of Beijing University of Chinese Medicine (license: SYXK (Beijing) 2015-0001) and maintained under the National Standards for Laboratory Animals of China (GB14925-2010). Our study was carried out in compliance with the ARRIVE guidelines. This study was approved by the Ethics Committee of Laboratory Animals of Dongzhimen Hospital of Beijing University of Chinese Medicine (No.21-10-01 and 21-46-01). Animals were kept separately in an SPF laboratory, with the breeding environment: temperature 25\u0026thinsp;\u0026plusmn;\u0026thinsp;1\u0026deg;C, humidity 50\u0026thinsp;\u0026plusmn;\u0026thinsp;10%, free of food and drinking water, 12-hour day and night alternation, as well as adaptable feeding for 5 days.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eDesign of Rat Hepatocellular Carcinoma Experiment\u003c/h2\u003e \u003cp\u003eThe hepatocellular carcinoma rat model was constructed by intraperitoneal injection of diethylnitrosamine (50 mg/kg/week, Psaitong, N60001, CN) for sixteen consecutive weeks. At the end of the 16th week, after 12 hours of fasting, the experimental animals were taken to observe the condition of the liver. The rats were anesthetized by intraperitoneal injection at 0.2 ml/100g of 3% pentobarbital sodium solution and the largest lobe of liver tissue was taken and kept in tissue fixation fluid, the rest of the liver tissue was placed in -80 ℃ refrigerator for storage.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCell Line of Experiment\u003c/h2\u003e \u003cp\u003eHepatoma cell lines including Huh7 were purchased from the American Type Culture Collection (Manassas, VA, USA) and maintained in Dulbecco\u0026rsquo;s modified eagle medium (DMEM) supplemented with 10% fetal bovine serum (FBS), 100 U/mL penicillin, 100 \u0026micro;g/mL streptomycin and 5% CO\u003csub\u003e2\u003c/sub\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eKnockdown of UGDH\u003c/h2\u003e \u003cp\u003e24 hours before transfection, 2\u0026times;10\u003csup\u003e5\u003c/sup\u003e cells were seeded in 400 \u0026micro;L of antibiotic-free medium at a cell confluency of 60\u0026ndash;80% for transfection. siRNA was transfected into wells of a plate using Lipo2000 transfection reagent, and diluted to a final concentration of 50 nM. 50 \u0026micro;L of Opti-MEM was used to dilute the siRNA, gently pipetting 3\u0026ndash;5 times to mix. The 1.0 \u0026micro;L transfection reagent was diluted in 50 \u0026micro;L of Opti-MEM, mixed gently by pipetting 3\u0026ndash;5 times, and left at room temperature for 5 minutes. The siRNA was combined with Lipofectamine 2000, gently mixed by pipetting 3\u0026ndash;5 times and incubated at room temperature for 20 minutes. The transfection complex (100 \u0026micro;L per well) was added evenly across the cell plate. The si-RNA used in this study is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe si-RNA sequence of UGDH gene\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eiUGDH-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGAUGUCAAUGAAUCAAGAATT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eantisense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUUCUUGAUUCAUUGACAUCTT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eiUGDH-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCAGCAUUAACUCCAUAAGUTT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eantisense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACUUAUGGAGUUAAUGCUGTT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eiUGDH-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGAUUAUGAACGCAUUCAUATT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eantisense\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUAUGAAUGCGUUCAUAAUCTT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCell Viability Assay\u003c/h2\u003e \u003cp\u003eAfter knocking down the UGDH according to the instruction manual, 5\u0026times;10\u003csup\u003e3\u003c/sup\u003e cells were seeded in 96-well plates with six duplications, after being incubated for 24, 48, and 72 hours. The CCK-8 assay kit (Solarbio, CN) was carried out to assess the ability of cell growth by measuring the absorbance at the wavelength of 450 nm by the TECAN infinite M200 Multimode microplate reader (Tecan, Mechelen, Belgium).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eDesign of Nude Mice Tumor Xenograft Assay\u003c/h2\u003e \u003cp\u003eThe detailed protocol for model construction of nude mice tumor xenograft was described in the previous study [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Injected the amount of 5 \u0026times; 10\u003csup\u003e6\u003c/sup\u003e Huh7 cells into the underarm skin of 4-week-old male BLAB/c nude mice. After 2 weeks of injection, the transplanted tumor formed. The mice were sacrificed and tumors were obtained after 2 weeks of regular feeding.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eQuantitative Real-time Polymerase Chain Reaction (qRT-PCR)\u003c/h2\u003e \u003cp\u003eThe reverse transcription of total RNA to cDNA was performed with a qPCR RT Master Mix kit (TOYOBO, JAN). qRT-PCR was performed using the Real-time PCR Detection System (Agilent Technologies, US) with the SYBR Green Real-time PCR Master Mix (TOYOBO, JAN). We used GAPDH and Actb as an internal control gene. The experiments were performed in triplicate and repeated 3 times. The primers used in this study are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe primers of genes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUGDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward Primer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCCCTGTGTGCTGTATATGAGC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse Primer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTGCTTATTCTCTGGGCAAGAAAA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGAPDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward Primer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGGAGCGAGATCCCTCCAAAAT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse Primer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGGCTGTTGTCATACTTCTCATGG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eUgdh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward Primer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eACTTGAATCTACAGGTTCTGTC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse Primer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCTCTGTCTGGGTTCTTTAGG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eActb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward Primer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCTTCCTGGGTATGGAATCCT\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReverse Primer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTCTTTACGGATGTCAACGTC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eWestern blot (WB)\u003c/h2\u003e \u003cp\u003eThe protein lysates were separated by 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) (Epizyme Biomedical Technology, PG112, CN) and then electrophoretically transferred onto the polyvinylidene fluoride membranes (Epizyme Biomedical Technology, WJ001, CN). The primary antibodies we used were anti-UGDH (Proteintech, 67360-1-Ig, 1:1000, US) and anti-GAPDH (MBL, M171-3, 1:5000, JPN). The second antibody we used was HRP-conjugated Affinipure Goat Anti-Mouse IgG (H\u0026thinsp;+\u0026thinsp;L) (Proteintech, SA00001-1, 1:8000, US). The Image J software was used to analyze the integrated density of the protein bands.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eExpression Landscape of UGDH\u003c/h2\u003e \u003cp\u003eThe mRNA and protein of UGDH were widely expressed in various organs and tissues, and most significantly expressed in the Liver, Gallbladder, and Gastrointestinal tract (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). The result obtained from the consensus dataset, which included 375 normal tissues in the HPA database and 13,084 samples in GTEx, showed the top 10 mRNA of UGDH expressed primarily in the Liver, Colon, Rectum, Urinary bladder, Adipose tissue, Stomach, Duodenum, Small intestineine, Gallbladder, and Placenta (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). The protein of UGDH data was acquired from the HPA database which has 144 individuals corresponding to 44 samples of different normal tissue types. The protein of UGDH is mainly expressed in the Bronchus, Esophagus, Stomach, Colon, Liver, Prostate, Endommetrium, and Appendix (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). UGDH mRNA expression in different tissues and cell lines was shown (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD), and the highest expression was found in Liver cancer, Gallbladder cancer, Kidney cancer, Non-cancerous, and Lung cancer.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eExpression of UGDH in the 33 Cancers\u003c/h2\u003e \u003cp\u003eThe cancer types and its abbreviation were shown in Table\u0026nbsp;3. The UGDH mRNA expression was evaluated in the 33 cancer types from TCGA in cancer and normal tissues with statistically significant differences in BRCA, COAD, GBM, HNSC, KICH, KIRC, KIRP, LIHC, LUAD, LUSC, PCPG, PRAD, READ, STAD, THCA, and UCEC (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The UGDH mRNA expression was evaluated in the 33 cancer types from TCGA and GTEx in cancer and normal tissues with statistically significant differences in ACC, BRCA, COAD, DLBC, ESCA, GBM, HNSC, KICH, LAML, LGG, LIHC, LUAD, LUSC, OV, PAAD, PCPG, PRAD, READ, SKCM, STAD, TGCT, THYM, UCEC, and UCS with statistical differences (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). MESO and UVM could not be analyzed due to the lack of sufficient non-cancerous tissue samples. Statistically significant differences in UGDH mRNA expression in BRCA, COAD, HNSC, KICH, LIHC, LUAD, LUSC, PRAD, READ, THCA, and UCEC were assessed in 23 carcinomas versus paracarcinomas by paired-sample analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;3 | The abbreviation 33 types of cancers in TCGA\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eDiagnostic Value of UGDH in the 33 Cancers\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA\u0026ndash;F, UGDH exhibited significant diagnostic potential across a diverse array of cancers. Its AUC was greater than 0.7 in 12 cancers (Figure supplement 1) and even more than 0.9 in 6 cancers, including GBM (AUC\u0026thinsp;=\u0026thinsp;0.978), LAML (AUC\u0026thinsp;=\u0026thinsp;0.997), LGG (AUC\u0026thinsp;=\u0026thinsp;0.952), LUAD (AUC\u0026thinsp;=\u0026thinsp;0.911), PCPG (AUC\u0026thinsp;=\u0026thinsp;0.964) and TGCT (AUC\u0026thinsp;=\u0026thinsp;0.925), indicating its high diagnostic value. It is worth noting that the number of cases in the PCPG was too low (N\u0026thinsp;=\u0026thinsp;3) and its results could be biased. While in ACC, BRCA, DLBC, ESCA, KICH, LUSC, PAAD, PRAD, SARC, THYM, UCEC, and UCS with AUC of the ROC exceeded 0.7, which also has some diagnostic implications (Figure Supplement 1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eSurvival Analysis of UGDH in the 33 Cancers\u003c/h2\u003e \u003cp\u003eTo evaluate the prognosis value of UGDH, we carried out the K-M analysis in 33 cancers. The types of cancers in which UGDH expression affects OS in 10 cancers, and another 23 cancer types whose effects were not apparent. Highly expressed UGDH in ACC, KIRC, KIRP, LICH, LUAD, and SARC had a lower survival rate, and lowly expressed UGDH in BRCA, LAML, SKCM, and UCS had a higher survival rate (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-J). The forest plot showed DSS and PFI information for 10 cancers in which UGDH expression was significantly associated with OS, based on Cox regression analyses of the cancers (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eK). For LAML, DSS and PFI lack of information. Among them, BRCA, LUAD, and SKCM were not statistically different in DSS, and LUAD and SKCM were still not statistically different in progression-free intervals. The relationship between UGDH and common clinical parameters tumors is shown in Figure Supplement 2.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eUGDH Expression in Different Immune and Molecular Subtypes of the 33 Cancers\u003c/h2\u003e \u003cp\u003eFrom previous results, we found that high or low levels of UGDH expression had an impact on OS in 10 cancers. Therefore, we analyzed UGDH expression in immune and molecular subtypes of 10 cancers. The results showed that UGDH was significantly differentially expressed in 9 of the 10 immune subtypes of cancer, including ACC, BRCA, KIRC, KIRP, LIHC, LUAD, SARC, SKCM, and UCS (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-I). For molecular subtypes, UGDH was significantly differentially expressed in 5 cancer types, including ACC, BRCA, KIRP, LIHC, and SKCM (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-E).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003eGenetic Alteration of UGDH\u003c/h2\u003e \u003cp\u003eMutations in UGDH expressed in cancer were analyzed by cBioPortal. We identified 84 mutation sites between amino acids 0 and 494, including 67 missense mutations, 12 truncating mutations, 1 in-frame mutation, 2 splices, and 2 fusions, with M432Cfs and Nfs being the most common mutation sites (Fig.\u0026nbsp;7A). UGDH mutations were most common in the top 10 cancers of UCEC, CHOL, STAD, ACC, LUAD, LIHC, SKCM, UCS, PAAD, and SARC (Fig.\u0026nbsp;7B). Among the 32 types of cancers, almost all cancers had shallow deletions in the expression of UGDH mRNA, gain is often present in Adrenocortical cancer, while amplification is more frequent in hepatobiliary cancer (Fig.\u0026nbsp;7C).\u003c/p\u003e \u003cp\u003eFIGURE 7 | Genetic alteration of UGDH in pan-cancers. (A) Mutation diagram of UGDH across protein domains. (B) Bar chart of UGDH mutations in 32 cancers. (C) Mutation counts and types of UGDH in 32 cancers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eThe PPI, Functional Enrichment, and GSEA of UGDH in Cancers\u003c/h2\u003e \u003cp\u003eA total of 32 genes closely related to UGDH were obtained from the String and the PPI network was constructed (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). The top 10 pivotal genes were UGDH, UGT1A1, UGT1A3, UGT1A4, UGT1A6, UGT1A7, UGT1A8, UGT1A9, UGT1A10 and GALE, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). The first 10 hub genes were mainly associated with BRCA, KIRC, KIRP, LIHC, and LUAD where UGDH expression affected OS prognosis (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003eC). These genes were subjected to GO and KEGG enrichment to analyze function in three categories including biological process (BP), molecular function (MF), and cellular component (CC). The top GO terms for BP were cellular glucuronidation, xenobiotic glucuronidation, and flavonoid glucuronidation. MF was glucuronosyltransferase activity, UDP-glycosyltransferase activity, and enzyme binding. Moreover, CC was the endoplasmic reticulum membrane, an integral component of the membrane, and endoplasmic reticulum. The major pathways of KEGG were Pentose and glucuronate interconversion, Ascorbate and alternate metabolism, and porphyrin metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003eD). Besides, UGDH was active in the DNA damage response, EMT, Hormone AR, Hormone ER, P13K/AKT, RAS/MAPK, and TSC/mTOR pathway were both activated and repressed (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003eE). The GSEA enrichment results for the 10 cancer types were shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003e, and common themes included pathways associated with cell cycle regulation, immune response, metabolism, and cell signaling. These common pathways suggested that UGDH expression has a coordinated role in specific cellular processes across cancer types.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eFunctional States of UGDH in scRNA-Seq Datasets\u003c/h2\u003e \u003cp\u003eWe investigated the functional status of UGDH across various cancer types using CancerSEA, analyzing its correlation with multiple functional states of cancer cells at the single-cell level. The findings indicated that UGDH expression impacted functional status varies widely across various cancers (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e10\u003c/span\u003eA). Further analysis of UGDH's correlation with specific cancers indicated LUAD is positively correlated with DNA repair and negatively correlated with quiescence, metastasis, angiogenesis, and differentiation. ALL and HNSCC are positively correlated with stemness, while NSCLC is negatively correlated with stemness. AML shows a positive correlation with quiescence, differentiation, inflammation, and proliferation. RCC and PC are positively correlated with hypoxia, whereas RB is positively correlated with differentiation, inflammation, and angiogenesis, and negatively correlated with DNA repair, Cell Cycle, and DNA damage. UM is negatively correlated with DNA damage and DNA repair. (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e10\u003c/span\u003eB-I).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eImmunogenomic Analyses of UGDH in the 33 Cancers\u003c/h2\u003e \u003cp\u003eTo assess the relationship between UGDH expression and immune infiltration and regulation, we generated heatmaps of UGDH with markers of immune cells or factors. Our findings indicated that UGDH expression is positively correlated with the infiltration levels of T helper cells, Tcm, and Th2 across 33 cancer types, and negatively correlated with the infiltration levels of pDC and Cytotoxic cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e11\u003c/span\u003eA). UGDH showed positive correlations with most MHC molecules in KICH, PCPG, and UVM, but negative correlations with most MHC molecules in LUAD, LUSC, and TGCT (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e11\u003c/span\u003eB). Interestingly, in terms of immune activation and suppression, there was a positive correlation in ACC, KICH, and UVM, and a negative correlation with LUAD and LUSC. The immunosuppressive factors TGFBR1 and KDR were positively correlated with the expression of UGDH in almost all cancers (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e11\u003c/span\u003eD). Most cytokines in KICH, KIRC, PCPG, and UVM were positively correlated with UGDH, while negative correlations were observed in BRCA, LUAD, and LUSC (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e11\u003c/span\u003eE). Regarding cytokine receptors, UGDH showed positive correlations with most cytokine receptors in BLCA, DLBC, KICH, KIRC, PCPG, PRAD, and UVM, and negative correlations in LUAD and LUSC (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e11\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eUGDH depletion inhibits the proliferation of liver cancer cells\u003c/h3\u003e\n\u003cp\u003eThe results in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e12\u003c/span\u003eA showed that the model group exhibited disrupted liver tissue structure, characterized by hepatocellular nodules displaying abnormal cellular proliferation and significant fibrous deposition, which revealed the modeling of rat hepatocellular carcinoma was successful. qRT-PCR and WB analyses showed elevated expression of UGDH in rat HCC tissues compared with the control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e12\u003c/span\u003eB and \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e12\u003c/span\u003eC). Further protein blotting analysis showed that UGDH was successfully knocked down in Huh7 cells using different siRNAs compared with the non-targeting control siRNA (Si-NC), and the UGDH protein level was significantly reduced (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e12\u003c/span\u003eD). siRNA-mediated knockdown resulted in a reduction of UGDH mRNA level in Huh7 cells, which enhanced the effectiveness of the gene silencing (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e12\u003c/span\u003eE). Cell viability assay (CCK-8 analysis) demonstrated that the knockdown of UGDH significantly inhibited the proliferative activity of Huh7 cells at 24 and 72 hours. Photographs of xenograft tumors from nude mice showed that siRNA-mediated knockdown of UGDH resulted in a significant reduction in tumor size, illustrating the potential of targeting UGDH in reducing tumor load (Figs.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e12\u003c/span\u003eF and \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e12\u003c/span\u003eG). Taken together, these results suggest that the UGDH plays an important role in the progression of hepatocellular carcinoma and that targeting UGDH may be a viable therapeutic strategy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eUGDH plays a key role in xenobiotic metabolism via the glucuronidation pathway, sugar metabolism, production of ECM precursors, and proteoglycan synthesis, which suggests that it may be a potential therapeutic target for a variety of diseases [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Previous studies have shown that the expression and localization of UGDH is an early serum diagnostic marker for lung cancer patients, as well as a prognostic indicator [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. UDPGA was shown to be a precursor for the synthesis of glycosaminoglycans and proteoglycans that promote the progression of invasive prostate cancer, and the UGDH content in the prostate tip serves as a new candidate biomarker [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The above suggests that UGDH may become a new diagnostic marker in clinical practice.\u003c/p\u003e \u003cp\u003ePrevious studies have shown that UGDH is positively associated with the development of epirubicin resistance and regulation of the ECM in breast cancer [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Upregulation of UGDH has been associated with metastasis in ovarian cancer, where it regulates tumor-initiating cells affecting the tumor microenvironment [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. And it may be related to melanoma development and progression [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Therefore, UGDH has a certain prognostic predictive value. Regarding common clinicopathological parameters of tumors, UGDH expression existed gender difference, pathologic T and M stage in LUAD. Past research has also shown that phosphorylation of UGDH at tyrosine 473 is associated with metastatic recurrence and poor prognosis in lung cancer patients [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Elevated UGDH expression correlated with male gender, vascular invasion, and histologic grade in LIHC. Investigations have shown that UGDH-mediated activation of UDPGA activates TGFβ/Smad signaling thereby promoting the migration of hepatocellular carcinoma cells [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and it is associated with sorafenib resistance [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Moreover, the UGDH was correlated with age and cytogenetic risk in LAML, pathologic T stage in SKCM, age in SARC, pathologic stage and gender in KIRP, and histologic grade and pathologic T and M stage in KIRC.\u003c/p\u003e \u003cp\u003eUGDH expression differed between various molecular or immune subtypes of cancers, with differences in immune subtypes present in 30 cancer types and differences in molecular subtypes present in 17 cancers. Except for LAML, the cancer types with differences in UGDH expression and immune subtypes were consistent with the cancer types whose expression affected survival prognosis, suggesting that UGDH may intervene in cancer prognosis by affecting immune subtypes. Moreover, in subsequent studies, we focused on analyzing the role of UGDH expression on immunomodulatory factors and major histocompatibility complex molecules and targeting immune lymphocytes. However, it has also been pointed out that genes are aberrantly expressed in specific subtypes of cancer, and the results may not be reflected in the overall patients of that cancer. Thus, differences in gene expression do not affect cancer survival in certain cancers, whereas its varied expression in different molecular or immune subtypes might play different roles in the prognosis of various cancers [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Genetic mutations could induce abnormal gene expression and tumorigenesis. We found that UGDH mutation sites mainly include missense, truncating, inframe, splice, and fusion in the TCGA pan-cancer Atlas study. UGDH has undergone genetic alteration in 24 types of cancer, and the cancer types with alteration frequency greater than 2% include UCEC, CHOL, STAD, ACC, and LUAD. In most cancers, the main type of UGDH gene alteration is mutation, followed by amplification. It is worth noting that the UGDH gene abnormalities in LICH are mainly amplification.\u003c/p\u003e \u003cp\u003eTo further explore the biological functions of UGDH, we established a PPI network and identified hub genes, and we showed the relationship between hub genes and cancer types in which UGDH expression affects the prognosis of cancer. For the hub genes obtained, GALE encodes UDP-galactose-4-epimerase. Previous studies have shown that it is associated with the differentiation grade of gastric cancer [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] promotes the proliferation and migration of glioblastoma cells [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], and is a potential marker for papillary thyroid carcinoma [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The other hub genes belong to the UDP glucuronosyltransferase family, which encodes UDP- glucuronosyltransferase, an enzyme of the glucuronidation pathway that transforms small lipophilic molecules, such as steroids, bilirubin, hormones, and drugs, into water-soluble, excretable metabolites [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. However, these enzymes have different preferences for substrates, and the preferred substrate of UGT1A1 is bilirubin. Substrates of UGT1A3 include estrone, 2-hydroxy estrone, and metabolites of benzo alpha-pyrene. Although UGT1A4 is more active on amines, steroids, and sapogenins. UGT1A6, UGT1A7, and UGT1A9 are active on phenols. UGT1A8 and UGT1A10 have glucuronidase activity on drugs such as coumarins [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Meanwhile, we performed GO and KEGG analysis of UGDH and its related genes. The most prominent BP enrichment result was cellular glucuronidation, followed by xenobiotic glucuronidation. For the MF analysis results, the top rank was glucuronosyltransferase activity, followed by UDP-glycosyltransferase activity. The CC enrichment results mainly included the endoplasmic reticulum membrane and its membrane, an integral component of membrane and intracellular membrane-bounded organelle. Whereas KEGG analysis was enriched to the most significant pathway was pentose and glucuronate interconversion, suggesting a high degree of correlation between the results of the GO enrichment analysis. It is known that UDP- glucuronosyltransferase is localized to the endoplasmic reticulum, and the active transport of the metabolite UDPGA involved in UGDH is thought to occur in hepatocytes [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], so the role of UGDH in hepatocellular carcinoma should be taken seriously.\u003c/p\u003e \u003cp\u003eThrough CancerSEA analysis results, the expression of UGDH exhibited different functional states at the single-cell level in various cancers. The expression of UGDH is generally positively correlated with cell apoptosis, invasion, and proliferation, while negatively correlated with cell cycle. GSEA functional enrichment analysis was applied to cancer types in which UGDH expression affects overall prognosis. Analysis results were repeated more than twice and were considered common enrichment pathways. Most of the common enrichment pathways are immunologically related. Immunoregulatory interactions between a lymphoid and a nonlymphoid cell pathway participated adaptive Immune system [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The two pathways related to B cells analyzed are antigen activates B cell receptor BCR leading to generation of second messengers and CD22 mediated BCR regulation pathway, CD22 is an inhibitory B cell coreceptor that regulates B cell development and activation by downregulating BCR signaling through activation of protein tyrosine phosphatase-1 [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. There are also 3 pathways related to complement, namely Initial triggering of complement, complement cascade, and Creation of C4 and C2 activators. Creation of C4 and C2 activators pathway participated initial triggering of complement. Current research shows that the complement system is one of the inflammatory mechanisms activated in the tumor microenvironment, beside exerting anti-tumor mechanisms such as complement-dependent cytotoxicity and phagocytosis induced by therapeutic monoclonal antibodies, the complement system may promote immunosuppression and tumor growth and invasiveness [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The analysis results also included Fc-gamma receptors (FCGR) activation and the Role of LAT2 Non-T cell activation linker (NTAL) lab on calcium mobilization pathway, the research suggests that FCGR are expressed on immune cells, bind to antibodies, and trigger antibody-induced cell-mediated antitumor responses when tumor-reactive antibodies are present [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The lipid raft resident adaptor molecules LAT1 and NTAL, also known as linkers for activation of B cells (LAB)/LAT2 are participants in the regulation of mast cell calcium responses [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In addition, the role of phospholipids in phagocytosis, scavenging of heme from plasma, and formation of the cornified envelope role in tumors still needs further investigation.\u003c/p\u003e \u003cp\u003eTo observe the immune status of UGDH and pan-cancer in-depth, we assessed the impact of UGDH on immune infiltration by analyzing its correlation with immune lymphocytes and immunomodulatory factors in the 33 cancers. UGDH expression was positively correlation with T helper cells, Tcm, and Th2 cells in most cancers, and negatively correlated with pDC and Cytotoxic cells. Primary T cells become effector T cells and memory T cells after being activated. Effector T cells include cytotoxic T cells and helper T (Th) cells. T cells are activated by dendritic cells (DC) and differentiate into various subtypes of Th cells, including Th2 cells [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Th2 cells induce an immunosuppressive protumorigenic response. Th2 cell infiltration in the tumor microenvironment is commonly associated with poor clinical outcomes in human cancers [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The Research that Tcm cells conferred superior anti-tumor immunity compared with effector memory T cells and effector T cells in CAR-T therapy [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. For other immune characteristics, UGDH preferred some correlation with the type of cancer. Thus, the role of UGDH in cancer immunity is complex, and its positive correlation with Th2 cells and negative correlation with cytotoxic cells might be associated with poor prognosis of some tumors.\u003c/p\u003e \u003cp\u003eFinally, we experimentally observed the expression and effect of UGDH in HCC. Compared with the normal group, the expression of UGDH was elevated in the liver tissue of HCC rats. Our previous experimental results also showed that the key genes AKR1B10 involved in the pentose and glucuronate interconversions pathway was consistent with the expression pattern of UGDH [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Subsequently, we knocked down the expression of UGDH in Huh7 cell lines and found that the knockdown of UGDH could inhibit the cell viability and in vitro tumourigenic ability of Huh7 cells. Of course, this study has some limits and did not explore the role of UGDH in more tumor types.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eIn conclusion, our study elucidates the role of UGDH in pan-cancer from multiple perspectives, including its associated biological functions, mutations, and immunomodulation. UGDH might be a potential diagnostic marker for lung adenocarcinoma, testicular cancer, and gliomas. And it could be a potential prognostic marker for HCC, lung cancers, and sarcomas. Inhibition of UGDH expression inhibits the proliferation of HCC cells.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate(Not applicable)\u003c/p\u003e\n\u003cp\u003eThe animal research ethics board of Beijing University of Chinese Medicine Dongzhimen Hospital approved this study.\u003c/p\u003e\n\u003cp\u003eConsent for publication(Not applicable)\u003c/p\u003e\n\u003cp\u003eAvailability of data and materials(Not applicable)\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or fnancial relationships that could be construed as potential conficts of interest.\u003c/p\u003e\n\u003cp\u003eFunding(Not applicable)\u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003eCao Xu: Data curation, Formal analysis, Investigation, Methodology, Project administration, Validation, Visualization, Writing-original draft, Writing-review \u0026amp; editing. Size Li: Data curation, Formal analysis, Investigation, Methodology, Project administration, Validation, Visualization, Writing-original draft, Writing-review \u0026amp; editing. Yongan Ye: Validation, Supervision. Liping Zhang: Funding acquisition, Validation. Xiaobin Zao: Validation, Supervision. Baiquan Xue: Conceptualization, Data curation. Li Hou: Conceptualization, Data curation. Shihao Zheng: Conceptualization, Data curation. Jiaxin Zhang: Formal analysis. Xiaoke Li: Investigation. Hongbo Du: Validation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZhou M, Wang H, Zeng X, Yin P, Zhu J, Chen W, et al. Mortality, Morbidity, and Risk Factors in China and Its Provinces, 1990\u0026ndash;2017: A Systematic Analysis for the Global Burden of Disease Study 2017. Lancet. 2019;394(10204):1145\u0026ndash;58. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s0140-6736(19)30427-1\u003c/span\u003e\u003cspan address=\"10.1016/s0140-6736(19)30427-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVasan N, Baselga J, Hyman DM. A View on Drug Resistance in Cancer. Nature. 2019;575(7782):299\u0026ndash;309. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41586-019-1730-1\u003c/span\u003e\u003cspan address=\"10.1038/s41586-019-1730-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHanahan D, Weinberg RA. Hallmarks of Cancer: The Next Generation. Cell. 2011;144(5):646\u0026ndash;74. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cell.2011.02.013\u003c/span\u003e\u003cspan address=\"10.1016/j.cell.2011.02.013\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLane AN, Higashi RM, Fan TWM. Metabolic reprogramming in tumors: Contributions of the tumor microenvironment. Genes Dis (. 2020;7(2):185\u0026ndash;98. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.gendis.2019.10.007\u003c/span\u003e\u003cspan address=\"10.1016/j.gendis.2019.10.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDoshi MB, Lee N, Tseyang T, Ponomarova O, Goel HL, Spears M, et al. Disruption of Sugar Nucleotide Clearance Is a Therapeutic Vulnerability of Cancer Cells. Nature. 2023;623(7987):625\u0026ndash;32. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41586-023-06676-3\u003c/span\u003e\u003cspan address=\"10.1038/s41586-023-06676-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZimmer BM, Barycki JJ, Simpson MA. Integration of Sugar Metabolism and Proteoglycan Synthesis by Udp-Glucose Dehydrogenase. J Histochem Cytochem. 2021;69(1):13\u0026ndash;23. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1369/0022155420947500\u003c/span\u003e\u003cspan address=\"10.1369/0022155420947500\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrice MJ, Nguyen AD, Byemerwa JK, Flowers J, Ba\u0026euml;ta CD, Goodwin CR. Udp-Glucose Dehydrogenase (Ugdh) in Clinical Oncology and Cancer Biology. Oncotarget. 2023;14:843\u0026ndash;57. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.18632/oncotarget.28514\u003c/span\u003e\u003cspan address=\"10.18632/oncotarget.28514\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang X, Liu R, Zhu W, Chu H, Yu H, Wei P, et al. Udp-Glucose Accelerates Snai1 Mrna Decay and Impairs Lung Cancer Metastasis. Nature. 2019;571(7763):127\u0026ndash;31. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41586-019-1340-y\u003c/span\u003e\u003cspan address=\"10.1038/s41586-019-1340-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo B, Xu X, Shao M, Yang X, He G, Qi K, et al. Udp-Glucose 6-Dehydrogenase Lessens Sorafenib Sensitivity Via Modulating Unfolded Protein Response. Biochem Biophys Res Commun. 2022;613:207\u0026ndash;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.bbrc.2022.05.048\u003c/span\u003e\u003cspan address=\"10.1016/j.bbrc.2022.05.048\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFan M, Huo S, Guo Y, Wang R, Hao W, Zhang Z, et al. Udp-Glucose Dehydrogenase Supports Autophagy-Deficient Pdac Growth Via Increasing Hyaluronic Acid Biosynthesis. Cell Rep. 2024;43(2):113808. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.celrep.2024.113808\u003c/span\u003e\u003cspan address=\"10.1016/j.celrep.2024.113808\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTeoh ST, Ogrodzinski MP, Lunt SY. Udp-Glucose 6-Dehydrogenase Knockout Impairs Migration and Decreases in Vivo Metastatic Ability of Breast Cancer Cells. Cancer Lett. 2020;492:21\u0026ndash;30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.canlet.2020.07.031\u003c/span\u003e\u003cspan address=\"10.1016/j.canlet.2020.07.031\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarrington BS, Kamdar R, Ning F, Korrapati S, Caminear MW, Hernandez LF, et al. Ugdh Promotes Tumor-Initiating Cells and a Fibroinflammatory Tumor Microenvironment in Ovarian Cancer. J Exp Clin Cancer Res. 2023;42(1):270. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13046-023-02820-z\u003c/span\u003e\u003cspan address=\"10.1186/s13046-023-02820-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang D, Casale GP, Tian J, Lele SM, Pisarev VM, Simpson MA, et al. Udp-Glucose Dehydrogenase as a Novel Field-Specific Candidate Biomarker of Prostate Cancer. Int J Cancer. 2010;126(2):315\u0026ndash;27. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/ijc.24820\u003c/span\u003e\u003cspan address=\"10.1002/ijc.24820\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOyinlade O, Wei S, Lal B, Laterra J, Zhu H, Goodwin CR, et al. Targeting Udp-Α-D-Glucose 6-Dehydrogenase Inhibits Glioblastoma Growth and Migration. Oncogene. 2018;37(20):2615\u0026ndash;29. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41388-018-0138-y\u003c/span\u003e\u003cspan address=\"10.1038/s41388-018-0138-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVitale DL, Caon I, Parnigoni A, Sevic I, Spinelli FM, Icardi A, et al. Initial Identification of Udp-Glucose Dehydrogenase as a Prognostic Marker in Breast Cancer Patients, Which Facilitates Epirubicin Resistance and Regulates Hyaluronan Synthesis in Mda-Mb-231 Cells. Biomolecules. 2021;11(2). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/biom11020246\u003c/span\u003e\u003cspan address=\"10.3390/biom11020246\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Q, Karvelsson ST, Johannsson F, Vilhjalmsson AI, Hagen L, de Miranda Fonseca D, et al. Udp-Glucose Dehydrogenase Expression Is Upregulated Following Emt and Differentially Affects Intracellular Glycerophosphocholine and Acetylaspartate Levels in Breast Mesenchymal Cell Lines. Mol Oncol. 2022;16(9):1816\u0026ndash;40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/1878-0261.13172\u003c/span\u003e\u003cspan address=\"10.1002/1878-0261.13172\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHagiuda D, Nagashio R, Ichinoe M, Tsuchiya B, Igawa S, Naoki K, et al. Clinicopathological and Prognostic Significance of Nuclear Ugdh Localization in Lung Adenocarcinoma. Biomed Res. 2019;40(1):17\u0026ndash;27. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2220/biomedres.40.17\u003c/span\u003e\u003cspan address=\"10.2220/biomedres.40.17\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGao Q, Cheng B, Chen C, Lei C, Lin X, Nie D, et al. Dysregulated Glucuronic Acid Metabolism Exacerbates Hepatocellular Carcinoma Progression and Metastasis through the Tgfβ Signalling Pathway. Clin Transl Med. 2022;12(8):e995. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/ctm2.995\u003c/span\u003e\u003cspan address=\"10.1002/ctm2.995\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShen S, Li H, Liu J, Sun L, Yuan Y. The Panoramic Picture of Pepsinogen Gene Family with Pan-Cancer. Cancer Med. 2020;9(23):9064\u0026ndash;80. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/cam4.3489\u003c/span\u003e\u003cspan address=\"10.1002/cam4.3489\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZao X, Cheng J, Shen C, et al. NFATc3 inhibits hepatocarcinogenesis and HBV replication via positively regulating RIG-I-mediated interferon transcription. Oncoimmunology. 2021;10(1):1869388. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/2162402X.2020.1869388\u003c/span\u003e\u003cspan address=\"10.1080/2162402X.2020.1869388\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEgger S, Chaikuad A, Kavanagh KL, Oppermann U, Nidetzky B. Udp-Glucose Dehydrogenase: Structure and Function of a Potential Drug Target. Biochem Soc Trans. 2010;38(5):1378\u0026ndash;85. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1042/bst0381378\u003c/span\u003e\u003cspan address=\"10.1042/bst0381378\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeen AJ, Arasu UT, Pasonen-Sepp\u0026auml;nen S, Hassinen A, Takabe P, Wojciechowski S, et al. Udp-Sugar Substrates of Has3 Regulate Its O-Glcnacylation, Intracellular Traffic, Extracellular Shedding and Correlate with Melanoma Progression. Cell Mol Life Sci. 2016;73(16):3183\u0026ndash;204. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s00018-016-2158-5\u003c/span\u003e\u003cspan address=\"10.1007/s00018-016-2158-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiang X, Li L, Fan Y, Diagnostic. Prognostic, and Immunological Roles of Hells in Pan-Cancer: A Bioinformatics Analysis. Front Immunol. 2022;13:870726. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2022.870726\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2022.870726\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Souza MFD, da Silva Filho AF, de Barros Albuquerque AP, Quirino MWL, de Souza Albuquerque MS, Cordeiro MF, et al. Overexpression of Udp-Glucose 4-Epimerase Is Associated with Differentiation Grade of Gastric Cancer. Dis Markers. 2019;2019:6325326. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1155/2019/6325326\u003c/span\u003e\u003cspan address=\"10.1155/2019/6325326\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun X, Xue H, Xiong Y, Yu R, Gao X, Qian M, et al. Gale Promotes the Proliferation and Migration of Glioblastoma Cells and Is Regulated by Mir-Let-7i-5p. Cancer Manag Res. 2019;11:10539\u0026ndash;54. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2147/cmar.S221585\u003c/span\u003e\u003cspan address=\"10.2147/cmar.S221585\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYe G, Zhang X, Li M, Lin Z, Xu Y, Dong H, et al. Integrated Analysis of Circulating and Tissue Proteomes Reveals That Fibronectin 1 Is a Potential Biomarker in Papillary Thyroid Cancer. BMC Cancer. 2023;23(1):412. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12885-023-10839-w\u003c/span\u003e\u003cspan address=\"10.1186/s12885-023-10839-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFujarewicz K, Jarzab M, Eszlinger M, Krohn K, Paschke R, Oczko-Wojciechowska M, et al. A Multi-Gene Approach to Differentiate Papillary Thyroid Carcinoma from Benign Lesions: Gene Selection Using Support Vector Machines with Bootstrapping. Endocr Relat Cancer. 2007;14(3):809\u0026ndash;26. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1677/erc-06-0048\u003c/span\u003e\u003cspan address=\"10.1677/erc-06-0048\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeech R, Mackenzie PI. Structure and Function of Uridine Diphosphate Glucuronosyltransferases. Clin Exp Pharmacol Physiol. 1997;24(12):907\u0026ndash;15. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/j.1440-1681.1997.tb02718.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1440-1681.1997.tb02718.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKing CD, Rios GR, Green MD, Tephly TR. Udp-Glucuronosyltransferases. Curr Drug Metab. 2000;1(2):143\u0026ndash;61. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2174/1389200003339171\u003c/span\u003e\u003cspan address=\"10.2174/1389200003339171\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBock KW, K\u0026ouml;hle C. Topological Aspects of Oligomeric Udp-Glucuronosyltransferases in Endoplasmic Reticulum Membranes: Advances and Open Questions. Biochem Pharmacol. 2009;77(9):1458\u0026ndash;65. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.bcp.2008.12.004\u003c/span\u003e\u003cspan address=\"10.1016/j.bcp.2008.12.004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNedvetzki S, Sowinski S, Eagle RA, Harris J, V\u0026eacute;ly F, Pende D, et al. Reciprocal Regulation of Human Natural Killer Cells and Macrophages Associated with Distinct Immune Synapses. Blood. 2007;109(9):3776\u0026ndash;85. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1182/blood-2006-10-052977\u003c/span\u003e\u003cspan address=\"10.1182/blood-2006-10-052977\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkatsu C, Alborzian Deh Sheikh A, Matsubara N, Takematsu H, Schweizer A, Abdu-Allah HHM, et al. The Inhibitory Coreceptor Cd22 Restores B Cell Signaling by Developmentally Regulating Cd45(-/-) Immunodeficient B Cells. Sci Signal. 2022;15(723):eabf9570. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1126/scisignal.abf9570\u003c/span\u003e\u003cspan address=\"10.1126/scisignal.abf9570\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlborzian Deh Sheikh A, Akatsu C, Abdu-Allah HHM, Suganuma Y, Imamura A, Ando H, et al. The Protein Tyrosine Phosphatase Shp-1 (Ptpn6) but Not Cd45 (Ptprc) Is Essential for the Ligand-Mediated Regulation of Cd22 in Bcr-Ligated B Cells. J Immunol. 2021;206(11):2544\u0026ndash;51. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4049/jimmunol.2100109\u003c/span\u003e\u003cspan address=\"10.4049/jimmunol.2100109\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMagrini E, Minute L, Dambra M, Garlanda C. Complement Activation in Cancer: Effects on Tumor-Associated Myeloid Cells and Immunosuppression. Semin Immunol. 2022;60:101642. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.smim.2022.101642\u003c/span\u003e\u003cspan address=\"10.1016/j.smim.2022.101642\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKolev M, Das M, Gerber M, Baver S, Deschatelets P, Markiewski MM. Inside-out of Complement in Cancer. Front Immunol. 2022;13:931273. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2022.931273\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2022.931273\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eErbe AK, Wang W, Goldberg J, Gallenberger M, Kim K, Carmichael L, et al. Fcgr Polymorphisms Influence Response to Il2 in Metastatic Renal Cell Carcinoma. Clin Cancer Res. 2017;23(9):2159\u0026ndash;68. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1158/1078-0432.Ccr-16-1874\u003c/span\u003e\u003cspan address=\"10.1158/1078-0432.Ccr-16-1874\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTessarz AS, Weiler S, Zanzinger K, Angelisov\u0026aacute; P, Horejs\u0026iacute; V, Cerwenka A. Non-T Cell Activation Linker (Ntal) Negatively Regulates Trem-1/Dap12-Induced Inflammatory Cytokine Production in Myeloid Cells. J Immunol. 2007;178(4):1991\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4049/jimmunol.178.4.1991\u003c/span\u003e\u003cspan address=\"10.4049/jimmunol.178.4.1991\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBasu A, Ramamoorthi G, Albert G, Gallen C, Beyer A, Snyder C, et al. Differentiation and Regulation of T(H) Cells: A Balancing Act for Cancer Immunotherapy. Front Immunol. 2021;12:669474. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2021.669474\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2021.669474\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRenaude E, Kroemer M, Borg C, Peixoto P, Hervouet E, Loyon R, et al. Epigenetic Reprogramming of Cd4(+) Helper T Cells as a Strategy to Improve Anticancer Immunotherapy. Front Immunol. 2021;12:669992. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2021.669992\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2021.669992\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKlebanoff CA, Gattinoni L, Torabi-Parizi P, Kerstann K, Cardones AR, Finkelstein SE, et al. Central Memory Self/Tumor-Reactive Cd8\u0026thinsp;+\u0026thinsp;T Cells Confer Superior Antitumor Immunity Compared with Effector Memory T Cells. Proc Natl Acad Sci U S A. 2005;102(27):9571\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1073/pnas.0503726102\u003c/span\u003e\u003cspan address=\"10.1073/pnas.0503726102\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZao X, Cao X, Liang Y, Zhang J, Chen H, Zhang N, et al. The Chinese herbal KangXianYiAi formula inhibits hepatocellular carcinoma by reducing glutathione and inducing ferroptosis. Pharmacol Res - Mod Chin Med. 2023;8:100276. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.prmcm.2023.100276\u003c/span\u003e\u003cspan address=\"10.1016/j.prmcm.2023.100276\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"UGDH, hepatocellular carcinoma, bioinformatics, pan-cancer, pentose and glucuronate interconversion","lastPublishedDoi":"10.21203/rs.3.rs-4632654/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4632654/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackgrounds:\u003c/strong\u003e Abnormalities in glycometabolism lead to carcinogenesis. UDP-glucose 6-dehydrogenase (UGDH) is the key enzyme of glucuronic acid metabolism and acts as a key mediator in several cancer developmental signaling pathways. In this study, our objective is to offer a more systematic and comprehensive elucidation of the involvement of UGDH in the onset and advancement of various malignancies via an in-depth analysis of UGDH in cancer contexts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod:\u003c/strong\u003e We investigated the role of UGDH in cancers using the Human Protein Atlas (HPA), The Cancer Genome Atlas (TCGA), and Genotype-Tissue Expression (GTEx) databases. And analyzed data using various R packages and websites, including TISIDB, cBioPortal, STRING, Cytoscape, GSCALite, and CancerSEA. A rat hepatocellular carcinoma (HCC) model was established using intraperitoneal injection of diethylnitrosamine. Hematoxylin-Eosin (HE) staining, MASSON staining, and KI67 immunohistochemistry of liver tissues were performed. Real-time quantitative PCR (qRT-PCR) and western blotting (WB) were used to detect the expression of UGDH. UGDH gene was knocked down in Huh7 cells, and CCK8 and nude mice tumor xenograft assays were further performed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e UGDH high expression is associated with poor clinical outcomes in hepatocellular carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, and sarcoma. And differentially expressed across molecular and immune subtypes. UGDH was primarily involved in the pentose and glucuronate interconversion pathway. Its expression positively correlated with T helper, Tcm, and Th2 cells in most cancers. Moreover, experimental results demonstrated that UGDH expression is elevated in liver cancer and promotes the proliferation of HCC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Our study elucidates that UGDH could be used as a valuable prognostic biomarker and potential therapeutic target in many cancers, especially liver and lung cancer. UGDH could promote the proliferation of HCC cells, possibly by modulating the pentose and glucuronate interconversion pathway.\u003c/p\u003e","manuscriptTitle":"Pan-cancer analysis of UDP-glucose 6-dehydrogenase and its carcinogenesis in hepatocellular carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-19 18:04:25","doi":"10.21203/rs.3.rs-4632654/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":"649e0935-c5d1-4224-b75a-83a0404bbe1b","owner":[],"postedDate":"July 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-13T14:23:46+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-19 18:04:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4632654","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4632654","identity":"rs-4632654","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}