The ‘Gene DE’ module of the TIMER 2.0 database (20) was employed to compare the UBE2T expression levels between tumor tissues and adjacent normal tissues across various cancer types. Gene expression distributions were visualized using box plots, with statistical significance assessed via the Wilcoxon test and denoted by asterisk notation. To reinforce and expand these comparisons, the ‘Box Plots’ feature in GEPIA2 (http://gepia2.cancer–pku.cn/#index) (21), integrating TCGA and GTEx datasets, was utilized under the thresholds of P<0.01 and log₂FoldChange=1. Protein expression data for UBE2T in pan-cancer contexts were retrieved from the UALCAN database (http://ualcan.path.uab.edu/index.html), which supports TCGA-based cancer data analysis. UBE2T mRNA profiles across cancer cell lines were sourced from the Cancer Cell Line Encyclopedia (https://sites.broadinstitute.org/ccle/tools) (22).

Expression levels of UBE2T mRNA and protein in pancreatic cancer cell lines [PANC1, ASPC, BXPC3, MIA2 (Mia-paca-2), SW1990 and CAPAN1] and normal pancreatic epithelial cells (HPDE) were assessed via reverse transcription-quantitative PCR (RT-qPCR) and western blotting. These 7 cell lines were purchased from American Type Culture Collection and underwent cultivation in Dulbecco's modified Eagle's medium (cat. no. C11995500BT; Gibco; Thermo Fisher Scientific, Inc.) with the addition of 10% fetal bovine serum (cat. no. 10099-141 C; Gibco; Thermo Fisher Scientific, Inc.), 100 U/ml penicillin and 100 mg/ml streptomycin (cat. no. 15140-122; Gibco; Thermo Fisher Scientific, Inc.). These cell cultures were nurtured under humidified conditions at 37°C with 5% CO₂ utilizing the Thermo Scientific HERACELL 240i CO₂ Incubator (240i; Thermo Fisher Scientific, Inc.). Western blotting was conducted using established protocols. Briefly, cell lysates were prepared in Radio Immunoprecipitation Assay buffer (cat. no. 87787; Thermo Fisher Scientific, Inc.) supplemented with protease (cat. no. 04693124001; Roche Diagnostics) and phosphatase inhibitors (cat. no. B15001-A; Selleck Chemicals), followed by incubation on ice for 30 min. The lysates underwent centrifugation at 13,580 × g for 15 min at 4°C, and the supernatant was subsequently collected with care. A total of 20 µg/lane of total protein was separated by 10% SDS-polyacrylamide gel electrophoresis. Post-electrophoresis, proteins were transferred onto polyvinylidene difluoride membranes (0.45 µm; MilliporeSigma). Membranes were incubated in 5% bovine serum albumin (cat. no. SLBN9354V; MilliporeSigma) to block non-specific binding sites for 1 h at room temperature. Primary antibodies specific to UBE2T (1:2,000; cat. no. A6853; Abclonal Biotech Co., Ltd.) and β-actin (1:2,000; cat. no. 4967S; Cell Signaling Technology, Inc.) were applied and maintained at 4°C overnight. After primary incubation, membranes were exposed to horseradish peroxidase (HRP)-conjugated goat anti-rabbit IgG (H+L) secondary antibodies (1:5,000; cat. no. 7074S; Cell Signaling Technology, Inc.) for 1 h at room temperature. Signal detection was achieved using Super ECL Detection Reagent (cat. no. 36208ES60; Shanghai Yeasen Biotechnology Co., Ltd.) and imaged with the Bio-Rad ChemiDoc MP Chemiluminescence Gel Imaging system (cat. no. 1708280; Bio-Rad Laboratories, Inc.). Quantification of western blot bands was performed using Image Lab™ software (Version 6.1; Bio-Rad Laboratories, Inc.).

For RT-qPCR, total RNA was extracted by lysing cells in 1 ml RNAiso Plus reagent (cat. no. 9109; Takara Biotechnology Co., Ltd.), ensuring complete homogenization. Following centrifugation (12,000 × g; 4°C; 15 min), the supernatant was removed and the RNA pellet was washed with 75% ethanol to improve purities. RNA extraction and subsequent reverse transcription were performed according to the protocols in the PrimeScript™ RT Master Mix (cat. no. RR036A; Takara Biotechnology Co., Ltd.) and TB Green^® Premix Ex Taq™ II (cat. no. RR820A; Takara Biotechnology Co., Ltd.) kits. qPCR was carried out on the 7500 Real-Time PCR System (cat. no. RR820A; Takara Biotechnology Co., Ltd.) using standard operational guidelines, and performed with the SYBR^® Premix TM kit (Invitrogen; Thermo Fisher Scientific, Inc.). The conditions for RT-qPCR were as follows: 95°C for 10 min, 40 cycles of 94°C for 30 sec, 60°C for 30 sec and 72°C for 30 sec, and a final extension of 10 min at 72°C. Relative mRNA expression levels were quantified using the 2^−ΔΔCq method (23) and normalized against β-actin as an internal control. The primer sequences used as follows: UBE2T forward (F), 5′-ATCCCTCAACATCGCAACTGT-3′ and reverse (R), 5′-CAGCCTCTGGTAGATTATCAAGC-3′; β-actin F, 5′-CGTGCGTGACATTAAGGAGAA-3′ and R, 5′-AGGAAGGAAGGCTGGAAGAG-3′.

Statistical analysis was conducted using GraphPad Prism version 9.01 (Dotmatics). Comparisons between each of the six pancreatic cancer cells and the single control group were performed using independent samples t-tests, with statistical significance assessed at a Bonferroni-corrected alpha level of α=0.01 (0.05/5 comparisons). Data are presented as mean ± standard deviation and were obtained from three independent repeats. All statistical analyses were two-sided. P<0.05 was considered to indicate a statistically significant difference.

The association between UBE2T expression and clinical phenotype was assessed using R software (version 4.2.1; http://www.r-project.org/). The results were visualized through violin plots and histograms. The diagnostic and prognostic relevance of UBE2T in tumors was further evaluated via Xiantao Academic (https://xiantaozi.com).

Kaplan-Meier plots for OS and disease-free survival (DFS) across 33 tumor types were generated using the ‘Survival Analysis’ module in the GEPIA2 database, with ‘Median’ specified as the group cut-off. Hazard ratios were estimated via the Cox proportional hazards model, with 95% confidence intervals indicated by dashed lines and statistical significance defined as P<0.05. Associations between UBE2T expression and OS, progression-free interval (PFI) and disease-specific survival (DSS) in pan-cancer contexts were further examined through Cox regression. The R packages ‘survival’ and ‘survminer’ were employed for this analysis, and outcomes were visualized using forest plots.

Genomic location, subcellular distribution, protein tertiary structure and known protein interactions of UBE2T were retrieved from the GeneCards database (https://www.genecards.org/). The cBioPortal platform (https://www.cbioportal.org/) integrates multi-dimensional genomic and clinical datasets (24), offering analyses of somatic mutations, DNA copy number variations (CNVs) and associated oncogenic alterations. Through modules such as ‘Cancer Types Summary’, ‘Plots’, ‘Mutations’ and ‘Survival’, cBioPortal was used to delineate the mutation prevalence, classification, prognostic relevance and potential epitope modification sites of UBE2T across various cancer types.

A database known as GSCALite (https://guolab.wchscu.cn/GSCA/#/) integrates genomic and pharmacological data including >10,000 tumor samples from 33 TCGA cancer types and >750 small-molecule agents from the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Therapeutics Response Portal (CTRP) repositories (25). UBE2T was systematically analyzed within this platform for its association with drug sensitivity, CNV and SNV profiles.

The ‘Gene_Corr’ module of the TIMER2.0 database was employed to assess the association between UBE2T expression and a spectrum of immune checkpoint genes, including TNFSF9, CD27, IDO1, CD274, TNFRSF9, CD28, TIGIT, CD40, CD70, PDCD1, CD86, LAG3, ICOS, HHLA2, ICOSLG, CTLA4, IDO2, CD80, CD276 and BTLA (26). Heatmap visualization was generated under the ‘Purity Adjustment’ mode to reduce the impact of sample composition variability. Key immunotherapy response predictors, such as mismatch repair (MMR), microsatellite instability (MSI), tumor mutation burden (TMB) and neoantigens (NEO), were further examined. Expression profiles for MSI and 5 core MMR genes (EPCAM, MSH6, PMS2, MSH2 and MLH1) were retrieved from TCGA. Pearson correlation analysis was conducted to quantify associations between UBE2T expression and MMR, MSI, TMB and NEO metrics. Statistical significance was determined at P<0.05.

The ESTIMATE algorithm infers stromal and immune cell infiltration in tumor tissues via single-sample gene set enrichment analysis (ssGSEA) based on transcriptomic profiles (27), yielding quantitative metrics termed ‘stromalscore’ and ‘immunescore’. This computational procedure, executed using the R package ‘estimate’, visualizes the outcomes through scatter plots. UBE2T expression data were retrieved from the normalized pan-cancer dataset available in the UCSC database (http://genome.ucsc.edu/cgi-bin/hgGateway), with expression levels log₂-transformed as log₂(x + 0.001). Immune cell infiltration associated with UBE2T across multiple cancer types was further evaluated using the R algorithms ‘Timer,’ ‘EPIC’ and ‘Cibersort’.

Given the elevated expression of UBE2T and its association with adverse prognosis across various malignancies, functional characterization was pursued through GO/KEGG and GSEA analyses. Additionally, KEGG analysis results for pancreatic cancer were derived from the LinkedOmics database (https://linkedomics.org/login.php). The specific steps were as follows: Firstly, ‘Pancreatic adenocarcinoma’ was selected as the ‘cancer cohort’, the data type was set to RNAseq, UBE2T was set as the dataset attribute, the data type in the Target dataset was also set as RNAseq, and finally, the Pearson correlation test was chosen as the statistical method. A total of 123 genes, identified as UBE2T-related or interacting partners via GEPIA2 and STRING databases (https://cn.string-db.org/), were subjected to GO and KEGG pathway enrichment using the R packages ‘clusterprofiler’ and ‘pathview’. To examine the biological relevance of UBE2T in tumor subtypes such as breast invasive carcinoma (BRCA) and pancreatic adenocarcinoma (PAAD), GSEA was conducted to assess pathway-level enrichment between phenotypic states. Moreover, the CancerSea platform (http://biocc.hrbmu.edu.cn/CancerSEA/), including 93,475 single cancer cells across 74 single cell RNA-sequencing datasets from 27 cancer types and defining 14 cellular functional states (28), was employed to interrogate UBE2T-related cellular programs at single-cell resolution.

Analysis using the TIMER2.0 database revealed differential expression of UBE2T between pan-cancer and normal tissues within TCGA dataset. Elevated UBE2T expression was observed in bladder urothelial carcinoma (BLCA), BRCA, cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), esophageal carcinoma (ESCA), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), PAAD, pheochromocytoma and paraganglioma (PCPG), prostate adenocarcinoma (PRAD), rectum adenocarcinoma (READ), stomach adenocarcinoma (STAD), thyroid carcinoma (THCA) and uterine corpus endometrial carcinoma (UCEC) compared with the matched normal tissues, whereas expression in kidney chromophobe (KICH) was reduced (Fig. 1A). Data from adrenocortical carcinoma (ACC), acute myeloid leukemia (LAML), mesothelioma (MESO), lymphoid neoplasm diffuse large B-cell lymphoma (DLBC), brain lower grade glioma (LGG), ovarian serous cystadenocarcinoma (OV), sarcoma (SARC), testicular germ cell tumor (TGCT), thymoma (THYM), uterine carcinosarcoma (UCS) and uveal melanoma (UVM) lacked sufficient normal tissue samples for reliable comparison. To strengthen and extend these observations, UBE2T expression across 33 tumor types and normal tissues was analyzed using the GEPIA2 platform. Consistent with the initial findings, UBE2T was generally upregulated in tumors, excluding MESO and UVM due to the absence of normal controls. No significant expression differences were identified for CHOL, KICH, KIRC, LGG, PCPG, PRAD, SARC and THCA (Fig. 1B). Notably, normal LAML tissues exhibited higher UBE2T expression relative to tumor counterparts. Results from both databases demonstrated notable concordance. The results shown in Fig. 1-E revealed increased UBE2T mRNA and protein expression in pancreatic cancer cell lines relative to normal pancreatic cells. Additionally, the results shown in Fig. 1F indicated higher UBE2T protein levels in LIHC, HNSC, LUSC, OV, LUAD, RCC and UCEC compared with the respective normal tissues.

Analysis across 33 tumor types revealed significant associations between UBE2T expression and clinical characteristics. As depicted in Fig. 2A, elevated UBE2T levels in ACC, BRCA, KIRP, KICH, LIHC, HNSC, KIRC and THCA were associated with advanced pathological stages. Furthermore, UBE2T expression was higher in individuals aged ≤65 in BRCA, ESCA, LIHC, LUAD, LUSC and THYM, whereas STAD exhibited an inverse pattern (Fig. 2B). Increased UBE2T expression was observed in male patients with BRCA, HNSC, KIRC, LAML, LUAD, LUSC and skin cutaneous melanoma (SKCM), in contrast to lower expression levels in men with SARC and STAD (Fig. 2C).

The identification of reliable biomarkers for early tumor detection and prognostication remains a primary objective in oncology. ROC analysis demonstrated that UBE2T achieved area under the curve (AUC) values >0.9 in BRCA, BLCA, CHOL, COADREAD, CESC, HNSC and LIHC, supporting its diagnostic applicability (Fig. 3A-C). Furthermore, UBE2T displayed AUC values >0.7 in predicting 1-, 3- and 5-year survival in ACC, KICH, GBMLGG and MESO (Fig. 3D), indicating its potential utility in prognostic stratification.

Patients were stratified into high- and low-expression cohorts according to the median UBE2T expression level, followed by survival analysis to evaluate the prognostic relevance of UBE2T. Analysis via GEPIA2 revealed that elevated UBE2T expression was associated with reduced DFS in ACC, DLBC, KICH, KIRC, KIRP, LIHC, LUAD, MESO, PAAD, PRAD, SARC and SKCM, whereas a favorable correlation with DFS was observed in STAD (Fig. 4B). Similarly, increased UBE2T expression was linked to decreased OS in ACC, BRCA, KIRC, KIRP, LGG, LIHC, LUAD, MESO, PAAD and SKCM, while improved OS outcomes were observed in OV and THYM (Fig. 4A). Further evaluation through Cox regression analysis reinforced the prognostic value of UBE2T across multiple cancer types. As illustrated in Fig. 4C, elevated UBE2T expression predicted unfavorable outcomes in ACC, BRCA, GBMLGG, KICH, KIPAN, KIRC, KIRP, LAML, LGG, LIHC, LUAD, MESO, PCPG, PAAD and UVM. Additionally, Fig. 4D indicated a negative association between high UBE2T expression and DFS in approximately half of the tumor types analyzed. In parallel, a shortened PFI in ACC, BRCA, KICH, PAAD, KIRP, LUAD and others was linked to high UBE2T expression (Fig. 4E). Collectively, the data suggest that UBE2T functions as a negative prognostic indicator in a broad spectrum of tumors and may offer diagnostic utility or therapeutic relevance in oncological contexts.

The UBE2T gene was mapped to chromosome 1q32.1 (Fig. 5A), with high-confidence localization to the nucleus, cytosol and mitochondria (Fig. 5B). The tertiary protein structure of UBE2T is illustrated in Fig. 5C. Protein-protein interaction analysis via the STRING database identified 25 UBE2T-associated proteins, including ZNF277, UBC, UBB, UBA7, UBA6, FANCF and CDK5R1 (Fig. 5D). Genomic alterations of UBE2T across 10,967 tumor samples from 33 cancer types were analyzed using cBioPortal. As presented in Fig. 5E, gene amplification represents the most frequent alteration, particularly in BRCA, followed by LIHC, CHOL and LUAD. Mutations are most prevalent in BLCA, while deep deletions were also observed in BLCA, SARC, PRAD and STAD. Predicted post-translational modifications of UBE2T included phosphorylation, acetylation, ubiquitination and sumoylation (Fig. 5F). Survival analysis revealed no statistically significant association between UBE2T mutations and OS (P=0.908), DSS (P=0.961), DFS (P=0.357) or PFI (P=0.812) for all individuals with cancer (Fig. 5G).

CNV and SNV represent prevalent genetic alterations in tumors. UBE2T was subjected to CNV, SNV and drug sensitivity profiling via the GSCALite platform. As illustrated in Fig. 6A, heterozygous amplification emerged as the dominant CNV subtype of UBE2T, frequently observed in malignancies including BRCA, LIHC, LUAD, CHOL, CESC, UCS and SKCM. The results shown in Fig. 6B indicated that UBE2T SNVs were more frequently detected in COAD, BLCA and UCEC, whereas no SNVs were identified in PAAD. Drug response data were obtained from GDSC and CTRP. Analysis based on the GDSC dataset revealed significant positive correlations between UBE2T expression and sensitivity to trametinib, selumetinib, RDEA119 and PD-0325901, while inverse correlations were noted with navitoclax, vorinostat and FK866 (Fig. 6C). Additionally, CTRP-based results demonstrated significant negative associations between UBE2T expression and responses to CD-437, mitomycin, SB-225002, vincristine and BI-2536 (Fig. 6D).

MMR, MSI, TMB and NEO serve as established indicators for predicting responses to tumor immunotherapy (29,30). The present analysis evaluated the relationship between UBE2T expression and these markers. In a pan-cancer context, UBE2T exhibited strong positive associations with nearly all MMR-related genes (EPCAM, PMS2, MLH1, MSH2 and MSH6) across diverse tumor types, with significant correlations observed in BLCA, BRCA-Basal, CESC, COAD, ESCA, HNSC, KIRC, KIRP, LGG, LIHC, LUSC, OV, PRAD and UVM (Fig. 7A). As shown in Fig. 7B, UBE2T expression was positively associated with the majority of immune checkpoint genes in BLCA, BRCA, HNSC, KIRC, LGG, LIHC, LUAD, PAAD, PRAD and TGCT, whereas inverse associations were detected in THCA and THYM. These patterns imply that UBE2T may modulate the tumor immune microenvironment through immune checkpoint gene regulation in select malignancies. Additionally, UBE2T demonstrated significant positive correlations with MSI in BLCA, UCS, UCEC, THCA, TGCT, STAD, SARC, PRAD, MESO, HNSC and ESCA (Fig. 7C). According to Fig. 7D, UBE2T expression showed positive correlations across most tumor types, except for THYM, which exhibited a negative association, and UVM, UCS, TGCT, LAML, KIRP, GBM, ESCA, DLBC, CHOL and CESC, where no significant correlation was noted. Fig. 7E indicated a significant positive relationship between UBE2T and NEO in GBM, GBMLGG, LUAD, COADREAD, BRCA and PRAD, while a negative correlation was identified in TGCT. Collectively, these data indicate that UBE2T expression is strongly associated with tumor immunogenicity across multiple cancer types.

Tumor-infiltrating immune cells represent a central component of the tumor microenvironment, exerting substantial influence on tumor progression and clinical outcomes (31–33). The present study assessed the relationship between UBE2T expression and both StromalScore and ImmuneScore. As shown in Fig. 8A, UBE2T expression exhibited a negative correlation with StromalScore in BRCA, stomach and esophageal carcinoma (STES), LUSC, SARC, STAD and TGCT, among others. A similar inverse association was observed between UBE2T and ImmuneScore in STES, STAD, LUSC, CESC, LUAD and ESCA (Fig. 8B).

To further characterize the immune landscape, TIMER, EPIC and CIBERSORT algorithms were applied to evaluate associations between UBE2T expression and immune cell infiltration. According to Fig. 8C, UBE2T expression showed positive correlations with B cells, CD4⁺ T cells, CD8⁺ T cells and dendritic cells (DCs) in THYM, KIRC, KIPAN, THCA and LIHC. Additionally, a strong positive association with cancer-associated fibroblasts (CAFs) was observed in PRAD, GBMLGG, KICH, KIPAN, MESO, THCA, KIRC and KIRP, while most remaining immune cell types demonstrated negative correlations. EPIC-based analysis (Fig. 8D) indicated that UBE2T expression was predominantly negatively associated with immune cell infiltration across pan-cancer, with the notable exception of a positive correlation with CD8⁺ T cells in THYM. Comparable patterns were identified using the CIBERSORT algorithm (Fig. 8E). Collectively, these data highlight the intricate and context-dependent role of UBE2T in modulating the tumor immune microenvironment. UBE2T may contribute to immune evasion through the expansion of immunosuppressive populations in certain tumors, whereas in others, it may enhance the recruitment or activity of cytotoxic immune subsets, thereby supporting antitumor responses.

The GO database classifies gene functions into three categories: Biological Process (BP), Cellular Component (CC) and Molecular Function (MF). As shown in Fig. 9A, UBE2T and its associated genes were predominantly involved in BPs such as ‘organelle fission’, ‘nuclear division’, ‘mitotic nuclear division’ and ‘chromosome segregation’. Corresponding CC terms included ‘chromosomal region’, ‘chromosome, centromeric region’ and ‘spindle’ (Fig. 9B), while MF annotations primarily included ‘microtubule binding’ and ‘tubulin binding’ (Fig. 9C). KEGG pathway enrichment (Fig. 9D) revealed significant associations with ‘cell cycle’, ‘Fanconi anemia pathway’, ‘ubiquitin mediated proteolysis’, ‘oocyte meiosis’, ‘DNA replication’ and ‘p53 signaling pathway’. Notably, ‘mismatch repair’ also appeared as a key pathway, indicating a strong link between UBE2T and tumor immune contexture, as well as potential responsiveness to immunotherapeutic strategies. The GSEA results for BRCA (Fig. 9E) identified BPs including ‘external encapsulating structure organization’, ‘regulation of membrane potential’ and ‘sensory perception of smell’. Enriched CCs involved ‘collagen-containing extracellular matrix’ and MF terms included ‘actin binding’. GSEA-based KEGG results for BRCA further implicated pathways such as ‘complement and coagulation cascades’, ‘focal adhesion’ and ‘PPAR signaling pathway’ in UBE2T-related mechanisms (Fig. 9F). Analysis of KEGG results for pancreatic cancer revealed the mechanism by which UBE2T promotes the occurrence and development of pancreatic cancer (Fig. 9G). The results in Fig. 9H showed that UBE2T induced different changes in cellular function in different tumor types, but mainly focused on ‘Cell cycle’, ‘DNA damage’, ‘DNA repair’, ‘proliferation’, ‘invasion’, ‘EMT’, ‘inflammation’ and ‘angiogenesis’.