Pan-cancer multi-omics profiling reveals ubiquitin D as a novel biomarker for diagnosis, immune microenvironment remodeling and prognostic prediction

Abstract

Ubiquitin D (UBD), a key member of the ubiquitin-like modifier (UBL) family, plays a critical role in targeting proteins for proteasomal degradation. Current research indicates that UBD is frequently overexpressed in various malignancies, with its elevated expression closely correlated with disease progression in cancers such as gliomas, colorectal carcinoma, hepatocellular carcinoma, and breast cancer. In this study, we conducted a pan-cancer analysis of UBD to elucidate its prognostic significance, immunological roles, and potential clinical applications. Using data from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and tools including Gene Expression Profiling Interactive Analysis (GEPIA2.0), CBio Cancer Genomics Portal (cBioPortal), University of Alabama at Birmingham CANcer data analysis Portal (UALCAN), Sangerbox and Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), we analyzed UBD expression, prognosis, promoter methylation, genetic alterations, immune infiltration, and pathway enrichment. UBD was overexpressed in 29 cancer types, linking it to poor prognosis and higher histological grades. The most common type of genetic variation was gene amplification, and patients with these alterations exhibited significantly reduced overall survival rates. Epigenetically, 16 cancer types showed reduced UBD promoter methylation. UBD expression was significantly correlated with tumor microenvironment features, including immune infiltration, checkpoints, microsatellite instability (MSI), tumor mutational burden (TMB), and neoantigens (NEO). Pathway analysis implicated UBD in neurodegeneration, proteolysis, and apoptosis. Collectively, our study demonstrates that UBD is a promising prognostic biomarker and a potential predictor of immunotherapy sensitivity in multiple cancer types.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12672-025-03545-5.

Introduction

Cancer remains one of the most pressing global public health challenges, a leading cause of death worldwide, and imposes a significant burden on healthcare systems and society as a whole [1]. Tumorigenesis is a multifactorial process involving complex mechanisms that regulate cancer cell proliferation, differentiation, tumor mutational burden (TMB), immune infiltration, and the tumor microenvironment (TME) [2]. Given this complexity, identifying key pan-cancer genes is essential for elucidating the fundamental mechanisms underlying tumor initiation and progression across diverse cancer types [3]. Such insights are critical for developing more effective diagnostic and therapeutic strategies, underscoring the urgent need to investigate the relationship between genetic factors and tumorigenesis.

Ubiquitination is a crucial post-translational modification (PTM) in eukaryotic cells, primarily tagging target proteins for proteasomal degradation via the 26 S proteasome pathway [4]. This process involves a cascade of reactions mediated by ubiquitin-activating enzyme E1, ubiquitin-conjugating enzyme E2, and ubiquitin ligase E3 [5, 6]. Beyond protein degradation, ubiquitination precisely regulates diverse cellular processes, including cell cycle progression, proliferation, apoptosis, differentiation, and signal transduction, thereby influencing nearly all fundamental biological activities [7]. In tumorigenesis, ubiquitination can modulate the activation or suppression of oncogenic pathways [8].

Ubiquitin D (UBD), also known as Human Leukocyte Antigen (HLA)-F adjacent transcript 10 (FAT10), is a key member of the ubiquitin-like modifier (UBL) family that mediates targeted protein degradation through the proteasome pathway [9]. This multifunctional protein participates in immune-mediated inflammation, cell cycle control, proliferation, signal transduction, and DNA damage repair. Previous studies demonstrate that UBD overexpression induces mitotic non-disjunction and chromosomal instability, contributing to tumor development [10]. Notably, UBD expression is strongly induced by pro-inflammatory cytokines, particularly IFN-γ and TNF-α, across diverse cell types and tissues [11].

Mechanistically, UBD engages in key oncogenic pathways—including NF-κB, Wnt, and SMAD2 signaling—and interacts with downstream effectors such as MAD2, p53, and β-catenin. These interactions collectively promote tumor survival, proliferation, invasion, and metastatic potential [12, 13]. Consistent with its oncogenic role, UBD is frequently overexpressed in multiple solid tumors (e.g., breast, liver, pancreatic, gastric, and colorectal cancers), with dysregulation strongly linked to tumor initiation and progression [14–16]. UBD is highly expressed in lymphoid tissues and modulates antigen presentation [17]. Additionally, ubiquitin-related pathways enhance anti-tumor immunity by promoting T-cell activation [18]. In hepatocellular carcinoma, FAT10 drives immune evasion by upregulating PD-L1, fostering an immunosuppressive TME [19]. Despite these advances, UBD research remains limited to specific cancer types. To address this gap, we conducted a systematic pan-cancer analysis of UBD’s functional roles.

Pan-cancer analysis represents a powerful bioinformatics approach that enables systematic identification of molecular commonalities and divergences across diverse cancer types through integrative multi-omics data analysis [20–22]. Here, we comprehensively investigated UBD using advanced computational methods to elucidate its oncogenic roles, evaluate its potential as a universal cancer biomarker, and explore its therapeutic implications for targeted treatment development.

Materials and methods

Data acquisition

Transcriptomic and clinical data for pan-cancer analysis were obtained from The Cancer Genome Atlas (TCGA) (https://www.cancer.gov/ccg/research/genome-sequencing/tcga) and the Genotype-Tissue Expression (GTEx) project (https://www.genome.gov/Funded-Programs-Projects/Genotype-Tissue-Expression-Project). Gene expression profiles were retrieved from two publicly available platforms: Gene Expression Profiling Interactive Analysis (GEPIA2.0) (http://gepia2.cancer-pku.cn/#index) and Sangerbox (http://vip.sangerbox.com/login.html), with the latter providing additional clinicopathological analysis tools. Protein expression and methylation level analyses were performed using University of Alabama at Birmingham CANcer data analysis Portal (UALCAN) (https://ualcan.path.uab.edu/index.html). For integrative exploration of cancer genomics and clinical data, we utilized CBio Cancer Genomics Portal (cBioPortal) (https://www.cbioportal.org/) for cancer genomics, which was also employed for gene mutation analysis. Common genes were identified by intersecting results from two datasets using Venn diagrams. Protein-protein interaction (PPI) networks for predicted UBD-interacting proteins were constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database (https://cn.string-db.org/). Additionally, we performed Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment analyses using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) (https://david.ncifcrf.gov/). The entire flowchart of our material analysis method is provided in the Supplementary Materials ESM (Supplementary Fig. 1).

This study encompassed 44 different cancer types, including: adrenocortical carcinoma (ACC), acute lymphoblastic leukemia (ALL), bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), cervical squamous cell carcinoma (CESC), cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), colorectal adenocarcinoma (COADREAD), lymphoid neoplasm diffuse large B cell lymphoma (DLBC), esophageal carcinoma (ESCA), glioblastoma (GBM), glioma (GBMLGG), brain lower grade glioma (LGG), head and neck squamous cell carcinoma (HNSC), renal chromophobe carcinoma (KICH), renal clear cell carcinoma (KIRC), renal papillary cell carcinoma (KIRP), pan-kidney cohort (KIPAN), acute myeloid leukemia (LAML), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), mesothelioma (MESO), ovarian serous cystadenocarcinoma (OV), Osteosarcoma(Os), pancreatic adenocarcinoma (PAAD), pheochromocytoma and paraganglioma (PCPG), prostate adenocarcinoma (PRAD), rectum adenocarcinoma (READ), sarcoma (SARC), skin cutaneous melanoma (SKCM), metastatic melanoma (SKCM-M), primary melanoma (SKCM-P), stomach adenocarcinoma (STAD), stomach and esophageal carcinoma (STES), testicular germ cell tumors (TGCT), thyroid carcinoma (THCA), thymoma (THYM), uterine corpus endometrial carcinoma (UCEC), uterine carcinosarcoma (UCS), uveal melanoma (UVM), and high-risk Wilms tumor (WT), neuroblastoma (NB).

Gene expression analysis

To investigate the role of UBD in human cancers, we conducted a systematic analysis of UBD expression using data from TCGA and GTEx projects. Differential mRNA expression of UBD between tumor tissues and adjacent normal tissues was analyzed using GEPIA 2.0. To validate these findings, we performed a comprehensive comparative analysis of UBD mRNA expression across pan-cancer tissues and their corresponding normal tissues using the Sangerbox database. Additionally, protein-level expression was assessed using the UALCAN online tool.

Analysis of UBD expression in relation to clinico-pathological parameters

To evaluate the clinical relevance of UBD expression, we analyzed its correlation with key clinico-pathological parameters (histological grade, clinical stage, and T-stage) using the Sangerbox database. Prior to analysis, we performed the following data preprocessing steps: (1) applied log2(x + 0.001) transformation to all expression values, (2) excluded samples with zero expression levels, and (3) removed cancer types with fewer than three representative samples. We then conducted logistic regression analyses to assess associations between UBD expression levels and various clinical grades/stages, evaluating UBD’s potential clinical significance in cancer progression.

Prognostic assessment in pan-cancer

To evaluate the prognostic significance of UBD expression across multiple cancer types, we conducted comprehensive survival analyses using TCGA data, employing the SangerBox platform to assess overall survival (OS) and disease-specific survival (DSS) through integrated Cox regression and Kaplan-Meier methods. After excluding samples with zero expression levels or follow-up durations shorter than 30 days, and removing cancer types with fewer than 10 samples, we retained 44 distinct cancer types for final analysis. Statistical analyses were performed using the R survival package (v2.1.6), including univariate Cox regression models and Kaplan-Meier survival curves analyzed with log-rank tests (significance threshold: p < 0.05).

DNA methylation analysis

We utilized UALCAN to analyze promoter DNA methylation levels of the UBD gene in both normal tissues and pan-cancer samples.

Genetic alteration analysis

We analyzed UBD gene mutation frequency, amplification, and other genetic alterations using cBioPortal, with all data sourced from the TCGA Pan-Cancer Atlas Studies. The analysis included genetic alteration data from 2683 samples representing 2565 pan-cancer patients.

Investigating tumor immune microenvironment of UBD

We systematically analyzed the relationships between UBD expression and immune biomarkers using the Sangerbox database. The TIMER and QUANTISEQ algorithms within the IOBR R package (version 2.1.6) were employed to assess associations between UBD expression levels and tumor immune cell infiltration across multiple tumor types. Using the ESTIMATE algorithm, we calculated immune scores for various tumors and performed Pearson correlation analyses to quantify relationships between UBD expression and both immune cell infiltration and immune checkpoint molecule expression. Furthermore, we examined correlations between UBD expression and immunoregulatory genes, including those encoding chemokines, chemokine receptors, immunostimulators, immunoinhibitors, and major histocompatibility complex (MHC) molecules.

Correlation analysis of UBD expression with tumor heterogeneity, stemness and gene mutation

We conducted Pearson correlation analyses to evaluate associations between UBD expression levels and critical tumor stemness features, focusing on established biomarkers of tumor heterogeneity including tumor purity, microsatellite instability (MSI), tumor mutational burden (TMB), and neoantigens (NEO). Resulting correlation patterns were visualized using Mutcet2 software and the maftools R package (version 2.8.05).

Enrichment analysis of UBD

We constructed the PPI network for UBD using the STRING database, setting minimum interaction score set at high confidence (0.700), and a maximum of 50 interactors included. After analyzing available UBD-binding proteins, we identified the top 520 UBD-related target genes through the “Co-expression” module in cBioPortal. Venn diagram analysis revealed 4 common genes between datasets, for which we established correlation with UBD expression using Pearson correlation tests via cBioPortal’s co-expression analysis. Furthermore, GO and KEGG enrichment analyses on 25 genes from STRING datasets, with gene lists obtained from DAVID database and results visualized using Sangerbox. Statistical significance was defined as p < 0.05.

Statistical analysis

We assessed differential molecular expression between normal and tumor samples from public databases using non-parametric Wilcoxon rank-sum tests based on data distribution characteristics. Continuous variables are expressed as mean ± standard deviation (SD), with all statistical analyses performed using R software (version 2.1.6). Statistical significance was defined as p < 0.05, with the following notation convention: *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001.

Results

mRNA and protein expression levels of UBD in human cancers

To elucidate UBD’s oncogenic role across malignancies, we performed comprehensive expression analyses using integrated TCGA and GTEx data. Compared with normal tissues, UBD expression was significantly upregulated in most tumor types, including liver, lung, colorectal, esophageal, gastric, kidney, sarcoma, breast, reproductive system, brain, and central nervous system (CNS) tumors (Fig. 1A). Analysis of 33 tumor-normal pairs via GEPIA 2.0 revealed significant UBD mRNA overexpression in 21 cancer types (BRCA, CESC, COAD, DLBC, ESCA, GBM, KIRC, LGG, LIHC, LUAD, LUSC, OV, PAAD, PRAD, READ, SKCM, STAD, TGCT, THYM, UCEC, and UCS), with downregulation observed only in KICH (Fig. 1B). Independent validation using Sangerbox confirmed UBD overexpression in 29 malignancies (GBM, GBMLGG, LGG, UCEC, BRCA, CESC, LUAD, ESCA, STES, KIPAN, COAD, COADREAD, PRAD, STAD, HNSC, KIRC, LUSC, LIHC, SKCM, BLCA, THCA, READ, OV, PAAD, TGCT, UCS, LAML, KICH, and CHOL) (Fig. 1C). Collectively, these results demonstrate consistent UBD upregulation across most human cancers.

Fig. 1.

UBD was differentially expressed in tumor and normal tissues. A The expression levels of UBD gene in different tumor and normal samples were represented by Body map; B The mRNA expression of UBD in 33 cancer types using GEPIA database, green means normal and red means tumor; C We conducted a comprehensive comparative analysis of UBD expression patterns between tumor and normal tissues using the Sangerbox database, with normal samples represented in blue and tumor samples in red

To complement our mRNA findings, we analyzed UBD protein expression using the UALCAN proteomic database, identifying significant upregulation specifically in hepatocellular carcinoma (LIHC) (Fig. 2). These results demonstrate that while UBD exhibits consistent mRNA upregulation across multiple cancers (as shown in Fig. 1), its protein-level dysregulation appears more tissue-specific. The pronounced protein overexpression in hepatocellular carcinoma coupled with UBD’s established role in oncogenic pathways, strongly supports its potential as a key molecular player in tumor initiation and progression.

Fig. 2.

The protein expression of the UBD in tumor and normal tissues, using UALCAN database. CPTAC Clinical Proteomic Tumor Analysis Consortium

Relationship of UBD with clinicopathological characteristics

We systematically evaluated UBD expression patterns in relation to clinicopathological parameters, including tumor histological grade, clinical stage, and T-stage across multiple tumor types using the Sangerbox database. Our analysis revealed significant associations between UBD expression and tumor histological grade in seven cancer types. In particular, the higher expression levels of UBD were showed in advanced histological grades in GBMLGG, LGG, ESCA, STES, STAD, HNSC, and LIHC (Fig. 3A). Comprehensive evaluation demonstrated stage-dependent UBD expression patterns in six malignancies (Fig. 3B), namely, CESC, COAD, COADREAD, KIRP, KIPAN, and PAAD. The results indicated that a relatively high level of UBD expression was observed in the late stage of KIPAN, PAAD, and KIRP. However, in CESC, COAD, COADREAD, UBD exhibited lower expression levels in the advanced stages (Fig. 3B). Furthermore, UBD expression showed significant correlations with T-stage progression in BRCA, CESC, HNSC, LUAD, KIRP, and KIPAN (Fig. 3C), underscoring its potential role in tumor progression.

Fig. 3.

The relationship of UBD expression with clinical metrics of various tumors. A UBD expression levels demonstrate significant association with the histological grade of various cancer types; B UBD expression levels show significant correlation with clinical stages across various cancer types; C The relationship of UBD expression with tumor T-stage

Prognostic role of UBD in human pan-cancer

UBD expression showed significant associations with clinicopathological characteristics across diverse tumor types. To assess its prognostic value, we performed univariate Cox regression and Kaplan-Meier survival analyses using the Sangerbox database, evaluating OS and DSS. Cox regression analysis identified high UBD expression as a risk factor for poor OS in seven cancers, including GBMLGG, LGG, KIRP, KIPAN, THYM, UVM and PAAD (Fig. 4A). While in patients with BRCA, SARC, HNSC, SKCM, BLCA, SKCM-M and OV those with low UBD expression had shorter survival (Fig. 4A). DSS analysis similarly revealed worse prognosis with high UBD expression in GBMLGG, LGG, KIRP, KIPAN, THYM, and UVM (Fig. 4B), but improved outcomes in SKCM, SKCM-M, OV, BLCA, CESC, SARC, and HNSC (Fig. 4B). The COX regression analysis integrating OS and DSS reveals that elevated UBD expression correlates with poorer prognosis in GBMLGG, LGG, KIRP, KIPAN, THYM, and UVM, while demonstrating an association with improved survival outcomes in SKCM, SKCM-M, OV, BLCA, SARC, and HNSC.

Fig. 4.

Univariate COX regression analysis was performed to examine the association of UBD with OS and DSS. A Forest plot of OS associations in 44 types of tumor; B Forest plot of association of UBD expression and DSS in 38 types of tumor

We further evaluated UBD’s prognostic significance through Kaplan-Meier analysis using the Kaplan-Meier plotter database. The analysis demonstrated that elevated UBD expression correlated with significantly worse OS in GBM, GBMLGG, KIPAN, KIRP, LGG, and UVM (Fig. 5A). DSS analysis similarly revealed poorer outcomes with high UBD expression in seven cancer types: GBM, GBMLGG, LGG, KIRP, KIPAN, UVM, and PAAD (Fig. 5B). Combined OS and DSS analyses consistently showed that increased UBD expression predicted adverse prognosis in GBM, GBMLGG, KIPAN, KIRP, LGG, and UVM, underscoring its clinical relevance in these malignancies.

Fig. 5.

Survival analysis using Kaplan-Meier (KM) curves was conducted to investigate the expression of UBD and its relationship with OS and DSS. A Illustrating the relationship between UBD expression profile and OS of patients with different tumors; B Illustrating the association between UBD expression and DSS in across various cancer types

Both Cox regression and Kaplan-Meier analyses consistently demonstrated that elevated UBD expression was significantly associated with poorer prognosis in GBMLGG, KIPAN, KIRP, LGG, and UVM. These findings highlight the critical importance of further investigating UBD’s role across cancer types, given its substantial clinical and scientific implications.

Genetic alterations and methylation landscapes of UBD in different tumors

DNA methylation represents a crucial epigenetic modification implicated in various diseases, with abnormal DNA methylation patterns serving as key biomarkers for cancer initiation and progression [23–25]. Aberrant promoter methylation can either silence tumor suppressor genes or deregulate oncogenes [26], demonstrating DNA methylation’s dual role in carcinogenesis: tumor suppressor gene silencing through hypermethylation and genomic instability promotion via genome-wide hypomethylation [27]. Using UALCAN, we compared UBD methylation levels between normal and tumor tissues, identifying significant downregulation in 16 cancer types, including BLCA, BRCA, CESC, CHOL, COAD, HNSC, KIRC, LIHC, LUAD, LUSC, PAAD, PCPG, READ, SARC, TGCT, and THCA (Fig. 6). Identifying patterns of DNA methylation that have a functional role in pan-cancer multi-omic analysis and distinguishing them from tissue-specific epigenetic information remains challenging [28].

Fig. 6.

The methylation level of the UBD promoter in 16 types of cancer was downregulated compared with that in normal tissues

To explore the frequency and types of UBD genetic alterations in various cancers, we analyzed its mutation status through the cBioPortal database(TCGA, Pan-cancer Atlas). The results demonstrated that UBD expression was altered in 125 samples collected from 2565 patients with different cancer types, accounting for 5% of alterations (Fig. 7A). UBD alterations occurred in 18 cancer types, as shown in Fig. 7B. The higher frequencies was observed in cases, including Melanoma(21.5%), Lung Cancer(13.16%), Hepatobiliary Cancer(11.21%), Bladder Cancer(8.7%), Breast Cancer(8.53%), Esophagogastric Cancer(7.98%), and Bone Cancer(6.56%). Notably, the most common alterations types observed of amplifification among the cancers. As shown in Fig. 7C, we found that missense and truncating mutations were the main type of UBD mutation sites. Whole-genome duplication analysis revealed that in the UBD-altered group, the whole-genome duplication rate was relatively high (Fig. 7D). Following this, we analyzed the correlation between consensus putative gene level copy-number alteration (CNA) and UBD mRNA expression in pan-cancer tissues (Fig. 7E). In addition, we investigated the potential links between genetic alterations of UBD and probability of OS in pan-cancers (Fig. 7F). It was observed that patients with genetic alterations of UBD had worse outcomes compared to those with unaltered group. This suggests that UBD gene alterations play a role in promoting pan-cancer progression.

Fig. 7.

Genetic alteration analysis. A OncoPrint visual summary of alterations in a query of UBD from cBioPortal; B The alteration frequency and corresponding mutation types of UBD in different cancers; C The specifific mutation sites of UBD are illustrated in; D The Whole Genome Duplication is affected by alterations; E The mRNA expression of UBD copy-number alteration (CNA) in pan-cancer tissues; F OS analyses for UBD genetic alterations in tumors

The relationship between UBD expression levels with tumor immune cell infiltration, the tumor immune infiltration status and tumour purity in pan-cancer datasets

The tumor immune microenvironment plays a pivotal role in cancer progression and patient outcomes. To systematically evaluate relationships between UBD expression and immune infiltration patterns, we performed pan-cancer analyses using TIMER and QUANTISEQ algorithms. TIMER analysis demonstrated significant positive correlations between UBD expression and infiltration levels of multiple immune cell types: B cells in 31 cancers, CD4 + T cells in 33 cancers, CD8 + T cells in 25 cancers, neutrophils in 35 cancers, macrophages in 19 cancers and dendritic cells in 34 cancers (Fig. 8A). Furthermore, we investigated the infiltration different immune cell subtypes at the pan-cancer level using the QUANTISEQ algorithm. We found a significant positive correlation between UBD expression and the infiltration immune cells: B cells in 27 cancers, Macrophages_ M cells in 36 cancers, Macrophages_ M2 cells in 28 cancers, CD8 T cells in 36 cancers, Neutrophils cells in 11 cancers, Tregs cells in 31 cancers. Moreover, UBD expression levels showed significant negative correlation with the tumor infiltration levels: Monocytes cells in 14 cancers, NK cells in 12 cancers, CD4 T cells in 19 cancers, Dendritic cells in 9 cancers, Other cells in 36 cancers (Fig. 8B). These comprehensive analyses establish UBD as a key modulator of tumor immune landscapes across cancer types.

Fig. 8.

Pan-cancer analysis of the UBD expression and immune cell infiltration. A The relationship between UBD expression and the immune cell infiltration in different cancers as analyzed by the TIMER algorithm; B The relationship between UBD expression and the immune cell infiltration in different cancers as analyzed by the QUANTISEQ algorithm

The immune score reflects the proportion of infiltrated immune cells within tumor tissues. Using the ESTIMATE algorithm, we calculated immune scores for UBD across 44 cancer types, revealing significant positive correlations between UBD expression and immune scores in 39 malignancies, including GBMLGG, KIPAN, KIRC, BRCA, PRAD, LUAD, LUSC, OV (Fig. 9A), and also analyzed other cancer types namely, ACC, BLCA, CESC, COAD, COADREDA, DLBC, ESCA, GBM, HNSC, KICH, KIRP, LGG, MESO, NB, PCPG, PAAD, READ, SARC, SKCM, SKCM-M, SKCM-P, STAD, STES, TGCT, THCA, UCEC, USC, UVM, THYM, LIHC, WT (Supplementary Fig. 2) are provided in ESM.

Fig. 9.

Correlation analysis between UBD expression with Immune Score (A) and Tumour Purity in pan-cancer (B)

Tumor purity reflects the proportion of tumor cells in the sample and significantly affects the clinical characteristics, genomic expression and biological characteristics of tumor patients. Our analysis revealed significant negative correlations between UBD expression and tumor purity in KIPAN. Among 11 tumor types (LUAD, BRCA, SARC, KIRP, READ, TGCT, UVM, UCS, BLCA, ACC, and DLBC), UBD expression showed moderate inverse correlations with tumor purity. Additionally, weak negative correlations were observed in 20 other malignancies, including GBM, CESC, COAD, COADREAD, ESCA, STES, STAD, PRAD, UCEC, HNSC, KIRC, LUSC, LIHC, THCA, MESO, PAAD, OV, PCPG, SKCM, KICH (Fig. 9B).

Elevated UBD expression shows significant positive associations with Immune Score and inverse correlations with tumor purity, indicating increased infiltration of non-tumor components (especially immune cells) in the tumor microenvironment; however, these bioinformatic observations necessitate further experimental validation.

Relationship between UBD expression and immunoregulators, immune checkpoint in human pan-cancer

The association between UBD expression and immunomodulatory factors is presented in Fig. 10A, encompassing chemokines, chemokine receptors, MHC-related genes, immunoinhibitors, and immunostimulators. Our analysis demonstrates significant positive correlations between UBD and multiple immunomodulator genes across most cancer types. There are only few cancers was no significantly associated with immunomodulators, including ALL, LAML, CHOL and WT. Notably, UBD expression exhibited particularly strong positive correlations with CXCL9 expression in nearly all examined cancers.

Fig. 10.

Pan-cancer analysis of the correlation between UBD expression and Immunoregulators, immune checkpoint. A Relationship of UBD expression and various immunoregulators; B Relationship of UBD expression and various immune checkpoint

Immune checkpoint molecules (ICPs) serve as primary immunotherapy targets and critically influence tumor immunotherapy responses and immune cell infiltration patterns. Our results demonstrate significant positive correlations between UBD expression and key immune checkpoint markers-including CD274 (PD-L1), CTLA4, SLAMF7, CXCL9, CXCL10, and CD80-across multiple cancer types (B). These findings strongly suggest UBD’s involvement in immune regulation and its potential utility as a prognostic biomarker in cancer patients.

Correlation between UBD expression and TMB, MSI, and NEO

TMB, MSI, and NEO represent key biomarkers of tumor microenvironment immunogenicity and potential predictors of immunotherapy response [29, 30]. Our analysis identified significant positive associations between UBD expression and: (1) TMB in THYM, BRCA, COAD, and COADREAD (Fig. 11A); (2) MSI in COAD, COADREAD, and THYM (Fig. 11B); and (3) NEO in COAD, COADREAD, SARC, THYM, and DLBC (Fig. 11C). Conversely, UBD showed negative correlations with MSI in GBMLGG, KIPAN, PRAD, PAAD, LUSC, and TGCT (Fig. 11B). These findings reveal a complex interaction between UBD expression and genomic biomarkers that may become novel strategies for influencing immunotherapy.

Fig. 11.

Association between UBD expression and various genomic biomarkers in pan-cancers. A Relationship between UBD expression and tumor mutational burden (TMB); B Relationship between UBD expression and microsatellite instability (MSI); C Correlation between UBD expression and neoantigen (NEO)

Functional enrichment analysis of UBD-related genes

To investigate UBD’s molecular mechanisms in tumorigenesis, we performed systematic bioinformatics analyses. First, we constructed a PPI network using STRING database, identifying 25 UBD-interacting proteins (Fig. 12A). These 25 gene underwent further enrichment analysis for GO and KEGG. As depicted in Fig. 12B, the GO biological process (BP) terms analysis showed that UBD was associated with ubiquitin-dependent protein catabolic process and negative regulation of apoptotic process. The cellular components (CC) were primarily the cytosol, nucleoplasm, cytoplasm, while the molecular functions (MF) focused on protein binding. KEGG pathway analysis showed that the UBD-binding genes were enriched in pathways such as Amyotrophic lateral sclerosis Pathways of neurodegeneration - multiple diseases, Parkinson disease, Prion disease, Alzheimer disease, Proteasome, Spinocerebellar ataxia and Ubiquitin mediated proteolysis (Fig. 12C). These findings collectively indicate UBD promotes tumor development by regulating apoptosis and protein degradation processes.

Fig. 12.

Functional enrichment analysis of UBD in pan-cancer. A PPI network analysis of UBD-binding proteins; B The GO enrichment analysis of UBD revealed its role in BP, CC and MF; C KEGG pathway analysis; D An intersection analysis of the UBD-binding genes and co-expression genes; (E–H) The correlation between expression of UBD and the 4 UBD-related genes

We identified the top 520 UBD co-expressed genes from cBioPortal, which when integrated with STRING data revealed four significantly correlated UBD-associated genes: CXCL9, CXCL10, HLA-F, and PSMB9 (Fig. 12D). Each demonstrated strong positive correlations with UBD expression (Fig. 12E–H).

Discussion

Cancer research remains a focal point in contemporary medicine. While tumor immunotherapy has achieved remarkable clinical advances in the past decade, its full therapeutic potential remains unrealized [31]. This underscores the critical need to identify novel biomarkers and therapeutic targets to enhance diagnostic accuracy and treatment efficacy [32].

UBD is a ubiquitin-like modifier protein that plays pivotal regulatory roles in diverse biological processes, including: cellular immunity, apoptosis regulation, cell cycle control, transcriptional modulation, signal transduction pathways and inflammatory responses [33]. Emerging evidence from recent studies has established a strong association between elevated UBD expression and tumor progression across multiple cancer types, particularly in hepatocellular carcinoma, glioma, colorectal cancer, oral squamous cell carcinoma, and ovarian cancer [19, 33–39]. UBD overexpression has been consistently associated with poor clinical outcomes across multiple cancer types [40]. In hepatocellular carcinoma, UBD mediates immune evasion by upregulating PD-L1 expression and confers resistance to anti-VEGF therapies [19, 34]. In alcoholic hepatitis UBD plays a critical role through the NFκB and/or STAT3 pathways, which are induced by TNF-α/IFN-γ signaling [9]. In colorectal cancer, UBD promotes tumor proliferation via p53 degradation [38]. In glioma, UBD drives tumor progression by activating EMT and PI3K/AKT/mTOR pathways, serving as a potential diagnostic and prognostic biomarker [37]. Other researchers have reported that UBD expression in tumors is somewhat tissue-specifc, with transcriptional upregulation observed in ovarian tissue [35]. Despite these advances, the therapeutic outcomes for advanced-stage patients remain suboptimal, and the pan-cancer mechanisms underlying UBD’s role remain elusive. This comprehensive analysis elucidates the multifaceted regulation of UBD in oncogenesis and highlights the critical need for developing targeted therapeutic strategies.

Accumulating evidence indicates that aberrant expression and dysfunction of UBD are closely associated with tumorigenesis and cancer progression [16]. In this study, we performed a comprehensive pan-cancer bioinformatics analysis of UBD expression and its functional implications using multi-database patient datasets. Although UBD is generally overexpressed in most cancers compared to normal tissues, we observed the opposite situation in KICH, suggesting that the regulation of UBD may vary depending on the tissue type, aligning with previous findings [16, 41]. While UBD mRNA expression is elevated in most cancers, a consistent increase in protein levels was only observed in LIHC. This indicates that the differences in UBD mRNA and protein levels among different cancer types suggest that post-transcriptional and post-translational regulation is complex. These findings strongly suggest that UBD plays a critical oncogenic role in these malignancies, highlighting its potential as a therapeutic target.

Although our analysis indicates that UBD mRNA upregulation is a common feature across various cancers, its prognostic significance varies significantly. COX regression analysis revealed that elevated UBD expression correlates with poor prognosis in GBMLGG, LGG, KIRP, KIPAN, THYM, and UVM, while demonstrating an association with improved survival outcomes in SKCM, SKCM-M, OV, BLCA, SARC, and HNSC. Kaplan-Meier analysis demonstrated that elevated UBD expression was significantly associated with worse prognosis in GBM, GBMLGG, KIPAN, KIRP, LGG, and UVM, consistent with prior research [40]. These findings suggest that UBD’s role in tumorigenesis may be influenced by tumor-specific factors, emphasizing the need for further research to clarify the potential mechanisms that control its different effects on cancer prognosis.

Our investigation of the relationship between UBD expression and clinicopathological cancer stages demonstrated that abnormal UBD expression may serve as a potential prognostic predictor. The study revealed significant associations between UBD expression levels and histological grades, clinical stages, and T-stages across multiple tumor types. Notably, increased UBD expression correlated with higher histological grades in seven cancer types. Furthermore, elevated UBD levels showed significant association with advanced clinical stages and T-stages in KIPAN cancers. These results indicate that UBD may serve as a valuable prognostic biomarker and could have important implications for clinical decision-making and personalized treatment strategies in patients with advanced-stage malignancies.

Epigenetic modifications refer to heritable changes in gene expression that occur without alterations to the DNA sequence, mediated by environmental and lifestyle factors including diet, toxins, and stress [42]. Due to their dynamic and reversible nature in response to external stimuli, these modifications have emerged as promising therapeutic targets for multiple cancer types [43]. DNA methylation, the most prevalent epigenetic modification, plays crucial roles in organismal development and disease progression [44]. In cancer biology, two hallmark methylation alterations have been well-characterized: genome-wide hypomethylation and localized hypermethylation of CpG islands in tumor suppressor genes and developmental regulators [45–48].

Our analysis revealed significant downregulation of UBD methylation levels across 16 cancer types, suggesting a potential negative correlation between UBD expression and methylation status. While tumor research has traditionally focused on tumor suppressor gene silencing through promoter hypermethylation [49–57], emerging evidence highlights the importance of DNA hypomethylation in oncogenesis. For example, the gene encoding the protease urokinase (PLAU/uPA) is overexpressed and hypomethylated in conjunction with tumor progression in breast cancers and prostate cancers (benign prostate hyperplasia vs. cancer) [58, 59]. This hypomethylation pattern, frequently observed in late-replicating genomic domains, appears to correlate with increased mitotic activity [60]. Mechanistically, DNA hypomethylation contributes to tumor progression by promoting genomic instability and activating oncogenic pathways [60, 61]. Our findings suggest that UBD may facilitate tumor cell proliferation through DNA hypomethylation-mediated mechanisms, thereby promoting tumorigenesis and disease progression.

Genetic alterations represent well-established drivers of tumorigenesis, with copy number variations (CNVs) frequently causing oncogene overexpression in affected genomic regions [62]. Our pan-cancer analysis revealed UBD genetic alterations in 5% of cases, predominantly gene amplification. The FAT10-MAD2 interaction has been shown to promote tumor progression by enhancing tumor growth in vivo and inducing aneuploidy, proliferation, migration, invasion, and anti-apoptotic effects in vitro [63]. Notably, gene amplification typically results in increased gene expression, and UBD demonstrates high-frequency alterations in several aggressive malignancies, including Melanoma, Lung cancer, Hepatobiliary cancer, Bladder cancer, Breast cancer, Esophagogastric cancer, Bone cancer, Cervical Cancer, Endometrial Cancer, Non-Small Cell Lung Cancer, Colorectal Cancer, Ovarian Cancer, Mature B-cell lymphoma, Head and Neck Cancer, Glioma. Furthermore, the copy number of UBD major diploid, gain and amplification in most samples. Clinically, these genetic alterations were associated with significantly reduced survival in affected patients. Therefore, it is necessary to study the association among several types of cancers with UBD mutations. These findings collectively suggest that UBD genomic alterations may contribute to tumor initiation, growth, and progression across multiple cancer types. Further investigation into the association between UBD mutations and specific cancer types is warranted.

Immunotherapy has become increasingly prominent in cancer treatment, with immune cells playing pivotal roles in tumor progression [64]. Our study reveals significant positive correlations between UBD expression and infiltration levels of multiple immune cell types, including: CD4 + and CD8 + T cells, B cells, Dendritic cells (DCs), Neutrophils, Macrophages. These findings position UBD as a potential biomarker reflecting tumor immunogenicity and progression. Mechanistically, in hepatocellular carcinoma, UBD upregates PD-L1 expression by activating the PI3K/AKT/mTOR pathway, leading to T-cell-mediated cytotoxic drug resistance enhancement inducing immunosuppression [19]. While ubiquitin-related pathways generally enhance T-cell activation and anti-tumor immunity [18], our findings show UBD specifically correlates with expression of immunomodulators (chemokines, chemokine receptors, immunoinhibitors, immunostimulators, and MHC-related genes) across most cancers.

Notably, UBD exhibits particularly strong association with CXCL9 expression, suggesting its potential as a combinatorial immunotherapy target. These findings position UBD as a multifaceted immune regulator and promising therapeutic target, with current research exploring UBD-targeted strategies to develop more precise immunotherapies [65].

The tumor microenvironment (TME) represents a complex ecosystem comprising diverse cellular components, including: immune cells (T lymphocytes, NK cells, macrophages, dendritic cells), stromal elements (fibroblasts, extracellular matrix), malignant tumor cells. Our study revealed that UBD expression positively correlates with immune cell scores, suggesting its role in promoting immune infiltration. Conversely, UBD expression negatively correlates with tumor purity across multiple cancer types, indicating its potential to enhance tumor heterogeneity and immune cell recruitment, thereby modifying the TME’s immunological landscape. These findings collectively demonstrate UBD’s critical role in shaping the TME by potentially modulating immune evasion mechanisms, stromal-tumor crosstalk, and therapeutic response patterns.

Immune checkpoint (ICP) genes play pivotal roles in modulating tumor immune infiltration and response to immunotherapy [66]. Our analysis revealed significant positive correlations between UBD expression and multiple critical immune checkpoints markers, including: CD274 (PD-L1), CTLA4, SLAMF7, CXCL9/CXCL10 chemokines, and CD80. These findings align with established mechanisms wherein tumor cells upregulate PD-L1 through UBD-mediated pathways, leading to T-cell dysfunction and immunosuppression [19]. This strong association positions UBD as a promising predictive biomarker for immunotherapy response across multiple cancer types.

Several studies have shown that TMB, MSI, and NEO are very attractive related biomarkers to guide tumor immunotherapy [67, 68]. Our results showed that the expression of UBD in THYM, BRCA, COAD, and COADREAD were positively correlated with TMB. And UBD expression was positively correlated with MSI in COAD, COADREAD, and THYM. Furthermore, UBD expression was positively correlated with NEO in patients with COAD, COADREAD, SARC, THYM, and DLBC. Recent studies have demonstrated that tumors with high MSI, TMB, and NEO show better response ICP inhibitors. These studies demonstrate that tumor cells with high TMB and MSI typically have higher levels of neoantigens, which help the immune system recognize and activate the antitumor effects of T cells [69]. Therefore, in tumors where UBD expression levels are positively correlated with TMB and MSI, the higher the TMB and MSI, the better the immunotherapy effect, and TMB is highly correlated with the efficacy of PD-1/PD-L1 inhibitors [69]. In summary, UBD demonstrates strong potential as a predictive biomarker for immunotherapy response, particularly in COAD, COADREAD, and THYM malignancies.

To characterize UBD’s biological functions, we performed GO and KEGG pathway analyses of UBD-associated genes. GO analysis identified UBD’s primary involvement in ubiquitin-dependent protein catabolism and apoptotic inhibition. KEGG pathway analysis revealed that UBD appears to function through multiple neurodegenerative and proteolytic pathways: Amyotrophic lateral sclerosis, Parkinson disease, Prion disease, Alzheimer disease, Proteasome, Spinocerebellar ataxia and Ubiquitin mediated proteolysis. Specifically, UBD inhibits the transcriptional activity of the tumor protein p53 (TP53) and promotes its degradation, thereby accelerating tumor development [38]. Furthermore, higher expression of UBD has been proven to increase the activation of tumor necrosis factor -α (TNF-α) and NF-κB, as well as the activation of transcription factors such as STAT3. These transcription factors play a core role as activators of anti-apoptotic gene expression and cell proliferation [12]. UBD exerts a carcinogenic effect by inhibiting apoptosis in glioma by regulating the PI3K/AKT/mTOR signaling pathway and the UBDP1/miR-6072/UBD network [37, 70]. Moreover, another previous researches have also indicated that UBD promotes tumor recurrence, metastasis and apoptosis through multiple signal transduction pathways, including PI3K/Akt, Wnt/β-catenin and nuclear factor κ-B signals [41]. Therefore, the roles of signaling pathways such as UBD expression, protein degradation and apoptosis in other tumors await further specific studies.

Protein-protein interaction network analysis identified CXCL9, CXCL10, HLA-F, PSMB9, and p53 as the most strongly associated partners of UBD. The p53 tumor suppressor serves as a master regulator of apoptosis and gene expression, with established evidence demonstrating UBD-mediated p53 degradation promotes colorectal cancer proliferation [38]. CXCL9 and CXCL10 belong to the CXC chemokine family and play a role in regulating immune responses in various tumors. In mmunomodulators, the expression of UBD is significantly positively correlated with the expression of the CXCL9 gene in most tumors. However, its role in immunotherapy has not yet been studied.

In conclusion, our findings demonstrate that UBD-associated molecular pathways may serve as valuable biomarkers for precision oncology, potentially guiding the development of more targeted therapeutic strategies for cancer patients.

Conclusion

Our results revealed that UBD expression was aberrantly upregulated in various cancers and closely associated with tumor prognosis, clinico-pathological stages, gene mutations, methylation patterns in multiple cancers. While these findings position UBD as a promising immunotherapeutic target based on its crucial role in tumor immunity, we acknowledge that these bioinformatics-derived observations require experimental validation. Future research should employ both in vitro and in vivo models to: (1) functionally characterize UBD’s oncogenic mechanisms, (2) elucidate its tumor-specific overexpression patterns, and (3) explore its therapeutic potential - essential steps for developing targeted UBD inhibitors and advancing precision immunotherapy approaches.

Author contributions

Y.S., conceptualization, data curation, formal analysis, methodology, writing— original draft preparation; Y.W. and F.L., data curation, formal analysis, methodology; Q.C., conceptualization, resources, supervision, project administration, writing—review and editing.

Funding

Authors received no specific funding for this work.

Data availability

All data associated with this study are available within the paper or the supplementary materials.

Declarations

Ethics approval and consent to participate

All authors agreed to participate.

Consent for publication

All authors agree to publish.

Competing interests

The authors declare no competing interests.