UBA6 serves as a prognostic biomarker and promotes tumor progression in pancreatic ductal adenocarcinoma

Introduction

Pancreatic ductal adenocarcinoma (PDAC) represents the most prevalent and aggressive form of pancreatic cancer, with a 5-year survival rate at 13% (1). Due to its asymptomatic presentation during early stages, more than 85% of patients are diagnosed at an advanced stage, precluding curative surgical resection (2). Even among the minority of patients eligible for surgery, the majority experience recurrence within two years after operation, despite receiving adjuvant therapies (3). Therefore, there is an urgent need to identify reliable molecular biomarkers for prognostic evaluation and individualized treatment planning.

Currently, survival prediction and therapeutic decision-making for PDAC patients largely depend on conventional clinicopathological parameters, such as the tumor-node-metastasis (TNM) staging system (4), surgical margin status (5), and histological grade (6). Nevertheless, the considerable intertumoral heterogeneity of PDAC limits the prognostic accuracy of these clinical factors alone (7). Recent advances in omics technologies and bioinformatics have highlighted the value of both single-gene and multi-gene prognostic signatures in predicting outcomes for PDAC (8-10). Such models have demonstrated strong predictive performance and may serve as useful complements to the conventional TNM staging framework.

Ubiquitin-like modifier activating enzyme 6 (UBA6) is an ubiquitin-activating enzyme that initiates ubiquitination cascades by activating and transferring ubiquitin to downstream target proteins (11-13). In addition to its role in ubiquitin activation, UBA6 also activates the ubiquitin-like protein FAT10 and directs it to substrate proteins, leading to proteasomal degradation independent of the ubiquitin pathway (14,15). Given its central function in UBA6-mediated post-translational modifications, UBA6 is involved in various pathogenic processes, including endoplasmic reticulum stress and cell death (16,17). Emerging evidence has implicated UBA6 in tumorigenesis in several cancers such as lung and esophageal carcinomas (18-20); however, its role in PDAC remains poorly understood.

In this study, we show that UBA6 is significantly upregulated in PDAC and exhibits strong prognostic relevance. Functional investigations further support its tumor-promoting role in PDAC progression. These findings identify UBA6 as a promising biomarker for improving survival prediction and guiding precision medicine approaches in PDAC management. We present this article in accordance with the TRIPOD reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0040/rc).

Methods

Data acquisition

Bulk RNA-sequencing data from patients with PDAC were obtained from seven publicly accessible cohorts: GSE21501 (n=102), GSE57495 (n=63), GSE62452 (n=65), GSE71729 (n=123), GSE85916 (n=78), E-MTAB-6134 (n=288), and TCGA (n=129). To minimize potential bias, samples with an overall survival (OS) of less than 30 days were excluded from analysis. Transcriptomic profiles from fourteen additional independent datasets (GSE15471, GSE16515, GSE28735, GSE32676, GSE41368, GSE60979, GSE62165, GSE62452, GSE71729, GSE71989, GSE130221, GSE132956, GSE183795, and GSE211398) were collected to enable comparative analysis of gene expression between tumor tissues and adjacent non-tumor pancreatic tissues. Gene expression data for PDAC cell lines and normal pancreatic ductal epithelial cells (HPNE) were sourced from the GSE138437 dataset. Gene expression matrices and corresponding clinical annotations for all GSE series were retrieved from the Gene Expression Omnibus (GEO) database (21). Metadata for the E-MTAB-6134 cohort were acquired from the ArrayExpress repository (22). Processed transcriptomic data for pancreatic adenocarcinoma (PAAD) from The Cancer Genome Atlas (TCGA) program were downloaded via the UCSC Xena platform (https://tcga.xenahubs.net) after exclusion of non-PDAC specimens as outlined in prior methodology (23). Additionally, proteomic datasets accompanied by survival information for PDAC patients were integrated from three previously published studies [Cell, 2021 (24); Journal of Hematology & Oncology, 2023 (25); Nature Medicine, 2024 (26)]. To analyze UBA6 transcription levels across multiple cancer types, pan-cancer data from TCGA were retrieved via the GEPIA3 database (27). Representative immunohistochemistry (IHC) images depicting UBA6 protein expression in PDAC tissues and matched adjacent normal samples were acquired from the Human Protein Atlas (HPA) database (28). The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Prognostic significance of UBA6

To evaluate the prognostic value of UBA6, patients in each cohort were categorized into low- and high-expression groups using the optimal cutoff determined by X-tile software (29). Kaplan-Meier (K-M) survival curves were generated to visualize OS differences between the two subgroups.

Nomogram development in the MTAB-6134 cohort

A nomogram incorporating UBA6 expression and key clinicopathological variables was developed to predict 1‑, 2‑, and 3‑year survival probabilities. Candidate covariates were initially selected through univariate Cox analysis. The predictive performance of the nomogram was subsequently evaluated using K‑M analysis, calibration curves, and receiver operating characteristic (ROC) analysis. Patients with missing data were excluded during the construction of the nomogram.

Single-cell landscape of UBA6

To profile UBA6 expression across singlecell subpopulations in PDAC, we interrogated the scCancerExplorer database (30) using the following filters: Organ = Pancreas, Cancer type = PAAD (pancreatic adenocarcinoma), and Gene name = UBA6.

Correlation between UBA6 expression and tumor immune infiltration

The CIBERSORT algorithm (https://cibersort.stanford.edu/) (31), a deconvolution method that estimates the proportions of 22 immune cell types based on the reference expression profiles of 534 biomarker genes, was employed to infer immune cell fractions across different samples. In previously published studies, Tang and colleagues applied the CIBERSORT algorithm to calculate immune cell infiltration in multiple PDAC cohorts, including E-MTAB-6134, GSE21501, GSE57495, GSE71729, GSE85916, and TCGA dataset (32). We obtained these pre-calculated immune infiltration data for subsequent analysis. Spearman correlation analysis was performed to evaluate the associations between UBA6 expression levels and the abundance of each immune cell type across the PDAC cohorts. Correlations were computed using the ‘cor.test’ function in the R package ’stats’ (R version 4.2.0), and the results were visualized using the R package ‘corrplot’.

Gene Set Enrichment Analysis (GSEA)

GSEA was conducted to identify functionally related gene sets associated with UBA6 expression levels. Using GSEA software (v 4.1.0) and the Molecular Signatures Database (MSigDB) (33), we compared transcriptomic profiles between low- and high-UBA6 expression groups. Enrichment scores and P values were derived by applying signal-to-noise normalization to the gene expression matrix, utilizing the software’s default parameters. Gene sets with a false discovery rate (FDR) <0.25 were considered statistically significant and selected for further visualization.

Cell culture and genetic manipulation

Human PDAC cell lines (BxPC-3, CFPAC-1, MiaPaCa-2, PANC-1), the immortalized human pancreatic ductal epithelial cell line hTERT‑HPNE, and HEK‑293T cells were sourced from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China). Cells were cultured under standard conditions (37 ℃, 5% CO₂) in the following media supplemented with 10 % fetal bovine serum (FBS) and 1% penicillin‑streptomycin (Beyotime, Shanghai, China): RPMI‑1640 (Gibco) for BxPC-3; high‑glucose DMEM (Gibco) for PANC-1, MiaPaCa-2, HPNE, and HEK-293T; and IMDM (Gibco) for CFPAC-1. All cell lines were routinely authenticated by STR profiling and confirmed to be free of mycoplasma contamination. For lentivirus production, target plasmids were co‑transfected with the packaging plasmids pMD2.G and psPAX2 into HEK‑293T cells. Viral supernatants were harvested 24 h later and filtered through 0.45 µm membranes (Millipore). For transduction, filtered virus was added to target cells in the presence of Polybrene (Beyotime). After 12–18 h, the medium was replaced with 10 mL of complete DMEM. Transduced cells were collected 72 h post‑transduction for subsequent experiments. Stable knockdown cell pools were selected using puromycin for 72 h. The shRNA sequences are listed below: sh‑UBA6#1: GCAGATATTGTTGAATCACTA and sh‑UBA6#2: CCATTGCAGAAGAAGATCAAT.

Western blotting

Proteins were extracted using RIPA buffer, separated by 10% SDS-PAGE, and electroblotted onto PVDF membranes (Millipore). Membranes were incubated with the following primary antibodies: anti‑UBA6 (1:1,000; 13211-1-AP, Proteintech, Rosemont, USA) and anti‑GAPDH (1:1,000; 60004‑1‑Ig, Proteintech). Protein signals were detected with enhanced chemiluminescence reagents (Epizyme). Experiments were performed in triplicate.

RNA extraction and real‑time quantitative polymerase chain reaction (RT‑qPCR)

Total RNA was extracted from cultured cells using the FastPure Cell/Tissue Total RNA Isolation Kit V2 (Vazyme, Nanjing, China) in accordance with the manufacturer’s instructions. For each sample, 1 µg of RNA was reverse transcribed into complementary DNA (cDNA) employing the HiScript III First Strand cDNA Synthesis Kit (Vazyme). Quantitative PCR analysis was carried out on a LightCycler 480 II System (Roche, Basel, Switzerland) using ChamQ Universal SYBR qPCR Master Mix (Vazyme). Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) served as the internal reference gene. Relative mRNA expression levels were determined by the 2^−ΔΔCT method. All primer sequences used in this study are listed in Table S1.

Cell proliferation assay

For Cell Counting Kit-8 (CCK-8) assays, transfected cells were seeded into 96‑well plates (six replicates) and cultured for five consecutive days. After adding 10 µL of CCK‑8 reagent (Beyotime), absorbance at 450 nm was measured following a 2h incubation. For colony‑formation assays, cells were plated in six‑well plates and cultured for 14 days. Colonies were fixed with 1% crystal violet (Beyotime) and enumerated manually.

Transwell assay

Transwell assays were performed using Corning Transwell inserts (8 µm pore size; Cat. #3422). Briefly, cells in 200 µL of serum‑free medium were seeded into the upper chamber, while the lower chamber contained 800 µL of medium supplemented with 10% FBS as a chemoattractant. After 24 h, migrated cells were fixed, stained with crystal violet, and counted in three randomly selected fields.

Statistical analysis

All bioinformatic analyses were conducted using R software (version 4.2.0). The ‘survival’ package was utilized for univariate Cox analysis, and forest plots were generated with the ‘forestplot’ package. K‑M survival curves, along with log‑rank tests, were created via the ‘survminer’ package. ROC curves were plotted using ‘survivalROC’, while calibration curves were generated with the ‘rms’ package. Boxplots were produced with ‘ggpubr’. In vitro experimental data were analyzed with GraphPad Prism (version 9.5.0) and are expressed as mean ± standard deviation (SD). Differences between two groups were assessed by Student’s t‑test. A P value <0.05 was considered statistically significant.

Results

UBA6 serves as a robust survival indicator in PDAC

To identify novel therapeutic targets and prognostic biomarkers for PDAC, we analyzed transcriptomic and clinical data from seven public datasets. Univariate Cox regression was first performed to screen for genes associated with OS across the independent cohorts. Venn analysis revealed three overlapping risk genes [hazard ratio (HR) >1]: UBA6, DCBLD2, and EREG (Figure 1A). No common protective genes (HR <1) were identified (Figure S1). Notably, while the oncogenic roles of DCBLD2 and EREG in PDAC have been well documented in previous studies (34-37), the biological functions and prognostic significance of UBA6 in this malignancy remain largely unexplored. This gap in knowledge prompted us to prioritize UBA6 for further investigation in the present study. As shown in Figure 1B, elevated UBA6 expression was significantly associated with poor OS in all seven cohorts: E‑MTAB‑6134 (HR =1.72, 95% CI: 1.16–2.54, P=0.007, n=288), GSE21501 (HR =1.85, 95% CI: 1.02–3.34, P=0.042, n=102), GSE57495 (HR =1.50, 95% CI: 1.05–2.14, P=0.03, n=63), GSE62452 (HR =1.64, 95% CI: 1.01–2.67, P=0.044, n=65), GSE71729 (HR =1.93, 95% CI: 1.19–3.12, P=0.008, n=123), GSE85916 (HR =3.64, 95% CI: 1.20–11.12, P=0.02, n=78), and TCGA (HR =1.68, 95% CI: 1.05–2.68, P=0.03, n=129). K-M survival analysis consistently showed that high UBA6 expression correlated with unfavorable prognosis in each cohort (Figure 1C-1I).

Figure 1 Identification of UBA6 as a robust prognostic indicator in PDAC. (A) Intersection analysis of seven independent PDAC cohorts identified three genes consistently associated with unfavorable OS (HR >1, P<0.05). (B) Forest plot summarizing univariate Cox regression results for UBA6 expression across the indicated cohorts. (C-I) K-M survival curves for patients stratified by high or low UBA6 expression in the E-MTAB-6134 (C), GSE21501 (D), GSE57495 (E), GSE62452 (F), GSE71729 (G), GSE85916 (H), and TCGA (I) datasets. CI, confidence interval; HR, hazard ratio; K-M, Kaplan-Meier; OS, overall survival; PDAC, pancreatic ductal adenocarcinoma; TCGA, The Cancer Genome Atlas; UBA6, ubiquitin-like modifier activating enzyme 6.

UBA6 is significantly upregulated in PDAC tissues

Transcriptomic analysis revealed a marked increase in UBA6 mRNA levels in PDAC tumor samples compared with unmatched adjacent normal tissues (Figure 2A). This finding was consistently supported by analyses of five additional independent cohorts comprising matched tumor‑normal pairs (Figure 2B-2F). Interrogation of the GEPIA3 database further demonstrated that UBA6 was significantly overexpressed in PAAD as well as in several other cancer types relative to corresponding normal tissues (Figure 2G). Consistent with the tissue‑level findings, UBA6 expression was also elevated in PDAC cell lines compared with normal pancreatic ductal epithelial cells (Figure 2H).

Figure 2 UBA6 mRNA is significantly upregulated in PDAC. (A) Comparison of UBA6 mRNA levels between tumor and unmatched normal tissue samples. (B-F) Paired analysis of UBA6 expression in tumor versus adjacent normal tissues from the GSE15471 (B), GSE16515 (C), GSE28735 (D), GSE62452 (E), and GSE130221 (F) datasets. (G) UBA6 expression across tumor and normal tissue types based on integrated TCGA pan-cancer analysis. (H) UBA6 mRNA levels in normal pancreatic ductal cells (HPNE) and PDAC cell lines from the GSE138437 dataset. ns, not significant; **, P<0.01; ***, P<0.001. PDAC, pancreatic ductal adenocarcinoma; TCGA, The Cancer Genome Atlas; UBA6, ubiquitin-like modifier activating enzyme 6.

At the protein level, proteomic data similarly showed upregulated UBA6 expression in PDAC tissues, both in unpaired and matched sample analyses (Figure 3A-3D). Survival analysis based on proteomic profiles further confirmed that higher UBA6 protein levels correlated with reduced OS, with statistical significance achieved in each cohort examined (Figure 3E-3G). IHC staining images from the HPA database likewise revealed stronger UBA6 immunoreactivity in PDAC specimens compared with adjacent non‑tumor tissues (Figure 3H). In summary, the consistent upregulation of UBA6 at both transcriptional and translational levels, together with its association with poorer patient outcomes, suggests that UBA6 may act as an oncogenic driver in PDAC pathogenesis.

Figure 3 Protein-level expression of UBA6 in PDAC. (A) UBA6 protein expression in PDAC compared with adjacent normal tissues across three independent cohorts. (B-D) UBA6 protein levels in paired PDAC and adjacent normal specimens from the Cell_2021 (B), JHO_2023 (C), and Nature Medicine_2024 (D) studies. (E-G) K-M survival analysis based on UBA6 protein expression in the Cell_2021 (E), JHO_2023 (F), and Nature Medicine_2024 (G) cohorts. (H) Representative IHC images of UBA6 expression in PDAC (available at https://www.proteinatlas.org/ENSG00000033178-UBA6/cancer/pancreatic+cancer#img) and matched normal tissues (available at https://www.proteinatlas.org/ENSG00000033178-UBA6/tissue/pancreas#img) from the Human Protein Atlas database. UBA6, ubiquitin-like modifier activating enzyme 6; PDAC, pancreatic ductal adenocarcinoma; IHC, immunohistochemistry.

Nomogram construction

Although UBA6 alone showed moderate prognostic ability for OS, we aimed to improve its clinical applicability by incorporating it into a practical nomogram. The final model integrated tumor grade, N stage, resection margin status, and UBA6 expression to generate individualized survival estimates (Figure 4A). K-M analysis confirmed that the nomogram effectively stratified patients into distinct risk groups with significantly different clinical outcomes (Figure 4B). ROC curves further validated the model’s predictive performance, with area under the curve (AUC) values for 1‑, 2‑, and 3‑year survival consistently approaching or exceeding 0.7 (Figure 4C). Calibration plots also demonstrated close agreement between predicted and observed survival probabilities at each time point (Figure 4D).

Figure 4 Construction and validation of a prognostic nomogram in the E-MTAB-6134 cohort. (A) A nomogram incorporating tumor grade, N stage, resection margin status, and UBA6 expression for predicting OS. Resection margin: 0= R0 resection, 1= R1 resection. (B) K-M survival curves stratified by nomogram-derived risk groups. (C) ROC curves assessing the nomogram’s accuracy for 1‑, 2‑, and 3‑year OS prediction. (D) Calibration plots comparing predicted versus observed survival probabilities. AUC, area under the curve; K-M, Kaplan-Meier; N, node; OS, overall survival; ROC, receiver operating characteristic; UBA6, ubiquitin-like modifier activating enzyme 6.

Single-cell analysis of UBA6 in tumor microenvironment (TME)

To delineate the expression pattern of UBA6 across different cellular components of the TME, we interrogated single‑cell RNA‑seq data from PDAC available in the scCancerExplorer database. Analysis of the GSE210347 dataset revealed that UBA6 was broadly distributed among multiple cell types, including normal epithelial cells, cancer cells, endothelial cells, lymphocytes, myeloid cells, fibroblasts, and plasma cells (Figure 5A). Consistent findings were observed in two additional independent datasets, HRA000433 (Figure 5B) and PRJCA001063 (Figure 5C), further confirming the widespread expression of UBA6 across diverse cell populations within the PDAC TME. Notably, across all three datasets, UBA6 expression appeared comparatively higher in cancer cells than in most other cell types.

Figure 5 Single‑cell RNA‑seq analysis reveals UBA6 expression across the PDAC tumor microenvironment. (A-C) UBA6 expression profiles at single‑cell resolution in the GSE210347 (A), HRA000433 (B), and PRJCA001063 (C) cohorts. For each cohort, panels show UMAP visualization of major cell types (left), UBA6 distribution in cell clusters (middle), and violin plots quantifying UBA6 expression levels (right) across different cell populations. PDAC, pancreatic ductal adenocarcinoma; RNA-seq, RNA-sequencing; UBA6, ubiquitin-like modifier activating enzyme 6; UMAP, Uniform Manifold Approximation and Projection.

UBA6 expression correlates with an immunosuppressive microenvironment

To investigate the relationship between UBA6 expression and immune infiltration in PDAC, we estimated the abundance of various immune cell types in each transcriptomic cohort using the CIBERSORT algorithm. Across six independent datasets, UBA6 levels showed consistently negative correlations with infiltrating CD8⁺ T cells (Figure 6A,6B). Inverse associations were also observed with monocytes in several cohorts. These findings suggest that elevated UBA6 expression may be linked to an immunosuppressive TME.

Figure 6 Association of UBA6 expression with immune cell infiltration in PDAC. (A) Heatmap displaying correlation coefficients between UBA6 expression and the estimated abundance of tumor‑infiltrating immune cells across six independent cohorts. Red indicates positive correlation; blue indicates negative correlation. (B) Heatmap of corresponding P values for the correlations shown in (A). *, P<0.05; **, P<0.01; ***, P<0.001; ****, P<0.0001. NK, natural killer; PDAC, pancreatic ductal adenocarcinoma; TCGA, The Cancer Genome Atlas; UBA6, ubiquitin-like modifier activating enzyme 6.

Pathway enrichment and protein-protein interaction analysis of UBA6

To explore the potential mechanisms linking high UBA6 expression to poor prognosis, patients in the E-MTAB-6134 and TCGA cohorts were dichotomized into high- and low-expression groups based on the median UBA6 level, followed by GSEA analysis. Three pathways were consistently enriched in both datasets: ubiquitin‑mediated proteolysis, cell cycle, and p53 signaling (Figure 7A,7B). Notably, the enrichment of the ubiquitin-mediated proteolysis pathway aligns closely with the established role of UBA6 as a second E1 ubiquitin-activating enzyme, functionally distinct from ubiquitin-like modifier-activating enzyme 1 (UBA1), in the ubiquitin conjugation cascade (38). Given its upstream position in this pathway, UBA6 is likely to exert its biological effects through the modulation of downstream substrates (39). To investigate this, we interrogated previously published data that identified 439 UBA6-specific ubiquitination substrates in HEK293T cells using an orthogonal ubiquitin transfer-based screening approach (40). Using the STRING database (41), we constructed a protein-protein interaction network linking UBA6 with these potential interactors (Figure S2), thereby providing a framework to guide future mechanistic studies.

Figure 7 Functional enrichment analysis of UBA6. (A,B) GSEA plots depicting the most significantly enriched pathways between the low- and high-UBA6 expression groups in the (A) E-MTAB-6134 and (B) TCGA cohorts. ES, enrichment score; FDR, false discovery rate; GSEA, Gene Set Enrichment Analysis; TCGA, The Cancer Genome Atlas; UBA6, ubiquitin-like modifier activating enzyme 6.

UBA6 is essential for PDAC cell proliferation and migration

Based on the aforementioned bioinformatic findings, we proceeded to investigate the biological functions of UBA6 in PDAC through in vitro experiments. Western blotting confirmed that UBA6 protein expression was elevated in PDAC cell lines compared with the non‑tumor pancreatic ductal epithelial cell line HPNE (Figure 8A). Given that BxPC‑3 and PANC‑1 cells exhibited relatively higher UBA6 levels, these two lines were selected for subsequent functional studies following knockdown. Two independent shRNAs effectively reduced UBA6 expression in both BxPC‑3 and PANC‑1 cells (Figure 8B,8C). Colony‑formation and CCK‑8 assays demonstrated that depletion of UBA6 significantly impaired the proliferative capacity of PDAC cells (Figure 8D,8E). Furthermore, Transwell assay revealed that UBA6 knockdown markedly attenuated the migratory abilities of PDAC cells (Figure 8F).

Figure 8 UBA6 is essential for PDAC cell proliferation and migration in vitro. (A) Western blotting analysis of UBA6 protein expression in a panel of PDAC cell lines compared to the normal pancreatic ductal cell line HPNE. (B,C) Validation of UBA6 knockdown efficiency at the mRNA (B) and protein (C) levels in cells transduced with scramble control (shLuc) or two independent UBA6‑targeting shRNAs. (D) Cell proliferation assessed by CCK‑8 assay in BxPC‑3 and PANC‑1 cells following UBA6 knockdown. (E) Colony‑forming ability of BxPC‑3 and PANC‑1 cells after UBA6 silencing (stain with 0.5% crystal violet). (F) Representative images and quantitative analysis of Transwell assays in control and UBA6‑knockdown PDAC cells. Migrated cells were stained with crystal violet and photographed under a microscope (×10 objective). **, P<0.01; ***, P<0.001. CCK-8, Cell Counting Kit-8; PDAC, pancreatic ductal adenocarcinoma; shRNA, short hairpin RNA; UBA6, ubiquitin-like modifier activating enzyme 6.

Molecular mechanism of UBA6-mediated suppression of CD8⁺ T cell infiltration in PDAC

We further investigated the potential mechanism underlying the negative correlation between UBA6 expression and CD8⁺ T cell infiltration. A previous study demonstrated that knockout of UBA6 in 4T1 cells upregulated the surface expression of MHC-I and increased the transcript levels of genes involved in MHC-I-mediated antigen presentation, including B2M, TAP1, PSMB8, and PSMB9 (42). Given that MHC-I-associated genes play a critical role in presenting antigens to CD8⁺ T cells and facilitating their infiltration (43), we hypothesized that UBA6 may suppress CD8⁺ T cell infiltration in PDAC through downregulation of these genes. To test this hypothesis, we performed RT-qPCR and found that knockdown of UBA6 in PANC-1 cells significantly reduced the expression of B2M, TAP1, PSMB8, and PSMB9 (Figure S3). These findings suggest that these genes may act as downstream effectors of UBA6, through which UBA6 potentially modulates CD8⁺ T cell infiltration and antitumor immunity.

Discussion

PDAC is a highly aggressive malignancy characterized by considerable molecular heterogeneity. Current clinical management, including risk stratification and treatment planning, depends largely on conventional clinicopathological parameters. However, these factors offer limited discriminatory accuracy for individual patient prognostication (44). In the era of precision oncology, there is a pressing need for reliable molecular prognosticators to inform the intensity of adjuvant therapy, tailor surveillance schedules, and facilitate early detection of recurrence. Consequently, substantial efforts have been devoted to developing robust prognostic models for PDAC (45-47). In this study, through integrated bioinformatic exploration and experimental validation, we identify UBA6 as a promising prognostic biomarker and a potent oncogenic driver in PDAC. Our multi-cohort, cross-platform analyses consistently establish UBA6 as a significant predictor of patient survival, providing complementary value to existing clinicopathological staging systems.

From the integrated analysis of seven independent transcriptomic cohorts, we identified three genes whose expression was consistently associated with poor prognosis. The use of multiple diverse cohorts, rather than a single dataset, enhances the generalizability of candidate biomarkers. Among these candidates, UBA6 had been the least studied in the context of PDAC, leading us to focus on its clinical and biological significance. K-M survival analysis demonstrated that UBA6 expression reliably distinguished between patients with short and long OS. This prognostic association was consistently observed across all seven cohorts, which included patients from North America, Australia, and Europe. The reproducibility of these findings across geographically distinct populations underscores the robustness and broad applicability of UBA6 as a prognostic indicator.

We observed an inverse correlation between UBA6 expression and CD8⁺ T cell infiltration in PDAC, suggesting a potential role for UBA6 in modulating antitumor immunity. This is supported by prior mechanistic studies: Lee et al. reported that UBA6 expression is elevated in T cells and critically regulates IFN‑γ production via modulation of the NFκB p65 pathway (48). Furthermore, Zhang et al. demonstrated that inosine‑mediated inhibition of UBA6 in tumor cells enhances tumor immunogenicity, leading to improved outcomes following immune checkpoint blockade (ICB) therapy in melanoma models. Consistently, murine melanoma and breast cancer models with genetic ablation of UBA6 exhibited increased sensitivity to ICB compared to wildtype tumors (42). Collectively, these findings highlight the importance of UBA6‑dependent pathways in regulating immune responses and suggest that targeting UBA6 may represent a promising strategy to overcome ICB resistance in PDAC.

Experimental findings substantiated the bioinformatic predictions: UBA6 was consistently overexpressed in PDAC cell lines and patient-derived tissues, and its knockdown effectively suppressed cellular proliferation and migration in vitro. Pathway enrichment analysis further linked high UBA6 expression to ubiquitin‑mediated proteolysis, cell cycle, and p53 signaling-pathways wellestablished to drive proliferation, invasion, and survival in PDAC (49-51). These findings provide a plausible mechanistic foundation for the oncogenic function of UBA6, positioning it within key cellular pathways that promote tumor progression.

Several limitations of this study should be acknowledged. Firstly, while our multi-cohort retrospective analysis strongly supports the prognostic value of UBA6, its clinical utility and therapeutic relevance require validation in prospective, large-scale clinical trials. Secondly, the heterogeneity and occasional incompleteness of clinical annotations across public datasets limited a fully comprehensive assessment of correlations between UBA6 expression and all relevant clinicopathological variables. Thirdly, the lack of nude mouse subcutaneous tumor models to validate the effect of UBA6 knockdown on tumor growth may reduce the translational value of the current findings. Finally, the direct transcriptional targets and precise molecular cascades downstream of UBA6 remain to be systematically identified and functionally characterized. Ongoing research is focused on elucidating these effector pathways to delineate the circuitry through which UBA6 governs PDAC biology.

In summary, through integrative bioinformatics and experimental validation, we establish UBA6 as a robust, independently verified prognostic biomarker and a potential therapeutic target in PDAC. These findings warrant further evaluation in larger, prospective cohorts to translate this molecular insight into clinical benefit for PDAC patients.

Conclusions

Through integrated bioinformatic discovery and functional validation, we have identified UBA6 as a robust, independently verified prognostic biomarker and a potent oncogenic driver in PDAC. Multi‑cohort and cross‑platform analyses consistently demonstrate that elevated UBA6 expression is strongly associated with poor patient survival, providing a molecular metric that complements conventional clinicopathological staging. Beyond its prognostic utility, UBA6 expression correlates significantly with immune cell infiltration, implicating its potential role in shaping an immunosuppressive TME and influencing immunotherapy sensitivity. Experimentally, we established that UBA6 is functionally essential for PDAC cell proliferation and migration, thereby supporting its direct contribution to tumor aggressiveness. These findings not only advance our understanding of PDAC pathogenesis by highlighting UBA6‑dependent regulatory networks but also suggest its translational promise. UBA6 may serve as an actionable target for risk stratification, therapeutic development, and potentially for overcoming immunotherapy resistance. Further investigation into the precise molecular mechanisms governed by UBA6 and its clinical applicability in prospective trials is warranted to translate these insights into improved outcomes for PDAC patients.

Acknowledgments

We acknowledge the contributions from GEO, TCGA, ArrayExpress, GEPIA3 and scCancerExplorer databases.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0040/rc

Data Sharing Statement: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0040/dss

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0040/prf

Funding: This work was supported by Bethune Charitable Foundation (No. Z04J2023E095).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2026-1-0040/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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