Skip to main content
Download PDF

Identification of USP39 as a prognostic and predictive biomarker for determining the response to immunotherapy in pancreatic cancer

BMC Cancer volume 25, Article number: 758 (2025) Cite this article

Abstract

Ubiquitin-Specific Protease 39 (USP39) has been implicated in numerous malignancies, however, its pathogenic mechanisms and impact on the tumor immune microenvironment (TIME) remain incompletely characterized. Based on The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases, we investigated the diagnostic and prognostic values of USP39 across various cancer types. Additionally, we examined the correlation between USP39 expression and immune-related gene signature, immune cell infiltration pattern, tumor microsatellite instability (MSI), and tumor mutation burden (TMB). This study specifically focused on exploring the clinical relevance and molecular functions of USP39 in pancreatic adenocarcinoma (PAAD), with particularly emphasis on its role in shaping the TIME and modulating responses to immunotherapy. The results demonstrated that evaluated USP39 expression significantly correlated with advanced tumor stage and unfavorable clinical outcomes across multiple cancer types, most notably in PAAD. Functional enrichment analysis indicated that USP39 potentially promotes tumor progression through multiple oncogenic signaling cascades. In vitro experimental validation confirmed that USP39 knockdown inhibited migration and proliferation of pancreatic cancer cells while inducing apoptosis. Additionally, we identified significant positive correlations between USP39 expression and immune checkpoint molecules, particularly prominent in PAAD. Furthermore, we observed associations between USP39 expression and TMB in 16 cancer types and MSI in 11 cancer types, suggesting that heightened USP39 expression may enhance responsiveness to immunotherapeutic interventions. Collectively, our findings establish USP39 as a valuable immune-related biomarker with both diagnostic and prognostic utility across multiple cancer types, especially PAAD, underscoring its potential as a promising therapeutic target for cancer immunotherapy.

Clinical trial number Not applicable.

Peer Review reports

Introduction

Ubiquitin-specific proteases (USPs) constitute a subfamily within the deubiquitinating enzymes (DUBs). These proteolytic enzymes catalyze the selective removal of ubiquitin moieties from specific substrate proteins through deubiquitination, thereby orchestrating protein homeostasis and functional modulation under physiological conditions [1,2,3]. Through the modulation of gene expression, epithelial-mesenchymal transition (EMT), apoptotic pathways, cell cycle progression, and DNA repair mechanisms in neoplastic cells, these proteases exhibit significant regulatory effects on tumor progression [4, 5]. Emerging evidence demonstrates that USPs serve as important modulators of immunotherapeutic responses [6,7,8]. In particularly, Li et al. reported that USP22 depletion reduces immunosuppressive myeloid cell infiltration while enhancing CD8+ T cell and natural killer (NK) cell recruitment, consequently augmenting the therapeutic efficacy of combination immunotherapy in pancreatic adenocarcinoma (PAAD) patients [9]. Wang et al. demonstrated six USPs (USP10, USP14, USP18, USP32, USP33, and USP39) as potential biomarkers and therapeutic targets for PAAD, linked to poor prognosis and immune infiltration [10]. Furthermore, Huang et al. revealed that USP22-mediated programmed death-ligand 1 (PD-L1) stabilization potentially underlies the limited efficacy of PD-L1 antagonists, suggesting that targeted USP22 inhibition represents a promising immunotherapeutic strategy [11]. Additional members of the USP family, including USP7 [12], USP8 [13] and USP2 [14], have been implicated in immune response regulation and T cell activation.

USP39 possesses distinctive characteristics due to mutations in critical amino acid residues within its catalytic domain, which render it devoid of ubiquitin hydrolase activity [15]. Consequently, previous studies have primarily focused on USP39’s role as a splicing-related factor, particularly its involvement in precursor mRNA maturation through the splicing of genes such as KN motif and ankyrin repeat domains 2 (KANK2) and WW domain containing transcription regulator 1 (WWTR1) [16, 17]. Interestingly, recent studies have revealed that USP39 possesses ubiquitin hydrolase activity, which stabilizes protein levels of β-Catenin [18], signal transducer and activator of transcription 1 (STAT1) [19] and zinc finger E-box binding homeobox 1 (ZEB1) [20] through deubiquitination. However, the molecular mechanisms by which USP39 influences the tumor immune microenvironment (TIME) remain incompletely understood.

Recent advances in tumor immunotherapy have yielded remarkable therapeutic breakthroughs. Strategic interventions, including immune checkpoint inhibitor (ICI) therapies, therapeutic vaccines, and T-cell targeted immunomodulatory agents, have demonstrated significant improvements in patient survival across multiple malignancies, notably non-small cell lung cancer (NSCLC), metastatic melanoma, and bladder urothelial carcinoma [21]. However, PAAD exhibits an immunologically “cold” tumor microenvironment (TME), characterized by extensive infiltration of myeloid-derived suppressor cells (MDSCs) and regulatory T cells (Tregs), coupled with a paucity of CD8 + effector T cells, resulting in intrinsic resistance to immune checkpoint blockade (ICB) [22, 23]. The TIME exerts profound influences on PAAD pathogenesis, progression, metastatic potential, and immunotherapeutic responsiveness [24,25,26]. In summary, the heterogeneity of the PAAD immune landscape presents significant challenges for therapeutic optimization. In this context, identifying effective biomarkers that can evaluate or provide valuable guidance for PAAD immunotherapy is particularly crucial.

In this study, we investigated the expression pattern of USP39 across various cancers through pan-cancer analysis, with particular focus on digestive tract tumors, especially PAAD. Using PAAD cell lines, we examined the effects of USP39 depletion on cellular proliferation and migration capabilities. Additionally, we analyzed the correlation between USP39 expression and immune cell infiltration within the TME across different malignancies, with specific emphasis on PAAD. We also explored potential associations between USP39 expression levels and anti-tumor drug sensitivity. This research provides insights into the expression patterns of USP39 in cancer and its potential relationship with tumor immunity, laying a foundation for future mechanistic studies of USP39 in pancreatic cancer.

Results

USP39 is highly expressed in various tumor tissues and is closely associated with prognosis, particularly in PAAD

We investigated the mRNA expression of USP39 in human tumors by analyzing the TCGA and GTEx databases. USP39 was significantly upregulated in numerous cancer types, including glioblastoma multiforme (GBM), brain lower grade glioma (LGG), breast invasive carcinoma (BRCA), lung adenocarcinoma (LUAD), esophageal carcinoma (ESCA), stomach and esophageal carcinoma (STES), colon adenocarcinoma (COAD), lung squamous cell carcinoma (LUSC), LIHC, high-risk wilms tumor (WT), ovarian serous cystadenocarcinoma (OV), PAAD, testicular germ cell tumors (TGCT), uterine carcinosarcoma (UCS), and cholangiocarcinoma (CHOL), when compared to normal tissues (Fig. 1A).

Fig. 1
figure 1

Expression profile and prognostic value analysis of USP39 across pan-cancer. A: Differential expression analysis of USP39 between tumor tissues and corresponding normal tissues. B: The impact of USP39 expression on disease-free interval (DFI), disease-specific survival (DSS), overall survival (OS), and progression-free interval (PFI) in 32 cancer types was evaluated using Cox proportional hazards regression model and log-rank test. Red boxes indicate USP39 as a risk factor (hazard ratio (HR) > 1 and P < 0.05), while green boxes indicate USP39 as a protective factor (HR < 1 and P < 0.05). C-H, Kaplan-Meier survival curves for OS and PFS in PAAD (C, D), LIHC (E, F), and KIRC (G, H) based on GSCA database. *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001

Full size image

Subsequently, the receiver-operating characteristic (ROC) curve analysis showed that AUCs were 0.977 for the diagnosis of PAAD, 0.627 for kidney renal papillary cell carcinoma (KIRP), 0.884 for LIHC, 0.512 for thyroid carcinoma (THCA), 0.592 for KIRC, 0.859 for COAD, and 0.826 for stomach adenocarcinoma (STAD) (Figure S1A-F). These results suggest that USP39 could serve as a potential diagnostic marker in multiple cancer types, including PAAD. To further explore the prognostic value of USP39 across various cancers, survival analyses were performed using both Cox regression and log-rank tests based on TCGA database (Fig. 1B). The results consistently demonstrated that USP39 expression was significantly associated with OS in multiple cancer types, including adrenocortical carcinoma (ACC), KIRC, KIRP, LGG, LIHC, mesothelioma (MESO), PAAD, pheochromocytoma and paraganglioma (PCPG), and uterine corpus endometrial carcinoma (UCEC). However, no consistent significant associations were observed between USP39 expression and DSS, DFI, or PFI in any cancer types. Detailed results of Cox regression analysis and Kaplan-Meier with log-rank tests are provided in Supplementary Tables S2 and S3, respectively. The prognostic impact of USP39 was further validated using the GSCA database, which confirmed its association with poor prognosis in both OS (Fig. 1C) and PFS (Fig. 1D) in PAAD. Similarly, poor prognostic associations were also observed in LIHC (Fig. 1E-F) and KIRC (Fig. 1G-H).

High USP39 expression is associated with significant activation of cell proliferation-related pathways in PAAD

To further clarify the role of USP39 expression in cancer progression, we conducted GSEA comparing patients with low virus high USP39 expression across multiple cancer types. Our analysis revealed that the high USP39 expression group exhibited significant enrichment of cancer-associated signaling pathways, including hallmark “DNA repair”, “mTORC1 signaling” “G2-M checkpoint” and “E2F targets” across most cancer types. Notably in PAAD, we observed enrichment of immune-related pathways, such as “TNF-α signaling via NF-κB,” “IFN-α response,” and “IFN-γ response” (Fig. 2A). Complementary pathway activity analysis using the GSCA database demonstrated that USP39 predominantly activated the cell cycle (47% activation vs. 0% inhibition), DNA damage response (34% activation vs. 0% inhibition), and apoptosis (25% activation vs. 3% inhibition) pathways, while inhibiting RAS/MAPK signaling (0% activation vs. 34% inhibition) (Fig. 2B). Further GSEA analysis of the TCGA-PAAD cohort confirmed significant enrichment of “Cell Cycle,” “G2-M checkpoint,” and “p53-dependent DNA damage response signaling pathway” in the high USP39 expression group compared to the low expression group (Fig. 2C-E). Additional validation using the CAMOIP database provided compelling evidence for USP39’s involvement in cancer proliferation and wound healing processes, as these pathways were significantly enriched in the high USP39 expression group within the TCGA-PAAD cohort (Fig. 2F).

Fig. 2
figure 2

Association between USP39 expression and cancer-related pathways. (A) GSEA analysis between the low and high USP39 expression patient groups across various cancer types. Normalized Enrichment Scores (NES) were utilized to evaluate pathway activation status. NES > 0 indicated by orange and red coloring, represented pathways or gene sets that were activated in the high USP39 expression group. Conversely, NES < 0 depicted in light and dark gray, denoted pathways or gene sets that were suppressed in the high USP39 expression group. (B) The distribution of USP39’s activation and inhibition effects on target pathways in tumor tissues was evaluated. When USP39 contributes to pathway activation, it is represented by “A”; when it contributes to inhibition, it is represented by “I”. The final results (displayed as numerical values on rectangular color-coded boxes, with larger values corresponding to redder colors) show the proportion of USP39’s impact on target pathways across all tumor tissues. For example, the number 47 indicates that in the 32 cancer types currently analyzed, USP39 exhibits an activation effect on the cell cycle in 47% of the cancers. (C–E) GSEA analysis between high and low USP39 expression groups in PAAD about cell cycle related pathway. (F) Comparison of proliferation and wound healing pathway activities between USP39 high- and low-expression groups in PAAD based on CAMOIP database analysis

Full size image

Depletion of USP39 inhibits cell proliferation and migration and induces cell death in human pancreatic cancer cells

Fig. 3
figure 3

USP39-knockdown inhibits cell proliferation and migration and promotes cell death in human pancreatic cancer cells. (A) Western blot analysis revealed that the protein levels of USP39 significantly decreased in PANC-1 and MIAPaCa-2 cells when transfected with USP39 shRNA. β-Actin served as an internal control. (B) Representative results of colony formation in PANC-1 and MIAPaCa-2 cells. (C) USP39 knockdown suppressed migration capabilities in PANC-1 and MIAPaCa-2 cells (scale bar: 200 μm). (D) Representative results of the EdU assays (scale bar: 100 μm) in PANC-1 and MIAPaCa-2 cells. (E) Representative results of the cell death assay (scale bar: 100 μm) in PANC-1 and MIAPaCa-2 cells. (ns, not significant; *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.)

Full size image

Based on our bioinformatic analyses suggesting a potential oncogenic role for USP39, we proceed to investigate its functional significance in human pancreatic cancer cells. We established stable USP39-knockdown cell models in MIAPaCa-2 and PANC-1 cell lines using lentivirus-mediated shRNA transfection. Western blot analysis confirmed efficient suppression of USP39 protein expression in both pancreatic cancer cell lines (Fig. 3A). To evaluate the biological functions of USP39 in pancreatic cancer, we performed a series of in vitro functional assays. Colony formation assays revealed that USP39 depletion significantly suppressed the long-term proliferative capacity of both cell lines (Fig. 3B). Moreover, wound healing assays showed that USP39 silencing substantially impaired the migratory abilities of MIAPaCa-2 and PANC-1 cells (Fig. 3C). Consistent with this finding, EdU incorporation assays further demonstrated that USP39 knockdown markedly reduced DNA synthesis and cell proliferation (Fig. 3D). Importantly, cell death analysis indicated that USP39 depletion promoted cancer cell death (Fig. 3E). Collectively, these results establish USP39 as a critical regulator of pancreatic cancer cell survival, proliferation, and migration, supporting its oncogenic function in pancreatic cancer progression.

USP39 is closely related to the TME across various tumor tissues, particularly influencing the infiltration of immune cells

We employed multiple analytical methods through the TIMER database to comprehensively evaluate the correlation between USP39 expression and the TME components. The results revealed positive correlations between USP39 expression and various immune cell infiltration levels, including CD4 + T cells, common lymphoid progenitors, and CD8 + T cells in most human cancers (Fig. 4, Figure S2). Additionally, USP39 expression exhibited a significant positive correlation with CAFs, which are key stromal components within the TME. These CAFs modulate immune responses through various mechanisms, including cytokine secretion and direct cell-cell interactions. In contrast, a notable negative correlation was observed between USP39 expression and both NK T cells and NK cells, as demonstrated in Fig. 4. To validate these findings, we employed MCP-counter as an alternative immune deconvolution method. This analysis confirmed positive correlations between USP39 expression and the infiltration levels of immune cells, including CD8 + T cells, B cells, myeloid DCs, neutrophils, and endothelial cells across most cancers, particularly in LIHC, KIRP, and PAAD (Fig. 4, Figure S2A). The analysis also revealed positive correlations with fibroblasts, further supporting the relationship between USP39 and stromal components. Using the EPIC algorithm, we observed consistent results: USP39 expression positively correlated with immune cell populations (CD8 + T cells and CD4 + T cells) and stromal components (CAFs) across various cancers (Fig. 4, Figure S2B).

Fig. 4
figure 4

Analysis of USP39 expression correlation with tumor immune microenvironment (TIME) using multiple analytical methods across pan-cancers. Heatmap showing correlations between USP39 expression and infiltration levels of various cell types in the TIME based on TIMER database analysis. Cell types include: B cells, cancer-associated fibroblasts (CAFs), activated myeloid dendritic cells (DCs), endothelial cells, eosinophils, macrophages, mast cells, monocytes, neutrophils, natural killer (NK) cells, γδT cells, T helper (Th) cells, CD4 + T cells, regulatory T cells (Tregs), and CD8 + T cells. Color scale indicates correlation strength (red: positive correlation; blue: negative correlation)

Full size image

USP39 is closely associated with immunomodulators, TMB, and MSI across various tumor tissues

Fig. 5
figure 5

Pan-cancer analysis of associations between USP39 expression and immune-related features. (A) Heatmap showing correlations between USP39 expression and 150 immunomodulators categorized into five groups: chemokines, receptors, MHCs, immunoinhibitors, and immunostimulators. (B and C) Analysis of correlations between USP39 expression and tumor genomic features: tumor mutation burden (TMB) and microsatellite instability (MSI) across cancer types. *P < 0.05; **P < 0.01; ***P < 0.001

Full size image

As USP39 expression plays a crucial role in the immune system, we investigated its relationship with 150 immunomodulators, including chemokines, receptors, MHCs, immunoinhibitors, and immunostimulators. Our findings, presented in Fig. 5A, reveal a positive correlation between USP39 expression and the majority of immunomodulators in various human cancers, such as the immune checkpoint genes PD-L1,PD1, Indoleamine 2,3-Dioxygenase 1, T Cell Immunoreceptor with Ig and ITIM Domains (TIGIT), and Cytotoxic T-Lymphocyte Associated Protein 4 (CTLA-4). Furthermore, we observed a positive association between USP39 expression and TMB in certain cancers, such as sarcoma (SARC), MESO, ACC, LUSC, PAAD, head and neck squamous cell carcinoma (HNSC), skin cutaneous melanoma (SKCM), prostate adenocarcinoma (PRAD), bladder urothelial carcinoma (BLCA), LGG, STAD, BRCA, and LUAD (Fig. 5B), while a negative correlation was found with MSI in LGG, COAD, diffuse large B cell lymphoma (DLBC), and LUSC (Fig. 5C).

USP39 is closely related to ICB signatures in PAAD

Four independent algorithms, namely TIMER, EPIC, QUANTISEQ, and MCP-counter, were applied to calculate the infiltration level of immune cells in different USP39 expression groups. As shown in Fig. 6A, the heatmap indicated that high USP39 expression resulted in higher infiltrates of immune cell subsets in PAAD, especially neutrophils, CD8 + T cells, DCs, B cells, and CAFs (Figure S3A-D). Subsequently, GSEA was conducted using TCGA-PAAD bulk RNA-seq data to identify USP39-related immune signaling pathways. The bladder cancer pathway signatures and the immunotherapy-predicted pathways were obtained from Hu et al. [27], and the activation levels of cancer-immunity cycle pathway were extracted from the tumor immunophenotype tracking web tool, TIP [28]; the pathway activity of cancer-immunity cycle should reflect the anticancer immune response [29]. The heatmap showed differences in pathway activity scores between PAAD patients with high and low USP39 expression (Figure S3E). Notably, significant differences were observed in the immunotherapy-predicted pathway (Fig. 6B) and bladder cancer pathway (Fig. 6C) signatures between the high- and low-USP39 expression groups. Correlations between USP39 expression and the immunotherapy-predicted and cancer-immunity cycle pathways were further analyzed. As demonstrated in Fig. 6D and Supplementary Table S4, USP39 was significantly positively correlated with immunotherapy-predicted pathways (such as P53, APM signal, DNA replication, and cell cycle signaling pathway). Moreover, in terms of cancer-immunity cycle pathways, we observed a positive correlation between USP39 expression and most of the individual steps involved in the cycle, which include the release of cancer cell antigens (Step 1), cancer antigen presentation (Step 2), priming and activation (Step 3), Th22 cell recruiting (Step 4), MDSC recruiting (Step 4), CD8 T cell recruiting (Step 4), B cell recruiting (Step 4), infiltration of immune cells into tumors (Step 5), and recognition of cancer cells by T cells (Step 6) (Fig. 6E and Supplementary Table S5).

Fig. 6
figure 6

USP39 expression levels as potential predictors of immune checkpoint blockade (ICB) response in pancreatic adenocarcinoma (PAAD). (A) Heatmap illustrating differential enrichment scores of immune-related signature pathways between USP39 high- and low-expression groups. (B-C) Comparative analysis of enrichment scores for immunotherapy-predicted pathways and cancer-immunity cycle pathways between USP39 expression groups. (D-F) Correlation analyses between USP39 expression and (D) immunotherapy-predicted pathway enrichment scores, (E) cancer immunity cycle steps, and (F) immune checkpoint marker expression. (G) Analysis of predicted immunotherapy responsiveness between USP39 expression groups in TCGA-PAAD cohort using SubMap analysis (GenePattern database). (H) Evaluation of immunotherapy response in the IMvigor210 cohort comparing high- versus low-USP39 expression groups. (I) Comparative analysis of macrophage regulation and TGF-β response pathway activities between USP39 expression groups based on CAMOIP database. *P < 0.05, **P < 0.01, ***P < 0.001

Full size image

  • ICBs, such as anti-CTLA-4 and anti-PD-1 therapies, are effective at treating a variety of cancers [30,31,32]. As such, a correlation analysis between USP39 expression and the expression of immune checkpoint factors was performed in our study (Fig. 6F). The results revealed that USP39 expression exhibited significant positive correlations with multiple common immune checkpoint regulators in PAAD. Notably strong associations were observed with TNFRSF14, CD276, LGALS9, CD70, TNFSF9, TNFRSF18 and TNFSF15, suggesting that USP39 may functionally interact with immune checkpoint pathways in the pancreatic TME. Differential expression analysis also revealed that expression of CD276, CD70, TNFSF9, and TNFSF15 was upregulated in the high USP39 expression group (Figure S4). In addition, we found that the high-USP39 group showed superior immune responses to anti-CTLA4 therapy based on the TCGA-PAAD cohort (nominal p-value = 0.038; Fig. 6G). Moreover, anti-PD-L1 cohort (obtained from IMvigor210) data were applied in our study to further validate the possibility of USP39 as an important immunotherapeutic target. As seen in Fig. 6H, patients with high USP39 expression have a more favorable response to immunotherapy compared to those with low USP39 expression. Moreover, we identified significant enrichments of the “macrophage regulation” and “TGF-beta response” pathways in the low USP39 expression group based on the TCGA-PAAD cohort from the CAMOIP database (Fig. 6I).

High expression of USP39 affects the sensitivity to anti-tumor drugs in PAAD

We also performed a chemotherapeutic drug sensitivity analysis between USP39 expression and common chemotherapeutic molecular drugs using the GDSC database. As shown in Fig. 7A, based on data obtained from the GSCA online database, we observed significant correlations between USP39 expression and drug sensitivity, with notably high FDR < 0.05 for several compounds including 17-AAG, bleomycin (50 µM), docetaxel, RDEA119, trametinib, and selumetinib. To further investigate the relationship between USP39 expression and drug sensitivity specifically in pancreatic cancer, we analyzed the TCGA-PAAD dataset. Our results revealed distinct drug response patterns associated with USP39 expression levels. PAAD patients with high USP39 expression showed increased sensitivity to DNA-damaging agents including Bleomycin (P = 0.0019), Doxorubicin (P = 0.041), Embelin (P = 0.05), and Gemcitabine (P = 0.036) (Fig. 7B-E). Conversely, high USP39 expression was associated with resistance to several targeted therapeutic agents, including axitinib (P = 9.4e-06), bosutinib (P = 0.0022), camptothecin (P = 0.013), dimethyloxalyl glycine (P = 0.04), gefitinib (P = 0.011), metformin (P = 7.8e-06), sunitinib (P = 0.0098), and temsirolimus (P = 2.8e-06) (Fig. 7F-M). These findings suggest that USP39 expression levels may serve as a potential predictor for drug response in PAAD patients.

Fig. 7
figure 7

Analysis of chemotherapeutic drug sensitivity in relation to USP39 expression in pancreatic adenocarcinoma (PAAD). (A) Pan-cancer correlation analysis between USP39 expression and drug sensitivity profiles from the GDSC database. (B-M) Comparative analysis of drug sensitivity between USP39 high- and low-expression groups in PAAD patients for multiple therapeutic agents: bleomycin, doxorubicin, embelin, gemcitabine, axitinib, bosutinib, camptothecin, dimethyloxallyl glycine, gefitinib, metformin, sunitinib, and temsirolimus

Full size image

Discussion

USP39, a member of the deubiquitinating enzyme family, is believed to be associated with cytoplasmic division and pre-mRNA maturation [33, 34]. Accumulating evidence demonstrates that USP39 is aberrantly overexpressed and functions as a pro-oncogenic factor across multiple malignancies, including gastric cancer [35], lung cancer [36], breast cancer [37], and colon cancer [38]. Furthermore, USP39 is involved in the response to DNA damage and resistance to chemoradiation in tumor cells and is associated with drug resistance in tumor cells [38, 39]. Despite these findings, a systematic analysis of USP39’s influence on clinical phenotypes and the TME across diverse cancer types remains to be elucidated.

In this study, utilizing TCGA and GTEx databases, we identified significant upregulation of USP39 expression across 30 cancer types compared to their corresponding normal tissues. We observed associations between USP39 expression and clinical outcomes in multiple cancer types, suggesting that USP39 represents a promising therapeutic target and diagnostic biomarker for cancer patients. GSEA revealed that USP39 likely contributes to tumor progression through multiple oncogenic signaling pathways, including DNA repair, mTORC1 signaling, and G2/M checkpoint regulation. A similar molecular signature was observed in PAAD, and our experimental findings demonstrated that elevated USP39 expression significantly enhanced cell proliferation and wound healing capacity in PAAD. Our previous studies have established that USP39 knockdown induces cell cycle arrest at the G2/M phase in non-small cell lung cancer (NSCLC) and colorectal cancer cells, subsequently triggering apoptosis through activation of the p53 pathway [40, 41]. Our results showed that USP39 knockdown reduced the proliferation and migration of pancreatic cancer cells and increased cell death in vitro, suggesting a potential role of USP39 in pancreatic cancer cell growth and survival. Cell death is a complex and precisely regulated biological process, where apoptosis plays a crucial role in maintaining tissue homeostasis [42, 43]. Existing studies have shown that deubiquitinases play important regulatory roles in cell death processes [44,45,46]. Our research demonstrates that USP39 silencing can induce cell death, which is consistent with the reported functions of other deubiquitinase family members such as USP47 [47, 48] and USP7 [49,50,51] in cell survival regulation. Although our study has established the impact of USP39 on cell survival through CCK-8 and colony formation assays, the precise molecular mechanisms underlying these effects warrant further investigation. Future studies should employ Annexin V/PI double staining flow cytometry to characterize the specific mode of cell death, complemented by Western blot analysis of apoptotic markers such as cleaved PARP and activated Caspase-3, thereby elucidating the molecular pathways involved in USP39-mediated cell death. Additionally, examination of markers associated with alternative cell death modalities, including ferroptosis and autophagy, would provide a more comprehensive understanding of USP39’s role within the broader cell death regulatory network.

In addition, we observed that USP39 plays a significant role in the enrichment of immune-related pathways, such as the TNF-α signaling via NF-kB, TGF-β signaling, IL-6/JAK/STAT3 signaling, and IL-2/STAT5 signaling pathways. These immune-related pathways prompted us to further investigate the relationship between USP39 and TIME. Tumor-infiltrating immune cells are critical components of the TIME, influencing both tumor progression and suppression through their dual pro- and anti-tumor effects. These cells serve as valuable biomarkers for predicting prognosis and immunotherapy response across various cancer types. Tumor-infiltrating CD8+ T cells are a predictor of both patient survival and immunotherapeutic response in many types of cancer [52]. In addition, immune checkpoint, TMB, and MSI status have been used as effective markers for the diagnosis and treatment of various cancers [53, 54].In this study, we demonstrated that USP39 expression significantly correlated with immune cell infiltration across multiple cancer types and showed positive associations with checkpoint, suppressor cell, and effector cell scores. Gene co-expression analysis revealed that USP39 exhibits extensive co-expression patterns with diverse immune-related genes, including those encoding MHC molecules, chemokines, immune receptors, and immunoregulatory factors. Furthermore, USP39 expression demonstrated significant correlations with TMB in 16 cancer types and MSI in 11 cancer types, suggesting its potential utility as a predictive biomarker for immunotherapy response. These comprehensive analyses collectively establish USP39 as a key regulator of tumor immunity across multiple cancer types, underscoring its potential as a promising therapeutic target for enhancing anti-tumor immune responses and improving immunotherapy outcomes.

PAAD is one of the most immune-resistant tumor types because of its unique TME and low cancer immunogenicity. The induction of more tumor-infiltrating effector immune cells and reversal of the immunosuppressive microenvironment are central to PAAD treatment [55]. Identifying tumor markers that target immune-related tumors can provide new research directions for the precise treatment of pancreatic cancer. Our research demonstrates a close association between USP39 expression in PAAD and multiple immune cells and immune checkpoint markers. Furthermore, we observed a significant positive correlation between the upregulation of USP39 and immune responses, as well as activation of the adaptive immune system in PAAD. These findings indicate that PAAD patients with high USP39 expression may have a better response to ICB treatment. Interestingly, our analysis further revealed a negative correlation between high USP39 expression and the TGF-β response. TGF-β plays the role of an immunosuppressive factor in the TIME, both in controlling adaptive immunity by suppressing effector T cell production [56], and activating TGF-β signaling has also been associated with poor survival and resistance to the ICB of PD-L1/PD-1 [57]. Therefore, we hypothesize that USP39 likely improves the TIME-suppressive environment by modulating the TGF-β signaling pathway, thereby enhancing the immunotherapeutic response. However, the physiological role of USP39 in immune function requires further experimental evidence. In conclusion, our data suggest that USP39 can be used as a potential biomarker in immunotherapeutic strategies for PAAD.

Chemotherapy is an important component of multimodal therapy for PAAD, and Gemcitabine is considered the global standard of care for postoperative adjuvant chemotherapy. Current studies on PAAD therapeutic targets mainly focus on growth factors and growth factor receptors, and some genes, such as Secreted Protein Acidic and Cysteine Rich (SPARC) [58], Pancreatic and Duodenal Homeobox 1 (PDX1) [59], ATM Serine/Threonine Kinase (ATM) [60], B-Raf Proto-Oncogene (BRAF) [61], Neurotrophic Receptor Tyrosine Kinase (NTRK) and Epidermal Growth Factor Receptor (EGFR) have also been shown to play an important role in the treatment of PAAD [62, 63]. For example, the FDA approved Olaparib, a PARP inhibitor, for first-line maintenance treatment of patients with metastatic pancreatic cancer carrying deleterious gBRCAm [64]. Our previous study showed that USP39 knockdown enhanced cisplatin-induced apoptosis in colon cancer cells by inducing oxidative stress and the DNA damage response [38], and other literature reported similar effects with carboplatin-induced apoptosis in ovarian cancer cells [39]. Interestingly, we observed differential effects of USP39 expression on various anti-tumor drugs in PAAD, which can be primarily categorized into several mechanistic groups. The first group includes classical chemotherapeutic agents such as Bleomycin, Doxorubicin, and Gemcitabine, which primarily target DNA damage and repair pathways. PAAD patients with high USP39 expression showed increased sensitivity to these agents, possibly due to USP39’s involvement in pre-mRNA splicing of DNA damage response genes. In contrast, high USP39 expression correlated with resistance to targeted therapeutic agents, including tyrosine kinase inhibitors (axitinib, gefitinib, sunitinib) and mTOR pathway inhibitors (temsirolimus). This divergent response pattern suggests that USP39 may differentially regulate distinct cellular pathways: while it may enhance cellular sensitivity to DNA-damaging agents, it might also activate alternative survival pathways that contribute to resistance against targeted therapies. This dual role of USP39 in drug response highlights the importance of considering USP39 expression levels when designing therapeutic strategies for PAAD patients. Although the detailed molecular mechanisms between USP39 and these chemotherapeutic agents remain to be fully elucidated, our findings suggest that combination therapies targeting both USP39-dependent and independent pathways might be more effective in overcoming drug resistance in PAAD treatment.

From a clinical perspective, our study underscores the significant potential clinical applications of USP39. First, regarding diagnostic and prognostic utility, USP39 functions as a valuable pan-cancer biomarker, particularly in PAAD, with its elevated expression correlating with advanced tumor stages and poor survival outcomes across multiple cancer types. This positions USP39 as a potential diagnostic and prognostic indicator for cancer patient stratification. Second, concerning immunotherapy response prediction, the significant correlation between USP39 expression and immune cell infiltration, immune checkpoint markers, and immune-related pathways in PAAD suggests that USP39 could serve as a predictive biomarker for immunotherapy response. These findings may guide personalized treatment strategies and improve therapeutic outcomes for PAAD patients. Nevertheless, several limitations of our study should be noted. First, although we observed significant correlations between USP39 and various immune parameters, the detailed molecular mechanisms underlying its regulation of the TIME require further investigation. Second, our findings are primarily based on bioinformatic analyses and limited in vitro experiments, necessitating validation in larger clinical cohorts. Third, we observed discrepancies between Cox regression and log-rank test results regarding USP39’s prognostic value in several cancer types, including PAAD. These inconsistencies likely arise from the inherent methodological differences between statistical approaches, as Cox regression analyzes USP39 expression as a continuous variable, while log-rank tests necessitate dichotomization of expression levels. While more sophisticated analyses incorporating time-dependent Cox regression and additional clinical parameters would be valuable for resolving these discrepancies, such investigations were beyond the scope of our current data resources. Finally, the development of specific therapeutic strategies targeting USP39 warrants further investigation.

Conclusions

In conclusion, our analysis demonstrates that USP39 serves as a potential immune-related biomarker for both diagnosis and prognosis across multiple cancer types, with particular significance in PAAD. The complex interplay between USP39’s roles in tumor progression and immune regulation varies among different cancer types, suggesting that its therapeutic targeting requires careful consideration of cancer-specific contexts. Our findings provide novel insights into the molecular mechanisms by which USP39 modulates the TIME and influences immunotherapeutic outcomes. However, further mechanistic investigations are essential to delineate the cancer type-specific functions of USP39 and to identify which patient populations might benefit from USP39-targeted interventions. Additionally, validation studies in larger clinical cohorts are needed to confirm the prognostic value of USP39 and to better understand how its diverse functions may impact potential therapeutic applications across different cancer subtypes.

Materials and methods

The impact of USP39 on tumorigenesis and prognosis

Data sources

Cancer-related data were downloaded and extracted from The Cancer Genome Atlas (TCGA) database [65], including gene expression data, somatic mutation information, tumor mutational burden (TMB), and microsatellite instability (MSI) across multiple cancer types. Sequencing data from normal human tissues were obtained from the Genotype-Tissue Expression (GTEx) database via the University of California, Santa Cruz (UCSC) platform [66]. Additionally, we used expression data and clinical information from the IMvigor 210 cohort (anti-PD-L1 cohort [67]) to assess the response to immunotherapy. All data were obtained from publicly accessible databases, and no additional ethical approval was required. The abbreviations for cancer types are detailed in Supplementary Table S1.

Differential and survival analysis

To elucidate differential expression patterns, we performed an integrative analysis of TCGA and GTEx datasets across diverse tissue specimens. The DESeq2 R package (version 1.38.3) was employed for differential expression analysis between malignant and normal tissues, utilizing its internal normalization procedures that account for sequencing depth and RNA composition biases [68]. Differentially expressed genes (DEGs) were identified using the following criteria: adjusted P-value (P adj) < 0.05 and|log2 fold change (log2 FC)| > 1 [69]. The prognostic significance of USP39 expression was evaluated across multiple cancer types using univariate Cox regression analysis and Kaplan-Meier survival models. The hazard ratio (HR) and P-value were used to assess the prognostic impact of USP39, where HR > 1 with statistical significance (P < 0.05) indicated USP39 as a risk factor, while HR < 1 with statistical significance suggested USP39 as a protective factor. Multiple survival endpoints were assessed, including overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI). To explore the prognostic value of USP39 in PAAD, we performed Kaplan-Meier survival analyses using the Gene Set Cancer Analysis (GSCA) database [70], with kidney renal clear cell carcinoma (KIRC) and liver hepatocellular carcinoma (LIHC) included as controls for comparison of OS and PFS.

Enrichment analysis between high and low USP39 expression groups

Based on the median expression level of USP39 across different cancer tissues, samples were stratified into high and low USP39 expression groups. Differential expression analysis was performed between these two groups to identify DEGs. Subsequently, Gene Set Enrichment Analysis (GSEA) was performed to identify differentially enriched pathways between these two groups [71], summarizing the potential biological processes influenced by USP39 across various cancer types. We used the R package “clusterProfiler” to conduct GSEA and determine pathway enrichment scores [72], with pathways having P < 0.05 considered statistically significant [73]. We utilized GSCALite (https://guolab.wchscu.cn/GSCA/, accessed on 10th August 2024 ) to evaluate the activity of cancer-related pathways [70]. These pathways included apoptosis, cell cycle, DNA damage response, epithelial-mesenchymal transition (EMT), estrogen receptor (ER), androgen receptor (AR), TSC complex subunit 1-mechanistic target of rapamycin kinase (TSC-mTOR), receptor tyrosine kinase (RTK), Ras/mitogen-activated protein kinase (MAPK), and phosphatidylinositol 3-kinase (PI3K)/protein kinase B (AKT) signaling pathways. Based on the pathway activity assessment method from the GSCA database, the principle is to analyze the correlation between USP39 expression and various pathways in each cancer type, calculating the correlation coefficients. These results are then adjusted for statistical significance using false discovery rate (FDR) correction (FDR ≤ 0.05). When displaying the results, the focus is on presenting the percentage of cancer types in which USP39 expression potentially impacts pathway activity. The findings were further validated using the CAMOIP database (https://www.camoip.net/, accessed on 10th August 2024) [74], specifically focusing on cell cycle, G2M checkpoints, and P53-independent DNA damage response pathways in PAAD. Additionally, pathway activity scores were compared between USP39-high and USP39-low expression groups based on the PAAD data in the CAMOIP database.

The effect of USP39 on proliferation and invasion in PAAD: in vitro experiments

Cell lines, cell culture, and cell transfection

The human pancreatic cancer cell lines PANC-1 and MIA PaCa-2 were obtained from the National Infrastructure of Cell Line Resources (NICR, China). PANC-1 and MIA PaCa-2 cells were grown in Dulbeccos modified Eagle’s medium (DMEM) with 10% fetal bovine serum (FBS) and cultured at 37 °C in a 5% CO2 incubator. The short hairpin RNA (shRNA) designed for USP39 (shUSP39) was induced using a lentiviral expression system (pMDL: pVSVG: pRev = 5:3:2). Stable cell lines were constructed as previously described [20]. shUSP39 Plasmids (GV-248-GFP-Puro) were purchased from GeneChem (Shanghai, China), the detailed sequences were as follows: shControl: 5′-TTCTCCGAACGTGTCACGT − 3′; shUSP39: 5′-CGGGTATTGTGGGACTGAA-3′.

Colony formation and 5-Ethynyl-2’-deoxyuridineassays

Colony formation and 5-Ethynyl-2’-deoxyuridine (EdU)assays were used to analyze cell proliferation, as described previously [75]. After transfection, 500 cells of either PANC-1 or MIA PaCa-2 cell line were inoculated onto 6-well plates and incubated for 2 weeks for the cell colony formation assay. The resulting colonies were then manually counted to quantify clonogenic capacity. For the EdU assay, PANC-1 and MIAPaCa-2 cells were seeded in 96-well plates at a density of 8 × 10 3 cells/well. The Cell-Light EdU DNA Cell Proliferation Kit (RiboBio, Guangzhou, China) was used according to the manufacturer’s instructions.

Wound healing assay

The wound healing assays were performed using a method previously described [20]. In this study, PANC1-control and PANC-1-shUSP39 cells were separately seeded in 6-well plates (approximately 5 × 105/well). After culturing for 12 h, a 10 µL pipette tip was used to scratch the cell layer and form a wound. The cells were cultured in a medium supplemented with 1% FBS. The closure of the wound was observed at specific time intervals (0, 24, and 48 h) using a 100 × field microscope, and the speed of closure was quantified using ImageJ2x software.

Cell death assay

The cell death assays were conducted according to Yuan et al. [41]. Stable transfected cells were seeded at a density of 6 × 103 cells per well in 96-well plates. After culturing for 24 h, the cells were rinsed with PBS and incubated with 100 µL of Hoechst33342(1 µg/mL) and propidium iodide solutions (0.5 µg/mL). The cells were then kept in the dark at 37 °C for 15 min before being observed using fluorescence microscopy (Carl Zeiss, Oberkochen, Germany).

Western blot analysis

The western blot analyses were conducted as described previously [20]. The extraction of proteins utilized radioimmunoprecipitation assay buffer, and quantification was carried out using a bicinchoninic acid (BCA) protein quantification kit (Thermo Fisher Scientific, Cat. No. 23227, MA, USA). We separated protein lysates (15–30 µg) using 10% sodium dodecyl-sulfate polyacrylamide gel electrophoresis and then transferred them onto a polyvinylidene fluoride membrane (Merck Millipore Ltd., Germany). Protein expression was assessed in the presence of rabbit antibodies against β-actin (1:40000, Sigma, code: A3854, St. Louis, MO, USA) and USP39 (1:2000; Abcam, code: ab131332, Cambridge, UK). The experiments were replicated three times.

Evaluating the relationship between USP39 expression and TME

We evaluated the correlation between USP39 expression and immune cell infiltration levels using multiple online databases: the TIMER web tool (http://timer.cistrome.org/, accessed on 10th August 2024) [76], Microenvironment Cell Populations (MCP)-counter [77], and the Estimating the Proportions of Immune and Cancer cells (EPIC) algorithm [78]. The analysis covered key components of the tumor microenvironment, including immune cell populations and stromal elements. The immune cell populations analyzed were B cells, activated myeloid dendritic cells (DCs), endothelial cells, eosinophils, macrophages, mast cells, monocytes, neutrophils, NK cells, γδT cells, T helper (Th) cells, CD4+ T cells, Tregs, and CD8+ T cells. Additionally, cancer-associated fibroblasts (CAFs) were examined as crucial stromal components that interact with these immune cells. Immunophenoscore (IPS) analysis was conducted using the method reported by Charoentong et al. [79]. Correlation analysis was performed to assess the relationship between USP39 and IPS scores across human cancers, including immune checkpoints (CPs), suppressor cells (SCs), major histocompatibility complexes (MHCs), average Z-scores (AZs), and effector cells (ECs). We collected 150 immunomodulators, including chemokines, immune-stimulators, MHC, receptors, and immune-inhibitors from the study by Charoentong et al. [79].

Evaluating the impact of USP39 expression on the drug sensitivity of anti-tumor

Drug sensitivity analysis was performed using the R package “pRRophetic” based on the Genomics of Drug Sensitivity in Cancer (GDSC) database [80]. At the pan-cancer level, we first assessed the correlation between USP39 expression and drug sensitivity, with results visualized through bubble plots. Subsequently, focusing on PAAD-specific drug sensitivity data, we investigated differential sensitivities to common anticancer agents between high and low USP39 expression groups, with the findings presented using box plots.

Statistical analysis

Statistical analyses were performed using R studio (version 4.1.1). Cox regression analysis was employed to determine HR, 95% confidence intervals (CI), and independent prognostic factors. Survival analyses were conducted using the Kaplan-Meier method, with statistical significance assessed by the log-rank test. For multiple group comparisons, Kruskal-Wallis or one-way ANOVA tests were applied as appropriate. Correlations were evaluated using Spearman’s or Pearson’s correlation coefficients, with R > 0.1 considered significant. All in vitro experiments were performed with at least three independent biological replicates. Quantitative data are presented as mean ± standard deviation (SD) and were analyzed using GraphPad Prism (version 8.0). Prior to statistical analysis, the normality of data distribution was assessed using the Shapiro-Wilk test. For normally distributed data, comparisons between two groups were performed using unpaired two-tailed Student’s t-tests. Statistical significance was defined as P < 0.05.

Data availability

TCGA raw data were obtained from both the official website (https://portal.gdc.cancer.gov/analysis_page?app=Downloads, accessed on 15th June 2024 ) and the UCSC Xena browser (https://xenabrowser.net/hub/, accessed on 15th June 2024). Normal pancreatic tissue sequencing data were downloaded from the GTEx database (https://www.gtexportal.org/home/downloads/adult-gtex/bulk_tissue_expression, accessed on 15th June 2024), where matching tissues were selected based on the V8 data catalog. Additional data materials can be obtained by contacting the first author or the corresponding author. All data used in this study are publicly accessible and were used in accordance with the relevant database usage policies.

References

  1. Gao H, Yin J, Ji C, Yu X, Xue J, Guan X, Zhang S, Liu X, Xing F. Targeting ubiquitin specific proteases (USPs) in cancer immunotherapy: from basic research to preclinical application. J Experimental Clin cancer Research: CR. 2023;42(1):225.

    Article  PubMed Central  Google Scholar 

  2. Gao H, Xi Z, Dai J, Xue J, Guan X, Zhao L, Chen Z, Xing F. Drug resistance mechanisms and treatment strategies mediated by Ubiquitin-Specific proteases (USPs) in cancers: new directions and therapeutic options. Mol Cancer. 2024;23(1):88.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Li KQ, Bai X, Ke AT, Ding SQ, Zhang CD, Dai DQ. Ubiquitin-specific proteases: from biological functions to potential therapeutic applications in gastric cancer. Biomed Pharmacother. 2024;173:116323.

    Article  CAS  PubMed  Google Scholar 

  4. Young MJ, Hsu KC, Lin TE, Chang WC, Hung JJ. The role of ubiquitin-specific peptidases in cancer progression. J Biomed Sci. 2019;26(1):42.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Wu W, Huang C. SUMOylation and desumoylation: prospective therapeutic targets in cancer. Life Sci. 2023;332:122085.

    Article  CAS  PubMed  Google Scholar 

  6. Xiao X, Shi J, He C, Bu X, Sun Y, Gao M, Xiang B, Xiong W, Dai P, Mao Q, et al. ERK and USP5 govern PD-1 homeostasis via deubiquitination to modulate tumor immunotherapy. Nat Commun. 2023;14(1):2859.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Yang H, Zhang X, Lao M, Sun K, He L, Xu J, Duan Y, Chen Y, Ying H, Li M, et al. Targeting ubiquitin-specific protease 8 sensitizes anti-programmed death-ligand 1 immunotherapy of pancreatic cancer. Cell Death Differ. 2023;30(2):560–75.

    Article  CAS  PubMed  Google Scholar 

  8. Nie H, Yu Y, Wang F, Huang X, Wang H, Wang J, Tao M, Ning Y, Zhou J, Zhao Q, et al. Comprehensive analysis of the relationship between ubiquitin-specific protease 21 (USP21) and prognosis, tumor microenvironment infiltration, and therapy response in colorectal cancer. Cancer Immunol Immunotherapy: CII. 2024;73(8):156.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Li J, Yuan S, Norgard RJ, Yan F, Yamazoe T, Blanco A, Stanger BZ. Tumor Cell-Intrinsic USP22 suppresses antitumor immunity in pancreatic cancer. Cancer Immunol Res. 2020;8(3):282–91.

    Article  CAS  PubMed  Google Scholar 

  10. Wang Y, Zhou D, Kong Y, Yang Q, Ding Y, Wang W. USPs in Pancreatic Ductal Adenocarcinoma: A Comprehensive Bioinformatic Analysis of Expression, Prognostic Significance, and Immune Infiltration. BioMed research international 2022, 2022:6109052.

  11. Huang X, Zhang X, Bai X, Liang T. Blocking PD-L1 for anti-liver cancer immunity: USP22 represents a critical cotarget. Cell Mol Immunol. 2020;17(7):677–9.

    Article  CAS  PubMed  Google Scholar 

  12. Wang Z, Kang W, Li O, Qi F, Wang J, You Y, He P, Suo Z, Zheng Y, Liu HM. Abrogation of USP7 is an alternative strategy to downregulate PD-L1 and sensitize gastric cancer cells to T cells killing. Acta Pharm Sin B. 2021;11(3):694–707.

    Article  CAS  PubMed  Google Scholar 

  13. Dufner A, Kisser A, Niendorf S, Basters A, Reissig S, Schonle A, Aichem A, Kurz T, Schlosser A, Yablonski D, et al. The ubiquitin-specific protease USP8 is critical for the development and homeostasis of T cells. Nat Immunol. 2015;16(9):950–60.

    Article  CAS  PubMed  Google Scholar 

  14. Kuang Z, Liu X, Zhang N, Dong J, Sun C, Yin M, Wang Y, Liu L, Xiao D, Zhou X, et al. USP2 promotes tumor immune evasion via deubiquitination and stabilization of PD-L1. Cell Death Differ. 2023;30(10):2249–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. van Leuken RJ, Luna-Vargas MP, Sixma TK, Wolthuis RM, Medema RH. Usp39 is essential for mitotic spindle checkpoint integrity and controls mRNA-levels of Aurora B. Cell Cycle. 2008;7(17):2710–9.

    Article  PubMed  Google Scholar 

  16. Ding K, Ji J, Zhang X, Huang B, Chen A, Zhang D, Li X, Wang X, Wang J. RNA splicing factor USP39 promotes glioma progression by inducing TAZ mRNA maturation. Oncogene. 2019;38(37):6414–28.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Zheng J, Wu S, Tang M, Xi S, Wang Y, Ren J, Luo H, Hu P, Sun L, Du Y, et al. USP39 promotes hepatocellular carcinogenesis through regulating alternative splicing in Cooperation with SRSF6/HNRNPC. Cell Death Dis. 2023;14(10):670.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Wang W, Lei Y, Zhang G, Li X, Yuan J, Li T, Zhong W, Zhang Y, Tan X, Song G. USP39 stabilizes beta-catenin by deubiquitination and suppressing E3 ligase TRIM26 pre-mRNA maturation to promote HCC progression. Cell Death Dis. 2023;14(1):63.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Peng Y, Guo J, Sun T, Fu Y, Zheng H, Dong C, Xiong S. USP39 serves as a deubiquitinase to stabilize STAT1 and sustains type I IFN-Induced antiviral immunity. J Immunol. 2020;205(11):3167–78.

    Article  CAS  PubMed  Google Scholar 

  20. Li X, Yuan J, Song C, Lei Y, Xu J, Zhang G, Wang W, Song G. Deubiquitinase USP39 and E3 ligase TRIM26 balance the level of ZEB1 ubiquitination and thereby determine the progression of hepatocellular carcinoma. Cell Death Differ. 2021;28(8):2315–32.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Singh S, Hassan D, Aldawsari HM, Molugulu N, Shukla R, Kesharwani P. Immune checkpoint inhibitors: a promising anticancer therapy. Drug Discov Today. 2020;25(1):223–9.

    Article  CAS  PubMed  Google Scholar 

  22. Sang M, Ge J, Ge J, Tang G, Wang Q, Wu J, Mao L, Ding X, Zhou X. Immune regulatory genes impact the hot/cold tumor microenvironment, affecting cancer treatment and patient outcomes. Front Immunol. 2024;15:1382842.

    Article  CAS  PubMed  Google Scholar 

  23. Zhang Y, Zu Z, Xu H, Weng S, Ren Y, Cheng Q, Luo P, Zhang J, Liu Z, Han X. Co-alteration of Myc and RTK-RAS pathways defines a liver-metastatic propensity and immune-cold subgroup of pancreatic adenocarcinoma. Genes Dis. 2024;11(3):100993.

    Article  CAS  PubMed  Google Scholar 

  24. Hosein AN, Dangol G, Okumura T, Roszik J, Rajapakshe K, Siemann M, Zaid M, Ghosh B, Monberg M, Guerrero PA, et al. Loss of Rnf43 accelerates Kras-Mediated neoplasia and remodels the tumor immune microenvironment in pancreatic adenocarcinoma. Gastroenterology. 2022;162(4):1303–e13181318.

    Article  CAS  PubMed  Google Scholar 

  25. Sun Y, Yao L, Man C, Gao Z, He R, Fan Y. Development and validation of cuproptosis-related LncRNAs associated with pancreatic cancer immune microenvironment based on single-cell. Front Immunol. 2023;14:1220760.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Binnewies M, Roberts EW, Kersten K, Chan V, Fearon DF, Merad M, Coussens LM, Gabrilovich DI, Ostrand-Rosenberg S, Hedrick CC, et al. Understanding the tumor immune microenvironment (TIME) for effective therapy. Nat Med. 2018;24(5):541–50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Hu J, Yu A, Othmane B, Qiu D, Li H, Li C, Liu P, Ren W, Chen M, Gong G, et al. Siglec15 shapes a non-inflamed tumor microenvironment and predicts the molecular subtype in bladder cancer. Theranostics. 2021;11(7):3089–108.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Xu L, Deng C, Pang B, Zhang X, Liu W, Liao G, Yuan H, Cheng P, Li F, Long Z, et al. TIP: A web server for resolving tumor immunophenotype profiling. Cancer Res. 2018;78(23):6575–80.

    Article  CAS  PubMed  Google Scholar 

  29. Chen DS, Mellman I. Oncology Meets immunology: the cancer-immunity cycle. Immunity. 2013;39(1):1–10.

    Article  PubMed  Google Scholar 

  30. Sun D, Guan X, Moran AE, Wu LY, Qian DZ, Schedin P, Dai MS, Danilov AV, Alumkal JJ, Adey AC, et al. Identifying phenotype-associated subpopulations by integrating bulk and single-cell sequencing data. Nat Biotechnol. 2022;40(4):527–38.

    Article  CAS  PubMed  Google Scholar 

  31. Robert C, Schachter J, Long GV, Arance A, Grob JJ, Mortier L, Daud A, Carlino MS, McNeil C, Lotem M, et al. Pembrolizumab versus ipilimumab in advanced melanoma. N Engl J Med. 2015;372(26):2521–32.

    Article  CAS  PubMed  Google Scholar 

  32. Weber JS, D’Angelo SP, Minor D, Hodi FS, Gutzmer R, Neyns B, Hoeller C, Khushalani NI, Miller WH Jr., Lao CD, et al. Nivolumab versus chemotherapy in patients with advanced melanoma who progressed after anti-CTLA-4 treatment (CheckMate 037): a randomised, controlled, open-label, phase 3 trial. Lancet Oncol. 2015;16(4):375–84.

    Article  CAS  PubMed  Google Scholar 

  33. Makarova OV, Makarov EM, Luhrmann R. The 65 and 110 kda SR-related proteins of the U4/U6.U5 tri-snRNP are essential for the assembly of mature spliceosomes. EMBO J. 2001;20(10):2553–63.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Lygerou Z, Christophides G, Seraphin B. A novel genetic screen for SnRNP assembly factors in yeast identifies a conserved protein, Sad1p, also required for pre-mRNA splicing. Mol Cell Biol. 1999;19(3):2008–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Wang X, Yu Q, Huang L, Yu P. Lentivirus-mediated Inhibition of USP39 suppresses the growth of gastric cancer cells via PARP activation. Mol Med Rep. 2016;14(1):301–6.

    Article  PubMed  Google Scholar 

  36. Lin Z, Xiong L, Lin Q. Ubiquitin-specific protease 39 is overexpressed in human lung cancer and promotes tumor cell proliferation in vitro. Mol Cell Biochem. 2016;422(1–2):97–107.

    Article  CAS  PubMed  Google Scholar 

  37. Wang H, Ji X, Liu X, Yao R, Chi J, Liu S, Wang Y, Cao W, Zhou Q. Lentivirus-mediated Inhibition of USP39 suppresses the growth of breast cancer cells in vitro. Oncol Rep. 2013;30(6):2871–7.

    Article  CAS  PubMed  Google Scholar 

  38. Yuan J, Li X, Zhang Y, Zhang G, Cheng W, Wang W, Lei Y, Song G. USP39 attenuates the antitumor activity of cisplatin on colon cancer cells dependent on p53. Cell Biol Toxicol. 2023;39(5):1995–2010.

    Article  CAS  PubMed  Google Scholar 

  39. Wang L, Chen T, Li X, Yan W, Lou Y, Liu Z, Chen H, Cui Z. USP39 promotes ovarian cancer malignant phenotypes and carboplatin chemoresistance. Int J Oncol. 2019;55(1):277–88.

    CAS  PubMed  Google Scholar 

  40. Yuan J, Zhang G, Li X, Ma Q, Cheng W, Wang W, Zhang B, Hu T, Song G. Knocking down USP39 inhibits the growth and metastasis of Non-Small-Cell lung cancer cells through activating the p53 pathway. Int J Mol Sci 2020, 21(23).

  41. Yuan J, Li X, Zhang G, Cheng W, Wang W, Lei Y, Ma Q, Song G. USP39 mediates p21-dependent proliferation and neoplasia of colon cancer cells by regulating the p53/p21/CDC2/cyclin B1 axis. Mol Carcinog. 2021;60(4):265–78.

    Article  CAS  PubMed  Google Scholar 

  42. Wu N, Zheng W, Zhou Y, Tian Y, Tang M, Feng X, Ashrafizadeh M, Wang Y, Niu X, Tambuwala M, et al. Autophagy in aging-related diseases and cancer: principles, regulatory mechanisms and therapeutic potential. Ageing Res Rev. 2024;100:102428.

    Article  CAS  PubMed  Google Scholar 

  43. Yang F, Liu W, Huang Y, Yang S, Shao Z, Cai X, Xiong L. Regulated cell death: implications for intervertebral disc degeneration and therapy. J Orthop Translat. 2022;37:163–72.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Bhattacharya S, Ghosh MK. Cell death and deubiquitinases: perspectives in cancer. Biomed Res Int 2014, 2014:435197.

  45. Gao L, Zhang W, Shi XH, Chang X, Han Y, Liu C, Jiang Z, Yang X. The mechanism of linear ubiquitination in regulating cell death and correlative diseases. Cell Death Dis. 2023;14(10):659.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Zhou Z, Song X, Kang R, Tang D. The emerging role of deubiquitinases in cell death. Biomolecules 2022, 12(12).

  47. Liu S, Tao Y, Wu S, Lin J, Fu S, Lu J, Zhang J, Fu B, Zhang E, Xu J, et al. Sanguinarine chloride induces ferroptosis by regulating ROS/BACH1/HMOX1 signaling pathway in prostate cancer. Chin Med. 2024;19(1):7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Naghavi L, Schwalbe M, Ghanem A, Naumann M. Deubiquitinylase USP47 promotes rela phosphorylation and survival in gastric cancer cells. Biomedicines 2018, 6(2).

  49. Kategaya L, Di Lello P, Rougé L, Pastor R, Clark KR, Drummond J, Kleinheinz T, Lin E, Upton JP, Prakash S, et al. USP7 small-molecule inhibitors interfere with ubiquitin binding. Nature. 2017;550(7677):534–8.

    Article  CAS  PubMed  Google Scholar 

  50. Lee SH, Chung KC. USP7 attenuates Endoplasmic reticulum stress-induced apoptotic cell death through deubiquitination and stabilization of FBXO7. PLoS ONE. 2023;18(10):e0290371.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Lim CH, Fang XQ, Kang H, Oh T, Lee S, Kim YS, Lim JH. ER Stress-Activated HSF1 governs cancer cell resistance to USP7 Inhibitor-Based chemotherapy through the PERK pathway. Int J Mol Sci 2024, 25(5).

  52. Jansen CS, Prokhnevska N, Master VA, Sanda MG, Carlisle JW, Bilen MA, Cardenas M, Wilkinson S, Lake R, Sowalsky AG, et al. An intra-tumoral niche maintains and differentiates stem-like CD8 T cells. Nature. 2019;576(7787):465–70.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Li L, Feng Q, Wang X. PreMSIm: an R package for predicting microsatellite instability from the expression profiling of a gene panel in cancer. Comput Struct Biotechnol J. 2020;18:668–75.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Vrana D, Matzenauer M, Neoral C, Aujesky R, Vrba R, Melichar B, Rusarova N, Bartouskova M, Jankowski J. From tumor immunology to immunotherapy in gastric and esophageal cancer. Int J Mol Sci 2018, 20(1).

  55. Wu J, Cai J. Dilemma and challenge of immunotherapy for pancreatic cancer. Dig Dis Sci. 2021;66(2):359–68.

    Article  PubMed  Google Scholar 

  56. Tauriello DVF, Sancho E, Batlle E. Overcoming TGFbeta-mediated immune evasion in cancer. Nat Rev Cancer. 2022;22(1):25–44.

    Article  CAS  PubMed  Google Scholar 

  57. Mariathasan S, Turley SJ, Nickles D, Castiglioni A, Yuen K, Wang Y, Kadel EE III, Koeppen H, Astarita JL, Cubas R, et al. TGFbeta attenuates tumour response to PD-L1 Blockade by contributing to exclusion of T cells. Nature. 2018;554(7693):544–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Sinn M, Sinn BV, Striefler JK, Lindner JL, Stieler JM, Lohneis P, Bischoff S, Blaker H, Pelzer U, Bahra M, et al. SPARC expression in resected pancreatic cancer patients treated with gemcitabine: results from the CONKO-001 study. Ann Oncol. 2014;25(5):1025–32.

    Article  CAS  PubMed  Google Scholar 

  59. Liu S, Wang XP, Brunicardi FC. Enhanced cytotoxicity of RIPTK gene therapy of pancreatic cancer via PDX-1 co-delivery. J Surg Res. 2007;137(1):1–9.

    Article  CAS  PubMed  Google Scholar 

  60. Zhang Q, Green MD, Lang X, Lazarus J, Parsels JD, Wei S, Parsels LA, Shi J, Ramnath N, Wahl DR, et al. Inhibition of ATM increases interferon signaling and sensitizes pancreatic cancer to immune checkpoint Blockade therapy. Cancer Res. 2019;79(15):3940–51.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Kinsey CG, Camolotto SA, Boespflug AM, Guillen KP, Foth M, Truong A, Schuman SS, Shea JE, Seipp MT, Yap JT, et al. Protective autophagy elicited by RAF–>MEK–>ERK Inhibition suggests a treatment strategy for RAS-driven cancers. Nat Med. 2019;25(4):620–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Reiss KA, Vonderheide RH. Alternatives to perpetual chemotherapy for metastatic pancreatic cancer. Clin Cancer Res. 2021;27(13):3540–2.

    Article  CAS  PubMed  Google Scholar 

  63. Verma HK, Kampalli PK, Lakkakula S, Chalikonda G, Bhaskar L, Pattnaik S. A retrospective look at Anti-EGFR agents in pancreatic cancer therapy. Curr Drug Metab. 2019;20(12):958–66.

    Article  CAS  PubMed  Google Scholar 

  64. Golan T, Hammel P, Reni M, Van Cutsem E, Macarulla T, Hall MJ, Park JO, Hochhauser D, Arnold D, Oh DY, et al. Maintenance Olaparib for germline BRCA-Mutated metastatic pancreatic cancer. N Engl J Med. 2019;381(4):317–27.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Hutter C, Zenklusen JC. The cancer genome atlas: creating lasting value beyond its data. Cell. 2018;173(2):283–5.

    Article  CAS  PubMed  Google Scholar 

  66. Perez G, Barber GP, Benet-Pages A, Casper J, Clawson H, Diekhans M, Fischer C, Gonzalez JN, Hinrichs AS, Lee CM et al. The UCSC genome browser database: 2025 update. Nucleic Acids Res 2024.

  67. Necchi A, Joseph RW, Loriot Y, Hoffman-Censits J, Perez-Gracia JL, Petrylak DP, Derleth CL, Tayama D, Zhu Q, Ding B, et al. Atezolizumab in platinum-treated locally advanced or metastatic urothelial carcinoma: post-progression outcomes from the phase II IMvigor210 study. Ann Oncol. 2017;28(12):3044–50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Love MI, Huber W, Anders S. Moderated Estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550.

    Article  PubMed  PubMed Central  Google Scholar 

  69. Feng Z, Gao L, Lu Y, He X, Xie J. The potential contribution of aberrant cathepsin K expression to gastric cancer pathogenesis. Discover Oncol. 2024;15(1):218.

    Article  CAS  Google Scholar 

  70. Liu CJ, Hu FF, Xie GY, Miao YR, Li XW, Zeng Y, Guo AY. GSCA: an integrated platform for gene set cancer analysis at genomic, Pharmacogenomic and Immunogenomic levels. Brief Bioinform 2023, 24(1).

  71. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545–50.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Wu T, Hu E, Xu S, Chen M, Guo P, Dai Z, Feng T, Zhou L, Tang W, Zhan L, et al. ClusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innov (Camb). 2021;2(3):100141.

    CAS  Google Scholar 

  73. Feng Z, Qiao R, Ren Z, Hou X, Feng J, He X, Chen D. Could CTSK and COL4A2 be specific biomarkers of poor prognosis for patients with gastric cancer in Asia?-a microarray analysis based on regional population. J Gastrointest Oncol. 2020;11(2):386–401.

    Article  PubMed  PubMed Central  Google Scholar 

  74. Lin A, Qi C, Wei T, Li M, Cheng Q, Liu Z, Luo P, Zhang J. CAMOIP: a web server for comprehensive analysis on multi-omics of immunotherapy in pan-cancer. Brief Bioinform 2022, 23(3).

  75. Xu M, Chen X, Lin K, Zeng K, Liu X, Pan B, Xu X, Xu T, Hu X, Sun L, et al. The long noncoding RNA SNHG1 regulates colorectal cancer cell growth through interactions with EZH2 and miR-154-5p. Mol Cancer. 2018;17(1):141.

    Article  PubMed  PubMed Central  Google Scholar 

  76. Li T, Fu J, Zeng Z, Cohen D, Li J, Chen Q, Li B, Liu XS. TIMER2.0 for analysis of tumor-infiltrating immune cells. Nucleic Acids Res. 2020;48(W1):W509–14.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Becht E, Giraldo NA, Lacroix L, Buttard B, Elarouci N, Petitprez F, Selves J, Laurent-Puig P, Sautes-Fridman C, Fridman WH, et al. Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biol. 2016;17(1):218.

    Article  PubMed  PubMed Central  Google Scholar 

  78. Racle J, Gfeller D. EPIC: A tool to estimate the proportions of different cell types from bulk gene expression data. Methods Mol Biol. 2020;2120:233–48.

    Article  CAS  PubMed  Google Scholar 

  79. Charoentong P, Finotello F, Angelova M, Mayer C, Efremova M, Rieder D, Hackl H, Trajanoski Z. Pan-cancer Immunogenomic analyses reveal Genotype-Immunophenotype relationships and predictors of response to checkpoint Blockade. Cell Rep. 2017;18(1):248–62.

    Article  CAS  PubMed  Google Scholar 

  80. Yang W, Soares J, Greninger P, Edelman EJ, Lightfoot H, Forbes S, Bindal N, Beare D, Smith JA, Thompson IR, et al. Genomics of drug sensitivity in cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res. 2013;41(Database issue):D955–961.

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We gratefully acknowledge the teams who developed and maintain the public databases used in this study. We also thank all data contributors who made their original datasets publicly available, enabling this research. Their commitment to open data sharing advances scientific discovery in this field. We thank co-authors contributions in this manuscript.

Funding

This work was supported by National Natural Science Foundation of China (Grant numbers: 82203638 and 82100661); Shenzhen Key Medical Discipline Construction Fund & Sanming Project of Medicine in Shenzhen (SZSM202111002); Shenzhen Science and Technology Innovation Commission (Grant number: RCBS20200714114958333). Medical-Engineering Interdisciplinary Research Foundation of Shenzhen University (2023YG019).

Author information

Authors and Affiliations

  1. Department of General Surgery, Institute of Precision Diagnosis and Treatment of Digestive System Tumors, Guangdong Provincial Key Laboratory of Chinese Medicine Ingredients and Gut Microbiomics, Carson International Cancer Center, Shenzhen University General Hospital, Shenzhen University, Shenzhen, Guangdong, 518055, China

    Jiahui Yuan, Xianbin Zhang & Peng Gong

  2. CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China

    Beibei Xu

  3. Xiamen Key Laboratory for Tumor Metastasis, Cancer Research School of Medicine, Xiamen University, Xiamen, 361102, China

    Yongcheng Su

  4. Department of Gastroenterology, Changhai Hospital, Naval Medical University, Shanghai, 200433, China

    Pingping Zhang

Authors
  1. Jiahui Yuan
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Beibei Xu
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Yongcheng Su
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Pingping Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Xianbin Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Peng Gong
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

J.Y., B.X. and P.G. conceived and designed this study. J.Y., B.X., Y.S., and P.Z. analyzed and validated the data. J.Y. and B.X. wrote and edited this draft. X.Z. and P.G. were responsible for supervising this project. All authors have reviewed this manuscript and approved this submitted version.

Corresponding author

Correspondence to Peng Gong.

Ethics declarations

Ethics approval and consent to participate

This study analyzed publicly available data from public databases. All participants provided informed consent through protocols approved by their respective institutional review boards. No additional ethics approval was required for this secondary analysis.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Supplementary Material 2

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yuan, J., Xu, B., Su, Y. et al. Identification of USP39 as a prognostic and predictive biomarker for determining the response to immunotherapy in pancreatic cancer. BMC Cancer 25, 758 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12885-025-14096-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12885-025-14096-x

Keywords

BMC Cancer

ISSN: 1471-2407

Contact us