Purine Nucleoside Phosphorylase (PNP) as a Biomarker and Therapeutic Target in Muscle-Invasive Bladder Cancer

Abstract

Background:

This study investigates the potential of purine nucleoside phosphorylase (PNP) as a biomarker and therapeutic target in muscle-invasive bladder cancer (MIBC). We aimed to explore PNP’s expression, prognostic value, and role in metabolic pathways, along with its association with gene mutations.

Methods:

We conducted multi-omics analyses using data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and other public databases to evaluate PNP expression across MIBC samples and its prognostic impact through Kaplan-Meier and Cox regression analyses. Functional enrichment and gene set variation analysis (GSVA) were performed to identify PNP-related pathways. In addition, in vitro siRNA knockdown experiments were carried out to assess PNP’s influence on MIBC cell proliferation.

Results:

Our findings revealed that PNP is significantly overexpressed in MIBC tissues and serves as an independent prognostic factor, correlating with poor clinical outcomes across multiple cohorts (TCGA: hazard ratio [HR] > 1.3, P < .05; GSE48075: HR > 1.5, P = .07; GSE169455: HR > 2.8, P < .001). Functional enrichment analysis identified PNP’s involvement in various metabolic pathways. Furthermore, we observed a high frequency of RB1 mutations in the PNP-high expression group. Based on this observation, we hypothesize that patients harboring RB1 mutations may benefit from PNP-targeted therapy. In vitro experiments demonstrated that PNP knockdown significantly reduces MIBC cell proliferation.

Conclusion:

This study underscores PNP’s role as a promising biomarker and therapeutic target in MIBC.

Introduction

Bladder cancer is the most common malignancy of the urinary system. ¹ In the United States, it ranks as the sixth most frequently diagnosed cancer and the ninth leading cause of cancer-related mortality. ² The incidence in men is significantly higher compared to women.^1,2 Muscle-invasive bladder cancer (MIBC), a particularly aggressive form of urothelial carcinoma (UC), is marked by the tumor’s invasion into the detrusor muscle layer of the bladder.^3,4 This form of cancer presents considerable heterogeneity and variability in prognosis, making treatment challenging.^5,6

Owing to the heterogeneity of MIBC, therapeutic interventions such as surgical resection, chemotherapy, and immune checkpoint blockade (ICB) often provide limited clinical benefits in certain patient populations, with suboptimal response rates and inevitable adverse effects. ⁷ , ⁸ Overall, the 5-year survival rate for MIBC remains approximately 40% to 60%. ⁷ Consequently, clinicians increasingly rely on predictive biomarkers to develop personalized treatment strategies.

Purine nucleoside phosphorylases (PNPs, EC 2.4.2.1) are crucial enzymes in the purine metabolic pathways. The gene encoding human PNP (hPNP), located on chromosome 14q13, spans 9 kb. ⁹ The enzyme’s primary function is to catalyze the conversion of nucleosides into their nitrogenous bases. ¹⁰ Purine nucleoside phosphorylase facilitates the degradation of (2′-deoxy) nucleosides derived from 6-oxopurines into corresponding purine bases and (2-deoxy) ribose-1-phosphate. It plays a key role in the nucleotide salvage pathway, which is present in a variety of tissues. Several studies have shown that abnormal PNP expression influences the progression of certain human diseases, such as T-acute lymphoblastic leukemia. ¹¹ Furthermore, it has been reported that PNP is highly expressed in prostate cancer tissues, where it promotes the proliferation, migration, and invasion of prostate cancer cells. ¹² However, its role in other solid tumors, particularly in MIBC, remains largely unexplored.

Materials and methods

Data collection and processing

A summary of the datasets is provided in Supplemental Table I. For the The Cancer Genome Atlas (TCGA) pan-cancer analysis, RNA sequencing data were obtained from the UCSC Xena data portal (http://xena.ucsc.edu/). The TCGA-BLCA cohort was also sourced from the UCSC Xena data portal. In addition, datasets GSE48075, GSE169455, and GSE69795 were downloaded from the Gene Expression Omnibus (GEO). The inclusion criteria for the MIBC cohorts were as follows: Bladder cancer patients (with duplicate samples removed); T stage ⩾ 2; follow-up duration greater than 30 days. Using these criteria, the following samples were included: TCGA cohort: 363 samples, GSE48075 cohort: 72 samples, GSE169455 cohort: 80 samples. In addition, RNA-seq data from different cohorts were analyzed only after removing batch effects. The IMvigor210 cohort, which involved patients treated with anti-programmed death ligand 1 (PD-L1) immunotherapy, was accessed through http://research-pub.Gene.com/imvigor210corebiologies/. ¹³ Immunohistochemistry (IHC) data were retrieved from the Human Protein Atlas (HPA).

Prognostic value assessment

We employed the R package “survival” ¹⁴ to conduct univariate Cox regression and Kaplan-Meier (KM) analyses, aiming to evaluate the prognostic relevance of gene expression levels. Specifically, Cox regression models were built using the coxph function from this R package to investigate the association between gene expression and clinical outcomes. The significance of the KM survival curves was determined through the log-rank test.

Functional enrichment

We evaluated the correlation between PNP expression and the expression of all other mRNAs using the Spearman correlation. This analysis helped identify activated or suppressed pathways. Based on this, genes were mapped to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway collections (https://www.genome.jp/kegg/), and enrichment analysis was performed using the SangerBox online tool (http://sangerbox.com/) to obtain enrichment results. ¹⁵

Metabolic pathways were retrieved from the KEGG and subjected to Gene Set Variation Analysis (GSVA). ¹⁶ The “limma” package was used to identify significantly altered pathways based on PNP expression levels. ¹⁷

Cell lines and cell culture

The 5637 (Catalog, CL-0002) and UM-UC-3 (Catalog, CL-0463) cell lines were obtained from Procell. Cells were cultured in RPMI-1640 (MA0548, MeilunBio) and MEM (MA0217, MeilunBio) media, respectively. Both media types were supplemented with 10% fetal bovine serum (FBS, FSS500, ExCell) and 1% penicillin-streptomycin (15140122, GIBCO). Cells were maintained in a humidified incubator at 37°C with 5% CO2.

RNA extraction and quantitative PCR (RT-qPCR)

Cell lysis was performed using NucleoZol (740404.200, BIOFIVEN), followed by the addition of ddH2O in a 1 mL: 200 µL ratio (NucleoZol: ddH2O). The lysate was centrifuged at 12 000 r/min for 15 min, and the supernatant was transferred to a fresh tube. An equal volume of isopropanol was then added, followed by another centrifugation at 12 000 r/min for 10 min. The supernatant was discarded, and the RNA pellet was washed twice with 500 µL of 75% ethanol, centrifuging at 8000 rpm for 3 min each time. The pellet was resuspended in 50 to 100 µL of ddH2O, and RNA concentration was measured using NanoDrop 2000 (ND 2000, Thermo Fisher Scientific). cDNA synthesis was performed using the All-In-One 5X RT MasterMix with gDNA Removal kit (G592, abm). Quantitative PCR (A40427, Thermo Fisher Scientific) was carried out using BlasTaq 2X qPCR MasterMix (G891, abm), with GAPDH as the internal control, and relative expression was calculated using the 2^−ΔΔCT method. Each experiment was conducted in triplicate. The sequence of primers targeting PNP was listed in Supplemental Table II.

Small interfering RNAs (siRNAs) transfection

SiRNAs targeting PNP were synthesized by Qsingke, and transfection was performed using reagents (siRNA-Mate) from GenePharma. Cells were seeded at a density of 2 × 106 per well in 6-well plates and transfected with siRNAs 24 hours later. At 48 hours post-transfection, the cells were collected for qPCR to verify transfection efficiency. The siRNA transfection protocol is as follows: Prepare two 1.5 mL EP tubes, each containing 200 μL of serum-free medium. To each tube, add 100 pmol of siRNA and 5 μL of siRNA-Mate transfection reagent separately, ensuring thorough mixing. Subsequently, combine the contents of both tubes and allow the mixture to incubate at room temperature for 15 min. Transfer the transfection mixture to the target cells, and after a 24-h incubation, replace the medium with fresh culture medium to maintain cell growth. si-NC was transfected as the negative control group. The sequence of siRNAs targeting PNP was listed in Supplemental Table III.

Cell assays

For cell counting kit-8 (CCK-8) assays, the transfected cells were seeded into 96-well plates at a density of 1000 cells per well. Cell proliferation was assessed daily over 5 days using the CCK-8 assay (MA0218, MeilunBio) by adding CCK-8 reagent to the culture medium at a 9:1 ratio. After a 2-h incubation, absorbance was measured on a Varioskan LUX multifunctional microplate reader (VL0L00D0, Thermo Fisher Scientific).

EdU assays were performed using the BeyoClick™ EdU-594 Cell Proliferation Assay Kit (C0078 S, Beyotime). Cells were seeded into 6-well plates at a density of 2 × 106 per well. EdU reagents were added to the medium at a concentration of 10 µM and incubated for 2 h. The EdU solution was then discarded, and 4% paraformaldehyde was added for fixation for 15 min. Cells were washed with wash buffer 3 times for 3 min each. After discarding the wash buffer, the cells were permeabilized with 0.3% Triton-X 100 for 10 min, followed by 2 washes with wash buffer for 3 min each. The wash buffer was discarded, and Click Additive Solution was added for a 30-min incubation at room temperature in the dark. Following this, the reaction buffer was discarded, and cells were washed 3 times with wash buffer for 3 min each. Finally, Hoechst 33342 (P0133, Beyotime) solution was added, and cells were incubated for 10 minutes before being washed 3 times with wash buffer for 3 min each. Fluorescent signals were observed using fluorescence microscopy (DFC7000T, Leica).

Mutation landscape

Differential genomic mutations and copy number variations (CNVs) were analyzed using the “maftools” R package (version 2.16). ¹⁸ Based on median PNP expression levels, groups were compared, and statistical significance was determined using the Wilcoxon rank-sum test.

Statistical analysis

All statistical analyses and graphical visualizations were performed using R version 4.3.3, with experimental data analysis and plotting conducted in GraphPad Prism version 8.0. Differences between 2 groups were assessed using the Wilcoxon rank-sum test for nonnormally distributed data or Student’s t-test for normally distributed data. For multiple group comparisons, the Kruskal-Wallis test (for non-normal distributions) or 1-way analysis of variance (ANOVA; for normal distributions) was applied. A P value of < .05 was considered statistically significant, indicating notable differences in median values between groups or among multiple samples.

Results

Flowchart of this study

Figure 1 illustrates the workflow of this study. First, a pan-cancer analysis was conducted to characterize the expression patterns of PNP across various cancer types. Next, we integrated multicenter MIBC cohorts (TCGA, GSE48075, GSE169455) to validate PNP as an independent prognostic risk factor for MIBC. Furthermore, we assessed the potential of PNP as a novel biomarker for MIBC through in vitro experiments, functional enrichment analyses, mutation data evaluation, and immunotherapy cohort analysis.

Figure 1.

Flow diagram of this research.

Expression patterns of PNP in tumors

We performed a differential analysis of PNP expression by integrating data from Genotype-Tissue Expression (GETx) and TCGA pan-cancer datasets. As shown in Figure 2A, PNP expression is significantly higher in bladder cancer tissues compared to normal tissues. Similarly, PNP exhibits higher expression in most tumor tissues compared to normal tissues, with the exception of lung cancer and adrenocortical carcinoma, where differences were minimal, and renal cancer, liver cancer, and melanoma, where PNP expression was lower. We further validated PNP expression at the protein level using the HPA database. As shown in Figure 2B, C, PNP expression is absent in normal urinary bladder tissues, but demonstrates moderate expression in bladder cancer tissues. The protein is predominantly localized in the cytoplasmic and membranous compartments.

Figure 2.

Patterns of PNP expression in various tumors. (A) PNP expression levels between tumor and normal tissues across various cancer types using data from the TCGA and GTEx datasets (Wilcoxon rank-sum test). (B, C) Immunohistochemistry for PNP in normal bladder tissue (B) and urothelial carcinoma tissue (C), obtained from the Human Protein Atlas (HPA). (D) PNP expression associated with overall survival, progression-free interval, and disease-specific survival across multiple cancer types (univariate Cox regression).

In addition, we assessed the prognostic value of PNP in the TCGA pan-cancer cohort through Cox regression analysis. As illustrated in Figure 2D, we identified the top 10 cancers where PNP expression had statistically significant correlations with overall survival (OS), progression-free interval (PFI), and disease-specific survival (DSS) (Supplemental Table IV). Notably, PNP emerged as an unfavorable prognostic factor in bladder cancer. These findings suggest that PNP has the potential to serve as a novel biomarker for bladder cancer.

Prognostic value of PNP in MIBC

Further investigation is warranted to better understand the role of PNP in MIBC, we performed a prognostic analysis of PNP across multicenter cohorts. In TCGA, using OS, PFI, and DSS as prognostic endpoints, KM survival curves consistently demonstrated poorer outcomes in groups with elevated PNP expression (Figure 3A). We then included age, gender, tumor node metastasis (TNM) stage, overall stage, and PNP expression in a Cox regression analysis. The results indicated that PNP is an independent risk factor for MIBC prognosis (Figure 3D). Similarly, in the US-based GSE48075 cohort, using OS and DSS as endpoints, the KM curves again showed worse prognosis in the higher PNP expression group (Figure 3B). While Cox regression identified PNP as an independent prognostic factor for OS (hazard ratio [HR] = 1.5), the multivariate Cox regression analysis for DSS yielded a P value of .07, which was not statistically significant (Figure 3E). Finally, in the Sweden-based GSE169455 cohort, using OS, RFS, and CSS as endpoints (Figure 3C), PNP consistently emerged as a significant adverse prognostic factor in both KM survival and Cox regression analyses (Figure 3F).

Figure 3.

Prognostic impact of PNP in multiple MIBC cohorts. (A) Kaplan-Meier survival curves for the TCGA cohort comparing overall survival (OS), progression-free interval (PFI), and disease-specific survival (DSS) between high and low PNP expression groups. (B) Kaplan-Meier survival curves for the GSE48075 cohort showing OS and DSS by PNP expression level. (C) Kaplan-Meier survival curves for the GSE169455 cohort illustrating OS, relapse-free survival (RFS), and cancer-specific survival (CSS) stratified by PNP expression level. (D-F) Univariate and multivariate Cox regression analyses assessing hazard ratios for PNP expression and other clinical variables in the TCGA (D), GSE48075 (E), and GSE169455 (F) cohorts.

In vitro experiments validate the oncogene role of PNP

To investigate the impact of PNP in MIBC cell lines, we conducted a series of in vitro assays. We designed 3 siRNA sequences and validated their knockdown efficiency. As shown in Figure 4A, qPCR indicated that siRNAs #2 and #3 had more pronounced knockdown effects. Using loss-of-function assays, we assessed changes in proliferative capacity through CCK-8 assays. As illustrated in Figure 4B, CCK-8 results demonstrated that PNP downregulation inhibited the proliferation of 5634 and UM-UC-3 cell lines. In addition, we used the siRNA with the highest knockdown efficiency (#3) in an EdU assay, which corroborated the CCK-8 findings, showing that PNP knockdown significantly restrained cell proliferation in both cell lines (Figure 4C). Collectively, these results suggest that PNP exhibits oncogenic activity in MIBC.

Figure 4.

Knockdown of PNP inhibited MIBC proliferation. (A) QPCR was utilized to assess the efficiency of PNP knockdown in 5637 and UM-UC-3 cell lines (student’s t-test). (B) CCK8 to detect the proliferation of 5637 and UM-UC-3 cell lines (student’s t-test). (C) EdU assay to detect the proliferation of 5637 and UM-UC-3 cell lines (student’s t-test).

PNP regulates metabolic pathways in MIBC

To investigate the biological functions through which PNP contributes to MIBC progression, we conducted KEGG functional enrichment analysis on TCGA-BLCA. Among the top 20 PNP-activated and -suppressed pathways, metabolic pathways were the most highly enriched (Figure 5, Supplemental Table V). Other enriched categories included cellular processes and organismal systems, such as cell death and immune system pathways. Given PNP’s role as a crucial enzyme in purine metabolism, its involvement in these pathways is not surprising.

Figure 5.

KEGG pathway enrichment analysis of PNP associated pathways in MIBC.

To gain a more comprehensive understanding of PNP’s role in regulating MIBC metabolism, we performed GSVA-based metabolic pathway analysis, stratified by PNP expression levels. As shown in Figure 6A, PNP is associated with several metabolic processes, including amino acid metabolism, glycan biosynthesis and metabolism, metabolism of cofactors and vitamins, carbohydrate metabolism, lipid metabolism, and nucleotide metabolism. This indicates that PNP’s influence extends beyond purine metabolism alone. We further identified the top 2 PNP-activated and -suppressed metabolic pathways (Figure 6B and C), with the activated pathways being the citrate cycle (TCA cycle) and 1-carbon pool by folate, while the suppressed pathways included linoleic acid metabolism and alpha-linolenic acid metabolism. These findings align with our KEGG enrichment analysis, which also showed that PNP significantly suppresses lipid metabolism in MIBC.

Figure 6.

Metabolic pathway analysis of PNP. (A) Gene Set Variation Analysis (GSVA) scores illustrating the enrichment of metabolic pathways associated with high and low PNP expression groups. (B) GSVA scores for representative metabolic pathways (Wilcoxon rank-sum test). (C) Correlation between PNP expression and GSVA scores (Spearman correlation).

Landscape of gene mutation and CNC associated with PNP

Gene mutations and CNVs are critical contributors to tumor heterogeneity. To gain a more comprehensive understanding of PNP’s role in MIBC, we analyzed the mutation landscape. As shown in Figure 7A, the top 10 most frequently mutated genes in MIBC within the TCGA dataset include TP53, TTN, KDM6A, KMT2D, MUC16, ARID1A, PIK3CA, SYNE1, MACF1, and RB1. Notably, when stratifying by PNP median expression, PIK3CA and RB1 mutations were significantly more frequent in the high PNP expression group (Figure 7B, P < .05). Similarly, the top 10 CNV gains and losses are illustrated in Figure 7C. Figure 7D shows that within PNP expression subgroups, there were more distinct CNV loss sites. Finally, we examined the impact of these gene mutations and CNVs on PNP expression levels (Figures 7E to F, Supplemental Figure 1).

Figure 7.

Mutation and copy number variation (CNV) landscape associated with PNP expression in MIBC. (A) Mutation frequencies of the top 10 most frequently mutated genes in MIBC. (B) Comparison of mutation frequencies between high and low PNP expression groups for these top 10 genes (Wilcoxon rank-sum test). (C) CNV profiles, displaying the most frequent gain and loss across chromosomes in MIBC samples. (D) Summary of CNV frequency in high and low PNP expression groups (Wilcoxon rank-sum test). (E) Box plot showing PNP expression in samples with RB1 mutations compared to wild-type RB1 (Wilcoxon rank-sum test). (F) Box plots illustrating PNP expression differences based on specific CNV gains and losses (Wilcoxon rank-sum test).

PNP as a biomarker for immunotherapy in MIBC

Immunotherapy has become a critical component in the management of MIBC patients, and we sought to determine whether PNP expression is associated with therapy response. First, we evaluated TIDE scores in the TCGA-BLCA cohort. As shown in Figure 8A, while there was no significant difference in Dysfunction scores, both TIDE and Exclusion scores were significantly higher in the high PNP expression group (P < .01). Moreover, we observed that PNP expression was elevated in the non-responsive group, with a higher proportion of non-responders in the high PNP expression group (Figure 8A). These findings suggest that elevated PNP expression may be an adverse prognostic factor for MIBC immunotherapy outcomes.

Figure 8.

PNP expression with therapy response. (A) Tumor Immune Dysfunction and Exclusion (TIDE) analysis of the TCGA-MIBC cohort, showing TIDE, Dysfunction, and Exclusion scores. (B) Kaplan-Meier overall survival curves for the IMvigor210 cohort. (C) Kaplan-Meier overall survival curves for the GSE69795 cohort.

We then validated our findings in the IMvigor210 cohort of metastatic bladder cancer patients receiving anti-PD-L1 therapy. KM survival analysis indicated that patients with high PNP expression had poorer outcomes, although this was not statistically significant (P = .31). Notably, patients with low PNP expression had a median survival approximately 2 months longer than those with high PNP expression (Figure 8B). In addition, we found that PNP also acted as an adverse prognostic factor in a neoadjuvant chemotherapy (NAC) cohort (Figure 8C).

Finally, we conducted a correlation analysis between PNP and various immune checkpoint molecules. We considered an absolute correlation coefficient greater than 0.3 with P < .05 as statistically significant. Based on this threshold, the significantly correlated immune checkpoints include TGFB1, CD274, CD80, PDCD1LG2, IDO1, PRF1, and TNFSF9, with TGFB1 showing the strongest correlation (Supplemental Figure 2) (Supplemental Table VI).

Discussion

MIBC is characterized by cancer invasion into the muscle layer, extending beyond the bladder mucosa and submucosa, and represents a major subset of bladder cancer cases. ³ While treatment advancements have been made in other cancers, the systemic management of bladder cancer has remained largely unchanged recently. ⁴ Nonetheless, an improved understanding of novel biomarkers and therapeutic targets for bladder cancer has spurred rapid therapeutic expansion in the last decade. ⁴ Such as Proteasome 26 S Subunit, Non-ATPase(PSMD) is a prognostic factor in MIBC. ¹⁹ Currently, the standard treatment for MIBC involves multimodal approaches, including radical cystectomy, NAC, and, for selected patients, bladder-sparing therapies such as transurethral resection, chemotherapy, and immunotherapy.^20,21 Moreover, various preclinical and clinical studies provide strong evidence supporting the potential repurposing of existing inhibitors, such as trastuzumab deruxtecan, nivolumab, pembrolizumab, and others, for the treatment of MIBC. ²²

In this study, we identified PNP as a potential biomarker with relevance across these diverse therapeutic approaches for bladder cancer. Our study identifies PNP as a potential biomarker for bladder cancer across various clinical dimensions. Our findings demonstrate that, in a multicenter cohort primarily composed of patients undergoing radical cystectomy, PNP serves as an independent prognostic factor across multiple clinical endpoints (Figure 2). In addition, PNP shows stratification potential within immunotherapy and neoadjuvant treatment cohorts (Figure 8). Immunohistochemistry analysis of clinical tissue samples revealed that PNP expression is elevated in MIBC tissues compared to normal bladder tissue (Figure 2). Furthermore, our in vitro experiments indicate that PNP knockdown significantly reduces the proliferative capacity of MIBC cell lines (Figure 4). It should be noticed that the sensitivity (SN) and specificity (SP) of PNP were assessed in the TCGA cohort, yielding values of 37% and 58%, respectively, with an SN/SP ratio of 64%. This result falls short of the ideal threshold of 90%, which we attribute to the limited sample size and the substantial imbalance between the case and control groups. In future studies, we plan to include additional cohorts and incorporate more comprehensive clinical parameters in our analyses to further optimize the clinical translational potential of PNP. Collectively, these results provide evidence supporting the clinical translational potential of PNP.

In previous studies, PNP is an enzyme crucial to the nucleotide salvage pathway, widely expressed across various tissues. Its primary role involves catalyzing the breakdown of nucleosides into nitrogenous bases. ²³ The deficiency of PNP could induce p53-meditated apoptosis. ²⁴ And it is not only involved in pathology process in bladder cancers, the suppression of it is expected to apply to cystitis. ²⁵ Although PNP has been investigated as a molecular target since the 1960s, no PNP inhibitor (PNPI)-based therapy has yet received Food and Drug Administration (FDA) approval. ²³ Peldesine (BCX-34), a potent PNP inhibitor, was tested in phase I trials for human immunodeficiency virus (HIV) treatment, progressed to phase III trials for cutaneous T-cell lymphoma, and was also investigated for T-cell-related autoimmune diseases, including rheumatoid arthritis and psoriasis. Nevertheless, these studies were discontinued due to a lack of significant efficacy compared to placebo. ²⁶ Besides, PNP inhibitors have shown effectiveness in inhibiting the growth of cancer cells.^27,28 In addition, PNP inhibitors is frequently used in combination with nucleoside-based anticancer and antiviral agents to prevent nucleoside cleavage, thereby boosting anticancer efficacy. ²⁹

To investigate the potential of PNP as a therapeutic target for MIBC, we explored its functional mechanisms from a multi-omics perspective. As expected, functional enrichment analysis revealed that PNP is primarily associated with metabolic pathways (Figure 4), particularly in amino acid metabolism, glycan biosynthesis and metabolism, metabolism of cofactors and vitamins, carbohydrate metabolism, lipid metabolism, and nucleotide metabolism (Figure 6). This suggests that combining PNP inhibitors with interventions targeting these metabolic pathways could be a promising avenue for further research. In addition, we observed a higher frequency of RB1 mutations in the high-PNP expression subgroup, with PNP expression levels also elevated in the presence of RB1 mutations. RB1, also known as RB Transcriptional Corepressor 1, is a critical regulator of cell division and functions as a tumor suppressor. ³⁰ High rates of inactivating mutations in RB1 are observed across various tumor genomes; loss of RB1 function limits its tumor-suppressive role, promoting tumor cell proliferation, invasion, apoptosis, and overall tumor development.^30,31 In bladder cancer, RB1 mutations significantly contribute to disease progression and treatment resistance. ³² In addition, these mutations are associated with enriched pathways involved in DNA repair, cell proliferation, and metabolic processes, which may drive disease progression and provide targets for therapeutic intervention. ³² Based on the findings above, we propose the hypothesis that a regulatory relationship may exist between PNP and RB1, and that MIBC patients with RB1 mutations might benefit from PNP-targeted therapies. However, this hypothesis requires further experimental validation.

TGFB1 is a well-established pro-tumorigenic factor that can attenuate tumor responses to PD-L1/PD-1 inhibitors, thereby reducing the efficacy of immunotherapy. ¹³ Our correlation analysis revealed a positive association between PNP and TGFB1 expression, which is consistent with the KM analysis results from the immunotherapy cohort in Figure 8, indicating poorer immunotherapy outcomes in patients with high PNP expression. In addition, CD274 (PD-L1) and CD80 exhibited strong correlations with PNP expression. PD-L1, a widely studied tumor immune checkpoint, binds to PD-1 and suppresses T-cell activation, facilitating tumor progression. ³³ Studies have demonstrated that PD-L1 inhibitors can improve BLCA patient outcomes. ³⁴ While high PD-L1 expression is typically associated with improved responses to immunotherapy, our correlation analysis found a positive association between PD-L1 and PNP expression, which contradicts the findings in Figure 8. CD80, on the contrary, has a dual role in immune regulation. It serves as a ligand for CD28 and CTLA-4, where its interaction with CD28 provides a stimulatory signal to activate T-cells, whereas its interaction with CTLA-4 inhibits T-cell activation. ³⁵ The precise role of CD80 in bladder cancer remains to be further explored. The tumor immune microenvironment is a highly complex network of molecular and immune cell interactions, making it challenging to interpret results based on a single factor alone. In future studies, we aim to integrate multi-omics approaches to further investigate the role of PNP-associated immune checkpoints.

Our study lacks validation of PNP as a biomarker in a local bladder cancer cohort with prognosis information, which we plan to establish in future work. In addition, we used siRNA only in this study without PNP inhibitors to show PNP’s anti-MIBC effects. More importantly, the molecular mechanisms of PNP in MIBC remain to be further explored in our upcoming research. Furthermore, we recognize that in vivo experiments play a crucial role in confirming the role of PNP in preclinical studies. Moreover, we recognize the importance of incorporating PNP-specific inhibitors in future experiments to mitigate the potential off-target effects of siRNA and minimize the influence of compensatory metabolic pathways. We will incorporate them into our future research endeavors.

Conclusion

Our multi-omics analysis results confirm that PNP is a promising prognostic biomarker in MIBC, with high expression often indicating poor prognosis for patients. In addition, PNP has the potential to serve as a predictive biomarker for immunotherapy response, as patients with high PNP expression typically exhibit poorer outcomes following immunotherapy. These findings are significant for improving prognostic risk assessments in MIBC and guiding clinical targeted therapies.

Supplemental Material

sj-jpg-1-onc-10.1177_11795549251359145 – Supplemental material for Purine Nucleoside Phosphorylase (PNP) as a Biomarker and Therapeutic Target in Muscle-Invasive Bladder Cancer

Supplemental material, sj-jpg-1-onc-10.1177_11795549251359145 for Purine Nucleoside Phosphorylase (PNP) as a Biomarker and Therapeutic Target in Muscle-Invasive Bladder Cancer by Yanfei Chen, Peiyi Xian, Jianming Lu, Le Zhang, Chao Cai and Weide Zhong in Clinical Medicine Insights: Oncology

Supplemental Material

sj-jpg-2-onc-10.1177_11795549251359145 – Supplemental material for Purine Nucleoside Phosphorylase (PNP) as a Biomarker and Therapeutic Target in Muscle-Invasive Bladder Cancer

Supplemental material, sj-jpg-2-onc-10.1177_11795549251359145 for Purine Nucleoside Phosphorylase (PNP) as a Biomarker and Therapeutic Target in Muscle-Invasive Bladder Cancer by Yanfei Chen, Peiyi Xian, Jianming Lu, Le Zhang, Chao Cai and Weide Zhong in Clinical Medicine Insights: Oncology

Supplemental Material

sj-xlsx-3-onc-10.1177_11795549251359145 – Supplemental material for Purine Nucleoside Phosphorylase (PNP) as a Biomarker and Therapeutic Target in Muscle-Invasive Bladder Cancer

Supplemental material, sj-xlsx-3-onc-10.1177_11795549251359145 for Purine Nucleoside Phosphorylase (PNP) as a Biomarker and Therapeutic Target in Muscle-Invasive Bladder Cancer by Yanfei Chen, Peiyi Xian, Jianming Lu, Le Zhang, Chao Cai and Weide Zhong in Clinical Medicine Insights: Oncology