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; [35]GSE48075: HR > 1.5, P = .07; [36]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.

   Keywords: Muscle-invasive bladder cancer, purine nucleoside
   phosphorylase, prognosis, immunotherapy, multi-omics

Introduction

   Bladder cancer is the most common malignancy of the urinary system.^
   [37]1 In the United States, it ranks as the sixth most frequently
   diagnosed cancer and the ninth leading cause of cancer-related
   mortality.^ [38]2 The incidence in men is significantly higher compared
   to women.^[39]1,[40]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.^[41]3,[42]4 This form of cancer presents considerable
   heterogeneity and variability in prognosis, making treatment
   challenging.^[43]5,[44]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.^ [45]7
   ,^[46]8 Overall, the 5-year survival rate for MIBC remains
   approximately 40% to 60%.^ [47]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.^ [48]9 The enzyme’s primary
   function is to catalyze the conversion of nucleosides into their
   nitrogenous bases.^ [49]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.^ [50]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.^ [51]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 [52]Supplemental Table I. For
   the The Cancer Genome Atlas (TCGA) pan-cancer analysis, RNA sequencing
   data were obtained from the UCSC Xena data portal
   ([53]http://xena.ucsc.edu/). The TCGA-BLCA cohort was also sourced from
   the UCSC Xena data portal. In addition, datasets [54]GSE48075,
   [55]GSE169455, and [56]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, [57]GSE48075 cohort: 72 samples, [58]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
   [59]http://research-pub.Gene.com/imvigor210corebiologies/.^ [60]13
   Immunohistochemistry (IHC) data were retrieved from the Human Protein
   Atlas (HPA).

Prognostic value assessment

   We employed the R package “survival”^ [61]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 ([62]https://www.genome.jp/kegg/), and enrichment analysis
   was performed using the SangerBox online tool
   ([63]http://sangerbox.com/) to obtain enrichment results.^ [64]15

   Metabolic pathways were retrieved from the KEGG and subjected to Gene
   Set Variation Analysis (GSVA).^ [65]16 The “limma” package was used to
   identify significantly altered pathways based on PNP expression
   levels.^[66]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 [67]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 [68]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).^ [69]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

   [70]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, [71]GSE48075, [72]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.

   [73]this diagram is about muscle-invasive bladder cancer
   gene-expression data using multiple methods.
   [74]Open in a new tab

   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 [75]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 [76]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.

   [77]The image shows a graph, histological images, and a table
   summarizing protein expression data across various cancer types. The
   graph compares PNP expression levels and standard deviations between
   tumor and normal tissues, with statistical significance marked. Below
   the graph, there are images depicting immunohistochemistry staining for
   PNP in normal bladder tissue and urothelial carcinoma tissue. The table
   below summarizes PNP expression associated with overall survival,
   progression-free interval, and disease-specific survival across
   multiple cancer types, with statistical significance indicated. The
   text highlights that the data for each cancer type and the statistical
   significance markers suggest the accuracy and reliability of the
   presented data.
   [78]Open in a new tab

   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).

   *P < .05; **P < .01; ***P < .001; ****P < .0001.

   In addition, we assessed the prognostic value of PNP in the TCGA
   pan-cancer cohort through Cox regression analysis. As illustrated in
   [79]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)
   ([80]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 ([81]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 ([82]Figure 3D).
   Similarly, in the US-based [83]GSE48075 cohort, using OS and DSS as
   endpoints, the KM curves again showed worse prognosis in the higher PNP
   expression group ([84]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 ([85]Figure 3E). Finally,
   in the Sweden-based [86]GSE169455 cohort, using OS, RFS, and CSS as
   endpoints ([87]Figure 3C), PNP consistently emerged as a significant
   adverse prognostic factor in both KM survival and Cox regression
   analyses ([88]Figure 3F).

Figure 3.

   [89]Cancer progression rate. Survival rates and related factors for the
   TCGA, GSE48075, and GSE169455 cohorts.
   [90]Open in a new tab

   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 [91]GSE48075 cohort showing OS and DSS by PNP expression
   level. (C) Kaplan-Meier survival curves for the [92]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), [93]GSE48075
   (E), and [94]GSE169455 (F) cohorts.

   Statistically significant hazard ratios (HR) with P values are
   indicated, with *P < .05; **P < .01; ***P < .001.

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 [95]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 [96]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 ([97]Figure 4C). Collectively,
   these results suggest that PNP exhibits oncogenic activity in MIBC.

Figure 4.

   [98]The image presents data on PNP knockdown and its effects on MIBC
   proliferation, including qPCR and cell viability assays for 5637 and
   UM-UC-3 cell lines.
   [99]Open in a new tab

   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).

   The data were displayed as the average ± standard deviation (SD) of the
   mean from 3 separate trials. si-NC: negative control group.

   *P < .05; **P < .01; ***P < .001; ****P < .0001.

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 ([100]Figure 5,
   [101]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.

   [102]KEGG pathway enrichment analysis of PNP associated pathways MIBC.
   Bar plot of KEGG pathways enriched in high versus low PNP expression
   groups.
   [103]Open in a new tab

   KEGG pathway enrichment analysis of PNP associated pathways in MIBC.

   Bar plot of KEGG pathways enriched in high versus low PNP expression
   groups.

   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 [104]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 ([105]Figure 6B and [106]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.

   [107]A heatmap and forest plot display gene and protein pathway
   analysis, highlighting significant correlations.
   [108]Open in a new tab

   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).

   *P < .05; **P < .01; ***P < .001.

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 [109]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 ([110]Figure 7B, P < .05). Similarly, the top 10
   CNV gains and losses are illustrated in [111]Figure 7C. [112]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 ([113]Figures 7E to [114]F,
   [115]Supplemental Figure 1).

Figure 7.

   [116]Title: "Mutation and Copy Number Variation Landscape Associated
   with PNK Expression in MIBC"Description: The graph illustrates the
   mutation frequencies of the top 10 most frequently mutated genes in
   MIBC, compares mutation frequencies in high and low PNK expression
   groups, displays CNV profiles, and shows PNK expression in samples with
   RB1 and specific CNV variations.
   [117]Open in a new tab

   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).

   *P < .05; **P < .01; ***P < .001.

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 [118]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 ([119]Figure 8A). These findings suggest
   that elevated PNP expression may be an adverse prognostic factor for
   MIBC immunotherapy outcomes.

Figure 8.

   [120]compare TIDE analysis in different therapy response groups.
   [121]Open in a new tab

   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 [122]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
   ([123]Figure 8B). In addition, we found that PNP also acted as an
   adverse prognostic factor in a neoadjuvant chemotherapy (NAC) cohort
   ([124]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 ([125]Supplemental
   Figure 2) ([126]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.^ [127]3 While treatment
   advancements have been made in other cancers, the systemic management
   of bladder cancer has remained largely unchanged recently.^ [128]4
   Nonetheless, an improved understanding of novel biomarkers and
   therapeutic targets for bladder cancer has spurred rapid therapeutic
   expansion in the last decade.^ [129]4 Such as Proteasome 26 S Subunit,
   Non-ATPase(PSMD) is a prognostic factor in MIBC.^ [130]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.^[131]20,[132]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.^ [133]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 ([134]Figure 2). In addition, PNP
   shows stratification potential within immunotherapy and neoadjuvant
   treatment cohorts ([135]Figure 8). Immunohistochemistry analysis of
   clinical tissue samples revealed that PNP expression is elevated in
   MIBC tissues compared to normal bladder tissue ([136]Figure 2).
   Furthermore, our in vitro experiments indicate that PNP knockdown
   significantly reduces the proliferative capacity of MIBC cell lines
   ([137]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.^ [138]23 The deficiency of PNP could induce p53-meditated
   apoptosis.^ [139]24 And it is not only involved in pathology process in
   bladder cancers, the suppression of it is expected to apply to
   cystitis.^ [140]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.^ [141]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.^[142]26 Besides, PNP inhibitors have shown effectiveness in
   inhibiting the growth of cancer cells.^[143]27,[144]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.^ [145]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 ([146]Figure 4),
   particularly in amino acid metabolism, glycan biosynthesis and
   metabolism, metabolism of cofactors and vitamins, carbohydrate
   metabolism, lipid metabolism, and nucleotide metabolism ([147]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.^ [148]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.^[149]30,[150]31 In bladder cancer, RB1 mutations
   significantly contribute to disease progression and treatment
   resistance.^ [151]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.^ [152]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.^ [153]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 [154]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.^ [155]33 Studies have demonstrated that
   PD-L1 inhibitors can improve BLCA patient outcomes.^ [156]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
   [157]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.^ [158]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
   [159]sj-jpg-1-onc-10.1177_11795549251359145.jpg^ (3.3MB, jpg)

   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
   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
   [160]sj-jpg-2-onc-10.1177_11795549251359145.jpg^ (813.1KB, jpg)

   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
   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
   [161]sj-xlsx-3-onc-10.1177_11795549251359145.xlsx^ (84.8KB, xlsx)

   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

Acknowledgments