Comprehensive bioinformatics and experimental analysis of PPOX reveals its carcinogenic effect in clear cell renal cell carcinoma

Introduction

Renal cell carcinoma (RCC) represents one of the most prevalent malignancies, accounting for approximately 2% to 3% of all cancer diagnoses and constituting over 90% of all renal neoplasms. Originating from the renal tubular epithelial cells, clear cell renal cell carcinoma (ccRCC) serves as the predominant histological subtype While a majority of patients presenting with early-stage, localized renal cancer can achieve favorable outcomes through surgical resection or ablative interventions, approximately one-third will eventually progress to metastatic disease, which profoundly compromises their long-term prognosis (1,2). Targeted therapy and immunotherapy serve as the primary treatment options for patients with advanced renal cancer. In addition, advances in sequencing technology and the advent of combination drug therapies have created opportunities to identify novel therapeutic targets, paving the way for precision medicine in the management of this disease.

Heme biosynthesis is a critical metabolic process that supports the rapid proliferation and survival of malignant tumors, with cancer cells often exploiting this pathway to meet their high energy demands (3). Accumulating evidence indicates that dysregulated heme metabolism drives the progression of various malignancies by reshaping cellular bioenergetics, particularly through promoting mitochondrial oxidative phosphorylation (4,5). Beyond its role in energy metabolism, abnormal accumulation of intracellular heme and its precursors is closely associated with enhanced oxidative stress and genomic instability, factors that collectively contribute to tumor initiation and progression (6).

Protoporphyrinogen oxidase (PPOX) is a protein-coding gene that encodes protoporphyrinogen oxidase, a key rate-limiting enzyme in the penultimate step of the heme biosynthesis pathway. Previous research has established that mutations in the PPOX gene are associated with several forms of porphyria, including variegate porphyria and its childhood-onset variant. Molecular genetic testing for pathogenic variants in the PPOX gene has been utilized to confirm the diagnosis of the acute hepatic porphyria (AHP) subtype (7). Notably, aberrant expression of PPOX may disrupt heme-mediated signal transduction and redox homeostasis, potentially constituting a metabolic vulnerability in ccRCC (8,9). However, the specific role of PPOX in ccRCC pathogenesis and progression remains largely unexplored, warranting further investigation.

Studies have reported that PPOX also serves a role in hepatocellular carcinoma (HCC) as a complication of acute porphyria. HCC represents a rare yet severe complication associated with AHP. In HCC tissues from patients with AHP, somatic secondary mutations result in near-complete inactivation of the enzyme catalyzing a critical step in the heme biosynthesis pathway. PPOX and hydroxymethylbilane synthase (HMBS) may exert a pivotal influence on the pathogenesis of HCC in these individuals (10,11).

Moreover, elevated expression of PPOX has been reported in colorectal cancer cell lines, with experimental evidence indicating that targeting PPOX both in vitro and in vivo markedly inhibits tumor growth. PPOX is highly expressed in faster-growing cell lines and primary tumors. Pharmacologic inhibition or small interfering RNA (siRNA)-mediated knockdown of PPOX has been reported to reduce colon cancer cell growth in vitro. In addition to the aforementioned findings, treatment with acifluorfen, a specific PPOX inhibitor, was reported to notably reduce the growth of colorectal cancer cell lines in a mouse xenograft model. The study also characterized differences between colorectal cancer cells with varying growth rates at the transcriptome level, identifying novel candidate chemotherapeutic targets for the treatment of colorectal cancer. Furthermore, the study elucidated the transcriptomic disparities among distinct colorectal cancer cells to facilitate the identification of novel chemotherapeutic targets for colorectal cancer (12).

Research has also highlighted the high expression of PPOX in gastric cancer, particularly in tubular adenocarcinoma. Immunohistochemical analysis was performed on 75 surgically resected specimens of early gastric cancer from diverse origins, and the expression of PPOX was reported to be more pronounced in tubular adenocarcinomas compared with that in signet ring cell carcinomas (13).

Finally, a study investigating the association between metastatic urothelial carcinoma of the bladder and multiple somatic cell copy number alterations reported that an increase in 1q23.3 was independently associated with shorter overall survival (OS). PPOX, located at 1q23.3, was suggested as a gene potentially associated with a worse prognosis (14).

However, the expression and functional role of PPOX in renal cancer remain poorly understood. Therefore, the present study aimed to investigate the expression profile of PPOX in ccRCC and evaluate its prognostic significance through comprehensive database analysis. The study also aimed to elucidate the association between PPOX with immune responses and drug sensitivity, whilst exploring its potential as a therapeutic target. Preliminary cellular experiments were also performed to uncover its molecular functions and underlying mechanisms. Ultimately, the present research aimed to provide new insights for the treatment of ccRCC, ultimately contributing to improved patient prognosis. We present this article in accordance with the TRIPOD reporting checklist (available at https://tau.amegroups.com/article/view/10.21037/tau-2026-1-0024/rc).

Methods

Materials acquisition

PPOX expression data across several cancer types were acquired from the Tumor IMmune Estimation Resource (TIMER) database (https://cistrome.shinyapps.io/timer) (15). This part of the study integrated The Cancer Genome Atlas (TCGA) RNA-seq data, where the raw counts were analyzed using the differential expression analysis tool edgeR to observe the overall expression pattern of the target gene across pan-cancer, providing a preliminary assessment of its role in cancer. The subcellular localization of PPOX and its expression profiles in ccRCC cell lines were downloaded from the Human Protein Atlas (HPA) database (https://www.proteinatlas.org/ENSG00000143224-PPOX) (16). Transcriptomic and clinical data for kidney renal clear cell carcinoma (KIRC) were downloaded from the TCGA database (https://portal.gdc.cancer.gov), comprising 613 samples (72 normal tissue samples and 541 tumor samples) derived from 537 unique patients. Only primary tumor samples (identified by sample codes ending in “-01A”) and their matched solid tissue normal samples (codes ending in “-11A”) were included; recurrent or metastatic samples were excluded. Raw read counts were normalized using the TMM (trimmed mean of M-values) method via the calcNormFactors function in the edgeR package. Subsequently, the normalized counts were transformed using the voom method to obtain log₂-transformed counts per million (log₂-CPM), which were used for all downstream differential expression and survival analyses. The GDC has uniformly re-aligned the TCGA-KIRC data to the GRCh38 reference genome, and the data are officially stated to be free of batch effects. Transcriptome expression data were organized using Perl, and relevant clinical information was extracted. Drug-related genes were obtained from the Drug Gene Interaction Database (DGIdb) database (https://www.dgidb.org) and cross-referenced with genes reported in the literature. Data IDs were retrieved from the MRC Integrative Epidemiology Unit (IEU) database (https://gwas.mrcieu.ac.uk), and drug-gene exposure data were extracted based on gene location information. Single nucleotide polymorphisms (SNPs) within 100 kilobases upstream and downstream of the genes were selected as instrumental variables for Mendelian randomization analyses. These instrumental variables were subsequently filtered and pruned: SNPs with an effect allele frequency below 0.01 were excluded; linkage disequilibrium (LD) was accounted for by clumping (window size: 10,000 kb, LD threshold: R²<0.001); and only SNPs with a strong association (F-statistic >10) were retained to mitigate bias from weak instruments. The FinnGen database was used to obtain outcome data for ccRCC (https://www.finngen.fi/en/access_results). Finally, single-cell RNA-sequencing data of ccRCC were downloaded from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo).

Single-cell sequencing analysis

Single-cell data for five patients with ccRCC were downloaded from the GEO database (GSE242299). After calculating mitochondrial ribosome metrics to remove low-quality cells, 13,899 cells remained. Subsequently, the 2,000 most variable genes were selected for downstream analysis. These were Z-score scaled and PCA-reduced. Harmony then removed batch effects. UMAP clustering revealed 17 cell clusters. SingleR and literature references annotated each cluster. Nebulosa mapped gene density and then Commpath analyzed cell-cell communication.

Differential expression analysis in individual tumors and paired samples

The ‘limma’ package was utilized to extract PPOX expression levels, categorize normal and tumor samples, and perform differential expression analysis for both individual tumors and paired sample comparisons.

Preliminary prognostic analysis and exploratory model construction

Data and cohort

Patients with ccRCC from the TCGA database were included. We selected only patients with complete data for OS, PPOX expression, and key clinical variables (age, gender, AJCC TNM stage, and tumor grade). Cases with any missing or ambiguous data (e.g., labeled “unknown”) were excluded. For subsequent modeling, the four-tiered WHO/ISUP tumor grade was dichotomized into a binary variable: low-grade (G1 + G2) and high-grade (G3 + G4). All analyses were performed using R software (v4.1.3).

Survival and correlation analysis

OS was defined as the time from diagnosis to death from any cause. Based on the median expression of PPOX, patients were stratified into high- and low-expression groups. Kaplan-Meier survival curves were generated and compared using the log-rank test (survival, survminer packages). Associations between PPOX expression and clinicopathological features were assessed using appropriate statistical tests (limma, ggpubr packages).

Model development

Univariate Cox regression analyses were performed for all candidate variables, including the binary tumor grade. Variables showing a potential association with OS (P<0.10) in univariate analysis were then entered into a multivariable Cox proportional hazards model for further selection. Variables that remained statistically significant (typically P<0.05) in the multivariable analysis were identified as independent prognostic factors. Results are presented as hazard ratios (HRs) with 95% confidence intervals (CIs).

Final model, visualization, and performance assessment

The independent prognostic factors identified by the multivariable Cox analysis were used to construct the final prediction model. This model was visualized as a nomogram to predict 1-, 3-, and 5-year OS probabilities (regplot package). The model’s discriminative ability was evaluated using the concordance index (C-index) with 95% CI (Hmisc package) and time-dependent receiver operating characteristic (ROC) analysis at 1, 3, and 5 years (timeROC package). Calibration curves at the same time points were generated using bootstrapping with 1,000 resamples (rms package) to assess the agreement between predicted and observed survival.

Note on model validation

This exploratory model was developed and assessed on the entire available cohort without external validation. Therefore, the reported performance metrics are optimistic. The primary aim of this analysis is to illustrate the independent prognostic value of PPOX and to generate hypotheses for future studies, not to propose a definitive clinical prediction tool.

Co-expression analysis

The samples were grouped according to PPOX expression to analyze differential genes. Visualization was performed using the ‘pheatmap’ package, with thresholds set at logfold change >1 and false discovery rate (FDR) <0.05. The PPOX co-expressed genes with the largest correlation coefficients were obtained with a P value threshold of <0.001. Co-expression circle plots were generated showing the five genes with the smallest correlation coefficients and the six genes with the largest correlation coefficients.

Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and Gene Set Enrichment Analysis (GSEA)

The R packages ‘org.Hs.eg.db’, ‘clusterProfiler’, and ‘enrichplot’ were utilized to perform GO functional analysis, KEGG pathway analysis and GSEA.

Immune analysis

The present study performed immune cell infiltration analysis, immune cell differential analysis, and analysis of the correlation between PPOX expression and immune cell characteristics using the ‘limma’ and ‘CIBERSORT’ packages. These approaches revealed the immune cell infiltration landscape in tumor samples and identified immune cells differentially enriched between high- and low-PPOX expression groups. Comparisons of immune cell infiltration scores between groups were performed using the Wilcoxon rank-sum test, with P values adjusted using the Benjamini-Hochberg method for multiple testing. A correlation test (P<0.001) was performed using the ‘limma’ and ‘reshape2’ packages to identify immune checkpoint genes correlated with PPOX in tumor samples. The ‘corrplot’ package was used to visualize these associations through heatmap representation. The relationship between PPOX expression and the tumor microenvironment (TME) in ccRCC was assessed using the Estimation of STromal and Immune cells in MAlignant Tumour tissues using Expression data (ESTIMATE) algorithm. Stromal, Immune, and ESTIMATE scores were calculated for each sample in the TCGA-KIRC cohort based on gene expression signatures, and their correlations with PPOX expression levels were analyzed. The association between PPOX expression and response to immune checkpoint blockade was evaluated using the IMvigor210 cohort, which includes transcriptomic data from patients with metastatic urothelial carcinoma treated with the anti-PD-L1 agent Atezolizumab. PPOX expression levels were compared across different clinical response groups.

Drug sensitivity analysis and molecular docking

Drug sensitivity analysis was performed using a computational prediction approach based on public databases. The R package ‘pRRophetic’ was employed to predict the sensitivity of each sample to specific drugs (reported as the predicted IC50 value) by comparing the gene expression profiles of the TCGA-KIRC cohort with the experimentally validated profiles of cancer cell lines in the GDSC database. The analysis utilized default parameters (including batch = “combat” to correct for batch effects), focusing on comparing the overall trends in predicted drug sensitivity between the high and low PPOX expression groups. The PubChem database was used to obtain molecular structure data of relevant drugs (https://pubchem.ncbi.nlm.nih.gov), and the PDB database was used to obtain the PPOX protein structure (https://www.rcsb.org). Molecular docking was conducted using the CB-Dock2 database (https://cadd.labshare.cn/cb-dock2/index.php). This tool is based on the AutoDock Vina 1.2.0 engine (https://vina.scripps.edu), utilizing the Vina scoring function, and outputting the predicted binding free energy (ΔG, in kcal/mol). The Vina score (in kcal·mol⁻¹) represents the approximate binding energy; a value ≤−7.0 kcal·mol⁻¹ is considered to indicate strong binding.

Cell culture

The human renal cortical proximal tubule epithelial cell line HK-2 (Homo sapiens, CSTR:19375.09.3101HUMGNHu47) and the ccRCC cell lines 786-O (Homo sapiens, CSTR:19375.09.3101HUMTCHu186) and 769-P (Homo sapiens, CSTR:19375.09.3101HUMTCHu215) were purchased from the Chinese Academy of Sciences Cell Bank. The ccRCC cell line A498 (Homo sapiens) was obtained from the National Experimental Cell Resource Sharing Platform (resource ID: 1101HUM-PUMC000171). The cells were stored at the Tianjin Institute of Urology. All experiments were conducted within 20 passages after cell resuscitation. Throughout the cell culture process, cellular morphology was closely monitored, and mycoplasma testing was performed regularly. Cells were cultured in RPMI-1640 basal medium. The medium was added with 10% fetal bovine serum and 1% penicillin-streptomycin. Cells were cultured at 37 ℃ and 5% CO₂.

RNA interference and reverse transcription-quantitative PCR

When cells reached the appropriate density, PPOX was silenced using siRNA oligonucleotides, with transfection performed using RFect. The siRNA sequences for PPOX used in the present study were as follows: Sense, 5'-GCUGAGCAAACCCAUCGUUTT-3'; antisense, 5'-AACGAUGGGUUUGCUCAGCTT-3'; sense 5'-ACUAGAGUCAGCUAGGCAATT-3'; antisense 5'-UUGCCUAGCUGACUCUAGUTT-3'. The negative control sequences used in the present study were as follows: Sense, 5'-UUCUCCGAACGUGUCACGUTT-3'; antisense, 5'-ACGUGACACGUUCGGAGAATT-3'. Trizol was used to isolate and purify total RNA from cell lines. The expression of PPOX was evaluated using reverse transcription-quantitative PCR. The primer sequences used are as follows: GAPDH-forward: 5'-GGAAGGTGAAGGTCGGAGTCA-3'; GAPDH-reverse: 5'-GTCATTGATGGCAACAATATCCACT-3'. PPOX-forward: 5'-CTGGATTCGCTCCGTTCGAG-3'; PPOX-reverse: 5'-CCCACGTAGAGGAACCTGT-3'.

Western blot

Total cellular protein was extracted using RIPA buffer, and PMSF and phosphatase inhibitors were added to prevent protein degradation. Protein samples were denatured by heat treatment after quantitative analysis by BCA assay. Proteins were separated using 10% SDS-PAGE electrophoresis and subsequently transferred to a PVDF membrane. Blocking of non-specific binding sites was achieved using skim milk powder at room temperature for 1 h. Membranes were incubated with primary antibodies at 4 ℃ overnight and then incubated with horseradish peroxidase-conjugated secondary antibodies for 1 h at room temperature. Target protein expression in cell lines was semi-quantified using ECL chemiluminescence. The antibodies used in the experiments were as follows: anti-β-catenin (Proteintech, Cat No. 51067-2-AP, AB_2086128), anti-GAPDH (Proteintech, Cat No. 60004-1-Ig, AB_2107436), anti-c-Myc (Abcam, ab32072, AB_731658), anti-PPOX (Proteintech, Cat No. 14870-1-AP, AB_10597704) and anti-Cyclin D1 (Proteintech, Cat No. 60186-1-Ig, AB_10793718).

Immunohistochemistry (IHC)

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of The Second Hospital of Tianjin Medical University (No. KY2026K009) and individual consent for this retrospective analysis was waived. Formalin-fixed paraffin-embedded tissue blocks were collected from seven paired surgical specimens of patients with ccRCC. Consecutive 4-µm sections were prepared from both tumor and adjacent non-tumor tissues for subsequent experimental analysis. Endogenous peroxidase activity was blocked (Beijing Zhongshan Golden Bridge Biotechnology Co., Ltd.). Samples were incubated with primary antibodies and incubated overnight in a 4 ℃ refrigerator. The samples were then incubated with secondary antibodies for 30 minutes, followed by color development using DAB reagent. The samples were counterstained with hematoxylin, washed to restore blue coloration, dehydrated, mounted, and finally examined under a microscope. Immunohistochemical staining results were quantified using ImageJ software (version 1.54). The H-DAB mode in the IHC Toolbox plug-in was applied to identify stained areas, and the positively stained area was measured for comparative analysis.

EdU, colony formation, and CCK-8 assays

Cells were inoculated into 24-well plates, cultured to the appropriate density and then incubated with EdU reagent. The incubated cells were fixed using a cell fixative, permeabilized with TritonX-100, and processed using the kit (Abbkine Scientific Co., Ltd., Wuhan, China). Nuclei were stained with DAPI, and the proportion of proliferating cells was observed under a fluorescence microscope. For colony formation assays, cells were seeded in six-well plates at a density of 500 cells per well and incubated for 1–2 weeks until visible cell clusters formed. Cells were subsequently fixed, stained with crystal violet and counted using the software Image J. For the CCK-8 assay, cells were cultured in 96-well plates, and optical density (OD) at 450 nm was measured after 3 h of incubation with CCK-8 reagent (APExBIO Technology LLC) to assess cell viability. OD values were recorded at 24, 48, and 72 h to generate a cell proliferation curve.

Wound healing assay

Cells were cultured using six-well plates until they reached ~80% confluence. A linear scratch was created using a pipette tip. The treated cells were cultured using a serum-free medium. Images were captured under a microscope after cells were washed with phosphate-buffered saline (PBS).

Transwell assay

Melted Matrigel was diluted with RPMI-1640 medium and coated onto the basal membrane of Transwell chambers. Serum-starved cells were inoculated into small chambers and cultured in serum-free medium. Medium containing 10% fetal bovine serum was added to the lower chambers. Cells on the upper surface of the membrane were removed with a cotton swab. The remaining cells were fixed with paraformaldehyde, stained with crystal violet and counted under a microscope after washing with PBS. Following a 24-h incubation period, the chambers were removed, the cells from the upper surface were wiped off, and cells from the lower membrane were fixed and stained for microscopic observation.

Cultivation of glycerol stock bacteria, plasmid extraction, and plasmid transfection

LB medium was prepared using tryptone, yeast extract, sodium chloride and deionized water, followed by autoclaving. The purchased plasmids containing the target genes were amplified in the LB medium, and the target strains were selected using ampicillin. The target plasmids were then extracted using a plasmid mini-extraction kit. When the cells reached the appropriate density, plasmid transfection was performed using RFect Plasmid Transfection Reagent (Changzhou Bio-generating Biotechnology Co., Ltd.). The transfection complexes were added to the culture medium, and cells were incubated at 37 ℃ with 5% CO₂. Depending on cell status, the medium was replaced with fresh medium 12–24 h after transfection. The experimental groups were transfected with the PPOX overexpression plasmid, while the negative control group was transfected with an equal amount of the empty vector pcDNA3.1(+), which shares the identical backbone (including the CMV promoter and the ampicillin resistance gene) but lacks the PPOX coding sequence. Transfection efficiency and target gene expression were typically assessed 48–72 hours after transfection by RT-qPCR and Western blot analysis.

TOP/FOP-Flash luciferase reporter assay

Cells were plated at equal densities in 24-well plates and transfected at ~70% confluence. According to experimental groupings, transfection was performed using siRNA, TOPFlash (Beyotime Biotechnology, Shanghai, China), FOPFlash (Beyotime Biotechnology), and pRL-TK plasmids (Beyotime Biotechnology). After 48 h, cells were lysed and analyzed using the Dual-Luciferase Reporter Assay Kit (cat.no.RG027, Beyotime Biotechnology), following the manufacturer’s protocol. Firefly and Renilla luciferase activities were then measured for subsequent normalization and analysis.

Activation of Wnt/β-catenin signaling

The canonical Wnt/β-catenin pathway was activated using CHIR-99021 (MedChemExpress, Cat# HY-10182), a selective glycogen synthase kinase-3β (GSK-3β) inhibitor. A 10 mM stock solution was prepared in dimethyl sulfoxide (DMSO) and stored at −20 ℃. For all experiments, the stock was diluted in complete cell culture medium to achieve a final working concentration of 5 µM. Cells were treated with CHIR-99021 or vehicle control and maintained at 37 ℃ under a 5% CO₂ atmosphere for 24 hours (for Western blot analysis) or for the entire duration of the respective functional assays (proliferation, migration, and invasion assays as described in subsequent sections).

Statistical analysis

All statistical analyses were performed using R (v.4.1.3) and GraphPad Prism 8. In the bioinformatics analysis, the Wilcoxon rank-sum test was used to compare PPOX expression between tumor and normal tissues from independent samples, while the paired Wilcoxon signed-rank test was applied to paired samples from the same patients. Survival differences were assessed using Kaplan-Meier curves and the log-rank test for both OS and progression-free survival (PFS). Associations between PPOX expression and categorical clinical variables were examined using the Wilcoxon rank-sum test (for two groups) or the Kruskal-Wallis test (for multiple groups). Within the TME, immune cell fractions were estimated using the CIBERSORT algorithm (based on support vector regression), and stromal/immune scores were calculated using the ESTIMATE algorithm. Group comparisons of these immune features employed the Wilcoxon rank-sum test, while correlations with PPOX expression were analyzed using Spearman’s rank correlation (for immune cells) or Pearson correlation (for immune checkpoint genes). Drug sensitivity was predicted using the pRRophetic algorithm (based on ridge regression) and compared between groups via the Wilcoxon rank-sum test. Genome-wide differential expression analysis was conducted using the Wilcoxon rank-sum test, with *P* values adjusted by the Benjamini-Hochberg (FDR) method. Functional enrichment was evaluated via the hypergeometric test (for GO/KEGG) and GSEA using permutation testing. Co-expression networks were constructed based on Pearson correlation analysis. Independent prognostic factors for OS were identified through univariate and multivariate Cox proportional hazards regression. For normally distributed data obtained from in vitro and in vivo experiments, group comparisons were performed as follows: the unpaired two-tailed t-test for two independent groups, the paired two-tailed t-test for two related or matched groups, and one-way analysis of variance (ANOVA) followed by Tukey’s HSD post-hoc test for comparisons across more than two groups. All experiments were performed with at least three independent biological replicates, and similar results were obtained. Data shown in the figures are representative or are presented as the mean ±standard deviation (SD) [or standard error of the mean (SEM)] of the biological replicates.

Results

Differential expression of PPOX in normal tissues and ccRCC

After excluding tumors without normal controls, the TIMER revealed that PPOX expression was upregulated in 12 cancers, including KIRC (Figure 1A). Moreover, assessment of ccRCC transcriptome data from TCGA demonstrated that PPOX expression was higher in tumor tissues (FDR =1.674e−07; Figure 1B). The paired sample analysis is presented in Figure S1A. Furthermore, subcellular localization analysis revealed that PPOX is predominantly detected in mitochondria, vesicles and cytoplasmic lysates (Figure 1C). In this study, tumor tissues and paired adjacent normal tissue samples were collected from 7 patients with pathologically confirmed ccRCC at our center and analyzed by IHC. The results showed that PPOX expression was significantly elevated in tumor tissues compared with adjacent normal tissues (Figure 1D). Furthermore, the HPA database demonstrated PPOX expression in renal cancer cell lines (Figure 1E). The results of cell cluster annotation revealed that PPOX is highly expressed in tumor tissue, macrophages, and T cells (Figure 1F). Finally, the results of RT-qPCR and western blotting experiments performed by our research team showed that PPOX expression was higher in A498 and 769-P cells than in HK2 cells (Figure 1G).

Figure 1 Differential expression of PPOX in normal tissue and ccRCC. (A) Pan-cancer expression profile of PPOX (red: tumor tissue; blue: normal tissue); in the figures, *, ** and *** indicate P<0.05, P<0.01 and P<0.001, respectively. (B) PPOX expression in normal and tumor samples (*** indicate FDR <0.001, FDR =1.674×10⁻⁷). (C) The expression of PPOX in subcellular localization (https://www.proteinatlas.org/ENSG00000143224-PPOX/subcellular). (D) PPOX expression in ccRCC tissues and non-tumor tissues; the right panel of the figure shows the quantification of immunohistochemistry results from seven paired tumor and adjacent normal tissue samples. The paired t-test was used for statistical analysis. Scale bar: 100 μm. (E) Expression profiles of PPOX in renal cancer cell lines documented in the HPA database (https://www.proteinatlas.org/ENSG00000143224-PPOX/cell+line). (F) The results of cell cluster annotation and the expression of PPOX in cell clusters. (G) The mRNA and protein expression levels of PPOX in cell lines. All experiments were performed with at least three independent replicates. Statistical comparisons between two groups were analyzed by unpaired Student’s t-test using GraphPad Prism 8. ccRCC, clear cell renal cell carcinoma; FDR, false discovery rate; PPOX, protoporphyrinogen oxidase; TPM, transcripts per million; UMAP, Uniform Manifold Approximation and Projection.

PPOX expression and prognosis of ccRCC

Kaplan-Meier survival analysis demonstrated that PPOX expression levels significantly influenced OS in patients with ccRCC. Patients with high PPOX expression exhibited worse OS outcomes compared with those with low PPOX expression (Figure 2A). Moreover, Mendelian randomization analysis revealed that the odds ratio (OR) for PPOX was greater than 1, suggesting that PPOX is a risk factor for ccRCC. The analysis used the inverse-variance weighted (IVW) method (Figure 2B). Furthermore, the results of the Mendelian randomization analysis of druggable genes associated with ccRCC are presented in Figure S1B,S1C. PPOX expression levels were also revealed to be associated with patient gender in ccRCC. Female patients with ccRCC exhibited higher PPOX expression levels compared with that of male patients (Figure 2C). The associations between PPOX expression levels and clinical characteristics are presented in Figure S1D. The results showed that PPOX expression levels were not significantly associated with patient age, M stage, N stage, or tumor grade. However, significant differences were observed across T stages and overall pathological stages. Specifically, PPOX expression was significantly higher in T2 stage tumors compared to T1 stage tumors, and in stage II patients compared to stage I patients.

Figure 2 Analysis of PPOX expression and ccRCC prognosis, clinical features and survival prediction. (A) Kaplan-Meier analysis of PPOX expression and overall survival in ccRCC. (B) OR results of IVW approach to analyze the relevant role of PPOX in ccRCC. (C) Correlation between PPOX expression and gender. (D) Univariate Cox regression analyses of PPOX. (E) Multivariate Cox regression analyses of PPOX. (F,G) Nomograms and calibration curves for predicting survival probabilities. *, P<0.05; ***, P<0.001; ****, P<0.0001. CI, confidence interval; OR, odds ratio; PPOX, protoporphyrinogen oxidase.

A total of 537 patients with ccRCC were initially identified from the TCGA database. After excluding 16 patients due to incomplete data, 521 patients were included in the final prognostic model analysis. During follow-up, 173 deaths occurred, while 348 patients remained alive at the last follow-up. The median follow-up time was 3.25 years (maximum: 12.43 years). The univariate Cox regression analysis identified PPOX expression, age, dichotomized tumor grade (high vs. low), and AJCC stage as significant factors associated with OS in ccRCC (all P<0.001; Figure 2D). Subsequently, these variables were included in a multivariate Cox proportional hazards model. The analysis confirmed PPOX (HR: 1.660, 95% CI: 1.216–2.266, P=0.001), age, dichotomized tumor grade, and stage as independent prognostic factors (all P<0.05; Figure 2E). These four independent factors were then used to construct a prognostic nomogram for predicting 1-, 3-, and 5-year OS probabilities (Figure 2F).

The overall discriminative ability of the nomogram, quantified by the concordance index (C-index), was 0.768 (95% CI: 0.732–0.804). Time-dependent ROC analysis further demonstrated its discriminatory power at specific time points, with area under the curve (AUC) values of 0.859 (95% CI: 0.808–0.909) at 1 year, 0.802 (95% CI: 0.753–0.851) at 3 years, and 0.767 (95% CI: 0.711–0.823) at 5 years. Calibration curves showed good agreement between the nomogram-predicted survival probabilities and actual observed outcomes (Figure 2G). The baseline characteristics of the 521 patients included in the final analysis are summarized in Table S1 (Part A). The full equation of the final multivariable Cox model, including all regression coefficients and baseline survival probabilities, is also provided in Table S1 (Part B).

Co-expression and pathway enrichment analyses reveal associations between PPOX and cancer-related genes and pathways

The heatmap in Figure 3A shows the genes with the most pronounced differences in the high- and low-PPOX expression groups. Moreover, Figure 3B presents the PPOX-co-expressed genes identified through TCGA analysis, visualized in a circular plot representation. These included WDR90, CDK10, TRMT2A, CENPT, UBXN11, ZNF692, SEL1L, TMX4, IPO7, MTPN and YWHAB, all with correlation coefficients of >0.6. Notably, several of these highly correlated genes are recognized as key functional drivers in ccRCC progression; for instance, ZNF692 is a known modulator of the Wnt/β-catenin pathway, suggesting that PPOX operates within a biologically cohesive oncogenic network rather than as an isolated metabolic enzyme. Figure 3B illustrates the five genes with the smallest correlation coefficients with PPOX (namely, SEL1L, TMX4, IPO7, MTPN, and YWHAB), and the six genes with the largest correlation coefficients (namely, WDR90, CDK10, TRMT2A, CENPT, UBXN11 and ZNF692). A scatterplot illustrating the genes exhibiting the strongest positive and negative correlations with PPOX is displayed in Figure S1E. The results of the GO enrichment analysis circle maps included GO:0022412 (‘cellular process involved in reproduction in multicellular organisms’), GO:0031514 (‘motile cilium’), GO:0005216 (‘monoatomic ion channel activity’), and GO:0004252 (’serine-type endopeptidase activity’) (Figure 3C). The enrichment in endopeptidase activity and ion channel regulation hints at the involvement of PPOX-associated networks in modulating the extracellular matrix and cellular homeostasis, which are critical for tumor invasion. Additionally, the GO enrichment analysis results encompassed terms such as non-motile cilium and calcium ion transmembrane transporter activity. KEGG enrichment analysis revealed significant pathway associations, including with the Ras signaling pathway and neuroactive ligand-receptor interactions (Figure 3D). The association with the Ras pathway further underscores the potential synergy between PPOX-mediated metabolism and classical mitogenic signaling. Moreover, the results of GSEA included ascorbate and aldarate metabolism, drug metabolism by other enzymes, and pentose and glucuronate interconversions (Figure 3E). These findings suggest that PPOX serves as a metabolic hub, where its heme-biosynthetic activity is integrated with broader metabolic reprogramming to support the survival and drug resistance of ccRCC cells. GSEA enrichment analysis also suggested associations between PPOX and several cancer types including endometrial cancer and colorectal cancer.

Figure 3 Co-expression analysis of genes, GO enrichment, KEGG pathway analysis, and GSEA. (A) Heatmap of differentially expressed genes in PPOX high and low expression groups. (B) Circular diagram of genes co-expressed with PPOX, red indicates positive correlation and green indicates negative correlation. (C) Circle plot of GO enrichment analysis results, with the outermost circle representing the GO ID, the second circle indicating the number of genes in the pathway, the third circle denoting the amount of differentially expressed genes. (D) KEGG enrichment analysis results. (E) Results of Gene Set Enrichment Analysis. GO, Gene Ontology; GSEA, Gene Set Enrichment Analysis; ID, identifier; KEGG, Kyoto Encyclopedia of Genes and Genomes; PPOX, protoporphyrinogen oxidase.

Analysis of tumor immunology correlations suggests that PPOX mediates negative regulation of immune responses while potentially conferring immunotherapy benefits

The present study further investigated the relationship between PPOX and immune cells. Samples were divided into high- and low-expression groups based on PPOX expression and the immune cell expression levels were compared. The findings demonstrated that immune cells such as activated Tregs, follicular helper T cells, CD8 T cells, activated NK cells, and resting dendritic cells were more highly expressed in the high PPOX expression group than in the low expression groups (Figure 4A). Cell types, such as M2 macrophages, resting CD4 memory T cells, M0 macrophages, neutrophils, resting mast cells, activated dendritic cells and naive B cells also displayed lower expression levels in the high PPOX expression group compared with that in the low expression group. Figure 4B illustrates the correlation between PPOX and immune cells. Moreover, Figure 4C presents a heatmap of the correlation analysis between PPOX and immune checkpoints. PPOX expression was positively correlated with genes including TNFRSF25, TNFRSF14, ADORA2A and IDO2, and negatively correlated with genes such as CD44 and NRP1. Furthermore, the relationship between PPOX expression and immunotherapy was analyzed and the results revealed that, compared with that in the low expression group, the PPOX high-expression group scored higher in the CTLA4-positive group. This suggests that patients with elevated PPOX expression may achieve more favorable outcomes with anti-CTLA4 immunotherapy (Figure 4D). Analysis using the ESTIMATE algorithm indicated that compared with the low-expression group, the high PPOX expression group exhibited a lower stromal score, while no significant differences were observed in the immune score or combined score (Figure S2A). Using the IMvigor210 cohort, we further analyzed the relationship between PPOX expression and treatment with atezolizumab (anti-PD-L1) in metastatic urothelial carcinoma. The analysis revealed that PPOX expression was higher in patients who responded to immunotherapy than in non-responders (Figure S2B).

Figure 4 Analysis of the correlation between PPOX and tumor immunity. (A) Results of the differential analysis of immune cells, red and blue colors represent high and low expression of PPOX, respectively. *, ** and *** indicate FDR <0.05, FDR <0.01, FDR <0.001, respectively. (B) Relationships between PPOX and various immune cells, with the horizontal coordinates and circle sizes representing correlation coefficients, and the vertical axis on the right indicating P values. (C) Correlation analysis between PPOX and various immune checkpoints. (D) Correlation between PPOX expression and immunotherapy. FDR, false discovery rate; PPOX, protoporphyrinogen oxidase.

Knockdown of PPOX inhibits the proliferation, migration, and invasion of ccRCC cells and downregulates the activity of the Wnt/β-catenin signaling pathway

All experiments in this section were performed by our research team. A498 and 769-P cells were selected for subsequent experiments. siRNA was used to interfere with PPOX expression and the knockdown efficiency of PPOX was assessed using RT-qPCR and western Blot analysis (Figure 5A,5B). Subsequent cell function experiments were performed using cells in which PPOX expression was successfully knocked down. CCK-8, colony formation and EdU assays were then employed to evaluate changes in cell proliferation. The CCK-8 assay results revealed a reduced cell proliferation profile and slower growth rate following PPOX knockdown (Figure 5C). The colony formation assay demonstrated a diminished capacity for single-cell clonogenicity following PPOX knockdown (Figure 5D). Furthermore, the EdU experiment demonstrated that interfering with PPOX reduced the number of proliferating cells compared with that of normal controls (Figure 5E). These results indicate a decrease in cell proliferation following interference with PPOX expression levels.

Figure 5 PPOX knockdown inhibits the proliferation of ccRCC. (A,B) Knockdown efficiency was verified by RT-qPCR and Western blot. (C-E) CCK-8, colony formation, and EdU assays (magnification, 100×) were employed to verify that PPOX knockdown inhibits cell proliferation. All experiments were performed with at least three independent replicates. Statistical comparisons between two groups were analyzed by unpaired Student’s t-test using GraphPad Prism 8. Scale bar: 100 μm. CCK-8, Cell Counting Kit-8; ccRCC, clear cell renal cell carcinoma; EdU, 5-Ethynyl-2'-deoxyuridine; NC, negative control; PPOX, protoporphyrinogen oxidase; RT-qPCR, reverse transcription quantitative real-time polymerase chain reaction.

Moreover, the wound healing assay revealed a reduced wound closure area in the PPOX knockdown group compared with that in control cells, suggesting that cell migration was impaired (Figure 6A). Knockdown of PPOX expression also reduced cell invasion, as evidenced by fewer cells invading to the bottom of the chamber (Figure 6B). These findings indicate that inhibition of PPOX expression levels diminishes cell migration and invasion capabilities. Furthermore, systematic experimental screening demonstrated that knockdown of PPOX in ccRCC cell lines led to the downregulation of β-catenin, c-Myc and Cyclin D1. This suggests a reduction of Wnt/β-catenin pathway activity (Figure 6C).

Figure 6 PPOX knockdown inhibits the migratory and invasive capacities of ccRCC cells and downregulates Wnt/β-catenin signaling pathway activity. (A) Wound healing assay confirmed that PPOX knockdown inhibited cell migration (magnification, 40×). (B) Transwell assay confirmed that PPOX knockdown inhibited cell invasion (stained with crystal violet; magnification, 100×). (C) PPOX knockdown reduced Wnt/β-catenin pathway activity. All experiments were performed with at least three independent replicates. Statistical comparisons between two groups were analyzed by unpaired Student’s t-test using GraphPad Prism 8. Scale bar: 100 μm. NC, negative control; PPOX, protoporphyrinogen oxidase.

PPOX overexpression enhances the proliferative, migratory, and invasive capacities of ccRCC cells and upregulates Wnt/β-catenin pathway activity

Experiments in this section were conducted in our laboratory. Overexpression plasmids successfully upregulated PPOX expression in A498 and 769-P cells (Figure 7A). Moreover, the results of the CCK-8, colony formation, and EdU assays indicated that PPOX upregulation significantly enhanced the proliferation of ccRCC cells (Figure 7B-7D). The wound healing assay also demonstrated that overexpression of PPOX was associated with an increase in cell migration (Figure 7E). Transwell assay results revealed that overexpression of PPOX increased cell invasion (Figure 7F), and the expression levels of β-catenin, c-Myc and Cyclin D1 were also demonstrated to be upregulated following PPOX overexpression (Figure 7G).

Figure 7 PPOX overexpression enhances the proliferative, migratory, and invasive capacities of ccRCC cells and upregulates Wnt/β-catenin pathway activity. (A) PPOX overexpression efficiency in A498 and 769-P cells. (B-D) CCK-8, colony formation, and EdU assays (EdU and DAPI staining; magnification, 100×) confirmed that cell proliferation was elevated after PPOX overexpression. (E,F) Wound healing (40×) and Transwell assays (stained with crystal violet; magnification, 100×) confirmed that PPOX overexpression enhances cell migration and invasion. (G) PPOX overexpression enhanced Wnt/β-catenin pathway activity. All experiments were performed with at least three independent replicates. Statistical comparisons between two groups were analyzed by unpaired Student’s t-test using GraphPad Prism 8. Scale bar: 100 μm. NC, negative control; OD, optical density; OE, overexpression; PPOX, protoporphyrinogen oxidase.

PPOX regulates malignant progression of ccRCC through the Wnt/β-catenin pathway

All experiments in this section were performed by our research team. TOP/FOP Flash reporter assays were performed to assess Wnt/β-catenin pathway activity. The lowest TOP/FOP Flash ratios occurred in A498 and 769-P cells with PPOX knockdown, and the highest ratios were observed in PPOX-overexpressing cells (Figure 8A). Western blot analysis demonstrated that the Wnt/β-catenin pathway activator CHIR-99021 (5 µM) upregulated β-catenin, c-Myc, and CCND1 expression in both cell lines. CHIR-99021 restored the expression of these pathway components in PPOX-knockdown cells (Figure 8B). EdU assays consistently demonstrated reduced proliferation in PPOX-knockdown cells. Subsequently, CHIR-99021 treatment enhanced proliferation and rescued the impaired growth in the knockdown cells (Figure 8C). Wound healing assays revealed decreased migration in PPOX-knockdown cells, and CHIR-99021 treatment enhanced migration and reversed this deficiency (Figure 8D). Finally, Transwell invasion assays demonstrated reduced invasion in PPOX-knockdown cells, and CHIR-99021 treatment partially restored the invasive capability (Figure 8E).

Figure 8 PPOX regulates malignant progression of ccRCC through the Wnt/β-catenin pathway. (A) TOP/FOP Flash reporter activity ratios in A498 and 769-P cells following PPOX knockdown or overexpression. (B) Protein expression levels of β-catenin, c-Myc and CCND1 in A498 and 769-P cells after PPOX knockdown and CHIR-99021 treatment. (C) Cell proliferation assessed by EdU assays (magnification, 100×) under PPOX knockdown and CHIR-99021 treatment. (D) Cell migration evaluated by wound healing assays (magnification, 40×). (E) Cell invasion analyzed by Transwell assays (stained with crystal violet; magnification, 100×). All experiments were performed with at least three independent replicates. Two-group comparisons were analyzed using the unpaired t-test, whilst multiple groups (>2) were compared using one-way ANOVA, followed by Tukey’s HSD test for post hoc pairwise comparisons where applicable. Scale bar: 100 μm. ANOVA, analysis of variance; ccRCC, clear cell renal cell carcinoma; HSD, Honestly Significant Difference; NC, negative control; PPOX, protoporphyrinogen oxidase.

Drug sensitivity analysis

The results of the drug sensitivity analysis demonstrated that the PPOX high-expression group exhibited lower predicted IC₅₀ values for 5-fluorouracil (5-FU), doxorubicin, erlotinib, gefitinib, SN-38, tipifarnib, tubastatin A, salubrinal, phenformin, OSI-027, LFM-A13, XMD13-2, and PF-4708671, compared with the low-expression group (Figure 9A) The remaining highly sensitive drugs are presented in Figure S2C. Moreover, Figure 9B demonstrates the results of molecular docking of these drugs with PPOX protein molecules. Molecular docking analysis using CB-Dock2 revealed distinct binding profiles for four compounds targeting the active region of PPOX (PDB: 3NKS), with precise binding sites elucidated as follows. 5-FU was predicted to bind to a surface hydrophobic pocket (Cavity ID: 1; center: −30.1, −3.2, 39.6; volume: ~2411 ų; docking score: −5.2). Its binding site involves 39 residues of Chain A, crucially encompassing the catalytic residues HIS333 and MET368 from the classical substrate-binding pocket, suggesting a potential allosteric or partial active-site engagement. In contrast, three anticancer drugs—doxorubicin, erlotinib, and SN-38—were all predicted to bind directly within the enzyme’s catalytic substrate-binding pocket (same center and cavity as above), but with distinct interaction patterns: Doxorubicin exhibited the strongest predicted affinity (Vina score: −10.0) and the most extensive binding interface (68 residues). Its binding site fully encompasses the canonical active site, including residues from the FAD-binding motif (GLY9, GLY10, GLY11, ILE12, SER13, ARG59), the substrate-anchoring “arginine triad” (ARG59, ARG97, ARG168), the essential catalytic residues (HIS333, MET368), and the surrounding hydrophobic wall (e.g., PHE331, LEU344, VAL347). SN-38 (active metabolite of irinotecan) also showed high-affinity binding (score: −9.2) with a broad interface (53 residues). Its binding site similarly covers the complete set of key functional residues, including the entire FAD-binding motif, the arginine triad, HIS333, MET368, and the core hydrophobic residues. Erlotinib displayed a more focused binding mode (score: −8.1; 42 residues). Its predicted binding site is centered on the substrate-recognition region, primarily engaging the “arginine triad” (ARG59, ARG97, ARG168) and HIS333, indicating a mechanism potentially focused on competitively disrupting substrate anchoring. The remaining molecular docking results are presented in Figure S2D. Table S2 includes key parameters necessary for assessing docking reliability, such as the cavity volume (volume of the predicted binding pocket), the center coordinates (x, y, z) of the predicted binding pocket in 3D space, and the docking size (dimensions of the search space box defined by CB-Dock2 for the docking calculation).

Figure 9 Correlation between drug sensitivity and PPOX expression levels. (A) Relationship between drug sensitivity and PPOX expression, red and blue colors represent high and low expression of PPOX, respectively. (B) Schematic representation of molecular docking between candidate drugs and the PPOX protein, with predicted binding free energies indicated in the diagram. IC₅₀, half maximal inhibitory concentration; PPOX, protoporphyrinogen oxidase.

Discussion

ccRCC is a common malignant tumor affecting human health and therefore is of great interest to the scientific community. Despite notable improvements in patient survival rates due to advancements in surgical techniques and other therapeutic approaches, there is an urgent need for further research into the precise management of individuals with advanced metastatic disease, with the ultimate goal of enhancing their prognostic outcomes.

Based on existing literature, PPOX associations with cancer are observed primarily in digestive and urinary system tumors. As PPOX functions as a key enzyme in heme biosynthesis, and these organ systems are notably responsible for heme intake, metabolism, and byproduct excretion, further investigation in this area holds considerable scientific merit. In particular, the expression and functional role of PPOX in renal cancer remain largely unexplored. During the investigation of druggable targets, in the present study, the potential significance of the gene PPOX in ccRCC was identified. Furthermore, by integrating database resources, clinical patient samples and established cellular models, the present study has initially established the role of PPOX as an oncogene that promotes tumor development. Furthermore, during Mendelian randomization screening for drug-eligible genes in ccRCC, PPOX was identified as a risk factor. Nevertheless, a limitation was the scarcity of SNPs obtained as instrumental variables from the relevant data, which constrained the credibility of the analysis results. Consequently, these SNP data serve primarily to illustrate the screening workflow and initial study design. The core findings of our research instead derive from subsequent experimental exploration and validation. The prognostic nomogram demonstrated promising initial performance within the development cohort, with a C-index of 0.768 and time-dependent AUCs of 0.859, 0.802, and 0.767 at 1, 3, and 5 years, respectively. The calibration curves also indicated satisfactory agreement. However, these results are based on internal assessment and are thus optimistic. The model’s generalizability requires confirmation through external validation in future independent studies.

To elucidate the potential mechanisms of PPOX in ccRCC, the present study investigated PPOX-related genes and pathways based on PPOX expression levels. Our co-expression analysis revealed that PPOX is not merely a metabolic byproduct, but more likely acts as a functional hub integrated within the oncogenic framework of ccRCC. SEL1L, the gene which had the greatest negative association with PPOX, is a key component of the endoplasmic reticulum-associated degradation complex. Deletion of SEL1L has been implicated in glomerular filtration dysfunction and nephrotic syndrome (17). Wang et al. reported that SEL1L3, as a homolog of SEL1L, could affect renal cancer progression by regulating ErbB/PI3K/mTOR, apoptosis and immune cell infiltration (18). This inverse relationship suggests that PPOX upregulation may contribute to the suppression of ER-associated degradation, potentially leading to the accumulation of various oncoproteins. Moreover, the ZNF692 gene had the greatest positive association with PPOX. Wang et al. reported that ZNF692 was highly expressed in ccRCC. It promoted ccRCC cell proliferation and migration, and may have mediated immune escape (19). In the co-expression analysis in the present study, genes strongly associated with PPOX were linked to the Wnt/β-catenin signaling pathway. Notably, ZNF692 serves as a transcriptional regulator of Wnt signaling, suggesting a potential synergistic effect between PPOX-mediated metabolism and ZNF692-driven signaling. The studies of Jiang et al. and Barbosa-Silva et al. indicated that YWHAB may also be involved in the Wnt/β-catenin pathway as a downstream of β-catenin, and affect the apoptosis and proliferation of gastric cancer cells (20,21). Moreover, Cao et al. reported that YWHAB exhibits tumor-suppressive properties through CHOP-mediated inhibition of the Wnt/β-catenin pathway activity in glioma (22). Wang et al. also reported that ZNF692 could regulate the activity of the Wnt pathway by affecting target genes such as LIN28A, HTR5A, IRF4, MAPK8IP2, FLT4, and ZC3H18 (19).

The association between PPOX expression and Wnt/β-catenin signaling, while observed in our preliminary experiments, warrants further mechanistic investigation. Based on existing literature, we propose several plausible models for this regulation. First, as a key enzyme in heme biosynthesis, the upregulation of PPOX increases the intracellular heme pool, establishing it as an effective metabolic rheostat. By increasing the intracellular heme pool, PPOX could affect the function of heme-containing proteins. For instance, heme-dependent modulation of cytochrome c, which exhibits functional crosstalk with the Wnt/β-catenin pathway (23,24), may alter mitochondrial respiratory activity and apoptotic thresholds, thereby creating a cellular environment conducive to pathway activation. Second, heme serves as the core prosthetic group of catalase. Enhanced PPOX activity could upregulate catalase function, thereby enabling the fine-tuned regulation of intracellular H₂O₂ levels. This precise modulation of the redox microenvironment is known to stabilize β-catenin by preventing its oxidative modification, which in turn promotes its nuclear translocation and transcriptional activity (25,26). Third, elevated heme levels may directly inhibit the tumor suppressor APC, a key negative regulator of the pathway. By acting as a metabolic inhibitor of the “destruction complex”, PPOX-derived heme releases the constraint on β-catenin degradation, providing a direct biochemical link between heme synthesis and Wnt activation (27-29).

Beyond this enzyme activity-dependent mechanism, PPOX may also function independently by directly interacting with components of the Wnt/β-catenin pathway. For example, mitochondrial-to-cytoplasmic translocation of PPOX could allow it to act as a stabilizing scaffold for β-catenin, binding directly to β-catenin, Axin, or GSK3β to prevent β-catenin phosphorylation and degradation (30). Alternatively, PPOX might participate in the trafficking of Wnt receptors, such as Frizzled or LRP5/6, by influencing their endocytosis and recycling—a critical step in signal transduction (31).

These non-canonical roles suggest a multifunctional capacity for PPOX in oncogenic signaling. In summary, these results indicate that PPOX may be a multi-modal regulatory factor linking metabolic reprogramming with the Wnt/β-catenin axis to drive the malignancy of ccRCC.

Previous studies have also demonstrated that Tregs inhibit anticancer immunity, protective immune surveillance and antitumor immune responses (32-34). Therefore, despite the high expression of activated NK cells and CD8 T cells in the PPOX-overexpressing group in the present study, the immunosuppressive effects of Tregs may prevent these cells from fully exerting their antitumor functions. The low expression of resting CD4 memory T cells, neutrophils, M0 macrophages, neutrophils, naive B cells and other immune cells in the PPOX high-expression group suggests suppression of anti-tumor immunity (35,36). PPOX expression demonstrated a positive correlation with resting dendritic cells and a negative correlation with activated dendritic cells. This pattern suggests impaired immune function of dendritic cells in the PPOX high-expression group (37). Moreover, PPOX expression showed positive correlations with several immune checkpoint molecules, including TNFRSF14, ADORA2A and IDO2. These associations suggest that PPOX may promote tumor progression and immune evasion through suppression of immune cell activity (38-40). Studies also indicate that patients with high PPOX expression may show improved response to anti-CTLA-4 immunotherapy. This observation suggests that PPOX-associated immune responses could provide new directions for investigating disease progression and developing therapeutic strategies. These findings indicate that the associated immune response may represent a novel direction for studying disease progression and therapeutic strategies. To translate these findings into therapeutic insights, future studies should first seek to functionally validate the role of Tregs by isolating or inducing them from models with differential PPOX expression and assessing their suppressive capacity in vitro. Subsequently, establishing mouse tumor models with modulated PPOX expression will be crucial to comprehensively profile the resultant changes in the intratumoral immune landscape via flow cytometry. Ultimately, therapeutic intervention in these models using agents such as anti-CTLA-4 antibodies will be essential to functionally test the predictions generated herein and evaluate the translational potential of targeting this immune axis.

Integrating the bioinformatics analysis from this study with existing literature, we propose a potential mechanistic model through which PPOX may regulate the tumor immune microenvironment. As a key enzyme in heme biosynthesis, alterations in PPOX activity may profoundly affect mitochondrial respiratory chain function and cellular metabolic states. Within the TME, this establishes a dual regulatory foundation. First, for T cells, mitochondrial metabolic stress may upregulate immune checkpoint molecules such as ADORA2A and IDO2, leading to an “activated but dysfunctional” phenotype (41). Concurrently, changes in mitochondrial functional status may inhibit apoptotic pathways, allowing such dysfunctional T cell subsets to persist in the microenvironment. Second, for innate immune cells such as macrophages, PPOX may influence mitochondrial OXPHOS and related NLRP3 inflammasome signaling (42), shifting their polarization balance toward the suppression of the M2 phenotype (43,44). Together, these effects shape a unique microenvironment characterized by activated effector T cells, enrichment of Tregs, yet accompanied by widespread immune checkpoint expression and a reduction in M2 macrophages. This model provides a plausible explanation for why tumors with high PPOX expression, despite presenting an immunosuppressive landscape, show greater sensitivity to PD-1/PD-L1 blockade therapy: the PD-1/PD-L1 pathway acts as a critical “brake” on T cell function in this setting, and its release can effectively reverse immune suppression. Of course, this model still requires rigorous experimental validation in subsequent studies.

Therapeutic modalities for ccRCC encompass surgical resection, targeted therapy, immunotherapy, radiotherapy and chemotherapy. Surgical intervention remains the primary treatment for early-stage renal cancer, whereas targeted therapy and immunotherapy are predominant strategies for advanced renal cancer. The present study identified potential novel therapeutic agents by assessing differential predicted IC₅₀ values, revealing that PPOX high-expression patients exhibit enhanced sensitivity to various anticancer agents. This suggests that PPOX may serve as a functional biomarker to guide personalized drug selection. The molecular basis for this increased sensitivity likely stems from the interplay between PPOX-mediated metabolism and oncogenic signaling. Notably, certain drug mechanisms of action are also associated with the Wnt/β-catenin signaling pathway. Studies have reported that there may be a dual influence of 5-FU on the Wnt/β-catenin pathway and this influence may be related to cell type, drug concentration and cellular microenvironment. A number of these hypotheses have been explored in cellular experiments (45). The combined administration of doxorubicin and zymosan nanoparticles has been reported to demonstrate potent anti-colorectal cancer activity, mediated through modulation of the Wnt signaling pathway and induction of apoptotic cell death (46). Doxorubicin-induced cardiomyocyte apoptosis has been reported to exhibit a strong association with suppression of the Wnt/β-catenin signaling pathway. Doxorubicin has also been reported to promote the dissociation of TBL1 from β-catenin, consequently suppressing the activity of the Wnt/β-catenin signaling pathway (47). Therefore, in PPOX-high ccRCC where the Wnt pathway is hyperactivated, these agents may exert a more profound inhibitory effect by targeting the signaling dependencies of PPOX-high ccRCC, leading to increased apoptotic cell death. In the literature, erlotinib was reported to markedly reduce the p-β-catenin/β-catenin protein ratio, suggesting that erlotinib may inhibit the phosphorylation of β-catenin. The activity of the Wnt/β-catenin signaling pathway may be attenuated due to reduced nuclear accumulation of β-catenin (48). In addition to the aforementioned agents, compounds such as SN-38, Tubastatin A and OSI-027 have been reported in the literature to exhibit direct or indirect associations with the Wnt/β-catenin signaling pathway.

The precise binding of 5-FU, doxorubicin, and SN-38 within the catalytic pocket of PPOX suggests that these compounds might act as competitive inhibitors of PPOX enzymatic activity. By potentially disrupting PPOX-mediated heme biosynthesis, these drugs could trigger a “double-hit” effect: directly inhibiting their canonical targets while simultaneously neutralizing the PPOX-Wnt metabolic-signaling axis. This mechanism titrates the intracellular redox state and releases the metabolic constraints that support tumor survival. From a clinical strategy perspective, these findings advocate for a stratified treatment approach. Patients identified with high PPOX expression may derive superior benefits from a combination of Wnt-targeting agents and conventional chemotherapeutics. Future therapeutic designs could focus on developing dual-action inhibitors that target both the enzymatic pocket of PPOX and its downstream signaling effectors to overcome the characteristic drug resistance of advanced ccRCC.

The present study presents an initial investigation of PPOX in ccRCC, focusing on its discovery process and summarizing its potential functions. Whilst preliminary in vitro experiments were performed, the findings lack in vivo validation. It should be noted that PPOX was identified as an independent prognostic factor based solely on a retrospective analysis from a single public database. The patient cohort in TCGA may not fully represent the broader ccRCC population. More importantly, the current model lacks external validation, and its real-world performance in independent cohorts remains to be confirmed. This limitation stems from the exploratory nature of the present study, whose primary aim was to verify the prognostic association of PPOX. Therefore, the model should be regarded as a proof-of-concept and visualization tool, rather than a mature clinical prediction model. Future work requires validation in multi-center prospective cohorts and should integrate multi-omics features. Moreover, although alterations in the Wnt pathway were observed, the underlying mechanisms require further elucidation. The proposed hypotheses also need additional experimental verification. Furthermore, all current analyses of the relationship between PPOX and the immune system rely solely on bioinformatic approaches. These findings lack validation through in vivo or in vitro experiments. These limitations should be addressed through subsequent in vivo studies. Additional investigations should also include in vitro co-culture systems and immunocompetent mouse tumor models. These approaches will enable deeper exploration of PPOX-immune system interactions. Furthermore, multi-omics sequencing should be performed to identify PPOX-interacting proteins and clarify its regulatory mechanisms within the Wnt/β-catenin pathway. Additionally, experimental validation of potential therapeutic compounds based on omics findings has yet to be performed. Given the large number and variety of drugs in the preliminary screening, the current results cannot provide more precise guidance for clinical research and therapy. Further experimental studies are needed to identify more effective agents. Based on our analysis, future experimental efforts will prioritize screening drugs that target DNA damage response and epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors. Additionally, potential therapeutic compounds should be screened based on omics results for experimental validation. Whilst the present study has initially outlined the discovery process of the oncogenic role of PPOX in ccRCC, subsequent investigations should establish a foundation for verifying the mechanisms of this gene and advance clinical translation. These efforts may ultimately provide new perspectives for ccRCC treatment research.

Conclusions

In conclusion, the present study provides the first comprehensive characterization, to the best of our knowledge, of PPOX expression and prognostic significance in ccRCC, while exploring underlying mechanisms. Moreover, the results indicate that PPOX may serve as a promising diagnostic and prognostic biomarker for ccRCC and could offer new insights and therapeutic targets for the management of related malignancies.

AcknowledgmentsW

We acknowledge all publicly available databases for providing accessible and comprehensive data, which have been instrumental in advancing this research.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist. Available at https://tau.amegroups.com/article/view/10.21037/tau-2026-1-0024/rc

Data Sharing Statement: Available at https://tau.amegroups.com/article/view/10.21037/tau-2026-1-0024/dss

Peer Review File: Available at https://tau.amegroups.com/article/view/10.21037/tau-2026-1-0024/prf

Funding: This research was funded by Tianjin Key Medical Discipline Construction Project (grant No. TJYXZDXK-3-003A); Clinical Research of Tianjin Medical University (No. 2018kylc004); Youth Scientific Research Fund of The Second Hospital of Tianjin Medical University (No. 2023ydey04); and Youth Scientific Research Fund of The Second Hospital of Tianjin Medical University (No. 2022ydey10).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tau.amegroups.com/article/view/10.21037/tau-2026-1-0024/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the Ethics Committee of The Second Hospital of Tianjin Medical University (No. KY2026K009) and individual consent for this retrospective analysis was waived.

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

References

1. Siegel RL, Kratzer TB, Giaquinto AN, et al. Cancer statistics, 2025. CA Cancer J Clin 2025;75:10-45. [Crossref] [PubMed]
2. Bex A, Ghanem YA, Albiges L, et al. European Association of Urology Guidelines on Renal Cell Carcinoma: The 2025 Update. Eur Urol 2025;87:683-96. [Crossref] [PubMed]
3. Fiorito V, Chiabrando D, Petrillo S, et al. The Multifaceted Role of Heme in Cancer. Front Oncol 2019;9:1540. [Crossref] [PubMed]
4. Adapa SR, Hunter GA, Amin NE, et al. Porphyrin overdrive rewires cancer cell metabolism. Life Sci Alliance 2024;7:e202302547. [Crossref] [PubMed]
5. Kaur P, Nagar S, Bhagwat M, et al. Activated heme synthesis regulates glycolysis and oxidative metabolism in breast and ovarian cancer cells. PLoS One 2021;16:e0260400. [Crossref] [PubMed]
6. Hooda J, Cadinu D, Alam MM, et al. Enhanced heme function and mitochondrial respiration promote the progression of lung cancer cells. PLoS One 2013;8:e63402. [Crossref] [PubMed]
7. Wang B, Bonkovsky HL, Lim JK, et al. AGA Clinical Practice Update on Diagnosis and Management of Acute Hepatic Porphyrias: Expert Review. Gastroenterology 2023;164:484-91. [Crossref] [PubMed]
8. Petrillo S, De Giorgio F, Kopecka J, et al. Endothelial Heme Dynamics Drive Cancer Cell Metabolism by Shaping the Tumor Microenvironment. Biomedicines 2021;9:1557. [Crossref] [PubMed]
9. Gong W, Ge M, Xi X, et al. ALAD as a prognostic biomarker regulates metabolism and immune responses in renal cell carcinoma through multi-omics analysis. Transl Oncol 2025;61:102524. [Crossref] [PubMed]
10. Schneider-Yin X, van Tuyll van Serooskerken AM, Siegesmund M, et al. Biallelic inactivation of protoporphyrinogen oxidase and hydroxymethylbilane synthase is associated with liver cancer in acute porphyrias. J Hepatol 2015;62:734-8. [Crossref] [PubMed]
11. Haverkamp T, Bronisch O, Knösel T, et al. Heterogeneous molecular behavior in liver tumors (HCC and CCA) of two patients with acute intermittent porphyria. J Cancer Res Clin Oncol 2023;149:2647-55. [Crossref] [PubMed]
12. Bazzocco S, Dopeso H, Carton-Garcia F, et al. Highly Expressed Genes in Rapidly Proliferating Tumor Cells as New Targets for Colorectal Cancer Treatment. Clin Cancer Res 2015;21:3695-704. [Crossref] [PubMed]
13. Kurumi H, Kanda T, Kawaguchi K, et al. Protoporphyrinogen oxidase is involved in the fluorescence intensity of 5-aminolevulinic acid-mediated laser-based photodynamic endoscopic diagnosis for early gastric cancer. Photodiagnosis Photodyn Ther 2018;22:79-85. [Crossref] [PubMed]
14. Riester M, Werner L, Bellmunt J, et al. Integrative analysis of 1q23.3 copy-number gain in metastatic urothelial carcinoma. Clin Cancer Res 2014;20:1873-83. [Crossref] [PubMed]
15. Li T, Fan J, Wang B, et al. TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells. Cancer Res 2017;77:e108-10. [Crossref] [PubMed]
16. Thul PJ, Åkesson L, Wiking M, et al. A subcellular map of the human proteome. Science 2017;356:eaal3321. [Crossref] [PubMed]
17. Yoshida S, Wei X, Zhang G, et al. Endoplasmic reticulum-associated degradation is required for nephrin maturation and kidney glomerular filtration function. J Clin Invest 2021;131:e143988. [Crossref] [PubMed]
18. Wang H, Ma X, Li S, et al. SEL1L3 as a link molecular between renal cell carcinoma and atherosclerosis based on bioinformatics analysis and experimental verification. Aging (Albany NY) 2023;15:13150-62. [Crossref] [PubMed]
19. Wang H, Zhao J, He H, et al. ZNF692 promote proliferation through transcriptional repression of essential genes in clear cell renal carcinoma. Biochem Biophys Res Commun 2023;671:255-62. [Crossref] [PubMed]
20. Jiang H, Xia J, Kang J, et al. Short hairpin RNA targeting beta-catenin suppresses cell proliferation and induces apoptosis in human gastric carcinoma cells. Scand J Gastroenterol 2009;44:1452-62. [Crossref] [PubMed]
21. Barbosa-Silva A, Magalhães M, Da Silva GF, et al. A Data Science Approach for the Identification of Molecular Signatures of Aggressive Cancers. Cancers (Basel) 2022;14:2325. [Crossref] [PubMed]
22. Cao L, Lei H, Chang MZ, et al. Down-regulation of 14-3-3β exerts anti-cancer effects through inducing ER stress in human glioma U87 cells: Involvement of CHOP-Wnt pathway. Biochem Biophys Res Commun 2015;462:389-95. [Crossref] [PubMed]
23. Shree Harini K, Ezhilarasan D. Cardionogen-1, a novel small molecule, induces cytotoxicity by inhibiting Wnt/β-catenin signalling pathway in Huh-7 cells. 3 Biotech 2026;16:57.
24. Kranz RG, Sutherland MC. Mechanisms and Control of Heme Transport and Incorporation into Cytochrome c. Annu Rev Microbiol 2025;79:23-43. [Crossref] [PubMed]
25. Catalano T, D'Amico E, Moscatello C, et al. Oxidative Distress Induces Wnt/β-Catenin Pathway Modulation in Colorectal Cancer Cells: Perspectives on APC Retained Functions. Cancers (Basel) 2021;13:6045. [Crossref] [PubMed]
26. Zhu J, Li X, Liang C, et al. Apatinib suppresses lung cancer stem-like cells by complex interplay between β-catenin signaling and mitochondrial ROS accumulation. Cell Death Discov 2021;7:102. [Crossref] [PubMed]
27. Hopp MT, Alhanafi N, Paul George AA, et al. Molecular Insights and Functional Consequences of the Interaction of Heme with Activated Protein C. Antioxid Redox Signal 2021;34:32-48. [Crossref] [PubMed]
28. Albrecht LV, Tejeda-Muñoz N, De Robertis EM. Cell Biology of Canonical Wnt Signaling. Annu Rev Cell Dev Biol 2021;37:369-89. [Crossref] [PubMed]
29. Saito-Diaz K, Benchabane H, Tiwari A, et al. APC Inhibits Ligand-Independent Wnt Signaling by the Clathrin Endocytic Pathway. Dev Cell 2018;44:566-581.e8. [Crossref] [PubMed]
30. Yu J, Liu D, Sun X, et al. CDX2 inhibits the proliferation and tumor formation of colon cancer cells by suppressing Wnt/β-catenin signaling via transactivation of GSK-3β and Axin2 expression. Cell Death Dis 2019;10:26. [Crossref] [PubMed]
31. MacDonald BT, He X. Frizzled and LRP5/6 receptors for Wnt/β-catenin signaling. Cold Spring Harb Perspect Biol 2012;4:a007880. [Crossref] [PubMed]
32. Zheng L, Wu D, Xie H, et al. Revisiting Tregs in cancer and beyond: immunological control and therapeutic potential. Front Immunol 2025;16:1581093. [Crossref] [PubMed]
33. Hao X, Liu L, Li S, et al. The regulatory role and mechanism of TGFBI on Tregs in the immune microenvironment of clear cell renal cell carcinoma. Int J Surg 2025;111:9134-46. [Crossref] [PubMed]
34. Li C, Jiang P, Wei S, et al. Regulatory T cells in tumor microenvironment: new mechanisms, potential therapeutic strategies and future prospects. Mol Cancer 2020;19:116. [Crossref] [PubMed]
35. Chaintreuil P, Kerreneur E, Bourgoin M, et al. The generation, activation, and polarization of monocyte-derived macrophages in human malignancies. Front Immunol 2023;14:1178337. [Crossref] [PubMed]
36. Jaillon S, Ponzetta A, Di Mitri D, et al. Neutrophil diversity and plasticity in tumour progression and therapy. Nat Rev Cancer 2020;20:485-503. [Crossref] [PubMed]
37. Gupta YH, Khanom A, Acton SE. Control of Dendritic Cell Function Within the Tumour Microenvironment. Front Immunol 2022;13:733800. [Crossref] [PubMed]
38. Nishizaki D, Kurzrock R, Miyashita H, et al. Viewing the immune checkpoint VISTA: landscape and outcomes across cancers. ESMO Open 2024;9:102942. [Crossref] [PubMed]
39. Ohta A, Gorelik E, Prasad SJ, et al. A2A adenosine receptor protects tumors from antitumor T cells. Proc Natl Acad Sci U S A 2006;103:13132-7. [Crossref] [PubMed]
40. Mondanelli G, Mandarano M, Belladonna ML, et al. Current Challenges for IDO2 as Target in Cancer Immunotherapy. Front Immunol 2021;12:679953. [Crossref] [PubMed]
41. Scharping NE, Menk AV, Moreci RS, et al. The Tumor Microenvironment Represses T Cell Mitochondrial Biogenesis to Drive Intratumoral T Cell Metabolic Insufficiency and Dysfunction. Immunity 2016;45:374-88. [Crossref] [PubMed]
42. Guan Q, Xiong H, Song X, et al. Suppression of NLRP3 inflammasome by a small molecule targeting CK1α-β-catenin-NF-κB and CK1α-NRF2-mitochondrial OXPHOS pathways during mycobacterial infection. Front Immunol 2025;16:1553093. [Crossref] [PubMed]
43. Wu X, Wu L, Wu Y, et al. Heme oxygenase-1 ameliorates endotoxin-induced acute lung injury by modulating macrophage polarization via inhibiting TXNIP/NLRP3 inflammasome activation. Free Radic Biol Med 2023;194:12-22. [Crossref] [PubMed]
44. Yu X, Song Y, Dong T, et al. Loss of PADI2 and PADI4 ameliorates sepsis-induced acute lung injury by suppressing NLRP3+ macrophages. JCI Insight 2024;9:e181686. [Crossref] [PubMed]
45. Zhang Y, Wang X. Targeting the Wnt/β-catenin signaling pathway in cancer. J Hematol Oncol 2020;13:165. [Crossref] [PubMed]
46. Rajabi A, Nejati M, Homayoonfal M, et al. Doxorubicin-loaded zymosan nanoparticles: Synergistic cytotoxicity and modulation of apoptosis and Wnt/β-catenin signaling pathway in C26 colorectal cancer cells. Int J Biol Macromol 2024;260:128949. [Crossref] [PubMed]
47. Lee SH, Lee J, Oh J, et al. Inhibition of TBL1 cleavage alleviates doxorubicin-induced cardiomyocytes death by regulating the Wnt/β-catenin signal pathway. Cardiovasc Res 2024;120:1037-50. [Crossref] [PubMed]
48. Karaca B, Bakır E, Yerer MB, et al. Doxazosin and erlotinib have anticancer effects in the endometrial cancer cell and important roles in ERα and Wnt/β-catenin signaling pathways. J Biochem Mol Toxicol 2021;35:e22905. [Crossref] [PubMed]