Myeloperoxidase inhibits prostate cancer progression, suppresses the PI3K/AKT signaling pathway and reshapes the immune microenvironment

Introduction

Prostate cancer (PCa) is the second most frequently diagnosed malignant tumor in men worldwide and a major cause of cancer-related mortality. According to statistics, over 1.4 million new cases and approximately 375,000 deaths were reported worldwide in 2020, with significant regional differences in both incidence and mortality (1). Although early-stage localized PCa can be effectively controlled by surgery or radiation therapy, metastatic castration-resistant PCa (mCRPC) still lacks effective treatment options, with a 5-year survival rate of less than 30% (2). Tumor metabolic reprogramming and immune microenvironment regulation have become hotspots in PCa research in recent years, but the key driving genes and their molecular mechanisms remain unclear (3).

Lactate metabolism is closely related to tumor progression and is one of the core features of cancer metabolic reprogramming. Tumor cells generate large amounts of lactate through the Warburg effect, which leads to acidification of the tumor microenvironment, promoting angiogenesis, immune evasion, invasion and metastasis (4,5). For example, lactate forms a metabolic symbiosis between tumor cells and stromal cells through the “lactate shuttle” mechanism mediated by monocarboxylate transporters (MCTs), enhancing the tumor cells’ resistance to oxidative stress (4,6). Recent studies have found that lactate is not only a byproduct of energy metabolism but also regulates gene expression through lactylation, a hallmark of metabolic reprograming. For instance, Zong et al. discovered that lactate catalyzed by AARS1 lactylates the p53 protein, inhibiting its tumor-suppressing function and directly driving tumorigenesis (7). Furthermore, lactate significantly suppresses anti-tumor immune responses. Ma et al. confirmed that high concentrations of lactate in the tumor microenvironment promote immune evasion by inhibiting CD8⁺ T cell activity, and targeting lactate metabolism (such as with lithium carbonate) can reverse this process (8). These studies suggest that targeting lactate production (e.g., lactate dehydrogenase A), transport (e.g., MCT1), or lactylation may represent novel anti-tumor strategies.

While the role of lactate in PCa is increasingly recognized, the regulatory genes connecting lactate metabolism to immune modulation remain poorly understood. Myeloperoxidase (MPO), a myeloid-derived enzyme with dual roles in oxidative stress and immune regulation, represents a promising candidate for such cross-talk. MPO plays a crucial role in tumor progression. As part of the innate immune response, MPO catalyzes the production of hypochlorous acid. In the early stages of melanoma growth, MPO exhibits antitumor effects, as evidenced by studies showing that MPO^+/+ mice experienced slower tumor growth compared to MPO^-/- mice. MPO may inhibit IκB kinase activity, leading to reduced nuclear factor κB (NF-κB) activation, which suppresses tumor growth (9). Additionally, MPO collaborates with neutrophils in tumor progression. In tumor-bearing mice, there was an increase in Ly6G+ neutrophils in the blood, which infiltrate organs such as the liver, lungs, and spleen. These neutrophils upregulate MPO and other enzymes, contributing to systemic tumor deterioration (10). Moreover, high MPO expression is associated with worse prognosis and malignant progression, influencing tumor cell migration, proliferation, invasion, and adhesion in colorectal cancer (11). Given the role of MPO in regulating immune responses and tumor progression, it may represent a potential target for cancer treatment and prognosis.

The PI3K/AKT pathway is crucial in the advancement of PCa, particularly in promoting tumor cell proliferation. Numerous studies have demonstrated that the activation of the PI3K/AKT pathway is strongly correlated with increased proliferation, invasion, and metastasis of PCa cells (12). In many cases, aberrant activation of this pathway results from the deletion or mutation of the phosphatase and tensin homolog (PTEN) gene, which enhances the proliferative and invasive capabilities of cancer cells (13). Additionally, the PI3K/AKT pathway regulates the phosphorylation of key cell cycle proteins, such as Retinoblastoma protein (Rb), thereby further driving the proliferation of PCa cells (13). In summary, the PI3K/AKT signaling pathway is integral to the growth of PCa, and targeting this pathway with specific inhibitors may offer promising therapeutic strategies for the treatment of the disease.

This research thoroughly examined the expression characteristics and prognostic value of lactate-associated genes (LAGs) in PCa. By integrating The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) data, 17 differentially expressed LAGs were identified, among which MPO was confirmed as an independent prognostic marker. Functional experiments showed that overexpression of MPO significantly inhibited tumor proliferation by suppressing the PI3K/AKT signaling pathway. Further analysis revealed that patients with high MPO expression were more sensitive to PI3K/AKT inhibitors, and Tumor Immune Dysfunction and Exclusion (TIDE) scores suggested a lower response rate to immunotherapy. Pan-cancer analysis expanded the clinical significance of MPO, confirming its prognostic value in various cancers, including colon cancer and glioblastoma. This study provides a new target for the metabolic-immunotherapy combination treatment of PCa. We present this article in accordance with the MDAR and ARRIVE reporting checklists (available at https://tau.amegroups.com/article/view/10.21037/tau-2025-390/rc).

Methods

Clinical tissue specimens

PCa samples, along with adjacent non-tumorous samples, were collected from individuals who underwent radical prostatectomy at Wuhan Union Hospital between 2022 and 2024. The diagnoses of all specimens were confirmed through both clinical evaluation and pathological examination. Informed consent was obtained from all the subjects involved in the study. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments and was approved by the Ethics Committee of Wuhan Union Hospital (No. S0136).

Cell culture

The human prostatic epithelial cell line RWPE-1, along with PCa cell lines C4-2, DU145, PC-3, and 22Rv1, were acquired from the Cell Bank of the Chinese Academy of Sciences (Shanghai, China). PCa cell lines were grown in RPMI-1640 medium supplemented with 10% FBS, while RWPE-1 cells were maintained in Prostate Epithelial Cell Medium (PEpiCM; ScienCell, CA, USA). All cell lines were incubated at 37 ℃ in an atmosphere containing 5% CO₂ throughout the duration of the experiments.

Quantitative real-time PCR (qRT-PCR)

Total RNA was isolated from cultured cell lines or freshly collected tissue samples using TRIzol reagent (Invitrogen, CA, USA). Then, RNA was reverse-transcribed into complementary DNA (cDNA) with the PrimeScript RT Reagent Kit (Takara, Japan). Quantification of gene expression was conducted using a StepOnePlus Real-Time PCR System (Applied Biosystems, CA, USA). Relative mRNA levels were determined using the 2^−ΔΔCT method, with GAPDH serving as the internal normalization control. All primers applied in this study were custom-synthesized by Sangon Biotech (Shanghai, China). Primers used for mRNA expression were:

MPO forward: 5'-GGTGATCGGTTTTGGTGGGA-3'; reverse: 5'-TTAGACACGGTGGTGATGCC-3'.

GAPDH forward: 5'-TCAAGAAGGTGGTGAAGCAG-3'; reverse: 5'-CGTCAAAGGTGGAGGAGTG-3'.

Western blot analysis

Proteins from cells and tissues were extracted using RIPA buffer, and their concentrations were measured with a BCA Protein Assay Kit from Thermo Scientific, MA, USA. Proteins were equally loaded, separated using 8–12% SDS-PAGE, and transferred to PVDF membranes. After blocking the membranes for an hour at room temperature, they were incubated overnight with specific primary antibodies at 4 ℃. After washing, membranes were incubated with appropriate HRP-conjugated secondary antibodies for 1 hour. The visualization of protein bands was achieved with an ECL detection system. This research employed primary antibodies, including anti-MPO (66177-1-Ig, Proteintech, USA), anti-p-PI3K (AP0427, ABclonal), anti-PI3K (A4860, ABclonal), anti-p-AKT (66444-1-Ig, Proteintech), anti-AKT (10176-2-AP, Proteintech), and anti-GAPDH (60004-1-Ig, Proteintech).

Lentivirus construction and infection

The cDNA of human MPO was amplified using PCR and inserted into a GV705 lentiviral vector from GeneChem in Shanghai, China. shRNA oligonucleotides targeting MPO were synthesized and cloned into the GV112 lentiviral vector (GeneChem). For the generation of stable PCa cell lines, cells were transduced with lentiviruses encoding either MPO (overexpression) or MPO-specific shRNAs (designated as MPO-Sh#1 and MPO-Sh#2). Following infection, transduced cells were selected using puromycin for at least 30 days to ensure stable integration. The oligonucleotide sequences used for shRNA construction targeting MPO mRNA were as follows: Non-Targeting Control (NTC)-shRNA, 5'-CCTAAGGTTAAGTCGCCCTCG-3'; MPO-Sh#1, 5'-GCCATGGTCCAGATCATCACT-3'; MPO-Sh#2, 5'-GCAGTACACTTCCTGCATTGA-3' (14).

Colony formation assay

Cells were seeded into 6-well plates at a density of 1,000 cells per well and maintained under standard culture conditions for approximately two weeks to allow colony formation. After incubation, colonies were fixed using 4% paraformaldehyde and subsequently stained with 0.1% crystal violet solution. Colony numbers were quantified using ImageJ software.

Xenografted tumor model

Male BALB/c nude mice aged 3 to 5 weeks were obtained from the Experimental Animal Center at Tongji Medical College. Approximately 2 million PCa cells were injected into the dorsal flanks of mice to develop the subcutaneous xenograft tumor model. Tumor volumes were calculated using the equation (L × W²)/2 every week. On day 35, mice were anesthetized and euthanized, and tumors were excised and weighed. All animal experiments in this research received approval from the Ethics Committee of Wuhan Union Hospital (No. 3842), in compliance with institutional guidelines for the care and use of animals. A protocol was prepared before the study without registration.

Data acquisition

Gene expression data and clinical information for prostate adenocarcinoma (PCa) were retrieved from TCGA (15). For pan-cancer analyses, MPO expression data, RNA-sequencing results, and corresponding clinical parameters were also acquired from the GDC Data Portal. Additionally, normal tissue expression data were sourced from the Genotype-Tissue Expression (GTEx) database (http://commonfund.nih.gov/GTEx/) (16). To further corroborate MPO expression patterns, the GSE14206 dataset was obtained from the GEO database (https://www.ncbi.nlm.nih.gov/geo/) (17).

Bioinformatics analysis

Unsupervised clustering was conducted based on the expression patterns of 17 LAGs in PCa patients using the ConsensusClusterPlus R package (version 1.56.0) (18). Principal component analysis (PCA) was performed to evaluate sample distribution and cluster separation, and results were visualized using ggplot2 (version 3.3.5). The clusterProfiler package (version 4.0.5) was used to perform functional enrichment analyses (19). Using the Benjamini-Hochberg method, P values from permutation tests were adjusted for multiple comparisons, with pathways considered significantly enriched if the adjusted P value or FDR was less than 0.05. To estimate immune cell infiltration in PCa tissues, the GSVA package (version 1.40.1) was employed via its single-sample gene set enrichment analysis (ssGSEA) algorithm, in conjunction with the Immunedeconv package (20). Visual representations of immune cell proportions were generated using ggplot2 (version 3.3.5) and ComplexHeatmap (version 2.8.0) (21). The survival package (version 3.2-13) and survminer package (version 0.4.9) were utilized for performing survival analyses (22). Cox proportional hazards regression models were employed to assess the prognostic significance of MPO across different types of cancer. Prognostic nomograms were developed using clinical factors [prostate-specific antigen (PSA); Gleason score] and MPO expression levels with the R package rms (version 6.3-0) to evaluate the 1-, 3-, and 5-year overall survival (OS) rates in PCa patients.

Drug sensitivity prediction

The drug response was predicted for each sample based on the Genomics of Drug Sensitivity in Cancer (GDSC) database (23). Predictions were made using the R package pRRophetic. Ridge regression was used to estimate the half-maximal inhibitory concentrations (IC₅₀) of samples (24).

Statistical analysis

All statistical analyses were conducted using RStudio (version 4.2.1) and SPSS software (version 16.0). The Mann-Whitney U test, a non-parametric method, was used to compare differences between two independent groups. For comparisons involving more than two groups, one-way analysis of variance (ANOVA) or the Kruskal-Wallis test was applied. Associations between two continuous variables were assessed using Spearman’s rank correlation analysis. Survival outcomes were evaluated using Kaplan-Meier (KM) survival curves, and differences between groups were tested with the log-rank test. In addition, univariate Cox proportional hazards regression was performed to calculate hazard ratios (HRs), 95% confidence intervals (CIs), and P values. The independent prognostic significance of MPO, alongside other clinical variables, was further assessed through univariate Cox regression analysis. Time-dependent receiver operating characteristic (ROC) curves were generated using the timeROC R package (version 0.4) to evaluate the predictive performance of genes for OS at 1-, 3-, and 5-year intervals (25). Data are presented as mean ± standard deviation (SD), derived from at least three independent replicates. P<0.05 was considered statistically significant. Statistical significance was determined as *, P<0.05; **, P<0.01; and ***, P<0.001.

Results

Identification of lactate associated differentially expressed genes (DEGs)

A total of 206 LAGs were obtained by analyzing the lactate-related literature (26). Data from RNA sequencing, including 52 normal samples and 502 PCa samples, were obtained from the TCGA database. Furthermore, the clinical profiles and prognostic information of 494 PCa patients were compiled from cBioPortal. As shown in the volcano plot and box plot, 17 lactate-related DEGs were identified in the PCa dataset, including 5 upregulated LAGs (PGK2, SLC16A8, PYCR1, NUP210, and GAPDHS) and 12 downregulated LAGs (ENO2, PIK3R1, SLC16A7, GCK, PFKFB3, EGLN3, AJUBA, MPO, CA9, HIF3A, IL18R1, and IL19) (Figure 1A,1B). To better understand the LAGs, we analyzed the gene distribution on chromosomes (Figure 1C). Chromosomal distribution of LAGs does not show significant specificity. The genes displaying high mutation frequency are visualized in Figure 1D, including GCK, NUP210, SLC16A7, PGK2, MPO, PYCR1, CA9, HIF3A, PIK3R1, and ENO2.

Figure 1 Dysregulated expression of LAGs in PCa and their prognostic implications. (A) Differential expression analysis of 206 LAGs in PCa. (B) Expression distributions of 17 LAGs between normal and PCa tissues. (C) Chromosomal localization of 17 LAGs. (D) Mutation frequency of 17 LAGs in the PCa cohort. (E) Unsupervised clustering of 17 LAGs and consensus matrix heatmap for k=2. (F,G) PCA and UMAP showing a remarkable transcriptome difference between LAGs patterns. (H-J) The Kaplan-Meier curves showing the overall survival (H), the disease-specific survival (I), and progress-free interval (J) for two LAGs clusters of PCa patients. ***, P<0.001. LAGs, lactate-associated genes; PCa, prostate cancer; PCA, principal component analysis; UMAP, uniform manifold approximation and projection.

Consensus clustering analysis was performed using 17 LAGs expression profiles. K=2 was selected from 2 to 6 as the optimal parameter using K-means (Figure 1E). Patients were segregated into cluster 1 and cluster 2. To further verify the transcriptional distinction between the two identified subtypes, PCA and UMAP were performed, both of which demonstrated clear separation between clusters (Figure 1F,1G). Additionally, KM survival analyses showed that patients in cluster 2 had significantly superior OS, disease-specific survival (DSS), and progress-free interval (PFI) relative to those in cluster 1 (Figure 1H-1J). These findings suggest that the two clusters display distinct expression patterns of the 17 LAGs, reflecting underlying molecular and prognostic heterogeneity.

MPO as a key prognostic biomarker in PCa

Among the 17 LAGs, MPO was uniquely associated with OS (HR =0.119, P=0.044; Figure 2A), underscoring its potential as a master regulator of PCa outcomes. The KM analysis showed that MPO was a protective factor (Figure 2B). MPO was downregulated in PCa tissues compared to normal controls (GSE14206, P<0.05; Figure 2C) and in high-risk tumors (Gleason score 6–7 vs. 8–10 tumors, P<0.05; Figure 2D). Subsequently, the time-dependent ROC curve was employed to assess the prognostic value of MPO concerning OS in PCa (Figure 2E). The univariate analyses suggested that MPO could be a significant prognostic indicator in PCa (Figure 2F). A nomogram integrating MPO mRNA expression, PSA, Gleason score, pathologic T stage, and pathologic N stage accurately predicted 1-, 3-, and 5-year OS (Figure 2G). Next, we evaluated MPO expression in 5 PCa tissue samples and 5 cell lines using qRT-PCR and Western Blot (Figure 2H-2K). MPO expression was lower in tumors than in adjacent noncancerous tissues (Figure 2I,2K). In addition, MPO was significantly downregulated in all 4 PCa cell lines compared with prostatic epithelial cells (Figure 2H,2J).

Figure 2 MPO expression is downregulated in PCa and is correlated with prognosis. (A) Univariate Cox regression analysis of 17 LAGs. (B) The Kaplan-Meier analysis of PCa patients with high and low MPO expression levels in the TCGA cohort for OS. (C) Validation set showing that MPO was expressed at low levels in PCa tissues compared to adjacent normal tissues in GSE14206. (D) MPO expression in Gleason score 6–7 vs. 8–10 tumors. (E) Time-dependent receiver operating characteristic analysis of MPO. (F) Forest plots based upon the outcomes of univariate Cox regression of MPO expression and other clinicopathological factors. (G) Nomogram integrating MPO expression, PSA, Gleason score, pathologic T stage, and pathologic N stage for predicting 1-, 3-, and 5-year OS in PCa patients. (H,J) MPO mRNA and protein expression in prostatic epithelial cells (RWPE-1) and different PCa cells (22Rv1, PC-3, C4-2, DU145). (I,K) MPO mRNA and protein expression in 5 PCa tissues (T) and paired adjacent non-tumor tissues (N). Data are represented as mean ± standard deviation of three independent experiments. *, P<0.05; **, P<0.01; ***, P<0.001. AUC, area under the curve; CI, confidence interval; FPR, alse positive rate; HR, hazard ratio; LAGs, lactate-associated genes; MPO, myeloperoxidase; OS, overall survival; PCa, prostate cancer; PSA, prostate-specific antigen; TCGA, The Cancer Genome Atlas; TPR, true positive rate.

MPO suppresses cell proliferation in vitro and suppresses tumor growth in vivo

To elucidate the functional role of MPO in PCa, gain- and loss-of-function experiments were conducted by transfecting PC-3 and DU145 cells with either MPO overexpression constructs or shRNA-mediated knockdown vectors (Figure 3A). Forced expression of MPO significantly suppressed cell proliferation, whereas silencing MPO led to enhanced proliferation in both cell lines, as shown in Figure 3B-3D.

Figure 3 MPO inhibits the proliferation of PCa cells. (A) Western blotting analysis of MPO expression in PC-3 and DU145 cells stably overexpressing or silencing MPO; GAPDH was used as a loading control. (B,C) MPO suppressed proliferation in PCa cancer cells, as determined by the CCK-8 assay. (D) MPO suppressed proliferation in PCa cancer cells, as determined by the colony formation assay. The colonies were stained with 0.1% crystal violet. Scale bar: 5 mm. (E) Representative images of tumor from a xenograft model in nude mice. (F) Tumor volumes were measured on the indicated days. (G) Tumor weight of all mice in each group. Data are represented as mean ± standard deviation of three independent experiments. ***, P<0.001. MPO, myeloperoxidase; PCa, prostate cancer.

To further assess the effect of MPO on tumor progression in vivo, PCa cells stably expressing MPO or MPO-targeting shRNAs were subcutaneously injected into the flanks of BALB/c nude mice. Tumor growth analysis revealed that xenografts with MPO overexpression developed markedly smaller and lighter tumors compared to control counterparts (Figure 3E-3G). Collectively, these findings support the tumor-suppressive function of MPO by inhibiting cellular proliferation in vitro and restraining tumor development in vivo.

MPO promotes cell proliferation by suppressing the PI3K/AKT pathway

Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were employed to explore the regulatory pathways underlying the distinct tumor immune microenvironments (TIME) observed between the two subtypes. DEGs revealed significant enrichment in biological processes such as the humoral immune response, B cell activation, immunoglobulin-mediated immunity, and the B cell receptor signaling pathway (Figure 4A).

Figure 4 MPO inhibits the PI3K/AKT signaling pathway. (A) Gene Ontology/Kyoto Encyclopedia of Genes and Genomes enrichment analysis of DEGs. (B) Gene set enrichment analysis of DEGs. (C) Western blot assay showing protein expression of p-PI3K, PI3K, p-AKT, and AKT. GAPDH was used as loading control. (D,E) Proliferation abilities of PCa cells determined using the CCK-8 assay and colony formation assay. The colonies were stained with 0.1% crystal violet. Scale bar: 5 mm. DEGs, differentially expressed genes; FDR, false discovery rate; NES, normalized enrichment score; MPO, myeloperoxidase; PCa, prostate cancer.

To further investigate the molecular mechanisms by which MPO influences PCa progression, GSEA was conducted using mRNA expression profiles. The analysis demonstrated a negative correlation between MPO expression and the activation of the PI3K/AKT signaling pathway (Figure 4B). To validate this finding, we assessed the expression levels of key downstream components of the PI3K/AKT cascade. Western blotting results showed a marked reduction in phosphorylated PI3K (p-PI3K) and AKT (p-AKT) levels in MPO-overexpressing cells, whereas MPO knockdown led to a notable increase in these phosphorylated proteins (Figure 4C). As expected, MK-2206 significantly reversed the promotion of PCa cell proliferation by MPO knockdown (Figure 4D,4E). Collectively, these findings suggest that MPO may inhibit PCa progression by suppressing the PI3K/AKT signaling pathway.

Immunological characteristics of MPO expression groups in PCa

The role of MPO in the TIME of PCa was next explored. First, the ssGSEA and MCPCOUNTER algorithms were performed to assess the differences between immune cell subpopulations in high and low MPO expression groups (Figure 5A-5D). The high MPO expression group had a higher proportion of most immune cell subpopulations. The ESTIMATE algorithm was then used to calculate the purity of the tumors in both groups (Figure 5E-5H). There was a significant positive correlation in stromal cells with MPO expression (R=0.558, P<0.001). The StromalScore, ImmuneScore, and ESTIMATEScore are derived from the ESTIMATE algorithm, which infers tumor microenvironment composition based on specific gene expression signatures. Specifically, the StromalScore reflects the abundance of stromal components within the tumor microenvironment, the ImmuneScore represents the extent of immune cell infiltration, and the ESTIMATEScore indicates the combined contribution of both stromal and immune components. Therefore, the significant positive correlations observed between MPO expression and these scores collectively suggest that higher MPO expression is associated with greater infiltration of immune cells as well as an increased presence of stromal components in the tumor microenvironment. In addition, the gene expression levels of immune checkpoint genes (ICGs) between the two groups were analyzed. Notably, there was a higher level of expression of all ICGs in the high MPO expression group (Figure 5I).

Figure 5 Evaluation of the tumor immune microenvironment and immune checkpoint genes between high and low MPO expression groups. (A) The ssGSEA scores in high and low MPO expression groups in PCa patients. (B) Scatterplot showing the correlation between MPO mRNA expression and the NK cells enrichment scores. (C) Lollipop chart showing the correlation between MPO mRNA expression and the ssGSEA scores. (D,E) The MCPCOUNTER and ESTIMATE scores in high and low MPO expression groups in PCa patients. (F-H) Scatterplot showing the correlation between MPO mRNA expression and the Immune score (F), ESTIMATE score (G), and Stromal score (H). (I) Expression levels of immune checkpoint genes in two groups. *, P<0.05; **, P<0.01; ***, P<0.001. MPO, myeloperoxidase; NK, natural killer; PCa, prostate cancer; ssGSEA, single sample gene set enrichment analysis.

Correlation between MPO expression and drug sensitivity

Next, we evaluated how groups with high and low MPO expression responded to anticancer drugs to identify possible treatment strategies for PCa. It was found that those with high levels of MPO expression might benefit more from targeted therapeutic approaches, such as temsirolimus, rapamycin, MK-2206, and AZD8055 (Figure 6A-6D). To further assess the potential response to immunotherapy, we applied the TIDE algorithm, which predicts tumor immune evasion by integrating two major mechanisms. Specifically, the Dysfunction score estimates the extent of functional impairment of infiltrating cytotoxic T lymphocytes (CTLs), while the Exclusion score reflects the extent to which CTLs are prevented from entering the tumor microenvironment by immunosuppressive factors. A higher overall TIDE score indicates a tumor microenvironment more capable of immune escape and is predictive of poorer responsiveness to immune checkpoint blockade (ICB) therapy. The high MPO expression group showed higher TIDE scores, predicting poorer response to ICB (Figure 6E-6H). Meanwhile, predictions showed a lower proportion of immune responders in the high MPO expression group (Figure 6I). These insights provide a theoretical basis for choosing clinical drugs in PCa therapy.

Figure 6 The role of MPO in PCa treatment drug sensitivity. (A-D) Correlation between MPO expression and IC₅₀ values of PI3K/AKT-targeting drugs. (E-H) Correlation between MPO expression and TIDE scores. (I) Predicted proportion of immune responder scores in high and low MPO expression groups in PCa patients. *, P<0.05; ***, P<0.001. IC₅₀, half-maximal inhibitory concentrations; MPO, myeloperoxidase; PCa, prostate cancer; TIDE, Tumor Immune Dysfunction and Exclusion.

Comprehensive analysis of pan-cancer MPO expression

Given that LAGs and immune cell infiltration contribute to the initiation and progression of various malignancies, we extended our analysis to explore the potential role of MPO across multiple cancer types. Specifically, we assessed whether MPO expression correlates with poor prognosis and alterations in the TIME. To this end, MPO expression levels were systematically compared between tumor and corresponding normal tissues using datasets from both TCGA and the GTEx project. According to the results, MPO was significantly upregulated in 4 (LAML, OV, PAAD, and SKCM) and downregulated in 20 cancer types (Figure 7A). The prognostic significance of MPO was validated in 32 additional types of cancer. A univariate Cox regression analysis across 33 cancer types revealed that low MPO expression correlated with poorer outcomes in three cancers: KICH, LAML, and PCa (Figure 7B). Furthermore, an analysis was conducted on the correlation between MPO and the 8 most common ICGs. These 8 ICGs included CD274, CTLA4, HAVCR2, LAG3, PDCD1, PDCD1LG2, SIGLEC15, and TIGIT. The results revealed that MPO was closely associated with the expression of ICGs in the majority of the 33 cancers (Figure 7C). Overall, the findings from the pan-cancer analysis indicate that MPO might influence other cancer types by impacting the TIME.

Figure 7 Comprehensive analysis of MPO in pan-cancers. (A) MPO was differentially expressed in most cancer types. (B) Univariate Cox regression analysis of MPO’s prognostic value in various cancers. (C) The association of MPO expression with the expression of 8 common immune checkpoint genes was observed in most cancer types. *, P<0.05; **, P<0.01; ***, P<0.001. CI, confidence interval; HR, hazard ratio; MPO, myeloperoxidase.

Discussion

Lactic acid, a byproduct of glycolysis, is produced in large quantities by tumor cells, even under oxygen-rich conditions, a phenomenon known as the Warburg effect (27). Lactic acid metabolism plays a pivotal role in the tumor microenvironment, influencing tumor growth, immune evasion, and therapeutic responses. The accumulation of lactate contributes to the acidification of the tumor microenvironment, which subsequently promotes immune suppression, angiogenesis, and metastasis (28). Elevated lactate levels can impair the function of immune effector cells, such as T cells and natural killer (NK) cells, while promoting the expansion of regulatory T cells, thus helping tumor cells evade immune surveillance (29). In recent years, therapeutic strategies targeting lactate metabolism have gained increasing attention. Inhibiting lactate dehydrogenase (LDH) and MCTs reduces lactate production and transport, improving the TIME and enhancing the efficacy of anti-tumor therapies (30). In this study, we identified 17 differentially expressed LAGs and demonstrated that their expression profiles were significant predictors of prognosis in PCa patients. This highlights the unique regulatory role of the lactate metabolic network in PCa and suggests potential avenues for targeted metabolic interventions.

MPO, identified as a key LAG in this study, was closely linked to poor prognosis in PCa attributable to its low expression levels. MPO is an enzyme involved in oxidative stress responses and may play a crucial role in the initiation and progression of PCa. A previous study found that the MPO -463G/A genotype (rs2333227) was linked to PCa risk in men with aggressive cancer, showing over a 2-fold decrease in risk for those with AA genotypes (31). Our study suggested that MPO exerted its tumor-suppressive effects by inhibiting the PI3K/AKT signaling pathway. Mechanistically, high MPO expression significantly decreased the phosphorylation levels of PI3K and AKT, which is consistent with the tumorigenic features of proliferation, metastasis, and therapeutic resistance associated with the excessive activation of the PI3K/AKT pathway.

When exploring the relationship between MPO and lactate, it is necessary to consider the role of MPO in inflammatory responses and its association with biochemical indicators such as LDH levels. MPO is an enzyme released by neutrophils that participates in oxidative stress and inflammation, while lactate is a product of cellular metabolism, often used to assess tissue hypoxia and metabolic status. MPO has been found to play a significant role in various inflammatory diseases. For instance, in COVID-19 patients, MPO-DNA complexes, as markers of neutrophil extracellular traps (NETs), are closely associated with disease severity. These NETs may exacerbate respiratory failure by promoting inflammation and microvascular thrombosis (32). Furthermore, studies indicate that MPO activity correlates with elevated levels of LDH, potentially reflecting increased tissue damage and inflammatory responses (33,34). In studies of hepatic ischemia-reperfusion injury, levels of MPO and LDH are significantly increased, indicating the presence of oxidative stress and cellular damage. Administration of antioxidants like Liv-52 can reduce MPO and LDH levels, thereby mitigating tissue injury (35). Similarly, another study showed that using Pycnogenol can lower MPO and LDH levels, reducing oxidative damage and DNA damage (36). The relationship between MPO and lactate in inflammation and tissue injury is complex and interrelated. As a key enzyme in inflammatory responses, MPO activity is closely linked to changes in metabolic indicators like LDH. These studies not only reveal the importance of MPO in pathophysiological processes but also provide new perspectives for developing therapeutic strategies targeting inflammatory diseases.

The infiltration patterns of immune cells not only influence tumor biology but also serve as potential prognostic biomarkers and therapeutic targets. Studies have demonstrated a close association between immune infiltration and the clinical phenotype and prognosis of PCa (37). Specifically, the differential expression of immune-related genes correlates with immune cell infiltration patterns in PCa, positioning these genes as potential biomarkers for immunotherapy (38). Remodeling of the immune microenvironment plays a critical role in PCa treatment, with androgen deprivation therapy (ADT) known to alter this microenvironment. One research indicates that ADT can induce immune microenvironment remodeling in PCa, which then impacts relapse-free survival and immune cell infiltration levels in patients (39). The expression of MPO is closely linked to immune cell infiltration across various cancers. MPO expression significantly correlates with the infiltration levels of multiple immune cells, suggesting that MPO may influence immune regulation in colorectal cancer (40). In breast cancer, MPO expression also shows a significant correlation with immune cell infiltration, particularly in macrophages and dendritic cells (41). Our study found that high MPO expression in PCa was associated with increased immune cell infiltration levels. Additionally, the expression of immune checkpoint molecules such as PD-L1 and CTLA4 was notably upregulated.

This study further demonstrated that the expression level of MPO was significantly correlated with the sensitivity to PI3K/AKT-targeted drugs, such as AZD8055 and MK-2206. The high-expression group exhibited improved drug responses, suggesting that MPO may serve as a potential biomarker for predicting therapeutic efficacy. Although our analyses demonstrated that MPO expression was positively correlated with increased immune cell infiltration in PCa (Figure 5), patients with high MPO expression exhibited higher TIDE scores and were predicted to respond poorly to ICB therapy (Figure 6F-6H). At first glance, these findings may appear contradictory. However, it is increasingly recognized that enhanced immune infiltration does not necessarily equate to effective anti-tumor immunity. In many tumor contexts, infiltrating immune cells, particularly T cells and myeloid subsets, may become functionally impaired, exhausted, or skewed toward immunosuppressive phenotypes. MPO, as a pro-inflammatory mediator, may contribute to a chronically inflamed tumor microenvironment that promotes the recruitment of immune cells while simultaneously impairing their cytotoxic activity. This dual role could explain why MPO expression is associated with both increased immune infiltration and an immunosuppressive TIME, ultimately resulting in diminished ICB responsiveness as reflected by elevated TIDE scores. These findings provide a theoretical framework for the clinical combination of ADT with immunotherapy. Specifically, for patients with low MPO expression, combining ADT with ICB may overcome resistance to monotherapy.

This study systematically elucidated a novel mechanism by which MPO inhibited PCa progression through suppression of the PI3K/AKT pathway, modulation of immune infiltration, and regulation of drug sensitivity. This not only deepens our understanding of the complexity of lactate metabolism networks but also provides a potential therapeutic target for precision treatment of PCa. Future research could further explore the direct role of MPO in lactate production and lactylation modifications, and develop combination therapies based on MPO expression stratification. Furthermore, the differential expression and prognostic value of MPO across various cancers suggested that it might function as a metabolic regulatory hub across multiple cancer types, warranting validation of its mechanism in a broader cancer context. In conclusion, this study offers theoretical support for targeting lactate metabolism to reverse tumor malignancy, with potential for clinical translation.

Conclusions

MPO suppresses PCa by inhibiting PI3K/AKT signaling and enhancing NK cell infiltration. Its expression serves as a prognostic biomarker and predicts sensitivity to PI3K/AKT inhibitors and ICBs. MPO represents a promising therapeutic target for metabolism-immune combined strategies.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the MDAR and ARRIVE reporting checklists. Available at https://tau.amegroups.com/article/view/10.21037/tau-2025-390/rc

Data Sharing Statement: Available at https://tau.amegroups.com/article/view/10.21037/tau-2025-390/dss

Peer Review File: Available at https://tau.amegroups.com/article/view/10.21037/tau-2025-390/prf

Funding: This study was supported by Municipal Financial Subsidy of Shenzhen Key Medical Discipline Construction (Grant No. SZXK021).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tau.amegroups.com/article/view/10.21037/tau-2025-390/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. Informed consent was obtained from all the subjects involved in the study. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments and was approved by the Ethics Committee of Wuhan Union Hospital (No. S0136). All animal experiments in this research received approval from the Ethics Committee of Wuhan Union Hospital (No. 3842), in compliance with institutional guidelines for the care and use of animals.

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

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