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Research Article

Association of MPO Expression with the Immune Microenvironment in Breast Cancer: Insights from Bioinformatics and Single-Cell Analyses

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DOI:

10.3791/71189

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August 14th, 2026

 ,  ,  ,  ,  , 
1Department of Traditional Chinese Medicine, Aerospace Center Hospital, 2Third Affiliated Hospital, Beijing University of Chinese Medicine, 3China Academy of Chinese Medical Sciences, 4Graduate School, Beijing University of Chinese Medicine, 5College of Traditional Chinese Medicine, Shandong University of Traditional Chinese Medicine

* These authors contributed equally

In This Article

Summary

This article presents a reproducible bioinformatics and single-cell workflow for exploring associations between myeloperoxidase (MPO) expression and immune/myeloid features in breast cancer. Because the analyses are based on public datasets and in silico methods, the findings are interpreted as exploratory and hypothesis-generating.

Abstract

Breast cancer remains a major cause of cancer-related mortality, and exploratory computational workflows can help prioritize immune-associated markers for further investigation. Here, we used the cancer genome atlas breast invasive carcinoma (TCGA-BRCA) bulk transcriptomic data and the public single-cell dataset GSE161529 to examine associations between myeloperoxidase (MPO) expression, clinical outcomes, immune infiltration, methylation, upstream-regulator annotations, single-cell expression patterns, virtual knockdown sensitivity outputs, drug–gene interaction retrieval, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) annotation. MPO expression was lower in breast cancer tissues than in adjacent non-tumor tissues. Higher MPO expression was associated with a longer progression-free interval, whereas its associations with overall survival and disease-specific survival were not statistically significant. Receiver operating characteristic (ROC) analysis suggested tumor–normal separation within the analyzed public dataset, but this should not be interpreted as clinical diagnostic validation. Immune deconvolution and enrichment analyses indicated that MPO expression mainly tracked with immune- and myeloid-related transcriptional features, rather than establishing tumor-intrinsic regulation of the immune microenvironment. At single-cell resolution, the MPO signal was sparse, with only 85 MPO-positive cells detected before k-nearest neighbor (KNN)-based neighborhood expansion. Detectable MPO signal and MPO-associated scores were interpreted cautiously because they may be influenced by sparse expression, cell-type annotation uncertainty, dropout, doublets, or ambient RNA. In silico virtual knockdown suggested candidate immune- and inflammatory-related transcriptional changes, but these results were considered exploratory and require validation. Drug-gene interaction database (DGIdb)-based drug-gene retrieval and ADMET annotation were used only as preliminary chemical annotations and were not interpreted as therapeutic evidence. Overall, this study provides a reproducible in silico workflow for generating hypotheses about MPO-associated immune/myeloid features in breast cancer, which require external cohort validation and experimental confirmation.

Introduction

Breast cancer is a highly heterogeneous immune-related malignancy1. Its disease progression, risk of recurrence and metastasis, and treatment response are closely associated with the composition and functional status of the tumor immune microenvironment (TIME)2. Despite ongoing optimization of comprehensive treatment strategies, some patients still experience progression or recurrence, underscoring the urgent need to identify molecular biomarkers that characterize TIME status and support risk stratification, while elucidating their underlying mechanisms.

Myeloperoxidase (MPO) is a heme-containing peroxidase predominantly expressed in neutrophils and, to a lesser extent, monocytes and macrophages. Through the generation of hypochlorous acid and other reactive oxidants, MPO contributes to antimicrobial defense but may also promote oxidative tissue injury and chronic inflammation. In cancer, the biological significance of MPO appears to be context-dependent3. On the one hand, MPO-mediated oxidative stress has been implicated in carcinogenesis and tumor progression through DNA damage, lipid and protein oxidation, inflammatory signaling, and remodeling of the tumor microenvironment4,5,6. On the other hand, MPO-positive innate immune or myeloid cell infiltration has been associated with a favorable prognosis or antitumor immune activity in certain tumor contexts7,8,9. These apparently conflicting findings suggest that the clinical and biological significance of MPO may depend on tumor type, disease stage, cellular source of MPO, and the immune composition of the tumor microenvironment. However, the expression pattern and prognostic relevance of MPO in breast cancer, particularly at the single-cell level, remain incompletely characterized.

The tumor immune microenvironment (TIME) contains heterogeneous myeloid, lymphoid, stromal, and epithelial compartments10. MPO is classically associated with neutrophils and other myeloid-lineage cells, and MPO-related signals in bulk tumor profiles may therefore reflect immune-cell composition rather than tumor-cell-intrinsic activity10. In breast cancer, the distribution of MPO signal across bulk and single-cell datasets, its association with immune-infiltration estimates, and the reproducibility limits of downstream computational analyses remain insufficiently characterized. This study, therefore, treats MPO as an immune-associated marker for exploratory workflow development, not as a proven causal regulator of the TIME or a validated therapeutic target. Compared with single-cohort differential expression analyses or single-platform immune-infiltration estimates, an integrated workflow combining bulk transcriptomics, immune deconvolution, methylation annotation, single-cell mapping, and computational perturbation can provide a broader exploratory view of gene-associated immune context. This approach is useful for prioritizing candidate markers and generating testable hypotheses, especially when experimental datasets are not yet available. However, such computational integration cannot by itself determine cellular source, causality, pharmacological activity, or clinical utility. With the advancement of large-scale public cancer cohorts and single-cell transcriptomic technologies, bioinformatics approaches can be used to explore associations among gene expression, clinical outcomes, immune-cell composition, and transcriptional states at both population and single-cell levels11. Computational perturbation methods based on single-cell gene regulatory networks may further provide hypothesis-generating information regarding gene-associated transcriptional sensitivity12,13. Therefore, this study aimed to characterize the expression pattern, survival association, immune/myeloid context, methylation profile, single-cell distribution, and exploratory computational perturbation profile of MPO in breast cancer. The overall workflow is shown in Figure 1.

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Protocol

Acquisition from the TCGA database

RNA sequencing data and clinical information for the TCGA breast invasive carcinoma (TCGA-BRCA) cohort were obtained from the genomic data commons portal14. STAR workflow RNA-seq data in transcripts per million (TPM) format were extracted together with matched clinical annotations. RNA-seq samples lacking corresponding clinical information were excluded. For expression-based analyses, TPM values were transformed as log2(TPM + 1). MPO expression was extracted using the gene symbol MPO and Ensembl gene ID ENSG00000005381.8. For analyses requiring MPO-high and MPO-low grouping, only TCGA-BRCA tumor samples were included, and adjacent normal samples were excluded from group assignment. Tumor samples were divided according to the median value of log2(TPM + 1)-transformed MPO expression among TCGA-BRCA tumor samples. Samples with MPO expression greater than or equal to the median were assigned to the MPO-high group, whereas samples below the median were assigned to the MPO-low group. This median-based grouping strategy was used for survival analysis, differential expression analysis, enrichment analysis, methylation grouping, and immune-cell enrichment comparisons unless otherwise specified. Clinical pathological characteristics, including sex, age, ethnicity, pathological T stage, histological grade, PAM50 subtype, pathological stage, tumor status, and survival endpoints, including overall survival (OS), progression-free interval (PFI), and disease-specific survival (DSS), were analyzed using R version 4.2.1.

Public Immunohistochemistry Image Retrieval

Representative MPO immunohistochemistry (IHC) images of adjacent normal breast tissue and breast cancer tissue were used as qualitative protein-level references. These images were not included in quantitative morphometric or statistical analyses. The boxed areas indicate regions shown at higher magnification. Scale bars indicate 100 µm in the 20× images and 50 µm in the 40× images.

Expression correlation analysis

The TCGA-BRCA dataset was used to examine genes co-varying with MPO expression in breast cancer. Genome-wide Pearson correlation coefficients were calculated between MPO and protein-coding genes, and the top 30 positively and top 30 negatively correlated genes were selected for visualization. For correlation analyses involving multiple tested genes, nominal p-values were adjusted using the Benjamini-Hochberg false discovery rate method. The MPO-associated protein-protein interaction (PPI) network was constructed using the search tool for the retrieval of interacting genes/proteins (STRING) database, with protein pairs displaying interaction scores greater than 0.40 retained for visualization15.

Functional enrichment analysis

Differentially expressed genes (DEGs) were identified by comparing the MPO-high and MPO-low TCGA-BRCA tumor groups using thresholds of |log2FC| > 1 and a Benjamini-Hochberg-adjusted p-value < 0.05. Functional enrichment analysis of DEGs was performed using the R package clusterProfiler version 4.4.4, including gene ontology (GO) biological process, cellular component, molecular function, and Kyoto encyclopedia of genes and genomes (KEGG) pathway analyses16,17,18,19,20. Enriched GO and KEGG terms were considered significant when the adjusted p-value was < 0.05.

Gene set enrichment analysis (GSEA) was performed using a pre-ranked gene list based on differential expression statistics between MPO-high and MPO-low groups. The MSigDB C2 Canonical Pathways collection c2.cp.all.v2022.1.Hs.symbols.gmt, corresponding to MSigDB v2022.1.Hs and containing 3,050 gene sets, was used21,22. Enriched terms were considered significant according to Benjamini–Hochberg adjusted p-value < 0.05, FDR q-value < 0.25, and |normalized enrichment score| > 1. Where applicable, Z-scores for significantly enriched terms were computed using the GOplot package for visualization.

Analysis of immune-cell enrichment in tumors

Immune and stromal components in the TCGA-BRCA cohort were evaluated using the ESTIMATE algorithm implemented in the R package estimate version 1.0.13. Log2(TPM + 1)-transformed expression data were used as input, and immune score, stromal score, and ESTIMATE score were calculated for each tumor sample. TIMER/TIMER2.0 was used to evaluate associations between MPO expression and estimated infiltration levels of major immune-cell populations in the TCGA-BRCA cohort, including B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells23,24,25. TIMER-based results were interpreted as immune-infiltration estimates derived from the corresponding online resource. For immune-cell enrichment analysis across 24 immune-cell types, single-sample gene set enrichment analysis (ssGSEA) was implemented using the R package GSVA version 1.46.026. The LM22 immune-cell signature matrix used for CIBERSORT-based deconvolution of 22 immune-cell types is provided in Supplementary Table 1. Correlations between MPO expression and immune-cell enrichment scores were evaluated using Spearman's rank correlation. Differences in immune-cell enrichment scores between the median-defined MPO-high and MPO-low tumor groups were compared using the Wilcoxon rank-sum test. For analyses involving multiple immune-cell types, p-values were adjusted using the Benjamini–Hochberg false discovery rate method.

DNA methylation of the MPO gene

DNA methylation patterns within the MPO locus were evaluated using MethSurv. CpG methylation beta values and survival associations for TCGA-BRCA were obtained from the MethSurv platform. Selected MPO-related CpG sites were visualized, and their associations with survival outcomes were evaluated using the survival analysis outputs provided by MethSurv27. For analyses involving multiple CpG sites, p-values were adjusted across the tested MPO-related CpG sites using the Benjamini-Hochberg false discovery rate method. These methylation analyses were interpreted as exploratory epigenetic annotations.

PPI network construction and correlation analysis of neutrophil-related genes

To examine the association between MPO and neutrophil-related biology, a systematic network analysis was performed. A gene set comprising established mediators of neutrophil activation and associated inflammatory processes was curated from the current literature. The complete neutrophil-related gene list is provided in Supplementary Table 2. Gene symbols were harmonized to official gene symbols, duplicate entries were removed, and available genes were intersected with the TCGA-BRCA expression matrix before STRING/PPI analysis, hub-gene prioritization, and MPO–hub gene correlation analysis. The PPI network among these genes was constructed using the STRING database (version 11.5) with a medium confidence interaction score threshold (>0.40). Hub genes within this network were algorithmically prioritized based on degree centrality, which quantifies the number of direct interactions per node. The top 20 genes with the highest degree scores were selected for downstream correlation analysis.

Subsequently, the expression profiles of these hub genes and MPO were extracted from the TCGA-BRCA transcriptomic dataset. The association between MPO and each hub gene was statistically evaluated using Spearman's rank correlation. To characterize correlation patterns among the hub genes themselves, a pairwise Spearman correlation matrix was computed across all tumor samples. These correlation analyses provided the quantitative basis for subsequent visualizations, including the lollipop plot of MPO-hub gene correlations and the chord diagram/heatmap depicting hub-gene correlation patterns.

Prediction of upstream transcription factors and miRNAs targeting MPO

The KnockTF database (https://bio.liclab.net/KnockTF/index.php)28,29, the ChIP database (http://chip-atlas.org/)30,31, and the GTRD database32,33 (https://gtrd.biouml.org/#!) were used to predict MPO's target TFs. In addition, the TargetScan database (https://www.targetscan.org/vert_80/) was utilized to predict potential miRNA binding sites targeting MPO. Venn diagrams were generated using the MicroBioinformatics website (https://www.bioinformatics.com.cn/static/others/jvenn/example.html)34.

Single-cell analysis of MPO

The specific dataset GSE161529 originates from the Gene Expression Omnibus (GEO). Data preprocessing first performed cell-level filtering to exclude low-quality cells—those meeting any of the following criteria: mitochondrial gene expression exceeding 25%, total unique molecular identifier (UMI) count below 5000, or fewer than 2500 detected genes. Subsequently, ambient RNA contamination and technical batch effects were corrected35. Principal component analysis (PCA) was performed for dimensionality reduction to assess cellular similarity, followed by UMAP for cell clustering and visualization. Then, according to the typical marker genes of cells, different clusters were annotated to cell types11. The MPO-associated gene set used for single-cell signature scoring is provided in Supplementary File 1. Before scoring, gene symbols were harmonized to official gene symbols, duplicate entries were removed, and available genes were intersected with the GSE161529 expression matrix. AUCell, Seurat AddModuleScore, and ssGSEA were used to calculate per-cell MPO-associated scores. Scores from the three methods were Z-score normalized, scaled to a comparable range, and integrated to generate a composite MPO-associated score for downstream descriptive analyses. Cell-cell interaction networks were interrogated to compare inferred ligand–receptor communication patterns involving epithelial tumor cells stratified by MPO-associated signal and diverse partner cell types. These outputs were interpreted as descriptive communication patterns rather than evidence that MPO-expressing cells directly mediate intercellular communication.

Single-cell virtual knockdown of MPO and pathway enrichment analysis using scTenifoldKnk

Single-cell virtual knockdown of MPO was performed by integrating Seurat and scTenifoldKnk. Following standard quality control (200–6,000 genes per cell; mitochondrial fraction < 10%), data were log-normalized, and 2,000 highly variable genes were selected for dimensionality reduction and clustering. To enrich for MPO-relevant contexts, cells scoring in the top 50% for a myeloid/neutrophil gene module were retained. From these cells, an MPO-neighborhood subset was defined by expanding from MPO-positive seeds using k = 40 nearest neighbors in PCA space. The expanded subset was not treated as a pure MPO-positive population, and no cell-type proportion conclusion was drawn from this KNN expansion step. This subset was subjected to virtual knockdown analysis via scTenifoldKnk, using the union of highly variable genes and MPO (expressed in ≥25 cells) as the gene set. Significantly perturbed genes were identified (FDR < 0.05, BH-adjusted). Resultant genes were further analyzed for functional enrichment in GO Biological Processes and KEGG pathways (q < 0.05).

Exploratory drug-gene retrieval and ADMET annotation

DGIdb was queried to obtain preliminary MPO-associated drug–gene or chemical–gene interaction records. Because database-derived interaction lists may include entries supported by heterogeneous evidence types and may not directly correspond to clinically actionable therapeutic agents, the retrieved compounds were treated as exploratory annotations rather than prioritized treatment candidates. SwissADME and ADMETlab were subsequently used to summarize predicted physicochemical, pharmacokinetic, and toxicological properties. These in silico annotations were used to provide preliminary context for compound-level interpretation and to highlight the need for further pharmacological, toxicological, and clinical curation before any therapeutic relevance can be considered36.

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Results

MPO expression patterns and exploratory survival associations in breast cancer

To describe MPO expression patterns across cancer datasets, we analyzed MPO RNA-seq data from the TCGA pan-cancer dataset and observed lower MPO expression in tumor tissues from bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), kidney chromophobe (KICH), liver hepatocellular carcinoma (...

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Discussion

This study presents an exploratory public-dataset and in silico workflow for examining associations between MPO expression and immune/myeloid features in breast cancer. The TCGA-BRCA analyses showed that MPO expression was lower in tumor tissues than in adjacent non-tumor tissues and that higher MPO expression was associated with a longer progression-free interval. However, overall survival and disease-specific survival were not statistically significant. Therefore, MPO should not be interpreted as a robust or establishe...

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Disclosures

The authors report no conflicts of interest in this work. An AI-based language editing tool was used only to assist with English language polishing and readability during manuscript revision. The tool was not used for study design, data analysis, figure generation, result interpretation, reference selection, or drawing scientific conclusions. All analyses, results, interpretations, references, and final text were carefully checked, reviewed, and approved by the authors, who take full responsibility for the content of the manuscript.

Acknowledgements

The authors gratefully acknowledge financial support from the Scientific Research Fund of Aerospace Center Hospital (YN202530).

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Materials

List of materials used in this article
NameCompanyCatalog NumberComments
CellChatR package/Open sourcehttps://github.com/sqjin/CellChatCell-cell communication analysis
ChIP-AtlasPublic databasehttps://chip-atlas.org/TF target screening; 2021 update 
clusterProfilerBioconductorhttps://bioconductor.org/packages/clusterProfiler/GO/KEGG enrichment analysis; v4.4.4
CytoscapeCytoscape Consortiumhttps://cytoscape.org/Network visualization and topology analysis
DGIdbWashington University/Public databasehttps://www.dgidb.org/Drug-gene interaction retrieval
GDC/TCGA-BRCANational Cancer Institutehttps://portal.gdc.cancer.gov/Bulk transcriptomic and clinical data source
Gene Expression Omnibus: GSE161529NCBIhttps://www.ncbi.nlm.nih.gov/geo/Single-cell dataset source
GSEA/MSigDBBroad Institutehttps://www.gsea-msigdb.org/gsea/msigdbGene set enrichment analysis and gene-set reference; Version 3.0 
GSVABioconductorhttps://bioconductor.org/packages/GSVA/Gene set variation/ssGSEA-related scoring; Version 1.46.0
GTRDPublic databasehttp://gtrd.biouml.org/TF target screening; 2021 
KnockTFPublic databasehttp://www.licpathway.net/KnockTF/index.htmlTF perturbation resource; Version 2.0 
RR Foundation for Statistical Computinghttps://www.r-project.org/Statistical computing environment
scTenifoldKnkR package/Open sourcehttps://github.com/cailab-tamu/scTenifoldKnkVirtual knockdown analysis
SeuratR package/Open sourcehttps://satijalab.org/seurat/Single-cell preprocessing and clustering
STRINGELIXIR/Public databasehttps://string-db.org/Protein-protein interaction analysis; v11 
SwissADMESIB Swiss Institute of Bioinformaticshttp://www.swissadme.ch/Drug-likeness assessment; 2017 release/web tool 
TIMERPublic web resourcehttps://timer.cistrome.org/Immune infiltration analysis; TIMER2.0 
UCSC Xena or linked TCGA portalUCSChttps://xenabrowser.net/Exploratory data access/validation 

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