Abstract Background The N-formyl peptide receptor family (FPRs) is implicated in the progression of diverse cancer types, yet studies specifically exploring their roles in breast cancer remain scarce. Methods A comprehensive analysis integrating bulk RNA-seq transcriptomics, methylomics, single-cell transcriptomics, and spatial single-cell transcriptomics data was conducted to elucidate the distinctive characteristics of FPRs in breast cancer. This study particularly focused on delineating the e xpression profiles of FPR3 across distinct breast cancer subtypes, while systematically investigating its prognostic implications and association with macrophage polarization patterns in breast cancer patients. Furthermore, molecular docking analysis was performed to screen potential therapeutic compounds targeting FPR3, providing insights into its druggability. Results Notably, FPR3 was found to be highly expressed in macrophages within breast cancer tissues, with a notably elevated level in HER2-positive and triple-negative breast cancer (TNBC) subtypes, both of which are associated with poor prognosis. FPR3 expression inversely correlates with promoter methylation levels. Further analysis of pan-cancer immune infiltration patterns uncovered a striking association between FPR3 and macrophage infiltration, as well as their polarization status. Knockdown of FPR3 expression in macrophages markedly enhanced the expression of IL6, TNF-α, and TGF-β, while significantly reducing IL10 levels, indicative of a shift towards an M1-like macrophage phenotype. Through computational molecular docking analyses, Otamixaban and Rivaroxaban emerged as promising candidate inhibitors of FPR3. Conclusions These findings underscore the profound infiltration of FPR3 + macrophages in breast cancer patients with adverse prognoses, highlighting FPR3 as a potential therapeutic target for intervening breast cancer aggressiveness. Supplementary Information The online version contains supplementary material available at 10.1186/s12935-025-03942-4. Keywords: FPR3, Breast cancer, Macrophage polarization, Immunity Introduction Breast cancer has surpassed lung cancer as the most commonly diagnosed cancer, with an estimated 2.3 million new cases annually, accounting for 11.6% of all cancer cases [[34]1]. Based on the expression of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2), breast cancer is typically classified into four subtypes: luminal A (ER+, PR+, HER2-), luminal B (ER+, PR+, HER2+), HER2-enriched (ER-, PR-, HER2+), and triple-negative breast cancer (TNBC) (ER-, PR-, HER2-) [[35]2–[36]5]. Among these, triple-negative and HER2-overexpressing breast cancers are widely recognized as the most aggressive and unfavorable prognostic subtypes of breast cancer [[37]6, [38]7]. Formyl peptide receptors (FPRs) belong to the G protein-coupled receptor (GPCR) subfamily, with three human FPR gene subtypes: FPR1, FPR2 (also known as FPRL1), and FPR3 (also known as FPRL2) [[39]8]. Members of the FPR family are involved in regulating various pathophysiological immune responses, encompassing neutrophil-mediated bacterial recognition, immune cell differentiation/proliferation, and tumorigenesis across multiple cancer types. For instance, bacterial-derived signal peptides exhibit selective agonism toward FPR1 and FPR2 receptors expressed on neutrophil membranes, triggering pathogen clearance mechanisms [[40]9]. FPR2-mediated signaling demonstrates pleiotropic functions: In dendritic cell (DC) lineages, it promotes phenotypic maturation from immature DCs (iDCs) to mature DCs (mDCs), while follicular dendritic cells (FDCs) expressing FPR2 upregulate CXCL13 chemokine and B cell-activating factor production, thereby potentiating B lymphocyte expansion and activation [[41]10]. Context-dependent oncogenic roles emerge from functional studies: FPR1 knockdown in neuroblastoma cells suppresses xenograft tumor progression in nude mice, whereas its silencing in gastric cancer cells paradoxically enhances tumorigenicity. Complementary evidence indicates FPR2 downregulation in colorectal carcinoma cell lines attenuates malignant properties, underscoring receptor-specific and tissue-context-dependent regulatory axes [[42]11–[43]13]. They are primarily expressed in inflammatory phagocytes, such as neutrophils and monocytes, but are also found in some non-immune cells and tumor cells [[44]10]. FPRs have been reported to interact with ligands, leading to their internalization and presence in intracellular vesicles or the nucleus [[45]14]. Members of the FPR family possess diverse ligands that can influence tumor progression through multiple pathways. For instance, breast cancer patients with the SNP rs867228 (1037:A > C) base mutation in FPR1 exhibit reduced metastasis-free survival and overall survival (OS) after adjuvant chemotherapy with anthracyclines [[46]15]. In female pancreatic cancer patients, FPR2 + macrophages induce T cell exhaustion, mediating immunosuppression [[47]16]. Additionally, FPR2 has been shown to favor M1 polarization of macrophages, limiting M2 polarization and enhancing anti-LLC (Lewis lung cancer) host mouse defense [[48]17]. Annexin-1 stimulates breast tumor cell proliferation by activating FPR1 and FPR2 in response to mitogenic stimuli [[49]18]. FPR3 regulates glycolysis and stemness mediated by NFATc1 in a calcium-dependent manner, and its overexpression inhibits glycolytic capacity and stemness in tumor cells, subsequently suppressing gastric cancer cell proliferation [[50]19]. In this study, we embarked on a meticulous, multi-omics-based systematic exploration of the FPR family and its intricate connections with breast cancer. We delved into the expression patterns of FPR3 across diverse breast cancer subtypes, meticulously analyzing the accompanying DNA methylation alterations and immune infiltration correlations. Furthermore, we delved into the intricate interplay between FPR3 and macrophage M1 and M2 polarization, shedding light on their mutual influence. This study aims to elucidate the pivotal role of FPR3 in driving breast cancer prognosis and uncover the underlying mechanisms. Materials and methods Specimens collection The paraffin-embedded pathological tissue samples used in this project are from the First Affiliated Hospital of Anhui Medical University. This study has been approved by the First Affiliated Hospital of Anhui Medical University (PJ2024-11-19). Data sources and processing The databases used in this study, including TCGA (The Cancer Genome Atlas, [51]https://portal.gdc.cancer.gov/), GEO (Gene Expression Omnibus, [52]https://www.ncbi.nlm.nih.gov/) [[53]20], CCLE (Cancer Cell Line Encyclopedia, [54]https://sites.broadinstitute.org/ccle) [[55]21], TISCH (Tumor Immune Single-cell Hub, [56]http://tisch.compbio.cn/home/) [[57]22], cBioPortal ([58]https://www.cbioportal.org/) [[59]23], GSCA (Gene Set Cancer Analysis, [60]https://guolab.wchscu.cn/GSCA/#/) [[61]24], bc-GenExMiner (Breast cancer gene-expression miner, [62]https://bcgenex.ico.unicancer.fr/BC-GEM/GEM-requete.php) [[63]25], KM (Kaplan Meier, [64]https://kmplot.com/analysis/) [[65]26]. The datasets used in this study, including [66]GSE19615 [[67]27], EMTAB8107 [[68]22], [69]GSE110686 [[70]28], [71]GSE114727 [[72]29], [73]GSE138536 [[74]30], [75]GSE143423, [76]GSE148673 [[77]31], [78]GSE150660 [[79]32], [80]GSE161529 [[81]33], [82]GSE176078 [[83]34], SRP114962, [84]GSE173634 [[85]35], [86]GSE1456 [[87]36], [88]GSE7390 [[89]37], E-MTAB-365 [[90]38], [91]GSE11121 [[92]39]. For TCGA database-derived data, we conducted searches using “TCGA-BRCA” as the keyword to download breast cancer transcriptomic profiles. These profiles were normalized to Transcripts Per Million (TPM) values to mitigate biases from sequencing depth and gene length variations. For GEO datasets prefixed with “GSE”, we directly retrieved corresponding expression matrix files and GPL annotation files from the GEO website using their accession numbers. In cases where multiple probes mapped to the same gene, the probe with the highest expression value was selected to represent gene-level expression. Genes showing zero expression across all samples in the dataset were subsequently excluded. CCLE database queries were performed using “FPR1”, “FPR2”, and “FPR3” as search terms to obtain respective gene expression files. For cBioPortal analyses, we selected the “Pan-cancer analysis of whole genomes” cohort and the “Breast” cancer-specific study for integrated genomic profiling. Remaining databases primarily utilized their built-in breast cancer datasets for downstream visualization of analysis results. Functional enrichment and immune infiltration analysis To uncover genes with similar functions and predict their biological roles, we leveraged the cBioPortal database. Through Spearman correlation analysis to calculate the correlation between gene expression level, we identified the top 20 genes most closely associated with FPR1, FPR2, and FPR3 in breast cancer samples, respectively. The Protein-Protein Interaction (PPI) network was constructed using the online STRING database ([93]https://cn.string-db.org/) [[94]40] and visualized with Cytoscape V3.9.1 software. Based on the identified associated genes, Gene Ontology (GO, Biological Processes) enrichment analysis was performed. To analyze the immune cell infiltration in the dataset sourced from the TCGA database, we employed three algorithms: MCPcounter (Microenvironment Cell Populations-counter) [[95]41], Estimate (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data) [[96]42], QUANTISEQ (Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts) [[97]43]. Single-cell RNA-seq (scRNA-seq) analysis The Seurat (V4.0) R package was utilized for quality control and further analysis of single-cell RNAseq data. Low-quality cells (with < 500 genes/cell, > 8,000 genes/cell, < 10 cells/gene, and > 20% mitochondrial genes) were excluded from the analysis. Batch effects across samples were systematically corrected using the ‘harmony’ algorithm. The filtered spatial transcriptomic matrix underwent normalization via SCTransform (v2) implementation. The top 3,000 highly variable genes (HVGs) identified through variance-stabilizing transformation were retained for downstream clustering resolution selection. Differential gene expression analysis between cell clusters was performed using the FindAllMarkers function in the Seurat package. For the [98]GSE176078 single-cell dataset, preexisting comprehensive cell type annotations were retained without additional computational label assignment, as the original study provided detailed cellular classification information. M1 and M2 polarization scores of macrophages were calculated by the ‘AddModuleScore’ function (Table S2) [[99]29]. Cell culture THP-1 cells were cultured in RPMI-1640 medium (1640, Gibco) supplemented with 10% fetal bovine serum (ShuangRu) and 1% P/S (Penicillin-Streptomycin). THP-1 differentiation into macrophages was induced by treatment with 100 ng/ml phorbol 12-myristate 13-acetate (PMA, Topscience) for 48 h. THP-1-derived macrophages were washed with phosphate-buffered saline (PBS) before use in experiments. Rivaroxaban was purchased from Targetmol (T1184). Macrophages derived from THP-1 cell differentiation were used. Cells were treated with RPMI-1640 medium containing Rivaroxaban at final concentrations of 1 µM and 5 µM. Total RNA was extracted after 24 h of treatment. The cell culture conditions were maintained at 5% CO2, 37 °C in a humidified atmosphere. Transfection The negative control sequence and FPR3-targeting siRNA were purchased from Sangon Biotech (Beijing, China). The siRNA sequence used in this study was sense (5’−3’): GGGUCGUAACUUCCAAGAA(dT)(dT), and antisense (5’−3’): UUCUUGGAAGUUACGACCC(dT)(dT). Transfection was carried out according to the experimental protocol provided by the transfection reagent manufacturer (TransIntro^® EL Transfection Reagent, FT-201). THP-1 cells were plated in 12-well plates at 1 × 10⁵ cells/well and differentiated with RPMI-1640 complete medium containing 100 ng/mL phorbol 12-myristate 13-acetate (PMA) for 24 h. After thorough removal of PMA via three PBS washes, cells were incubated in fresh medium for 4 h. Transfection complexes were prepared by mixing 1.6 µg plasmid DNA with 3 µL TransIntro EL transfection reagent in 100 µL Opti-MEM, followed by 20-minute incubation. The mixture was added to cells, and after 5 h, the medium was replaced with fresh complete medium for continued culture (18–72 h) before downstream experiments. Immunofluorescence Immunofluorescence (IF) analyses were performed on paraffin-embedded tissue sections. The slides were dewaxed in xylene and hydrated through a gradient of ethanol concentrations. Antigen retrieval was performed using high-pressure steam with citrate buffer at pH 6.0. Non-specific binding was blocked by incubating the slides with 10% goat serum albumin (BSA) followed by overnight incubation at 4 °C with the corresponding primary antibodies: FPR3 (Immunoway, #YT1771), CD68 (Immunoway, #YM3050), CD45 (Immunoway, #PT0471R). The sections were incubated for 50 min at room temperature with a mixture of secondary antibodies Alexa Fluor 488 (Invitrogen, GB25301) and Cy3 590 (Invitrogen, GB21303). Subsequently, the cell nuclei were counterstained with DAPI (4’,6-diamidino-2-phenylindole), and an autofluorescence quencher was added to remove tissue autofluorescence. Finally, images were captured under a fluorescence microscope. Quantitative Real-Time polymerase chain reaction (qRT-PCR) Cells were collected, and total RNA was extracted using an RNA extraction kit (TIANGEN, Y2107, China). A cDNA reverse transcription kit (TRAN, AT301, China) was used to convert 500 ng of RNA into cDNA. The qRT-PCR program was as follows: initial denaturation at 94 °C for 30 s, followed by 42 cycles of 94 °C for 5 s, 60 °C for 15 s, and 72 °C for 10 s. The primers were produced by Sangon Biotech, and their sequences are presented in Table [100]1. A HealForce X960 real-time fluorescence quantitative PCR system was used for real-time fluorescence quantification. ACTB was used as an endogenous control. After the reaction, the relative expression levels of mRNA were determined using the 2-ΔΔCt method. Table 1. Primer sequences Gene Forward (5′−3′) Reverse (5′−3′) FPR3 GACTGATTCGCTCTTTGCCCAC TCTCCTCAGGAGGTGAAGCAGA IL6 CTGGATTCAATGAGGAGACTTGC TCAAATCTGTTCTGGAGGTACTCTAGG IL10 AAGAAGGCATGCACAGCTCA AAGTGGGTGCAGCTGTTCTC TNFα TGCTTGTTCCTCAGCCTCTT ATCACTCCAAAGTGCAGCAG TGFβ CAATTCCTGGCGATACCTCAG GCACAACTCCGGTGACATCAA ACTB TCCCTGGAGAAGAGCTACGA AGCACTGTGTTGGCGTACAG [101]Open in a new tab Small-molecule compounds screening and molecular Docking To explore interactions between small-molecule compounds and FPR3, we conducted molecular docking using AutoDock Vina [[102]44]. Small-molecule compounds associated with FPR3 expression and activity were retrieved from the Comparative Toxicogenomics Database [[103]45] (CTD, [104]http://ctdbase.org/) and the Drug-Gene Interaction Database [[105]46] (DGIdb, [106]https://dgidb.org/) (see Supplementary Table [107]1 for search results). 3D structural data of these molecules were obtained from the PubChem database [[108]47] ([109]https://pubchem.ncbi.nlm.nih.gov/) while the protein structure of FPR3 was acquired via the UniProt database [[110]48] ([111]https://www.uniprot.org/). Docking of FPR3 with small-molecule compounds followed AutoDock Vina’s standard protocols. A threshold of −5 kcal/mol was applied to distinguish potentially active from inactive molecules, with values below this cutoff considered biologically active (Table S4). PyMol was used for visualization. All analyses were performed on an Ubuntu 24.04 LTS system. Statistical analysis Statistical differences between two groups were evaluated using Student’s t-test. Spearman’s and Pearson’s correlation analyses were performed to determine correlations between variables. Multiple hypothesis testing corrections were conducted via the Benjamini-Hochberg false discovery rate (FDR) method. Survival characteristics were compared using the Kaplan-Meier method. All statistical analyses and visualizations were executed in R software. The R packages ComplexHeatmap [[112]49] and ggpubr are employed for data visualization and statistical analysis. P value < 0.05 was considered statistically significant. Results Characterization ofFPR3Gene Expression Across Pan-Cancer Contexts Through an integrative multi-omics framework encompassing mRNA sequencing (bulk RNA, single-cell RNA, spatial RNA) and DNA methylation profiling (Fig. [113]1A), this study systematically characterized the expression landscape and functional implications of the formyl peptide receptor (FPR) family. Pan-cancer TCGA analysis revealed tissue-specific expression patterns: FPR3 showed significant upregulation in breast cancer (BRCA) compared to adjacent normal tissue (Fig. [114]1B), while other FPR members exhibited tumor-type dependent expression. Validation using independent breast cancer cohorts ([115]GSE1456/[116]GSE7390) confirmed FPR3’s prognostic significance, showing elevated expression correlated with worse overall survival (OS) (Fig. S1), which is consistent with previous study [[117]50]. Paradoxically, analysis of 62 breast cancer cell lines from the CCLE database showed generally low FPR family mRNA levels, though cell line-specific variations were observed: FPR1 was enriched in MDA-MB-231 and CAL-85-1 cells, while FPR3 exhibited higher expression in UACC-812 lines (Fig. [118]1C). Single-cell RNA sequencing ([119]GSE173634) corroborated these cell line-specific patterns through UMAP visualization and expression heatmaps (Fig. [120]1D). To resolve this discrepancy, we performed cellular deconvolution using 10 breast cancer single-cell datasets from the TISCH database. This analysis revealed FPR3 enrichment predominantly in tumor-infiltrating immune cells rather than malignant epithelial cells (Fig. [121]1E). Fig. 1. [122]Fig. 1 [123]Open in a new tab Analysis of FPR Gene Family Expression. (A) Research Flowchart. (B) Violin plots illustrating the differential expression of FPR1, FPR2, and FPR3 genes in 26 types of tumors and their adjacent normal tissues from the TCGA database. (C) Heatmap showing the expression of FPR1, FPR2, and FPR3 genes in 62 breast cancer cell lines from the CCLE database. (D) Expression plots of FPR1, FPR2, FPR3, and ACTB genes in 32 breast cancer cell lines from the [124]GSE173634 dataset. (E) Heatmap depicting the expression of FPR3 in stromal cells, immune cells, and malignant tumor cells across multiple integrated breast cancer single-cell datasets from the TISCH database Functional enrichment and methylation analysis for FPR family Above analysis indicated that the FPR family exhibits similar expression patterns across multiple tumor types. To investigate potential interactions within the FPR family, we leveraged TCGA/ICGC pan-cancer data from the c-BioPortal database [[125]51] to analyze their expression’s correlation. The analysis revealed coordinated expression patterns among FPR family genes. Notably, FPR1 and FPR2 demonstrated strong positive correlation (Spearman r = 0.81) (Fig. [126]2A), suggesting coregulatory mechanisms. To explore functional interactions, we constructed a PPI network using the top 20 genes correlated with each FPR member. This analysis revealed distinct interaction landscapes: FPR1 and FPR2 shared numerous common binding partners, forming a tightly connected module, while FPR3 exhibited limited overlap indicating functional divergence (Fig. [127]2B). GO enrichment of the PPI network highlighted FPR family involvement in inflammatory response pathways, including leukocyte migration and cytokine signaling (Fig. [128]2C). GSEA further revealed FPR3-specific enrichment in apoptosis and EMT pathways in breast cancer (Fig. [129]2D). Epigenetic analysis using the BCNTB database uncovered differential DNA methylation patterns: FPR1 and FPR3 showed promoter hypomethylation in breast tumors compared to normal tissues, while FPR2 maintained hypermethylation (Fig. [130]2E). Survival analysis demonstrated that FPR3 methylation status significantly correlated with OS and disease-specific survival (DSS) (Fig. [131]2F, S2). Notably, FPR3 methylation inversely correlated with mRNA expression (Fig. [132]2G), suggesting methylation-mediated transcriptional regulation. Therefore, FPR3 exhibits breast cancer-specific hypomethylation, apoptosis/EMT pathway involvement, and independent prognostic significance. Fig. 2. [133]Fig. 2 [134]Open in a new tab Functional enrichment analysis and promoter methylation analysis for the FPR family. (A) Analysis of the correlation of FPR family gene expression using the TCGA pan-cancer cohort from the c-BioPortal database. (B) PPI network diagram of 41 genes highly correlated with FPR1, FPR2, and FPR3. (C) The circular plot of GO-BP (Gene Ontology-Biological Process) pathway analysis for the 41 genes highly correlated with FPR1, FPR2, and FPR3. (D) Single-gene pathway enrichment analysis of FPR3 in breast cancer cohorts from the GSCA database. (E) Differential promoter methylation levels of FPR1, FPR2, and FPR3 genes between breast cancer and adjacent normal tissues from the BCNTB database. (F) Correlation analysis between the methylation levels of FPR1, FPR2, and FPR3 and the prognosis of breast cancer patients from the GSCA database. (G) Correlation analysis between the methylation levels of FPR1, FPR2, and FPR3 and their corresponding mRNA expression levels in breast cancer cohorts from the GSCA database FPR3 expression patterns and prognostic impact in breast cancer subtypes Subtype-specific analysis using bc-GenExMiner v4.5 and [135]GSE19615 dataset revealed FPR3 overexpression in aggressive breast cancer subtypes, with HER2 + and Triple-Negative Breast Cancer (TNBC) patients showing higher FPR3 mRNA levels compared to luminal subtypes (Fig. [136]3A). This expression disparity was unique to FPR3, as FPR1/FPR2 showed no subtype-specific differential expression (Fig. [137]3B). Progression analysis demonstrated FPR3 upregulation across clinical stages (I-IV), with a progressive increase from stage I to IV (Fig. [138]3C). Kaplan-Meier survival analysis revealed that patients with high FPR3 expression had significantly shorter OS and Relapse-Free Survival (RFS) compared to low-expression groups (Fig. [139]3D). Collectively, FPR3 overexpression distinctively correlates with aggressive breast cancer subtypes (HER2+/TNBC), advanced-stage progression, and poor survival outcomes, positioning it as a unique prognostic biomarker compared to non-differential FPR1/FPR2 expression. Fig. 3. [140]Fig. 3 [141]Open in a new tab Expression Analysis of FPR3 among Different Subtypes of Breast Cancer Patients. (A) Violin plot from the integrated cohort of all breast cancer patients in the bc-GenExMiner database, showing FPR3 gene expression across different ER, PR, HER status groups, and molecular subtypes of breast cancer patients (*P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001). (B) Gene expression levels of FPR1, FPR2, and FPR3 in different molecular subtypes of breast cancer from the [142]GSE19615 dataset (*P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001). (C) Expression levels of FPR3 gene in different pathological stages of breast cancer from the GSCA database cohort (number: I = 183, II = 622, III = 250, IV = 20). (D) Kaplan-Meier (KM) plots illustrating the difference in overall survival between breast cancer patients with high and low FPR3 gene expression from the [143]GSE1456 and [144]GSE7350 datasets, and the difference in relapse-free survival between patients with high and low FPR3 gene expression from the E-MTAB-365 and [145]GSE1112 datasets FPR3 enrichment in Tumor-Associated macrophages correlates with aggressive breast cancer subtypes A systematic analysis of multiple breast cancer single-cell data results from the TISCH database and a large breast cancer myeloid cell atlas [[146]52] revealed that FPR3 is expressed at higher levels in macrophages and cDC2_CD1C compared to tumor cells (Fig. [147]4A-C). Compared to the ER subtype, TNBC and HER2 subtype breast cancer patients exhibited greater macrophage infiltration (Fig. [148]4D, E). Furthermore, patients with TNBC and HER2 subtypes exhibited higher expression levels of FPR3 in macrophages (Fig. [149]4F, G). Spatial transcriptomic analysis reveals that the macrophage marker gene CD68 and the FPR3 gene exhibit similar spatial distribution patterns in expression. Furthermore, compared to ER + breast cancer patients, TNBC (triple-negative breast cancer) patients demonstrate higher levels of FPR3 + macrophage infiltration (Fig. [150]4H, I). Multiplex immunofluorescence detected FPR3-positive macrophages in breast cancer tissue (Fig. [151]4J). Fig. 4. [152]Fig. 4 [153]Open in a new tab Macrophages infiltrating breast cancer patient tissues exhibit high expression of FPR3. (A) Heatmap of FPR3 expression in different cell subsets from the integrated multiple breast cancer single-cell datasets in the TISCH database. (B) Breast cancer myeloid cell single-cell UMAP Dimensionality Reduction Plot. (C) The UMAP plot shows the expression distribution of the FPR3 gene. (D) Dimensionality reduction plot of major cell subsets from the large breast cancer single-cell cohort [154]GSE176078. (E) The abundance of various cell types in TNBC, HER+, and ER + patients from the large breast cancer single-cell cohort [155]GSE176078. (F) The UMAP plot shows the expression distribution of the FPR3 gene in [156]GSE176078. (G) Differences in FPR3 gene expression in myeloid cells among TNBC, HER+, and ER + subtypes of breast cancer patients. (H) Single-cell spatial transcriptomics analysis of breast cancer patients, with FPR3 scaled deconvolution values overlaid onto tissue points defined in the tissue section. (I) Single-cell spatial transcriptomics analysis of ER and TNBC subtype breast cancer patients, with FPR3 scaled deconvolution values overlaid onto tissue points defined in the tissue section. (J) Immunofluorescence staining of a breast cancer tissue section for CD68 (green), CD45 (green), and FPR3 (red) FPR3 promotes Tumor-Associated macrophage polarization toward Pro-Tumoral M2 phenotype in breast cancer Immune infiltration analysis revealed significant positive correlations between FPR3 expression and tumor-infiltrating immune cells (TICs) and cancer-associated fibroblasts (CAFs). Stromal/immune score analysis demonstrated that high FPR3 expression correlated with elevated tumor microenvironment remodeling (StromalScore r = 0.71, ImmuneScore r = 0.68; Fig. [157]5B). Notably, FPR3 showed the strongest correlation with the infiltration levels of both M1 and M2 macrophages (Fig. [158]5C). To investigate FPR3 expression in macrophages from non-tumor tissues, we selected a single-cell sequencing dataset encompassing breast cancer tumor tissues, normal tissues, blood, and lymph node tissues. The results showed that FPR3 enrichment specifically in tumor-associated macrophages (TAMs) compared to normal tissue macrophages (Fig. [159]5D-F). Subsequently, we scored macrophages with different FPR3 expression levels based on the gene set of M1 and M2 macrophage markers. The results indicated that the macrophage subpopulation with higher FPR3 expression exhibited a higher M2 polarization score (Fig. [160]5G). Knocking down FPR3 in macrophages (Fig. [161]5H) significantly increased the expression levels of M1 macrophage-associated cytokines IL6 and TNFα (Fig. [162]5I), while significantly decreasing the expression level of the M2 macrophage-associated cytokine IL10 (Fig. [163]5J), indicating a shift towards M1 macrophage differentiation. Therefore, high FPR3 expression may facilitate macrophage polarization towards the M2 phenotype, establishing its role as a critical immunoregulatory node in breast cancer progression. Fig. 5. [164]Fig. 5 [165]Open in a new tab FPR3 Influences Macrophage Polarization. (A) Heatmap showing the correlation between FPR3 gene expression levels and the infiltration abundance of different cells in breast cancer. (B) Heatmap showing the correlation between FPR3 gene expression levels and immune and stromal scores in the breast cancer tumor microenvironment. (C) Heatmap showing the correlation between FPR3 gene expression levels and different cell subtypes in breast cancer. (D) Dimensionality reduction plot of cell subsets from the [166]GSE114725 breast cancer single-cell dataset. (E) Expression levels of FPR1, FPR2, and FPR3 genes in different cell subsets and tissue locations from the [167]GSE114725 breast cancer single-cell dataset. (F) Dot plot of marker gene expression for different cell subpopulations in the [168]GSE114725 dataset. (G) M1/M2 polarization scores of macrophages with different FPR3 expression levels. Cluster macrophages in the [169]GSE114725 dataset based on FPR3 expression levels and calculate their polarization scores using AddModuleScore. (H) Bar chart displaying FPR3 expression levels in siFPR3-knockdown macrophages and control groups detected by qPCR. (I) Bar chart showing TNFα and IL6 expression levels in siFPR3-knockdown macrophages and control groups detected by qPCR. (J) Bar chart displaying TGFβ and IL10 expression levels in siFPR3-knockdown macrophages and control groups detected by qPCR Discovery of FPR3-Targeting small molecules through computational screening We performed in silico screening using DGIdb and CTD databases to identify potential small-molecule inhibitors of FPR3 expression and activity. Molecular docking analysis with Autodock-vina revealed two promising compounds, Otamixaban and Rivaroxaban, which showed stable and specific binding to the FPR3 active site (Fig. [170]6A, B), suggesting their therapeutic potential for modulating FPR3-mediated processes. In subsequent experiments, THP-1-derived macrophages were treated with varying concentrations of Rivaroxaban. At 1 µM, Rivaroxaban significantly upregulated M1-associated genes (IL6, TNFα) while downregulating M2-associated genes (IL10, TGFβ), consistent with expected FPR3 inhibitiont (Fig. [171]6C). Fig. 6. [172]Fig. 6 [173]Open in a new tab Potential drug screening targeting FPR3. (A) Molecular docking illustration of Otamixaban interaction with FPR3. (B) Molecular docking illustration of Rivaroxaban interaction with FPR3. (C) Bar chart displaying FPR3, IL6, TNFα, IL10, and TGFβ expression levels in macrophages detected by qPCR in Rivaroxaban-treated and untreated control groups (*P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001) Discussion Through comprehensive multi-omics analysis, we systematically characterized the expression profile and functional significance of FPR3, with particular focus on its involvement in macrophage polarization and clinical outcomes. Our findings demonstrate that FPR3 exhibits significantly elevated expression in breast cancer tumor tissues, and importantly, its expression levels serve as a robust prognostic biomarker capable of stratifying patients with distinct clinical outcomes. Single-cell sequencing and spatial transcriptomic analysis analysis revealed that FPR3 expression is predominantly localized to tumor-infiltrating macrophages within the breast cancer microenvironment. Notably, the infiltration density of FPR3-positive macrophage subpopulations was markedly higher in HER2-positive and triple-negative breast cancer (TNBC) subtypes compared to other molecular subtypes. Mechanistically, FPR3 was found to be functionally linked to multiple oncogenic signaling pathways. Intriguingly, genetic knockdown of FPR3 in macrophages promoted their phenotypic shift towards the tumor-suppressive M1 phenotype, suggesting its potential role in modulating tumor immunity. Formyl peptide receptors (FPRs), belonging to pattern recognition receptors (PRRs), play a pivotal role in host defense and inflammation processes [[174]53]. Pan-cancer analysis has illuminated that the FPR family, particularly FPR3, is frequently overexpressed in a wide array of cancers. Notably, the high expression of FPR3 in breast cancer tumor tissues may be governed by demethylation, with promoter methylation levels and FPR3 gene expression serving as effective biomarkers for stratifying breast cancer patient prognosis. Previous studies have underscored the correlation between an increased relative abundance of M2 tumor-associated macrophages (TAMs) and poorer 5-year survival rates among cancer patients [[175]54]. In this study, an in-depth analysis of immune infiltration revealed a pronounced association between FPR3 and the infiltration levels of both M1 and M2 macrophages. Specifically, when FPR3 was knocked down in M0 macrophages, we observed an upregulation of M1 macrophage-associated cytokines IL6 and TNFα, accompanied by a downregulation of IL10 levels. This phenomenon may be attributed to FPR3’s activation of the p-IκB/NF-κB axis, thereby influencing macrophage polarization [[176]55]. Our findings underscore the intricate interplay between FPR3, macrophage polarization, and patient prognosis in breast cancer. FPR3 presents a double-edged sword in the context of cancer patients, as its antitumor or tumor-suppressing effects hinge crucially on the nature of its ligands. For instance, the successful therapeutic agent, Nocardia rubra cell wall skeleton (Nr-CWS), targets FPR3 on dendritic cells (DCs), triggering a potent Th1 immune response against cervical human papillomavirus infections [[177]56]. Conversely, another study reported that sensitized lipocalin binds to FPR3 in DCs, inhibiting T cells through the release of IL-12 and promotion of IL-10 production, leading to a Th2 immune response upon FPR3 activation [[178]57]. These examples underscore the divergent effects FPR3 can exert on T cell differentiation, contingent upon the specific ligand involved. In gastric cancer, elevated FPR3 levels have been shown to inhibit GC proliferation and progression, possibly by disrupting cytoplasmic calcium fluxes, reducing nuclear translocation of calcium-dependent NFATc1, and subsequently suppressing the transactivation of downstream NOTCH3 and SOX2, thereby impeding the activation of the AKT/mTORC1 signaling pathway and halting tumor progression [[179]19]. Moreover, FPR3 has been implicated in the interplay with fibroblasts, as it promotes the transdifferentiation of fibroblasts into myofibroblasts in human nasal polyp tissue through the activation of the PKA/Rap1/ERK1/2 axis [[180]55]. Collectively, these findings highlight the multifaceted and context-dependent role of FPR3 in modulating the tumor microenvironment, emphasizing its potential as a therapeutic target or biomarker that necessitates careful consideration of its interaction with various ligands and cell types within the tumor milieu. Our study revealed a striking uptrend in FPR3 expression levels as breast cancer progresses, and a robust association with several cancer-related signaling pathways, including EMT, NF-κB, and PI3K. Notably, FPR3 was found to be highly expressed in macrophages within breast cancer tissues, contributing to the maintenance of an M2 polarization phenotype, thereby suggesting its potential as an efficacious anticancer target. However, the underlying mechanisms governing the upregulation of FPR3 protein levels in macrophages of breast cancer patients, as well as the specific molecular pathways linking FPR3 to macrophage polarization and adverse patient prognosis, remain elusive. Moreover, the scarcity of FPR3 inhibitors has hindered in-depth research into FPR3. To fully harness FPR3’s potential as a therapeutic target in breast cancer, further in-depth investigations are imperative. In summary, through the integration and comprehensive analysis of multi-omics data from breast cancer, we conducted a systematic investigation of the Formyl Peptide Receptor family, with a particular focus on exploring the potential impact of the FPR3 gene on breast cancer. The findings of this study offer novel insights into the relationship between FPR3 and macrophages and provide fresh perspectives for understanding the progression of breast cancer as well as predicting poor prognosis. These findings significantly expand our understanding of breast cancer biology while simultaneously identifying FPR3 as a promising therapeutic target. The translational implications of this work extend to both prognostic biomarker development and precision medicine approaches targeting the FPR3 signaling axis. Supplementary Information [181]Supplementary Material 1.^ (16.8KB, xlsx) [182]Supplementary Material 2.^ (1.1MB, docx) Acknowledgements