anti fpr3  (Biorbyt)

Journal: Cell Reports Medicine

Article Title: Myeloid cells coordinately induce glioma cell-intrinsic and cell-extrinsic pathways for chemoresistance via GP130 signaling

doi: 10.1016/j.xcrm.2024.101658

Figure Lengend Snippet:

Article Snippet: FPR3 TaqMan Genexpression Assay , ThermoFisher Scientific , Cat#: Hs01574392_m1.

Techniques: Plasmid Preparation, Recombinant, Transfection, Fluorescence, Staining, Reverse Transcription, Gene Expression, Liposomes, Mutagenesis, shRNA, Control, Construct, Software, Imaging, Functional Assay, Dissection, Sequencing, Real-time Polymerase Chain Reaction

Journal: Cancer Cell International

Article Title: FPR3 orchestrates macrophage polarization in breast cancer: multi-omics dissection of prognostic relevance and therapeutic targeting

doi: 10.1186/s12935-025-03942-4

Figure Lengend Snippet: 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 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

Article Snippet: The negative control sequence and FPR3 -targeting siRNA were purchased from Sangon Biotech (Beijing, China).

Techniques: Expressing, Quantitative Proteomics

Journal: Cancer Cell International

Article Title: FPR3 orchestrates macrophage polarization in breast cancer: multi-omics dissection of prognostic relevance and therapeutic targeting

doi: 10.1186/s12935-025-03942-4

Figure Lengend Snippet: 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

Article Snippet: The negative control sequence and FPR3 -targeting siRNA were purchased from Sangon Biotech (Beijing, China).

Techniques: Functional Assay, Methylation, Gene Expression, Expressing

Journal: Cancer Cell International

Article Title: FPR3 orchestrates macrophage polarization in breast cancer: multi-omics dissection of prognostic relevance and therapeutic targeting

doi: 10.1186/s12935-025-03942-4

Figure Lengend Snippet: 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 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 GSE1456 and GSE7350 datasets, and the difference in relapse-free survival between patients with high and low FPR3 gene expression from the E-MTAB-365 and GSE1112 datasets

Article Snippet: The negative control sequence and FPR3 -targeting siRNA were purchased from Sangon Biotech (Beijing, China).

Techniques: Expressing, Gene Expression

Journal: Cancer Cell International

Article Title: FPR3 orchestrates macrophage polarization in breast cancer: multi-omics dissection of prognostic relevance and therapeutic targeting

doi: 10.1186/s12935-025-03942-4

Figure Lengend Snippet: 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 GSE176078 . ( E ) The abundance of various cell types in TNBC, HER+, and ER + patients from the large breast cancer single-cell cohort GSE176078 . ( F ) The UMAP plot shows the expression distribution of the FPR3 gene in 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)

Article Snippet: The negative control sequence and FPR3 -targeting siRNA were purchased from Sangon Biotech (Beijing, China).

Techniques: Expressing, Gene Expression, Immunofluorescence, Staining

Journal: Cancer Cell International

Article Title: FPR3 orchestrates macrophage polarization in breast cancer: multi-omics dissection of prognostic relevance and therapeutic targeting

doi: 10.1186/s12935-025-03942-4

Figure Lengend Snippet: 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 GSE114725 breast cancer single-cell dataset. ( E ) Expression levels of FPR1 , FPR2 , and FPR3 genes in different cell subsets and tissue locations from the GSE114725 breast cancer single-cell dataset. ( F ) Dot plot of marker gene expression for different cell subpopulations in the GSE114725 dataset. ( G ) M1/M2 polarization scores of macrophages with different FPR3 expression levels. Cluster macrophages in the 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

Article Snippet: The negative control sequence and FPR3 -targeting siRNA were purchased from Sangon Biotech (Beijing, China).

Techniques: Gene Expression, Expressing, Marker, Knockdown, Control

Journal: Cancer Cell International

Article Title: FPR3 orchestrates macrophage polarization in breast cancer: multi-omics dissection of prognostic relevance and therapeutic targeting

doi: 10.1186/s12935-025-03942-4

Figure Lengend Snippet: 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)

Article Snippet: The negative control sequence and FPR3 -targeting siRNA were purchased from Sangon Biotech (Beijing, China).

Techniques: Drug discovery, Expressing, Control

Journal: NPJ Precision Oncology

Article Title: Machine learning algorithms for predicting glioma patient prognosis based on CD163+FPR3+ macrophage signature

doi: 10.1038/s41698-024-00692-w

Figure Lengend Snippet: A UMAP plot displaying identified clusters in glioma. B WGCNA identifies M0, M1, and M2-like macrophage correlated modules. C The volcano plot showing differentially expressed genes between gliomas with high- and low-M2 macrophage ssGSEA score. D Venn diagram demonstrating the overlapping of genes in brown modules and differentially expressed genes. E Expression of CD163 and FPR3 overlaid on the UMAP space. F Correlation analysis of FPR3 and M2 macrophage ssGSEA score in glioma. G Correlation analysis of FPR3 and CD163 expression in TCGA cohort. H IF staining of FPR3 and CD163 in human glioma were shown. I Representative IHC staining of FPR3 and CD163 in glioma microarray; scale bar represents 100 µm.

Article Snippet: IHC was performed using a primary antibody targeting FPR3 (Proteintech, China).

Techniques: Expressing, Staining, Immunohistochemistry, Microarray

Journal: NPJ Precision Oncology

Article Title: Machine learning algorithms for predicting glioma patient prognosis based on CD163+FPR3+ macrophage signature

doi: 10.1038/s41698-024-00692-w

Figure Lengend Snippet: The expression level of FPR3 in the normal tissues and glioma tissues in TCGA cohort ( A ), GSE16011 ( B ), Rembrandt cohort ( C ) and in-house cohort ( D ). E The expression of FPR3 in peritumor and glioma tissues was analyzed by RT-qPCR. F The expression level of FPR3 in the normal tissues and glioma tissues was analyzed by immunohistochemical staining. G The expression of FPR3 in glioma and peritumor tissues was analyzed by IHC. H Kaplan–Meier survival plot of FPR3 expression of glioma patients in the six cohorts. I Kaplan–Meier survival plot of FPR3 expression of glioma patients by RT-qPCR. J Kaplan–Meier survival plot of FPR3 expression of glioma patients by IHC. P < 0.05, log-rank test. (“**, ***” respectively means P < 0.01, P < 0.001).

Article Snippet: IHC was performed using a primary antibody targeting FPR3 (Proteintech, China).

Techniques: Expressing, Quantitative RT-PCR, Immunohistochemical staining, Staining, Paraffin-embedded Immunohistochemistry

Journal: NPJ Precision Oncology

Article Title: Machine learning algorithms for predicting glioma patient prognosis based on CD163+FPR3+ macrophage signature

doi: 10.1038/s41698-024-00692-w

Figure Lengend Snippet: A A UMAP projection of de novo clustered myeloid cells. B Expression of CD163 and FPR3 overlaid on the UMAP space. C GSEA analysis of specific gene of the CD163+FPR3+ macrophage subset. D Two immune phenotypes in the glioma microarray cohort were detected using IF. E The expression level of FPR3 between glioma with two immune phenotypes. F The expression of PD-L1, CD40, PD-1, P65, and STAT3 expression in glioma with the CD163+FPR3+ and CD163+FPR3− macrophage subset. (“*”means P < 0.05).

Article Snippet: IHC was performed using a primary antibody targeting FPR3 (Proteintech, China).

Techniques: Expressing, Microarray

Journal: NPJ Precision Oncology

Article Title: Machine learning algorithms for predicting glioma patient prognosis based on CD163+FPR3+ macrophage signature

doi: 10.1038/s41698-024-00692-w

Figure Lengend Snippet: A The C-index of 101 kinds of prediction models built by machine learning algorithms for the CD163+FPR3+ macrophage-associated signatures in the six glioma cohorts. B An ensemble of 17 cell death-associated genes with Cox coefficients. C The number of trees for determining the CD163+FPR3+ macrophage-associated signature with minimal error and the importance of CD163+FPR3+ macrophage-associated signatures based on the RSF algorithm. D The identification of optimal cutoffs for survival analyses in TCGA cohort. Kaplan–Meier survival curves of OS between high-score and low-score patients identified the scoring system in TCGA ( E ), CGGA-693 ( F ), CGGA-301 ( G ), CGGA-325 ( H ), GSE16011, ( I ) and Rembrandt ( J ) datasets. P values were calculated by the log-rank test, and P < 0.05 was considered significant.

Article Snippet: IHC was performed using a primary antibody targeting FPR3 (Proteintech, China).

Techniques:

Journal: NPJ Precision Oncology

Article Title: Machine learning algorithms for predicting glioma patient prognosis based on CD163+FPR3+ macrophage signature

doi: 10.1038/s41698-024-00692-w

Figure Lengend Snippet: A Time-dependent ROC curves displaying the prognostic accuracy of the risk score at 1, 3, and 5 years in TCGA, CGGA-693, CGGA-301, CGGA-325, GSE16011, and Rembrandt cohorts. B C-index of the risk score in the six cohorts. The performance of risk score was compared with other clinical and molecular variables in predicting prognosis in TCGA ( C ), CGGA-693 ( D ), GSE16011 ( E ), and Rembrandt ( F ) datasets. The performance of risk score + grade was compared with risk score and grade alone in predicting prognosis in TCGA ( G ), CGGA-693 ( H ), GSE16011 ( I ), and Rembrandt ( J ) datasets. K The 1-year, 2-year, and 3-year calibration curves of the CD163 + FPR3+ macrophage-associated signature in the four datasets. The P values are labeled above each boxplot with asterisks (-, no significant, “*, **, ***” respectively means P < 0.05, P < 0.01, P < 0.001).

Article Snippet: IHC was performed using a primary antibody targeting FPR3 (Proteintech, China).

Techniques: Labeling

Journal: Cell Reports Medicine

Article Title: Myeloid cells coordinately induce glioma cell-intrinsic and cell-extrinsic pathways for chemoresistance via GP130 signaling

doi: 10.1016/j.xcrm.2024.101658

Figure Lengend Snippet: GP130 is essential for humanin-induced chemoresistance (A) Quantitative reverse-transcription PCR (RT-PCR) for the humanin receptor subunit IL6ST (encoding GP130) was performed; note that IL6ST levels are much higher in humanin-sensitive than humanin-insensitive hGBMs. (B) hGBMs were stimulated with HN or HNG, partly sc144 was coapplied, which consistently abrogated the protumorigenic effect of HN and HNG (dashed line: controls without HN or sc144). (C) Expansion of hGBM1-HN-WT, HN-C8A, or HN-L9R cells, with or without sc144. (D) Humanin expression levels in brain slices with hiPSC-derived microglia and hGBM1 cells were attenuated after addition of sc144 (graphically summarized in E). The number of biological replicates is indicated (dots in graphs indicate data from individual experiments); all error bars are presented as mean ± SDM. Statistical significance is shown by one-way ANOVA in (A) and t test in (B–D): ∗∗ p < 0.01; ∗∗∗∗ p < 0.0001; NS, not significant.

Article Snippet: IL6ST TaqMan Genexpression Assay , ThermoFisher Scientific , Cat#: Hs00174360_m1.

Techniques: Reverse Transcription, Reverse Transcription Polymerase Chain Reaction, Expressing, Derivative Assay

Journal: Cell Reports Medicine

Article Title: Myeloid cells coordinately induce glioma cell-intrinsic and cell-extrinsic pathways for chemoresistance via GP130 signaling

doi: 10.1016/j.xcrm.2024.101658

Figure Lengend Snippet: Humanin-mediated BTB formation is blunted by GP130 blockage increasing chemotherapeutic efficacy and survival (A) Quantification of vascular mural coverage on tumor vessels in orthotopic HN-WT or HN-C8a GBMs with or without TMZ (indicated by blue bars). (B and C) Vascular mural coverage of tumor vessels in TMZ-treated orthotopic HN-WT GBMs with or without BZA. (D) Endothelial cells and pericytes were purified from a transgenic GBM mouse model (GL261, n = 11; infused with humanin, HN, or aCSF, control) and analyzed by transcriptomics; GSEA indicated enrichment for angiogenic traits in HN-stimulated endothelia (as compared to aCSF). (E) Signaling pathways between endothelia and pericytes were analyzed from HN-infused versus control GBMs; in HN-infused GBMs, pericytes promote IL6ST (GP130) signaling in endothelia; in HN-infused GBMs, endothelia promote BMP signaling in pericytes. (F) In HN-WT GBMs, receiving TMZ cotreatment with BZA reduced pericyte (PDGFRB+) coverage of tumor vessels (CD31 + ; as compared to cotreatment with vehicle). (G) Intravenous application of 70 kDA dextran as a tracer for vessel tightness showed that BZA-treated gliomas had significantly increased leakage (across CD31 + vessels) into the tumor parenchyma, as compared to vehicle-treated mice. (H) In mice with orthotopic HN-WT GBMs, intracerebral infusion of sc144 (10 μM) during TMZ chemotherapy prolonged survival as compared to intracerebral infusion of vehicle ( n = 12 per group). (I) Schematic summary: the BTB and DDR protect humanin-sensitive GBMs from TMZ. GP130 inhibitors reduce BTB tightness and blunt chemoresistance. Scale bars indicate 200 μm in (A) and 20 μm in (G). The number of biological replicates is indicated (dots in graphs indicate data from individual mice); all error bars are presented as mean ± SDM. Statistical significance is shown by one-way ANOVA (A), t test (F, G), or by Mantel-Cox test (F): ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001.

Article Snippet: IL6ST TaqMan Genexpression Assay , ThermoFisher Scientific , Cat#: Hs00174360_m1.

Techniques: Purification, Transgenic Assay, Control, Protein-Protein interactions

Journal: Cell Reports Medicine

Article Title: Myeloid cells coordinately induce glioma cell-intrinsic and cell-extrinsic pathways for chemoresistance via GP130 signaling

doi: 10.1016/j.xcrm.2024.101658

Figure Lengend Snippet:

Article Snippet: IL6ST TaqMan Genexpression Assay , ThermoFisher Scientific , Cat#: Hs00174360_m1.

Techniques: Plasmid Preparation, Recombinant, Transfection, Fluorescence, Staining, Reverse Transcription, Gene Expression, Liposomes, Mutagenesis, shRNA, Control, Construct, Software, Imaging, Functional Assay, Dissection, Sequencing, Real-time Polymerase Chain Reaction

Journal: Schizophrenia Research: Cognition

Article Title: Relationship of cognitive measures to mRNA levels in lymphocytes from patients with schizophrenia and controls

doi: 10.1016/j.scog.2024.100321

Figure Lengend Snippet: Gene symbols and TaqMan primers for mRNAs assayed.

Article Snippet: FPRL2 , Hs00266666_s1 , Formyl peptide receptor 3.

Techniques: TaqMan Assay, Gene Expression, Sequencing, Variant Assay, Derivative Assay, Protein Binding